system

Abstract

The system according to this embodiment aims to provide appropriate nutritional advice by individually managing the health status and diet of pregnant women. [Solution] The system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a consultation unit. The collection unit collects the pregnant woman's health status and dietary history. The analysis unit analyzes the data collected by the collection unit. The provision unit provides individual nutritional advice and recipes based on the analysis results obtained by the analysis unit. The consultation unit answers the pregnant woman's questions based on the advice provided by the provision unit.

Description

Technical Field

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to individually manage the health status and diet of pregnant women, and it is difficult to provide appropriate nutritional advice.

[0005] The system according to the embodiment aims to individually manage the health status and diet of pregnant women and provide appropriate nutritional advice.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, and a consultation unit. The data collection unit collects the pregnant woman's health status and dietary history. The analysis unit analyzes the data collected by the data collection unit. The data provision unit provides individual nutritional advice and recipes based on the analysis results obtained by the analysis unit. The consultation unit answers the pregnant woman's questions based on the advice provided by the data provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can individually manage the health status and diet of pregnant women and provide appropriate nutritional advice. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F​​​The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI agent system according to an embodiment of the present invention is a system that manages the diet and nutritional balance of pregnant women and provides individualized nutritional advice and recipes. This AI agent system monitors the pregnant woman's health status and dietary history 24 hours a day and provides advice in real time. It also responds to consultations and questions in chat format, resolving the pregnant woman's anxieties and doubts. For example, the AI ​​agent system records in detail what the pregnant woman eats and the nutrients she ingests, and understands her health status in real time. For example, by recording the contents of the meals the pregnant woman eats and the amount of vitamins and minerals she ingests, it is possible to manage her nutritional balance. Next, the AI ​​agent system analyzes the pregnant woman's health status in real time and provides individualized nutritional advice and recipes. For example, it can suggest meals and recipes to supplement nutrients that the pregnant woman is lacking. This allows the pregnant woman to appropriately consume the nutrients she needs. Furthermore, the AI ​​agent system responds to consultations and questions in chat format, resolving the pregnant woman's anxieties and doubts. For example, the pregnant woman can consult about her questions and anxieties regarding her diet via chat, and the AI ​​agent system will respond quickly, increasing the pregnant woman's sense of security. This system allows pregnant women to receive appropriate nutritional management, enabling them to have a safe and healthy pregnancy. For example, if a pregnant woman has questions or concerns about her diet, the AI ​​agent system can respond quickly, increasing her sense of security. It can also support the pregnant woman's health by suggesting meals and recipes to supplement any nutrients she may be lacking. The AI ​​agent system collects the pregnant woman's health status and dietary history, analyzes the results, and provides personalized nutritional advice, recipes, and answers questions.

[0029] The AI ​​agent system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, and a consultation unit. The data collection unit collects the pregnant woman's health status and dietary history. The data collection unit records, for example, the contents of the meals the pregnant woman ate and the amount of vitamins and minerals she ingested. The data collection unit records, for example, the contents of the meals the pregnant woman ate in detail and understands the amount of nutrients she ingested. The data collection unit records, for example, the types and amounts of meals the pregnant woman ate and manages nutritional balance. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes, for example, the pregnant woman's health status in real time. The analysis unit analyzes, for example, the pregnant woman's health status in detail and evaluates nutritional balance. The analysis unit continuously monitors the pregnant woman's health status and detects abnormalities. The data provision unit provides individual nutritional advice and recipes based on the analysis results obtained by the analysis unit. The data provision unit suggests, for example, meals and recipes to supplement nutrients that the pregnant woman is deficient in. The data provision unit provides, for example, appropriate nutritional advice based on the pregnant woman's health status. The provision department provides, for example, a specific meal plan to improve the nutritional balance of pregnant women. The consultation department answers questions from pregnant women based on the advice provided by the provision department. The consultation department answers questions and concerns about pregnant women's diets in a chat format, for example. The consultation department allows pregnant women to consult with the consultation department about their diet questions via chat and receive quick answers. The consultation department provides, for example, specific advice to alleviate the anxieties and concerns of pregnant women. As a result, the AI ​​agent system according to this embodiment can collect information on the pregnant woman's health status and dietary history, provide personalized nutritional advice and recipes based on the analysis results, and answer questions.

[0030] The data collection unit collects information on the pregnant woman's health status and dietary history. Specifically, it meticulously records the contents of the meals the pregnant woman eats daily and the amounts of vitamins and minerals she consumes. For example, it records specific meals such as fruit and yogurt eaten for breakfast, salad and soup for lunch, and fish and vegetables for dinner. It also meticulously records the type and quantity of meals to understand the amounts of nutrients contained in these meals, such as vitamin A, vitamin C, calcium, and iron. Furthermore, it collects information on supplements and vitamin drinks consumed by the pregnant woman to manage her overall nutritional balance. The data collection unit streamlines data collection by allowing pregnant women to easily input their meal details through smartphone apps and wearable devices. For example, it provides a function that automatically analyzes the meal content and calculates the amount of nutrients by allowing pregnant women to take and upload photos of their meals. It also collects health data such as the pregnant woman's weight, blood pressure, and blood sugar levels to understand her overall health status. As a result, the data collection unit can collect detailed and accurate information on the pregnant woman's health status and dietary history, which can be used for subsequent analysis and advice provision.

[0031] The analysis department analyzes data collected by the data collection department in real time to understand the health status of pregnant women. Specifically, it evaluates the nutritional balance of pregnant women based on the collected data on diet and nutrients. For example, it analyzes the amount of vitamins and minerals consumed by pregnant women to determine whether they are deficient in necessary nutrients. It also analyzes health data such as weight, blood pressure, and blood sugar levels of pregnant women to detect changes or abnormalities in their health status. Using AI-based analysis algorithms, the collected data is processed quickly and accurately, and the health status of pregnant women is monitored in real time. For example, image recognition technology is used to analyze photos of meals and automatically extract the contents of the meals and the amount of nutrients. Machine learning models can also be used to detect abnormal patterns in pregnant women's health data and issue early warnings. Furthermore, by utilizing past data and statistical information, it is possible to analyze trends in pregnant women's health status and conduct long-term risk assessments. For example, based on past dietary history and health data, it is possible to predict periods when specific nutrients are likely to be deficient and patterns of health status fluctuations, and to plan future countermeasures. In this way, the analysis department can monitor the health status of pregnant women in detail and continuously, and support appropriate nutritional management and health management.

[0032] The service provider will provide pregnant women with individualized nutritional advice and recipes based on the analysis results obtained by the analysis department. Specifically, they will suggest meals and recipes to supplement nutrients that pregnant women are lacking. For example, if a pregnant woman is iron deficient, they will provide recipes using iron-rich ingredients to encourage iron intake. They will also provide appropriate nutritional advice based on the pregnant woman's health condition. For example, if a pregnant woman has high blood pressure, they will suggest a low-salt diet to help manage her blood pressure. Furthermore, they will provide specific meal plans to improve the pregnant woman's nutritional balance. For example, they will create a one-week meal plan and suggest menus for breakfast, lunch, and dinner. This will make it easier for pregnant women to eat a balanced diet. The service provider will make it easy for pregnant women to access advice and recipes through smartphone apps and websites. The service provider will also provide individually customized advice and recipes, taking into account the pregnant woman's preferences and allergy information. For example, if a pregnant woman is allergic to a particular ingredient, they will suggest recipes that do not include that ingredient. In this way, the service provider can provide nutritional advice and recipes that meet the individual needs of pregnant women and support their health management.

[0033] The consultation department answers pregnant women's questions based on advice provided by the service department. Specifically, it answers pregnant women's questions and anxieties about diet in a chat format. For example, if a pregnant woman asks about the nutritional value and amount of a particular food, the consultation department will provide a quick answer. Pregnant women can also consult about diet-related questions via chat and receive quick answers. For example, if a pregnant woman asks about menu choices when eating out, the consultation department will suggest healthy options. Furthermore, it provides specific advice to alleviate pregnant women's anxieties and doubts. For example, if a pregnant woman is worried about weight gain, it will provide advice on appropriate weight management methods and exercise. The consultation department can respond to pregnant women's questions 24 hours a day using an AI chatbot. The AI ​​chatbot uses natural language processing technology to understand pregnant women's questions and provide appropriate answers. It can also escalate to experts as needed to provide more detailed advice. This allows the consultation department to ensure that pregnant women can resolve their diet-related questions and anxieties anytime, anywhere, and manage their health with peace of mind.

[0034] The data collection unit can record the contents of meals eaten by pregnant women and the amounts of vitamins and minerals they ingest. For example, the data collection unit can record the contents of meals eaten by pregnant women in detail. For example, the data collection unit can determine the amounts of vitamins and minerals ingested by pregnant women. For example, the data collection unit can record the types and amounts of meals eaten by pregnant women and manage nutritional balance. This allows for the management of nutritional balance by recording the contents of meals eaten by pregnant women and the amounts of vitamins and minerals they ingest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the contents of meals eaten by pregnant women into AI, and the AI ​​can automatically calculate the amount of nutrients.

[0035] The analysis unit can analyze the health status of pregnant women in real time. For example, the analysis unit can analyze the health status of pregnant women in detail. For example, the analysis unit can continuously monitor the health status of pregnant women and detect abnormalities. For example, the analysis unit can evaluate the health status of pregnant women and provide appropriate nutritional advice. This allows for the provision of appropriate nutritional advice by analyzing the health status of pregnant women in real time. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the health data of pregnant women into AI, and the AI ​​can automatically analyze their health status.

[0036] The service provider can suggest meals and recipes to supplement nutrients that pregnant women are lacking. For example, the service provider can provide a specific meal plan to supplement nutrients that pregnant women are lacking. For example, the service provider can provide appropriate nutritional advice based on the pregnant woman's health condition. For example, the service provider can suggest recipes to improve the nutritional balance of pregnant women. This improves nutritional balance by suggesting meals and recipes to supplement nutrients that pregnant women are lacking. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the pregnant woman's nutritional data into AI, and the AI ​​can automatically suggest appropriate meals and recipes.

[0037] The consultation service can answer pregnant women's questions and concerns about diet in a chat format. For example, a pregnant woman can consult the consultation service via chat about her diet and receive a quick response. The consultation service can provide specific advice to alleviate the pregnant woman's anxieties and concerns. For example, a pregnant woman can consult the consultation service via chat about her diet questions and concerns, and the AI ​​will provide a quick response. This allows pregnant women to feel more at ease by having their diet questions and concerns answered in a chat format. Some or all of the above processes in the consultation service may be performed using AI, or not. For example, the consultation service can input a pregnant woman's question into the AI, and the AI ​​can automatically generate an answer.

[0038] The data collection unit can analyze a pregnant woman's past dietary history and select the optimal recording method. For example, the data collection unit can provide a template that allows for easy recording based on the meals the pregnant woman has frequently eaten in the past. For example, the data collection unit can suggest an automatic input function to reduce the effort of recording based on the pregnant woman's past dietary history. For example, the data collection unit can analyze the pregnant woman's past eating patterns and suggest the optimal recording frequency and method. This allows for the selection of the optimal recording method by analyzing the pregnant woman's past dietary history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the pregnant woman's past dietary data into AI, which can then automatically select the optimal recording method.

[0039] The data collection unit can filter the recorded meal content based on the pregnant woman's current physical condition and activity level. For example, if the pregnant woman is tired, the data collection unit can provide a simpler recording method to reduce her burden. For example, if the pregnant woman is very active, the data collection unit can encourage detailed recording to collect accurate data. For example, if the pregnant woman is unwell, the data collection unit can suggest temporarily suspending recording and supplementing it later. This improves the accuracy of the recording by filtering based on the pregnant woman's current physical condition and activity level. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the pregnant woman's physical condition data into AI, which can then automatically perform the filtering.

[0040] The data collection unit can prioritize recording meals that are highly relevant to the pregnant woman's location, taking into account her geographical location. For example, if the pregnant woman is in a specific region, the data collection unit will prioritize recording ingredients and dishes from that region. For example, if the pregnant woman is traveling, the data collection unit will record meals in detail at her travel destination. For example, if the pregnant woman is at home, the data collection unit will prioritize recording her daily meals. This improves the accuracy of the records by prioritizing the recording of meals that are highly relevant to the pregnant woman's location, taking into account her geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the pregnant woman's geographical location data into the AI, which can then automatically select highly relevant meals.

[0041] The data collection unit can analyze the pregnant woman's social media activity when recording meal content and record relevant meal content. For example, the data collection unit can automatically record meal content shared by the pregnant woman on social media. For example, the data collection unit can prioritize recording ingredients and dishes mentioned by the pregnant woman on social media. For example, the data collection unit can analyze meal trends from the pregnant woman's social media activity and record relevant content. By analyzing the pregnant woman's social media activity and recording relevant meal content, the accuracy of the recording can be improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the pregnant woman's social media data into AI, and the AI ​​can automatically select relevant meal content.

[0042] The analysis unit can optimize its analysis algorithm by referring to the pregnant woman's past health data when analyzing her health status. For example, the analysis unit optimizes an algorithm to predict her current health status based on her past health data. For example, the analysis unit predicts specific health risks from her past health data and incorporates them into the analysis. For example, the analysis unit refers to her past health data and performs an analysis tailored to her individual health status. By optimizing the analysis algorithm by referring to her past health data, the accuracy of the analysis can be improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the pregnant woman's past health data into AI, and the AI ​​can automatically optimize the analysis algorithm.

[0043] The analysis unit can perform health status analyses while considering the pregnant woman's lifestyle and environmental factors. For example, the analysis unit can analyze health status while considering the pregnant woman's lifestyle (exercise, sleep, etc.). For example, the analysis unit can analyze health status while considering the pregnant woman's environmental factors (living environment, work environment, etc.). For example, the analysis unit can comprehensively evaluate the pregnant woman's lifestyle and environmental factors and analyze her health status. By considering the pregnant woman's lifestyle and environmental factors in the analysis, the accuracy of the analysis can be improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the pregnant woman's lifestyle data into AI, and the AI ​​can automatically analyze her health status.

[0044] The analysis unit can perform health status analyses while considering the geographical distribution of pregnant women. For example, the analysis unit can perform analyses while considering the health risks of the area in which the pregnant women live. For example, the analysis unit can analyze region-specific health risks based on the geographical distribution of pregnant women. For example, the analysis unit can perform comparative analyses of health status by region, taking into account the geographical distribution of pregnant women. By doing so, the accuracy of the analysis can be improved by considering the geographical distribution of pregnant women. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical data of pregnant women into AI, and the AI ​​can automatically analyze their health status.

[0045] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on pregnant women when analyzing their health status. For example, the analysis unit can refer to the latest research literature on the health status of pregnant women and incorporate it into the analysis. For example, the analysis unit can refer to past research literature on the health status of pregnant women and improve the accuracy of the analysis. For example, the analysis unit can comprehensively evaluate relevant literature on the health status of pregnant women and incorporate it into the analysis. In this way, the accuracy of the analysis can be improved by referring to relevant literature on pregnant women. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input data on relevant literature on pregnant women into AI, and the AI ​​can automatically incorporate it into the analysis.

[0046] The service provider can adjust the level of detail in the advice and recipes provided based on the pregnant woman's nutritional status. For example, if the pregnant woman's nutritional status is good, the service provider will provide simple advice and recipes. If the pregnant woman's nutritional status is poor, the service provider will provide detailed advice and recipes. For example, the service provider will provide specific advice and recipes to supplement necessary nutrients based on the pregnant woman's nutritional status. By adjusting the level of detail in the advice and recipes based on the pregnant woman's nutritional status, the accuracy of the advice and recipes can be improved. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the pregnant woman's nutritional data into AI, and the AI ​​can automatically adjust the level of detail in the advice and recipes.

[0047] The service provider can apply different service algorithms depending on the pregnant woman's dietary preferences when providing advice and recipes. For example, the service provider can provide recipes using preferred ingredients based on the pregnant woman's dietary preferences. For example, the service provider can provide recipes that accommodate allergies and dietary restrictions depending on the pregnant woman's dietary preferences. For example, the service provider can provide a variety of recipes considering the pregnant woman's dietary preferences. By applying different service algorithms depending on the pregnant woman's dietary preferences, the accuracy of the provided content can be improved. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the pregnant woman's dietary preference data into AI, and the AI ​​can automatically apply a service algorithm.

[0048] The service provider can prioritize the content of advice and recipes based on the pregnant woman's dietary history. For example, the service provider can provide advice and recipes that prioritize the intake of necessary nutrients based on the pregnant woman's past dietary history. For example, the service provider can provide recipes that use ingredients that the pregnant woman frequently consumes based on her dietary history. For example, the service provider can analyze the pregnant woman's dietary history and provide advice and recipes to provide a balanced diet. This improves the accuracy of the content provided by prioritizing the content based on the pregnant woman's dietary history. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the pregnant woman's dietary history data into AI, and the AI ​​can automatically determine the priority of the content to be provided.

[0049] The service provider can adjust the order of advice and recipes based on the pregnant woman's relevance. For example, the service provider might provide the most important advice or recipe first based on the pregnant woman's health status. For example, the service provider might prioritize recipes using preferred ingredients based on the pregnant woman's dietary preferences. For example, the service provider might prioritize advice and recipes to supplement necessary nutrients based on the pregnant woman's nutritional status. By adjusting the order of the content based on the pregnant woman's relevance, the accuracy of the content can be improved. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the pregnant woman's relevant data into AI, which can then automatically adjust the order of the content.

[0050] The consultation department can provide the most appropriate answer when responding to a consultation by referring to the pregnant woman's past consultation history. For example, the consultation department can provide the most appropriate answer to similar questions based on the pregnant woman's past consultation history. For example, the consultation department can prioritize providing answers to frequently asked questions based on the pregnant woman's past consultation history. For example, the consultation department can provide answers tailored to individual needs by referring to the pregnant woman's past consultation history. This improves the accuracy of the answers by providing the most appropriate answer by referring to the pregnant woman's past consultation history. Some or all of the above processes in the consultation department may be performed using AI, for example, or not using AI. For example, the consultation department can input the pregnant woman's past consultation history data into AI, and the AI ​​can automatically generate the most appropriate answer.

[0051] The consultation department can customize its responses to inquiries based on the pregnant woman's current health condition. For example, the consultation department can provide appropriate advice considering the pregnant woman's current health condition. For example, the consultation department can provide specific answers regarding necessary nutrients and diet based on the pregnant woman's health condition. For example, the consultation department can monitor the pregnant woman's health condition in real time and provide the most appropriate answers. This improves the accuracy of responses by customizing them based on the pregnant woman's current health condition. Some or all of the above processes in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input the pregnant woman's health data into AI, which can then automatically customize the responses.

[0052] The consultation department can provide the most appropriate response to a consultation by considering the pregnant woman's geographical location. For example, the consultation department can provide information on medical institutions and support services in the area where the pregnant woman lives. For example, the consultation department can provide advice on region-specific health risks based on the pregnant woman's geographical location. For example, the consultation department can provide responses tailored to the health conditions of each region, taking into account the pregnant woman's geographical location. This improves the accuracy of responses by providing the most appropriate response by considering the pregnant woman's geographical location. Some or all of the above processing in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input the pregnant woman's geographical location data into AI, which can then automatically generate the most appropriate response.

[0053] The consultation department can analyze the pregnant woman's social media activity and suggest appropriate responses when answering questions. For example, the consultation department can provide relevant answers based on information shared by the pregnant woman on social media. For example, the consultation department can provide answers based on the pregnant woman's interests and concerns from her social media activity. For example, the consultation department can analyze the pregnant woman's social media activity and provide answers that align with current trends. By analyzing the pregnant woman's social media activity and suggesting appropriate responses, the accuracy of the answers can be improved. Some or all of the above processes in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input the pregnant woman's social media data into AI, which can then automatically generate relevant answers.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The data collection unit can automatically recognize ingredients and nutrients by taking photos of meals when recording a pregnant woman's diet, using image analysis technology. For example, when a pregnant woman takes a photo of her meal, the data collection unit analyzes the image and automatically recognizes the type and quantity of ingredients. This eliminates the need for the pregnant woman to manually input the information. In addition, by taking photos of meals, the data collection unit can evaluate the appearance and balance of the food presentation. Furthermore, by taking photos of meals, the data collection unit can evaluate the color and freshness of the food. This allows for more accurate recording of the pregnant woman's diet and better management of her nutritional balance.

[0056] The analysis department can consider pregnant women's sleep data when analyzing their health status. For example, it can analyze the duration and quality of a pregnant woman's sleep and evaluate its impact on her health. This allows for an understanding of the effects of sleep deprivation or excessive sleep on a pregnant woman's health and enables the provision of appropriate nutritional advice. The analysis department can also provide advice for improving sleep based on the pregnant woman's sleep data. Furthermore, the analysis department can comprehensively analyze the pregnant woman's sleep data in combination with other health data to provide a more accurate assessment of her health status. This allows for a more accurate understanding of the pregnant woman's health status and the provision of appropriate nutritional advice.

[0057] The service provider can take the season and weather into consideration when providing nutritional advice and recipes to pregnant women. For example, they can suggest recipes using seasonal ingredients. This allows pregnant women to consume fresh and nutritious ingredients. They can also suggest meals that are appropriate for the weather. For example, they can suggest recipes using ingredients that warm the body in cold seasons and recipes using ingredients that cool the body in hot seasons. Furthermore, by providing nutritional advice tailored to the season and weather, the service provider can support pregnant women in managing their health. This allows pregnant women to consume appropriate nutrients according to the season and weather and maintain their health.

[0058] The consultation department can consider the opinions of the pregnant woman's family and partner when answering questions and concerns about pregnant women's diets. For example, when a pregnant woman consults about planning meals to share with her family and partner, the consultation department can provide advice that reflects their opinions. This allows pregnant women to plan healthy meals in cooperation with their family and partner. The consultation department can also suggest recipes that take into account the dietary preferences and allergy information of family and partner members. Furthermore, the consultation department can provide advice to facilitate communication with family and partner members. This allows pregnant women to enjoy healthy meals in cooperation with their family and partner.

[0059] The data collection unit can automatically recognize ingredients and nutrients by taking photos of meals when recording a pregnant woman's diet, using image analysis technology. For example, when a pregnant woman takes a photo of her meal, the data collection unit analyzes the image and automatically recognizes the type and quantity of ingredients. This eliminates the need for the pregnant woman to manually input the information. In addition, by taking photos of meals, the data collection unit can evaluate the appearance and balance of the food presentation. Furthermore, by taking photos of meals, the data collection unit can evaluate the color and freshness of the food. This allows for more accurate recording of the pregnant woman's diet and better management of her nutritional balance.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects information on the pregnant woman's health status and dietary history. For example, it records the contents of the meals the pregnant woman ate and the amount of vitamins and minerals she consumed to manage her nutritional balance. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the health status of pregnant women in real time, evaluates their nutritional balance, and detects abnormalities. Step 3: The provision department provides individualized nutritional advice and recipes based on the analysis results obtained by the analysis department. For example, they suggest meals and recipes to supplement nutrients that pregnant women may be lacking and provide specific meal plans. Step 4: The consultation department answers pregnant women's questions based on the advice provided by the service department. For example, they answer pregnant women's questions and concerns about diet in a chat format and provide specific advice.

[0062] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that manages the diet and nutritional balance of pregnant women and provides individualized nutritional advice and recipes. This AI agent system monitors the pregnant woman's health status and dietary history 24 hours a day and provides advice in real time. It also responds to consultations and questions in chat format, resolving the pregnant woman's anxieties and doubts. For example, the AI ​​agent system records in detail what the pregnant woman eats and the nutrients she ingests, and understands her health status in real time. For example, by recording the contents of the meals the pregnant woman eats and the amount of vitamins and minerals she ingests, it is possible to manage her nutritional balance. Next, the AI ​​agent system analyzes the pregnant woman's health status in real time and provides individualized nutritional advice and recipes. For example, it can suggest meals and recipes to supplement nutrients that the pregnant woman is lacking. This allows the pregnant woman to appropriately consume the nutrients she needs. Furthermore, the AI ​​agent system responds to consultations and questions in chat format, resolving the pregnant woman's anxieties and doubts. For example, the pregnant woman can consult about her questions and anxieties regarding her diet via chat, and the AI ​​agent system will respond quickly, increasing the pregnant woman's sense of security. This system allows pregnant women to receive appropriate nutritional management, enabling them to have a safe and healthy pregnancy. For example, if a pregnant woman has questions or concerns about her diet, the AI ​​agent system can respond quickly, increasing her sense of security. It can also support the pregnant woman's health by suggesting meals and recipes to supplement any nutrients she may be lacking. The AI ​​agent system collects the pregnant woman's health status and dietary history, analyzes the results, and provides personalized nutritional advice, recipes, and answers questions.

[0063] The AI ​​agent system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, and a consultation unit. The data collection unit collects the pregnant woman's health status and dietary history. The data collection unit records, for example, the contents of the meals the pregnant woman ate and the amount of vitamins and minerals she ingested. The data collection unit records, for example, the contents of the meals the pregnant woman ate in detail and understands the amount of nutrients she ingested. The data collection unit records, for example, the types and amounts of meals the pregnant woman ate and manages nutritional balance. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes, for example, the pregnant woman's health status in real time. The analysis unit analyzes, for example, the pregnant woman's health status in detail and evaluates nutritional balance. The analysis unit continuously monitors the pregnant woman's health status and detects abnormalities. The data provision unit provides individual nutritional advice and recipes based on the analysis results obtained by the analysis unit. The data provision unit suggests, for example, meals and recipes to supplement nutrients that the pregnant woman is deficient in. The data provision unit provides, for example, appropriate nutritional advice based on the pregnant woman's health status. The provision department provides, for example, a specific meal plan to improve the nutritional balance of pregnant women. The consultation department answers questions from pregnant women based on the advice provided by the provision department. The consultation department answers questions and concerns about pregnant women's diets in a chat format, for example. The consultation department allows pregnant women to consult with the consultation department about their diet questions via chat and receive quick answers. The consultation department provides, for example, specific advice to alleviate the anxieties and concerns of pregnant women. As a result, the AI ​​agent system according to this embodiment can collect information on the pregnant woman's health status and dietary history, provide personalized nutritional advice and recipes based on the analysis results, and answer questions.

[0064] The data collection unit collects information on the pregnant woman's health status and dietary history. Specifically, it meticulously records the contents of the meals the pregnant woman eats daily and the amounts of vitamins and minerals she consumes. For example, it records specific meals such as fruit and yogurt eaten for breakfast, salad and soup for lunch, and fish and vegetables for dinner. It also meticulously records the type and quantity of meals to understand the amounts of nutrients contained in these meals, such as vitamin A, vitamin C, calcium, and iron. Furthermore, it collects information on supplements and vitamin drinks consumed by the pregnant woman to manage her overall nutritional balance. The data collection unit streamlines data collection by allowing pregnant women to easily input their meal details through smartphone apps and wearable devices. For example, it provides a function that automatically analyzes the meal content and calculates the amount of nutrients by allowing pregnant women to take and upload photos of their meals. It also collects health data such as the pregnant woman's weight, blood pressure, and blood sugar levels to understand her overall health status. As a result, the data collection unit can collect detailed and accurate information on the pregnant woman's health status and dietary history, which can be used for subsequent analysis and advice provision.

[0065] The analysis department analyzes data collected by the data collection department in real time to understand the health status of pregnant women. Specifically, it evaluates the nutritional balance of pregnant women based on the collected data on diet and nutrients. For example, it analyzes the amount of vitamins and minerals consumed by pregnant women to determine whether they are deficient in necessary nutrients. It also analyzes health data such as weight, blood pressure, and blood sugar levels of pregnant women to detect changes or abnormalities in their health status. Using AI-based analysis algorithms, the collected data is processed quickly and accurately, and the health status of pregnant women is monitored in real time. For example, image recognition technology is used to analyze photos of meals and automatically extract the contents of the meals and the amount of nutrients. Machine learning models can also be used to detect abnormal patterns in pregnant women's health data and issue early warnings. Furthermore, by utilizing past data and statistical information, it is possible to analyze trends in pregnant women's health status and conduct long-term risk assessments. For example, based on past dietary history and health data, it is possible to predict periods when specific nutrients are likely to be deficient and patterns of health status fluctuations, and to plan future countermeasures. In this way, the analysis department can monitor the health status of pregnant women in detail and continuously, and support appropriate nutritional management and health management.

[0066] The service provider will provide pregnant women with individualized nutritional advice and recipes based on the analysis results obtained by the analysis department. Specifically, they will suggest meals and recipes to supplement nutrients that pregnant women are lacking. For example, if a pregnant woman is iron deficient, they will provide recipes using iron-rich ingredients to encourage iron intake. They will also provide appropriate nutritional advice based on the pregnant woman's health condition. For example, if a pregnant woman has high blood pressure, they will suggest a low-salt diet to help manage her blood pressure. Furthermore, they will provide specific meal plans to improve the pregnant woman's nutritional balance. For example, they will create a one-week meal plan and suggest menus for breakfast, lunch, and dinner. This will make it easier for pregnant women to eat a balanced diet. The service provider will make it easy for pregnant women to access advice and recipes through smartphone apps and websites. The service provider will also provide individually customized advice and recipes, taking into account the pregnant woman's preferences and allergy information. For example, if a pregnant woman is allergic to a particular ingredient, they will suggest recipes that do not include that ingredient. In this way, the service provider can provide nutritional advice and recipes that meet the individual needs of pregnant women and support their health management.

[0067] The consultation department answers pregnant women's questions based on advice provided by the service department. Specifically, it answers pregnant women's questions and anxieties about diet in a chat format. For example, if a pregnant woman asks about the nutritional value and amount of a particular food, the consultation department will provide a quick answer. Pregnant women can also consult about diet-related questions via chat and receive quick answers. For example, if a pregnant woman asks about menu choices when eating out, the consultation department will suggest healthy options. Furthermore, it provides specific advice to alleviate pregnant women's anxieties and doubts. For example, if a pregnant woman is worried about weight gain, it will provide advice on appropriate weight management methods and exercise. The consultation department can respond to pregnant women's questions 24 hours a day using an AI chatbot. The AI ​​chatbot uses natural language processing technology to understand pregnant women's questions and provide appropriate answers. It can also escalate to experts as needed to provide more detailed advice. This allows the consultation department to ensure that pregnant women can resolve their diet-related questions and anxieties anytime, anywhere, and manage their health with peace of mind.

[0068] The data collection unit can record the contents of meals eaten by pregnant women and the amounts of vitamins and minerals they ingest. For example, the data collection unit can record the contents of meals eaten by pregnant women in detail. For example, the data collection unit can determine the amounts of vitamins and minerals ingested by pregnant women. For example, the data collection unit can record the types and amounts of meals eaten by pregnant women and manage nutritional balance. This allows for the management of nutritional balance by recording the contents of meals eaten by pregnant women and the amounts of vitamins and minerals they ingest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the contents of meals eaten by pregnant women into AI, and the AI ​​can automatically calculate the amount of nutrients.

[0069] The analysis unit can analyze the health status of pregnant women in real time. For example, the analysis unit can analyze the health status of pregnant women in detail. For example, the analysis unit can continuously monitor the health status of pregnant women and detect abnormalities. For example, the analysis unit can evaluate the health status of pregnant women and provide appropriate nutritional advice. This allows for the provision of appropriate nutritional advice by analyzing the health status of pregnant women in real time. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the health data of pregnant women into AI, and the AI ​​can automatically analyze their health status.

[0070] The service provider can suggest meals and recipes to supplement nutrients that pregnant women are lacking. For example, the service provider can provide a specific meal plan to supplement nutrients that pregnant women are lacking. For example, the service provider can provide appropriate nutritional advice based on the pregnant woman's health condition. For example, the service provider can suggest recipes to improve the nutritional balance of pregnant women. This improves nutritional balance by suggesting meals and recipes to supplement nutrients that pregnant women are lacking. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the pregnant woman's nutritional data into AI, and the AI ​​can automatically suggest appropriate meals and recipes.

[0071] The consultation service can answer pregnant women's questions and concerns about diet in a chat format. For example, a pregnant woman can consult the consultation service via chat about her diet and receive a quick response. The consultation service can provide specific advice to alleviate the pregnant woman's anxieties and concerns. For example, a pregnant woman can consult the consultation service via chat about her diet questions and concerns, and the AI ​​will provide a quick response. This allows pregnant women to feel more at ease by having their diet questions and concerns answered in a chat format. Some or all of the above processes in the consultation service may be performed using AI, or not. For example, the consultation service can input a pregnant woman's question into the AI, and the AI ​​can automatically generate an answer.

[0072] The data collection unit can estimate the pregnant woman's emotions and adjust the frequency of recording her meals based on the estimated emotions. For example, if the pregnant woman is stressed, the data collection unit can reduce the recording frequency to alleviate her burden. For example, if the pregnant woman is relaxed, the data collection unit can encourage more detailed recording to collect more accurate data. For example, if the pregnant woman is anxious, the data collection unit can provide a simpler recording method to lower the barrier to recording. This reduces the burden of recording by adjusting the frequency of recording meals based on the pregnant woman's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the pregnant woman's emotion data into an AI, which can automatically estimate her emotions and adjust the recording frequency.

[0073] The data collection unit can analyze a pregnant woman's past dietary history and select the optimal recording method. For example, the data collection unit can provide a template that allows for easy recording based on the meals the pregnant woman has frequently eaten in the past. For example, the data collection unit can suggest an automatic input function to reduce the effort of recording based on the pregnant woman's past dietary history. For example, the data collection unit can analyze the pregnant woman's past eating patterns and suggest the optimal recording frequency and method. This allows for the selection of the optimal recording method by analyzing the pregnant woman's past dietary history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the pregnant woman's past dietary data into AI, which can then automatically select the optimal recording method.

[0074] The data collection unit can filter the recorded meal content based on the pregnant woman's current physical condition and activity level. For example, if the pregnant woman is tired, the data collection unit can provide a simpler recording method to reduce her burden. For example, if the pregnant woman is very active, the data collection unit can encourage detailed recording to collect accurate data. For example, if the pregnant woman is unwell, the data collection unit can suggest temporarily suspending recording and supplementing it later. This improves the accuracy of the recording by filtering based on the pregnant woman's current physical condition and activity level. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the pregnant woman's physical condition data into AI, which can then automatically perform the filtering.

[0075] The data collection unit can estimate the pregnant woman's emotions and determine the priority of meals to record based on the estimated emotions. For example, if the pregnant woman is stressed, the data collection unit prioritizes recording important nutrients and omits detailed recording. For example, if the pregnant woman is relaxed, the data collection unit records all meals in detail. For example, if the pregnant woman is anxious, the data collection unit prioritizes simple recordings and supplements details later. This improves the efficiency of recording by determining the priority of meals to record based on the pregnant woman's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input the pregnant woman's emotion data into an AI, which can automatically estimate emotions and determine the priority of recordings.

[0076] The data collection unit can prioritize recording meals that are highly relevant to the pregnant woman's location, taking into account her geographical location. For example, if the pregnant woman is in a specific region, the data collection unit will prioritize recording ingredients and dishes from that region. For example, if the pregnant woman is traveling, the data collection unit will record meals in detail at her travel destination. For example, if the pregnant woman is at home, the data collection unit will prioritize recording her daily meals. This improves the accuracy of the records by prioritizing the recording of meals that are highly relevant to the pregnant woman's location, taking into account her geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the pregnant woman's geographical location data into the AI, which can then automatically select highly relevant meals.

[0077] The data collection unit can analyze the pregnant woman's social media activity when recording meal content and record relevant meal content. For example, the data collection unit can automatically record meal content shared by the pregnant woman on social media. For example, the data collection unit can prioritize recording ingredients and dishes mentioned by the pregnant woman on social media. For example, the data collection unit can analyze meal trends from the pregnant woman's social media activity and record relevant content. By analyzing the pregnant woman's social media activity and recording relevant meal content, the accuracy of the recording can be improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the pregnant woman's social media data into AI, and the AI ​​can automatically select relevant meal content.

[0078] The analysis unit can estimate the pregnant woman's emotions and adjust the health analysis method based on the estimated emotions. For example, if the pregnant woman is stressed, the analysis unit will focus on stress reduction. For example, if the pregnant woman is relaxed, the analysis unit will perform a detailed analysis of her overall health. For example, if the pregnant woman is anxious, the analysis unit will provide analysis results to provide reassurance. This improves the accuracy of the analysis by adjusting the health analysis method based on the pregnant woman's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input the pregnant woman's emotion data into an AI, which can automatically estimate the emotions and adjust the analysis method.

[0079] The analysis unit can optimize its analysis algorithm by referring to the pregnant woman's past health data when analyzing her health status. For example, the analysis unit optimizes an algorithm to predict her current health status based on her past health data. For example, the analysis unit predicts specific health risks from her past health data and incorporates them into the analysis. For example, the analysis unit refers to her past health data and performs an analysis tailored to her individual health status. By optimizing the analysis algorithm by referring to her past health data, the accuracy of the analysis can be improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the pregnant woman's past health data into AI, and the AI ​​can automatically optimize the analysis algorithm.

[0080] The analysis unit can perform health status analyses while considering the pregnant woman's lifestyle and environmental factors. For example, the analysis unit can analyze health status while considering the pregnant woman's lifestyle (exercise, sleep, etc.). For example, the analysis unit can analyze health status while considering the pregnant woman's environmental factors (living environment, work environment, etc.). For example, the analysis unit can comprehensively evaluate the pregnant woman's lifestyle and environmental factors and analyze her health status. By considering the pregnant woman's lifestyle and environmental factors in the analysis, the accuracy of the analysis can be improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the pregnant woman's lifestyle data into AI, and the AI ​​can automatically analyze her health status.

[0081] The analysis unit can estimate the pregnant woman's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the pregnant woman is tense, the analysis unit provides a simple and highly visible display method. For example, if the pregnant woman is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the pregnant woman is feeling anxious, the analysis unit provides a display method that provides reassurance. By adjusting the display method of the analysis results based on the pregnant woman's emotions, the accuracy of the display can be improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the pregnant woman's emotion data into AI, which can automatically estimate the emotions and adjust the display method.

[0082] The analysis unit can perform health status analyses while considering the geographical distribution of pregnant women. For example, the analysis unit can perform analyses while considering the health risks of the area in which the pregnant women live. For example, the analysis unit can analyze region-specific health risks based on the geographical distribution of pregnant women. For example, the analysis unit can perform comparative analyses of health status by region, taking into account the geographical distribution of pregnant women. By doing so, the accuracy of the analysis can be improved by considering the geographical distribution of pregnant women. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical data of pregnant women into AI, and the AI ​​can automatically analyze their health status.

[0083] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on pregnant women when analyzing their health status. For example, the analysis unit can refer to the latest research literature on the health status of pregnant women and incorporate it into the analysis. For example, the analysis unit can refer to past research literature on the health status of pregnant women and improve the accuracy of the analysis. For example, the analysis unit can comprehensively evaluate relevant literature on the health status of pregnant women and incorporate it into the analysis. In this way, the accuracy of the analysis can be improved by referring to relevant literature on pregnant women. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input data on relevant literature on pregnant women into AI, and the AI ​​can automatically incorporate it into the analysis.

[0084] The service provider can estimate the pregnant woman's emotions and adjust the way advice and recipes are presented based on the estimated emotions. For example, if the pregnant woman is stressed, the service provider will provide simple and easy-to-understand language. For example, if the pregnant woman is relaxed, the service provider will provide language that includes detailed information. For example, if the pregnant woman is anxious, the service provider will provide language that provides reassurance. By adjusting the way advice and recipes are presented based on the pregnant woman's emotions, the accuracy of the presentation can be improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the pregnant woman's emotion data into an AI, which can automatically estimate the emotions and adjust the presentation.

[0085] The service provider can adjust the level of detail in the advice and recipes provided based on the pregnant woman's nutritional status. For example, if the pregnant woman's nutritional status is good, the service provider will provide simple advice and recipes. If the pregnant woman's nutritional status is poor, the service provider will provide detailed advice and recipes. For example, the service provider will provide specific advice and recipes to supplement necessary nutrients based on the pregnant woman's nutritional status. By adjusting the level of detail in the advice and recipes based on the pregnant woman's nutritional status, the accuracy of the advice and recipes can be improved. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the pregnant woman's nutritional data into AI, and the AI ​​can automatically adjust the level of detail in the advice and recipes.

[0086] The service provider can apply different service algorithms depending on the pregnant woman's dietary preferences when providing advice and recipes. For example, the service provider can provide recipes using preferred ingredients based on the pregnant woman's dietary preferences. For example, the service provider can provide recipes that accommodate allergies and dietary restrictions depending on the pregnant woman's dietary preferences. For example, the service provider can provide a variety of recipes considering the pregnant woman's dietary preferences. By applying different service algorithms depending on the pregnant woman's dietary preferences, the accuracy of the provided content can be improved. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the pregnant woman's dietary preference data into AI, and the AI ​​can automatically apply a service algorithm.

[0087] The service provider can estimate the pregnant woman's emotions and adjust the length of advice and recipes based on the estimated emotions. For example, if the pregnant woman is stressed, the service provider will provide short, concise advice and recipes. If the pregnant woman is relaxed, the service provider will provide longer advice and recipes with more detailed explanations. If the pregnant woman is anxious, the service provider will provide reassuring advice and recipes. By adjusting the length of advice and recipes based on the pregnant woman's emotions, the accuracy of the provided content can be improved. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input the pregnant woman's emotion data into an AI, which can automatically estimate the emotions and adjust the length of advice and recipes.

[0088] The service provider can prioritize the content of advice and recipes based on the pregnant woman's dietary history. For example, the service provider can provide advice and recipes that prioritize the intake of necessary nutrients based on the pregnant woman's past dietary history. For example, the service provider can provide recipes that use ingredients that the pregnant woman frequently consumes based on her dietary history. For example, the service provider can analyze the pregnant woman's dietary history and provide advice and recipes to provide a balanced diet. This improves the accuracy of the content provided by prioritizing the content based on the pregnant woman's dietary history. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the pregnant woman's dietary history data into AI, and the AI ​​can automatically determine the priority of the content to be provided.

[0089] The service provider can adjust the order of advice and recipes based on the pregnant woman's relevance. For example, the service provider might provide the most important advice or recipe first based on the pregnant woman's health status. For example, the service provider might prioritize recipes using preferred ingredients based on the pregnant woman's dietary preferences. For example, the service provider might prioritize advice and recipes to supplement necessary nutrients based on the pregnant woman's nutritional status. By adjusting the order of the content based on the pregnant woman's relevance, the accuracy of the content can be improved. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the pregnant woman's relevant data into AI, which can then automatically adjust the order of the content.

[0090] The consultation department can estimate the pregnant woman's emotions and adjust the way it answers the consultation based on the estimated emotions. For example, if the pregnant woman is feeling stressed, the consultation department will provide a simple and easy-to-understand answer. For example, if the pregnant woman is relaxed, the consultation department will provide an answer that includes detailed information. For example, if the pregnant woman is feeling anxious, the consultation department will provide an answer that provides reassurance. In this way, the accuracy of the answers can be improved by adjusting the way the consultation is answered based on the pregnant woman's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the consultation department may be performed using AI, for example, or not using AI. For example, the consultation department can input the pregnant woman's emotion data into an AI, which can automatically estimate the emotions and adjust the answer method.

[0091] The consultation department can provide the most appropriate answer when responding to a consultation by referring to the pregnant woman's past consultation history. For example, the consultation department can provide the most appropriate answer to similar questions based on the pregnant woman's past consultation history. For example, the consultation department can prioritize providing answers to frequently asked questions based on the pregnant woman's past consultation history. For example, the consultation department can provide answers tailored to individual needs by referring to the pregnant woman's past consultation history. This improves the accuracy of the answers by providing the most appropriate answer by referring to the pregnant woman's past consultation history. Some or all of the above processes in the consultation department may be performed using AI, for example, or not using AI. For example, the consultation department can input the pregnant woman's past consultation history data into AI, and the AI ​​can automatically generate the most appropriate answer.

[0092] The consultation department can customize its responses to inquiries based on the pregnant woman's current health condition. For example, the consultation department can provide appropriate advice considering the pregnant woman's current health condition. For example, the consultation department can provide specific answers regarding necessary nutrients and diet based on the pregnant woman's health condition. For example, the consultation department can monitor the pregnant woman's health condition in real time and provide the most appropriate answers. This improves the accuracy of responses by customizing them based on the pregnant woman's current health condition. Some or all of the above processes in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input the pregnant woman's health data into AI, which can then automatically customize the responses.

[0093] The consultation department can estimate the pregnant woman's emotions and prioritize the consultation content based on the estimated emotions. For example, if the pregnant woman is feeling stressed, the consultation department will prioritize answering important consultation topics. For example, if the pregnant woman is relaxed, the consultation department will provide detailed answers to all consultation topics. For example, if the pregnant woman is feeling anxious, the consultation department will prioritize answering consultation topics that will provide reassurance. This improves the accuracy of the answers by prioritizing consultation content based on the pregnant woman's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the consultation department may be performed using AI, for example, or not using AI. For example, the consultation department can input the pregnant woman's emotion data into an AI, which can automatically estimate the emotions and determine the priorities.

[0094] The consultation department can provide the most appropriate response to a consultation by considering the pregnant woman's geographical location. For example, the consultation department can provide information on medical institutions and support services in the area where the pregnant woman lives. For example, the consultation department can provide advice on region-specific health risks based on the pregnant woman's geographical location. For example, the consultation department can provide responses tailored to the health conditions of each region, taking into account the pregnant woman's geographical location. This improves the accuracy of responses by providing the most appropriate response by considering the pregnant woman's geographical location. Some or all of the above processing in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input the pregnant woman's geographical location data into AI, which can then automatically generate the most appropriate response.

[0095] The consultation department can analyze the pregnant woman's social media activity and suggest appropriate responses when answering questions. For example, the consultation department can provide relevant answers based on information shared by the pregnant woman on social media. For example, the consultation department can provide answers based on the pregnant woman's interests and concerns from her social media activity. For example, the consultation department can analyze the pregnant woman's social media activity and provide answers that align with current trends. By analyzing the pregnant woman's social media activity and suggesting appropriate responses, the accuracy of the answers can be improved. Some or all of the above processes in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input the pregnant woman's social media data into AI, which can then automatically generate relevant answers.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The data collection unit can automatically recognize ingredients and nutrients by taking photos of meals when recording a pregnant woman's diet, using image analysis technology. For example, when a pregnant woman takes a photo of her meal, the data collection unit analyzes the image and automatically recognizes the type and quantity of ingredients. This eliminates the need for the pregnant woman to manually input the information. In addition, by taking photos of meals, the data collection unit can evaluate the appearance and balance of the food presentation. Furthermore, by taking photos of meals, the data collection unit can evaluate the color and freshness of the food. This allows for more accurate recording of the pregnant woman's diet and better management of her nutritional balance.

[0098] The analysis department can consider pregnant women's sleep data when analyzing their health status. For example, it can analyze the duration and quality of a pregnant woman's sleep and evaluate its impact on her health. This allows for an understanding of the effects of sleep deprivation or excessive sleep on a pregnant woman's health and enables the provision of appropriate nutritional advice. The analysis department can also provide advice for improving sleep based on the pregnant woman's sleep data. Furthermore, the analysis department can comprehensively analyze the pregnant woman's sleep data in combination with other health data to provide a more accurate assessment of her health status. This allows for a more accurate understanding of the pregnant woman's health status and the provision of appropriate nutritional advice.

[0099] The service provider can take the season and weather into consideration when providing nutritional advice and recipes to pregnant women. For example, they can suggest recipes using seasonal ingredients. This allows pregnant women to consume fresh and nutritious ingredients. They can also suggest meals that are appropriate for the weather. For example, they can suggest recipes using ingredients that warm the body in cold seasons and recipes using ingredients that cool the body in hot seasons. Furthermore, by providing nutritional advice tailored to the season and weather, the service provider can support pregnant women in managing their health. This allows pregnant women to consume appropriate nutrients according to the season and weather and maintain their health.

[0100] The consultation department can consider the opinions of the pregnant woman's family and partner when answering questions and concerns about pregnant women's diets. For example, when a pregnant woman consults about planning meals to share with her family and partner, the consultation department can provide advice that reflects their opinions. This allows pregnant women to plan healthy meals in cooperation with their family and partner. The consultation department can also suggest recipes that take into account the dietary preferences and allergy information of family and partner members. Furthermore, the consultation department can provide advice to facilitate communication with family and partner members. This allows pregnant women to enjoy healthy meals in cooperation with their family and partner.

[0101] The data collection unit can estimate the pregnant woman's emotions and customize the method of recording her meals based on the estimated emotions. For example, if the pregnant woman is stressed, it can provide a simpler recording method to reduce her burden. If the pregnant woman is relaxed, for example, the data collection unit can encourage more detailed recording to collect more accurate data. If the pregnant woman is anxious, for example, the data collection unit can provide a recording method that provides reassurance. By customizing the method of recording meals based on the pregnant woman's emotions, the burden of recording can be reduced and accurate data can be collected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The analysis unit can estimate a pregnant woman's emotions and customize the health analysis results based on the estimated emotions. For example, if a pregnant woman is stressed, the analysis unit can provide analysis results that focus on stress reduction. If a pregnant woman is relaxed, the analysis unit can perform a detailed analysis of her overall health. If a pregnant woman is anxious, the analysis unit can provide analysis results that provide reassurance. By customizing the health analysis results based on the pregnant woman's emotions, appropriate advice can be provided that meets the pregnant woman's needs. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The service provider can estimate the pregnant woman's emotions and adjust the timing of advice and recipe delivery based on the estimated emotions. For example, if the pregnant woman is feeling stressed, advice and recipes can be provided during times when she can relax. If the pregnant woman is relaxed, the service provider can provide advice and recipes with more detailed information. If the pregnant woman is feeling anxious, the service provider can provide advice and recipes to help her feel at ease. By adjusting the timing of advice and recipe delivery based on the pregnant woman's emotions, appropriate support can be provided that meets the pregnant woman's needs. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The counseling service can estimate the emotions of pregnant women and customize the way it answers their questions based on those estimated emotions. For example, if a pregnant woman is feeling stressed, it can provide a simple and easy-to-understand answer. If a pregnant woman is relaxed, it can provide an answer that includes detailed information. If a pregnant woman is feeling anxious, it can provide an answer that provides reassurance. By customizing the way it answers questions based on the emotions of pregnant women, it is possible to provide appropriate support that meets their needs. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The service provider can estimate the pregnant woman's emotions and customize the way advice and recipes are presented based on those estimated emotions. For example, if the pregnant woman is stressed, it can provide simple and easy-to-understand language. If the pregnant woman is relaxed, it can provide language that includes detailed information. If the pregnant woman is anxious, it can provide language that provides reassurance. By customizing the way advice and recipes are presented based on the pregnant woman's emotions, appropriate support can be provided that meets the pregnant woman's needs. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The data collection unit can automatically recognize ingredients and nutrients by taking photos of meals when recording a pregnant woman's diet, using image analysis technology. For example, when a pregnant woman takes a photo of her meal, the data collection unit analyzes the image and automatically recognizes the type and quantity of ingredients. This eliminates the need for the pregnant woman to manually input the information. In addition, by taking photos of meals, the data collection unit can evaluate the appearance and balance of the food presentation. Furthermore, by taking photos of meals, the data collection unit can evaluate the color and freshness of the food. This allows for more accurate recording of the pregnant woman's diet and better management of her nutritional balance.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data collection unit collects information on the pregnant woman's health status and dietary history. For example, it records the contents of the meals the pregnant woman ate and the amount of vitamins and minerals she consumed to manage her nutritional balance. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the health status of pregnant women in real time, evaluates their nutritional balance, and detects abnormalities. Step 3: The provision department provides individualized nutritional advice and recipes based on the analysis results obtained by the analysis department. For example, they suggest meals and recipes to supplement nutrients that pregnant women may be lacking and provide specific meal plans. Step 4: The consultation department answers pregnant women's questions based on the advice provided by the service department. For example, they answer pregnant women's questions and concerns about diet in a chat format and provide specific advice.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and consultation unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit records the pregnant woman's diet and health status using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A collects the data. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data in real time. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates individual nutritional advice and recipes based on the analysis results. The consultation unit is implemented, for example, by the control unit 46A of the smart device 14, and answers the pregnant woman's questions in a chat format. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and consultation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit records the pregnant woman's diet and health status using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A collects the data. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data in real time. The provision unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and generates individual nutritional advice and recipes based on the analysis results. The consultation unit is implemented, for example, in the control unit 46A of the smart glasses 214, and answers the pregnant woman's questions in a chat format. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and consultation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit records the pregnant woman's diet and health status using the camera 42 and microphone 238 of the headset terminal 314, and the control unit 46A collects the data. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and analyzes the collected data in real time. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and generates individual nutritional advice and recipes based on the analysis results. The consultation unit is implemented by, for example, the control unit 46A of the headset terminal 314, and answers the pregnant woman's questions in a chat format. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and consultation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the robot 414 to record the pregnant woman's diet and health status, and the control unit 46A collects the data. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and analyzes the collected data in real time. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and generates individual nutritional advice and recipes based on the analysis results. The consultation unit is implemented by, for example, the control unit 46A of the robot 414, and answers the pregnant woman's questions in a chat format. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] 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.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A collection department that collects information on the health status and dietary history of pregnant women, An analysis unit analyzes the data collected by the aforementioned collection unit, A provision unit provides individual nutritional advice and recipes based on the analysis results obtained by the aforementioned analysis unit. The system includes a consultation unit that answers questions from pregnant women based on the advice provided by the aforementioned service unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Record the contents of the meals eaten by pregnant women and the amount of vitamins and minerals they consume. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Analyze the health status of pregnant women in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, We suggest meals and recipes to supplement nutrients that pregnant women may be lacking. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned consultation department, We answer questions and concerns about pregnant women's diets in a chat format. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the emotions of pregnant women and adjusts the frequency of recording their meals based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the past dietary history of pregnant women and select the optimal recording method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When recording food intake, filter the data based on the pregnant woman's current physical condition and activity level. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates the pregnant woman's emotions and determines the priority of the meals to be recorded based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When recording meal content, prioritize recording meals that are highly relevant to the pregnant woman's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When recording dietary information, analyze the social media activity of pregnant women and record related dietary information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is We estimate the emotions of pregnant women and adjust the health analysis method based on the estimated emotions of pregnant women. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is When analyzing health status, the analysis algorithm is optimized by referring to the pregnant woman's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is When analyzing a pregnant woman's health status, the analysis should take into account her lifestyle and environmental factors. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is The system estimates the emotions of pregnant women and adjusts the display method of the analysis results based on the estimated emotions of the pregnant women. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is When analyzing health status, the geographical distribution of pregnant women should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is When analyzing health status, we refer to relevant literature on pregnant women to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, The system estimates the emotions of pregnant women and adjusts the wording of advice and recipes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing advice or recipes, we adjust the level of detail based on the pregnant woman's nutritional status. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing advice or recipes, different serving algorithms are applied depending on the pregnant woman's dietary preferences. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, The system estimates the pregnant woman's emotions and adjusts the length of advice and recipes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing advice or recipes, we prioritize the content based on the pregnant woman's dietary history. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing advice or recipes, the order of the content will be adjusted based on its relevance to the pregnant woman. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned consultation department, The system estimates the pregnant woman's emotions and adjusts the method of responding to her inquiries based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned consultation department, When responding to a consultation, we refer to the pregnant woman's past consultation history to provide the most appropriate answer. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned consultation department, When responding to inquiries, the response will be customized based on the pregnant woman's current health condition. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned consultation department, The system estimates the pregnant woman's emotions and prioritizes the topics of consultation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned consultation department, When responding to inquiries, we will provide the most appropriate answer by taking into account the pregnant woman's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned consultation department, When responding to inquiries, we analyze the pregnant woman's social media activity and propose appropriate responses. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection department that collects information on the health status and dietary history of pregnant women, An analysis unit analyzes the data collected by the aforementioned collection unit, A provision unit provides individual nutritional advice and recipes based on the analysis results obtained by the aforementioned analysis unit. The system includes a consultation unit that answers questions from pregnant women based on the advice provided by the aforementioned service unit. A system characterized by the following features.

2. The aforementioned collection unit is Record the contents of the meals eaten by pregnant women and the amount of vitamins and minerals they consume. The system according to feature 1.

3. The aforementioned analysis unit is Analyze the health status of pregnant women in real time. The system according to feature 1.

4. The aforementioned supply unit is, We suggest meals and recipes to supplement nutrients that pregnant women may be lacking. The system according to feature 1.

5. The aforementioned consultation department, We answer questions and concerns about pregnant women's diets in a chat format. The system according to feature 1.

6. The aforementioned collection unit is The system estimates the emotions of pregnant women and adjusts the frequency of recording their meals based on these estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the past dietary history of pregnant women and select the optimal recording method. The system according to feature 1.

8. The aforementioned collection unit is When recording food intake, filter the data based on the pregnant woman's current physical condition and activity level. The system according to feature 1.

9. The aforementioned collection unit is The system estimates the pregnant woman's emotions and determines the priority of the meals to be recorded based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is When recording meal content, prioritize recording meals that are highly relevant to the pregnant woman's geographical location. The system according to feature 1.

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