system

Abstract

The system according to this embodiment aims to modify the meal plan in real time based on the patient's daily meal log and physical condition data. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a modification unit, and a response unit. The collection unit collects the patient's daily meal logs and physical condition data. The analysis unit analyzes the data collected by the collection unit. The modification unit modifies the meal plan based on the analysis results obtained by the analysis unit. The response unit responds to the patient's questions and concerns.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, a meal plan is not modified in real time based on a patient's daily meal log and physical condition data, and there is room for improvement.

[0005] The system according to the embodiment aims to modify a meal plan in real time based on a patient's daily meal log and physical condition data.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a modification unit, and a response unit. The data collection unit collects the patient's daily meal logs and physical condition data. The analysis unit analyzes the data collected by the data collection unit. The modification unit modifies the meal plan based on the analysis results obtained by the analysis unit. The response unit responds to the patient's questions and concerns. [Effects of the Invention]

[0007] The system according to this embodiment can modify the meal plan in real time based on the patient's daily meal log and physical condition data. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a reception 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 reception 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) A health management system according to an embodiment of the present invention is a system that autonomously monitors a patient's daily meal logs and physical condition data, and modifies the meal plan in real time as needed. This health management system collects the patient's daily meal logs and physical condition data, analyzes it with AI, and modifies the meal plan as needed. In addition, if the patient has questions or concerns about their diet, the AI ​​responds immediately in a chat format. This helps to improve the patient's health. For example, the health management system collects data entered by the patient using a smartphone or wearable device. Next, the health management system's AI analyzes the collected data and modifies the meal plan as needed. For example, if the patient's blood sugar level is high, the meal plan is modified to reduce sugar intake. The health management system can also create personalized meal plans based on previous patient cases and the latest medical research. For example, the health management system analyzes data based on previous patient cases and the latest medical research to provide the patient with the optimal meal plan. Furthermore, if the patient has questions or concerns about their diet, the AI ​​responds immediately in a chat format. For example, if the patient sends a question about their diet via chat, the AI ​​provides an immediate answer. This allows the patient to quickly resolve their questions about their diet. This allows the health management system to autonomously monitor patients' daily meal logs and physical condition data, and modify meal plans in real time as needed.

[0029] The health management system according to this embodiment comprises a collection unit, an analysis unit, a modification unit, and a response unit. The collection unit collects the patient's daily meal logs and physical condition data. The collection unit collects data entered by the patient using, for example, a smartphone or wearable device. The collection unit can collect the data entered by the patient in real time and store it in a database. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, AI to evaluate the patient's health status. The analysis unit can analyze the data based on previous patient cases and the latest medical research. The modification unit modifies the meal plan based on the analysis results obtained by the analysis unit. The modification unit modifies the meal plan to reduce sugar intake if the patient's blood glucose level is high, for example. The modification unit can modify the meal plan in real time according to the patient's health status. The response unit responds to the patient's questions and concerns. The response unit responds immediately in a chat format when the patient has questions or concerns about their diet. The response unit can provide answers to the patient's questions using AI. As a result, the health management system according to this embodiment can autonomously monitor the patient's daily meal logs and physical condition data, and modify the meal plan in real time as needed.

[0030] The data collection unit collects patients' daily meal logs and physical condition data. For example, it collects data entered by patients using smartphones or wearable devices. Specifically, through a smartphone application, patients can record their daily meals with photos and text, and input calorie and nutrient information. Wearable devices automatically record physical condition data such as heart rate, steps, calories burned, and sleep patterns, and transmit this data to the data collection unit. This data is collected in real time and stored in a central database. To ensure data accuracy and consistency, the data collection unit standardizes data entry methods and formats, making it easy for patients to input data. Furthermore, to ensure data privacy and security, the data collection unit protects data using encryption technology to prevent the leakage of patients' personal information. In addition, the data collection unit regularly backs up patient data to prevent data loss or corruption. This allows the data collection unit to efficiently and securely collect patients' daily meal logs and physical condition data, improving the overall reliability of the system.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses AI to analyze the data and assess the patient's health status. Specifically, the AI ​​uses machine learning algorithms to extract patterns and trends from the collected data and assess the patient's health status. For example, it analyzes calorie intake and nutrient balance from meal logs and assesses exercise levels and sleep quality from physical condition data. Furthermore, the analysis unit can analyze data based on previous patient cases and the latest medical research. This allows the analysis unit to comprehensively assess the patient's health status and detect potential health risks early. The analysis unit can analyze data in real time using AI and respond quickly to changes in the patient's health status. For example, if a patient's blood glucose level suddenly rises, the analysis unit can immediately identify the cause and propose appropriate countermeasures. Additionally, the analysis unit can analyze long-term trends in the patient's health status based on past data and predict future health risks. This allows the analysis unit to continuously monitor the patient's health status and support appropriate health management.

[0032] The modification unit modifies the meal plan based on the analysis results obtained by the analysis unit. For example, if a patient's blood glucose level is high, the modification unit modifies the meal plan to reduce sugar intake. Specifically, the modification unit uses AI to automatically generate an optimal meal plan tailored to the patient's health condition. For example, if a patient's blood glucose level is high, it suggests recipes using low-carbohydrate ingredients to reduce sugar intake. It also selects ingredients containing necessary vitamins and minerals, taking into account the patient's nutritional balance, and incorporates them into the meal plan. The modification unit can modify the meal plan in real time according to the patient's health condition. For example, if a patient's weight increases, it suggests recipes using low-calorie ingredients to reduce calorie intake. Also, if a patient's exercise level increases, it suggests an appropriate calorie intake for energy replenishment. The modification unit regularly reviews the patient's meal plan and provides the optimal plan based on the latest health condition. This allows the modification unit to provide an effective meal plan to maintain and improve the patient's health condition.

[0033] The support unit handles patients' questions and concerns. For example, if a patient has questions or concerns about their diet, the support unit will respond immediately in a chat format. Specifically, the support unit can use AI to provide answers to patients' questions. For example, if a patient asks about the nutritional value of a particular food item, the AI ​​will search the database for the relevant information and provide a quick answer. Also, if a patient has questions about their meal plan, the AI ​​will provide appropriate advice based on data from the analysis and modification units. In addition to providing answers to patients' questions, the support unit can also collect patient feedback and use it to improve the overall system. For example, if a patient is not satisfied with the answer provided, that feedback can be used as training data to improve the accuracy of the AI's answers. Furthermore, the support unit can quickly respond to urgent questions and concerns regarding the patient's health condition. For example, if a patient complains of a sudden illness, the AI ​​will suggest appropriate measures and, if necessary, recommend a visit to a medical institution. In this way, the support unit can respond quickly and accurately to patients' questions and concerns and support their health management.

[0034] The data collection unit can collect data entered by patients using smartphones or wearable devices. For example, if a patient enters a meal log using a smartphone, the data collection unit can collect that data. The data collection unit can also collect data if a patient enters physical condition data using a wearable device. For example, the data collection unit can collect data from wearable devices such as smartwatches or fitness trackers. This improves the convenience of data collection by collecting data entered by patients using smartphones or wearable devices. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data acquired from smartphones or wearable devices into a generating AI and have the generating AI perform the data collection.

[0035] The analysis unit can analyze data based on previous patient cases and the latest medical research. For example, the analysis unit can retrieve previous patient cases from a database and use them in the analysis. The analysis unit can also refer to the latest medical research and incorporate it into the analysis. For example, the analysis unit can analyze data based on the latest papers and clinical trial results. By analyzing data based on previous patient cases and the latest medical research, the accuracy of the analysis is improved. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data from previous patient cases and the latest medical research into a generating AI and have the generating AI perform the data analysis.

[0036] The modification unit can modify the meal plan to reduce sugar intake when the patient's blood glucose level is high. For example, if the patient's blood glucose level exceeds a certain standard, the modification unit will modify the meal plan to reduce sugar intake. The modification unit can also modify the meal plan in real time according to the patient's health condition. For example, if the patient's blood glucose level is high, the modification unit will not only reduce sugar intake but also modify the meal plan considering the balance of other nutrients. This improves the patient's health condition by modifying the meal plan to reduce sugar intake when the patient's blood glucose level is high. Some or all of the above processing in the modification unit may be performed using AI or not. For example, the modification unit can input the patient's blood glucose data into a generating AI and have the generating AI perform the modification of the meal plan.

[0037] The support unit can immediately respond to patients' questions and concerns regarding their diet in a chat format. For example, when a patient sends a question about their diet via chat, the AI ​​provides an immediate answer. The support unit can also provide answers to patients' questions in real time. For example, when a patient has a question about their diet, the support unit uses a chatbot to provide an immediate answer. This allows for the rapid resolution of patients' concerns by providing immediate chat support when they have questions or concerns about their diet. Some or all of the above-described processes in the support unit may be performed using AI or not. For example, the support unit can input the patient's question into a generating AI and have the generating AI generate the answer.

[0038] The data collection unit can analyze the patient's past meal logs and physical condition data to select the optimal data collection method. For example, the data collection unit can analyze patterns in data previously entered by the patient to determine the optimal collection time. The data collection unit can also analyze the patient's response to specific meals from their past data and adjust the collection method. For example, the data collection unit can optimize the frequency of data collection based on the patient's physical condition data. This allows the optimal data collection method to be selected by analyzing the patient's past data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the patient's past data into a generating AI and have the generating AI select the optimal data collection method.

[0039] The data collection unit can filter data based on the patient's current health status and lifestyle. For example, the data collection unit can collect only important data based on the patient's current health status. The data collection unit can also adjust the timing of data collection, taking into account the patient's lifestyle. For example, the data collection unit can temporarily suspend data collection if the patient's health status deteriorates. This allows for the collection of only important data by filtering the data based on the patient's current health status and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input patient health status data into a generating AI and have the generating AI perform data filtering.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the patient's geographical location information during data collection. For example, if the patient is out, the data collection unit can prioritize the collection of meal data taken at the patient's location. If the patient is at home, the data collection unit can also prioritize the collection of meal data taken at home. For example, if the patient is in a specific location, the data collection unit can collect data related to that location. This allows for the priority collection of highly relevant data by considering the patient's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the patient's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0041] The data collection unit can analyze patients' social media activity and collect relevant data during data collection. For example, the data collection unit can collect information about meals posted by patients on social media. The data collection unit can also analyze dietary trends from patients' social media activity and reflect them in data collection. For example, the data collection unit can collect data necessary to modify meal plans based on patients' social media activity. This allows for the collection of relevant data by analyzing patients' social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input patients' social media data into a generating AI and have the generating AI collect relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on general data. For example, the analysis unit performs a rapid analysis on urgent data. This allows for detailed analysis of important data by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a nutrition analysis algorithm to dietary data. The analysis unit can also apply a health analysis algorithm to physical condition data. For example, the analysis unit can apply an emotion analysis algorithm to emotion data. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0044] The analysis unit can determine the priority of analysis based on the data collection period during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can also perform analysis while referring to past data. For example, the analysis unit may prioritize the analysis of data collected during a specific period. This allows for the prioritization of the analysis of the most recent data by determining the priority of analysis based on the data collection period. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI perform the determination of the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. The analysis unit may also postpone the analysis of less relevant data. For example, the analysis unit may analyze the relevance of the data and perform the analysis in the optimal order. This allows for prioritizing the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit may input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0046] The editing unit can analyze the patient's past meal plans and select the optimal editing method during the editing process. For example, the editing unit can edit based on the meals the patient has enjoyed eating in the past. The editing unit can also prioritize suggesting menus that were effective from the patient's past meal plans. For example, the editing unit can analyze the patient's past meal plans and edit them while considering nutritional balance. In this way, the optimal editing method can be selected by analyzing the patient's past meal plans. Some or all of the above processing in the editing unit may be performed using AI or not. For example, the editing unit can input the patient's past meal plan data into a generating AI and have the generating AI select the optimal editing method.

[0047] The modification unit can customize the meal plan modifications based on the patient's current health condition during the modification process. For example, if the patient's blood glucose level is high, the modification unit may modify the plan to reduce sugar intake. If the patient's blood pressure is high, the modification unit may also modify the plan to reduce salt intake. For example, if the patient's weight is increasing, the modification unit may modify the plan to reduce calorie intake. This allows for the provision of a more appropriate meal plan by customizing it based on the patient's current health condition. Some or all of the above-described processes in the modification unit may be performed using AI or not. For example, the modification unit can input patient health data into a generating AI and have the generating AI perform the meal plan modifications.

[0048] The correction unit can select the optimal correction method by considering the patient's geographical location information during the correction process. For example, the correction unit may consider the food ingredients of the area where the patient lives. If the patient is traveling, the correction unit may also consider the food ingredients of the travel destination. For example, if the patient is in a specific region, the correction unit may consider the food culture of that region. This allows for the selection of a more appropriate correction method by considering the patient's geographical location information. Some or all of the above-described processes in the correction unit may be performed using AI or not. For example, the correction unit can input the patient's geographical location information into a generating AI and have the generating AI select the optimal correction method.

[0049] The revision unit can analyze the patient's social media activity and suggest revisions to the meal plan during the revision process. For example, the revision unit may revise the meal plan based on the content of meals posted by the patient on social media. The revision unit can also analyze dietary trends from the patient's social media activity and reflect them in the revisions. For example, the revision unit collects information necessary for revising the meal plan based on the patient's social media activity. This allows the revision unit to suggest more appropriate revisions to the meal plan by analyzing the patient's social media activity. Some or all of the above processes in the revision unit may be performed using AI or not. For example, the revision unit can input the patient's social media data into a generating AI and have the generating AI perform the revisions to the meal plan.

[0050] The support unit can provide the most appropriate answer by referring to the patient's past question history during chat support. For example, the support unit can provide relevant answers based on questions the patient has asked in the past. The support unit can also prioritize providing answers to common questions from the patient's past question history. For example, the support unit can analyze the patient's past question history and provide the most appropriate answer. This allows for the provision of more appropriate answers by referring to the patient's past question history. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support unit can input the patient's past question history into a generating AI and have the generating AI perform the task of providing the most appropriate answer.

[0051] The support unit can customize its responses during chat support based on the patient's current health status. For example, if the patient has high blood sugar levels, the support unit can provide advice on sugar intake. If the patient has high blood pressure, the support unit can also provide advice on salt intake. For example, if the patient has gained weight, the support unit can provide advice on calorie intake. This allows for more appropriate advice to be provided by customizing responses based on the patient's current health status. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support unit can input patient health data into a generating AI and have the generating AI perform the customization of the responses.

[0052] The support unit can provide the most appropriate response method when responding via chat, taking into account the patient's device information. For example, if the patient is using a smartphone, the support unit can provide a response method adapted to the screen size. If the patient is using a tablet, the support unit can also provide a response method optimized for a larger screen. For example, if the patient is using a smartwatch, the support unit can provide a concise and highly visible response method. This allows for the provision of more appropriate responses by considering the patient's device information. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support unit can input the patient's device information into a generating AI and have the generating AI perform the task of providing the most appropriate response method.

[0053] The support unit can analyze the patient's social media activity during chat support and provide relevant answers. For example, the support unit can provide relevant answers based on what the patient has posted on social media. The support unit can also analyze dietary trends from the patient's social media activity and reflect them in its answers. For example, the support unit can provide the best answer to a question about diet based on the patient's social media activity. This allows for the provision of more appropriate answers by analyzing the patient's social media activity. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support unit can input the patient's social media data into a generating AI and have the generating AI provide 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 health management system can further collect patient exercise data and link it with meal plans. For example, the data collection unit collects exercise data recorded by the patient using a smartwatch or fitness tracker. The analysis unit evaluates the patient's exercise volume and calorie expenditure based on the collected exercise data. The adjustment unit adjusts the meal plan based on the exercise data and suggests calorie intake appropriate to the exercise level. The response unit allows AI to provide immediate answers to questions the patient has about exercise. This makes it possible to provide meal plans that take the patient's exercise data into consideration, enabling more comprehensive health management.

[0056] The health management system can further collect patient sleep data and link it with meal plans. For example, the data collection unit collects sleep data recorded by the patient using a smartwatch or sleep tracker. The analysis unit evaluates the patient's sleep quality and duration based on the collected sleep data. The adjustment unit adjusts the meal plan based on the sleep data and suggests nutrients to improve sleep quality. The response unit allows AI to provide immediate answers to questions the patient has about sleep. This makes it possible to provide meal plans that take the patient's sleep data into consideration, enabling more comprehensive health management.

[0057] The health management system can further collect patient allergy data and link it to meal plans. For example, the data collection unit collects data entered by the patient regarding allergy test results. The analysis unit evaluates the patient's allergic reactions based on the collected allergy data. The modification unit adjusts the meal plan based on the allergy data, excluding allergy-causing ingredients. The response unit allows AI to provide immediate answers when the patient asks questions about allergies. This makes it possible to provide meal plans that take the patient's allergy data into consideration, resulting in safer health management.

[0058] The health management system can further collect patients' genetic data and link it to their meal plans. For example, the data collection unit collects data entered by patients based on the results of genetic testing. The analysis unit evaluates the patient's genetic tendencies based on the collected genetic data. The modification unit adjusts the meal plan based on the genetic data and suggests diets to mitigate genetic risks. The response unit allows AI to provide immediate answers to patients' questions about their genes. This makes it possible to provide meal plans that take the patient's genetic data into consideration, enabling more personalized health management.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The data collection unit collects the patient's daily meal logs and physical condition data. The data collection unit collects data entered by the patient using, for example, a smartphone or wearable device. The data collection unit can collect the data entered by the patient in real time and store it in a database. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, use AI to analyze the data and evaluate the patient's health status. The analysis unit can analyze the data based on previous patient cases and the latest medical research. Step 3: The modification unit modifies the meal plan based on the analysis results obtained by the analysis unit. For example, the modification unit modifies the meal plan to reduce sugar intake if the patient's blood glucose level is high. The modification unit can modify the meal plan in real time according to the patient's health condition. Step 4: The support unit responds to the patient's questions and concerns. For example, if a patient has questions or concerns about food, the support unit will respond immediately in a chat format. The support unit can use AI to provide answers to the patient's questions.

[0061] (Example of form 2) A health management system according to an embodiment of the present invention is a system that autonomously monitors a patient's daily meal logs and physical condition data, and modifies the meal plan in real time as needed. This health management system collects the patient's daily meal logs and physical condition data, analyzes it with AI, and modifies the meal plan as needed. In addition, if the patient has questions or concerns about their diet, the AI ​​responds immediately in a chat format. This helps to improve the patient's health. For example, the health management system collects data entered by the patient using a smartphone or wearable device. Next, the health management system's AI analyzes the collected data and modifies the meal plan as needed. For example, if the patient's blood sugar level is high, the meal plan is modified to reduce sugar intake. The health management system can also create personalized meal plans based on previous patient cases and the latest medical research. For example, the health management system analyzes data based on previous patient cases and the latest medical research to provide the patient with the optimal meal plan. Furthermore, if the patient has questions or concerns about their diet, the AI ​​responds immediately in a chat format. For example, if the patient sends a question about their diet via chat, the AI ​​provides an immediate answer. This allows the patient to quickly resolve their questions about their diet. This allows the health management system to autonomously monitor patients' daily meal logs and physical condition data, and modify meal plans in real time as needed.

[0062] The health management system according to this embodiment comprises a collection unit, an analysis unit, a modification unit, and a response unit. The collection unit collects the patient's daily meal logs and physical condition data. The collection unit collects data entered by the patient using, for example, a smartphone or wearable device. The collection unit can collect the data entered by the patient in real time and store it in a database. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, AI to evaluate the patient's health status. The analysis unit can analyze the data based on previous patient cases and the latest medical research. The modification unit modifies the meal plan based on the analysis results obtained by the analysis unit. The modification unit modifies the meal plan to reduce sugar intake if the patient's blood glucose level is high, for example. The modification unit can modify the meal plan in real time according to the patient's health status. The response unit responds to the patient's questions and concerns. The response unit responds immediately in a chat format when the patient has questions or concerns about their diet. The response unit can provide answers to the patient's questions using AI. As a result, the health management system according to this embodiment can autonomously monitor the patient's daily meal logs and physical condition data, and modify the meal plan in real time as needed.

[0063] The data collection unit collects patients' daily meal logs and physical condition data. For example, it collects data entered by patients using smartphones or wearable devices. Specifically, through a smartphone application, patients can record their daily meals with photos and text, and input calorie and nutrient information. Wearable devices automatically record physical condition data such as heart rate, steps, calories burned, and sleep patterns, and transmit this data to the data collection unit. This data is collected in real time and stored in a central database. To ensure data accuracy and consistency, the data collection unit standardizes data entry methods and formats, making it easy for patients to input data. Furthermore, to ensure data privacy and security, the data collection unit protects data using encryption technology to prevent the leakage of patients' personal information. In addition, the data collection unit regularly backs up patient data to prevent data loss or corruption. This allows the data collection unit to efficiently and securely collect patients' daily meal logs and physical condition data, improving the overall reliability of the system.

[0064] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses AI to analyze the data and assess the patient's health status. Specifically, the AI ​​uses machine learning algorithms to extract patterns and trends from the collected data and assess the patient's health status. For example, it analyzes calorie intake and nutrient balance from meal logs and assesses exercise levels and sleep quality from physical condition data. Furthermore, the analysis unit can analyze data based on previous patient cases and the latest medical research. This allows the analysis unit to comprehensively assess the patient's health status and detect potential health risks early. The analysis unit can analyze data in real time using AI and respond quickly to changes in the patient's health status. For example, if a patient's blood glucose level suddenly rises, the analysis unit can immediately identify the cause and propose appropriate countermeasures. Additionally, the analysis unit can analyze long-term trends in the patient's health status based on past data and predict future health risks. This allows the analysis unit to continuously monitor the patient's health status and support appropriate health management.

[0065] The modification unit modifies the meal plan based on the analysis results obtained by the analysis unit. For example, if a patient's blood glucose level is high, the modification unit modifies the meal plan to reduce sugar intake. Specifically, the modification unit uses AI to automatically generate an optimal meal plan tailored to the patient's health condition. For example, if a patient's blood glucose level is high, it suggests recipes using low-carbohydrate ingredients to reduce sugar intake. It also selects ingredients containing necessary vitamins and minerals, taking into account the patient's nutritional balance, and incorporates them into the meal plan. The modification unit can modify the meal plan in real time according to the patient's health condition. For example, if a patient's weight increases, it suggests recipes using low-calorie ingredients to reduce calorie intake. Also, if a patient's exercise level increases, it suggests an appropriate calorie intake for energy replenishment. The modification unit regularly reviews the patient's meal plan and provides the optimal plan based on the latest health condition. This allows the modification unit to provide an effective meal plan to maintain and improve the patient's health condition.

[0066] The support unit handles patients' questions and concerns. For example, if a patient has questions or concerns about their diet, the support unit will respond immediately in a chat format. Specifically, the support unit can use AI to provide answers to patients' questions. For example, if a patient asks about the nutritional value of a particular food item, the AI ​​will search the database for the relevant information and provide a quick answer. Also, if a patient has questions about their meal plan, the AI ​​will provide appropriate advice based on data from the analysis and modification units. In addition to providing answers to patients' questions, the support unit can also collect patient feedback and use it to improve the overall system. For example, if a patient is not satisfied with the answer provided, that feedback can be used as training data to improve the accuracy of the AI's answers. Furthermore, the support unit can quickly respond to urgent questions and concerns regarding the patient's health condition. For example, if a patient complains of a sudden illness, the AI ​​will suggest appropriate measures and, if necessary, recommend a visit to a medical institution. In this way, the support unit can respond quickly and accurately to patients' questions and concerns and support their health management.

[0067] The data collection unit can collect data entered by patients using smartphones or wearable devices. For example, if a patient enters a meal log using a smartphone, the data collection unit can collect that data. The data collection unit can also collect data if a patient enters physical condition data using a wearable device. For example, the data collection unit can collect data from wearable devices such as smartwatches or fitness trackers. This improves the convenience of data collection by collecting data entered by patients using smartphones or wearable devices. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data acquired from smartphones or wearable devices into a generating AI and have the generating AI perform the data collection.

[0068] The analysis unit can analyze data based on previous patient cases and the latest medical research. For example, the analysis unit can retrieve previous patient cases from a database and use them in the analysis. The analysis unit can also refer to the latest medical research and incorporate it into the analysis. For example, the analysis unit can analyze data based on the latest papers and clinical trial results. By analyzing data based on previous patient cases and the latest medical research, the accuracy of the analysis is improved. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data from previous patient cases and the latest medical research into a generating AI and have the generating AI perform the data analysis.

[0069] The modification unit can modify the meal plan to reduce sugar intake when the patient's blood glucose level is high. For example, if the patient's blood glucose level exceeds a certain standard, the modification unit will modify the meal plan to reduce sugar intake. The modification unit can also modify the meal plan in real time according to the patient's health condition. For example, if the patient's blood glucose level is high, the modification unit will not only reduce sugar intake but also modify the meal plan considering the balance of other nutrients. This improves the patient's health condition by modifying the meal plan to reduce sugar intake when the patient's blood glucose level is high. Some or all of the above processing in the modification unit may be performed using AI or not. For example, the modification unit can input the patient's blood glucose data into a generating AI and have the generating AI perform the modification of the meal plan.

[0070] The support unit can immediately respond to patients' questions and concerns regarding their diet in a chat format. For example, when a patient sends a question about their diet via chat, the AI ​​provides an immediate answer. The support unit can also provide answers to patients' questions in real time. For example, when a patient has a question about their diet, the support unit uses a chatbot to provide an immediate answer. This allows for the rapid resolution of patients' concerns by providing immediate chat support when they have questions or concerns about their diet. Some or all of the above-described processes in the support unit may be performed using AI or not. For example, the support unit can input the patient's question into a generating AI and have the generating AI generate the answer.

[0071] The data collection unit can estimate the patient's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the patient is stressed, the data collection unit will collect data during times when the patient is relaxed. If the patient is tired, the data collection unit may postpone data collection until the next day. For example, if the patient is relaxed, the data collection unit will collect data immediately. This allows for data collection at a more appropriate time by adjusting the timing of data collection based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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. For example, the data collection unit can input patient emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0072] The data collection unit can analyze the patient's past meal logs and physical condition data to select the optimal data collection method. For example, the data collection unit can analyze patterns in data previously entered by the patient to determine the optimal collection time. The data collection unit can also analyze the patient's response to specific meals from their past data and adjust the collection method. For example, the data collection unit can optimize the frequency of data collection based on the patient's physical condition data. This allows the optimal data collection method to be selected by analyzing the patient's past data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the patient's past data into a generating AI and have the generating AI select the optimal data collection method.

[0073] The data collection unit can filter data based on the patient's current health status and lifestyle. For example, the data collection unit can collect only important data based on the patient's current health status. The data collection unit can also adjust the timing of data collection, taking into account the patient's lifestyle. For example, the data collection unit can temporarily suspend data collection if the patient's health status deteriorates. This allows for the collection of only important data by filtering the data based on the patient's current health status and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input patient health status data into a generating AI and have the generating AI perform data filtering.

[0074] The data collection unit can estimate the patient's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the patient is stressed, the data collection unit will prioritize collecting stress-related data. If the patient is relaxed, the data collection unit can also perform normal data collection. For example, if the patient is tired, the data collection unit will collect only important data. This allows for the priority collection of important data by determining the priority of data to collect based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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. For example, the data collection unit can input patient emotion data into a generative AI and have the generative AI perform the data prioritization.

[0075] The data collection unit can prioritize the collection of highly relevant data by considering the patient's geographical location information during data collection. For example, if the patient is out, the data collection unit can prioritize the collection of meal data taken at the patient's location. If the patient is at home, the data collection unit can also prioritize the collection of meal data taken at home. For example, if the patient is in a specific location, the data collection unit can collect data related to that location. This allows for the priority collection of highly relevant data by considering the patient's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the patient's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0076] The data collection unit can analyze patients' social media activity and collect relevant data during data collection. For example, the data collection unit can collect information about meals posted by patients on social media. The data collection unit can also analyze dietary trends from patients' social media activity and reflect them in data collection. For example, the data collection unit can collect data necessary to modify meal plans based on patients' social media activity. This allows for the collection of relevant data by analyzing patients' social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input patients' social media data into a generating AI and have the generating AI collect relevant data.

[0077] The analysis unit can estimate the patient's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the patient is stressed, the analysis unit can provide a simple analysis result. If the patient is relaxed, the analysis unit can also provide a detailed analysis result. For example, if the patient is in a hurry, the analysis unit can provide a concise analysis result. By adjusting the presentation of the analysis based on the patient's emotions, the analysis results can be made easier for the patient to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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. For example, the analysis unit can input patient emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0078] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on general data. For example, the analysis unit performs a rapid analysis on urgent data. This allows for detailed analysis of important data by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0079] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a nutrition analysis algorithm to dietary data. The analysis unit can also apply a health analysis algorithm to physical condition data. For example, the analysis unit can apply an emotion analysis algorithm to emotion data. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0080] The analysis unit can estimate the patient's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the patient is in a hurry, the analysis unit can perform a short, concise analysis. If the patient is relaxed, the analysis unit can also perform a detailed analysis. For example, if the patient is agitated, the analysis unit can perform a visually stimulating analysis. By adjusting the length of the analysis based on the patient's emotions, the analysis unit can provide the patient with an analysis result of the optimal length. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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. For example, the analysis unit can input patient emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0081] The analysis unit can determine the priority of analysis based on the data collection period during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can also perform analysis while referring to past data. For example, the analysis unit may prioritize the analysis of data collected during a specific period. This allows for the prioritization of the analysis of the most recent data by determining the priority of analysis based on the data collection period. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI perform the determination of the analysis priority.

[0082] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. The analysis unit may also postpone the analysis of less relevant data. For example, the analysis unit may analyze the relevance of the data and perform the analysis in the optimal order. This allows for prioritizing the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit may input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0083] The modification unit can estimate the patient's emotions and adjust the method of modifying the meal plan based on the estimated emotions. For example, if the patient is stressed, the modification unit may suggest a meal plan that is effective in reducing stress. If the patient is relaxed, the modification unit may also suggest a normal meal plan. For example, if the patient is tired, the modification unit may suggest a highly nutritious meal plan. In this way, by adjusting the method of modifying the meal plan based on the patient's emotions, the optimal meal plan for the patient can be provided. 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 modification unit may be performed using AI or not using AI. For example, the modification unit can input the patient's emotion data into the generative AI and have the generative AI perform the adjustment of the method of modifying the meal plan.

[0084] The editing unit can analyze the patient's past meal plans and select the optimal editing method during the editing process. For example, the editing unit can edit based on the meals the patient has enjoyed eating in the past. The editing unit can also prioritize suggesting menus that were effective from the patient's past meal plans. For example, the editing unit can analyze the patient's past meal plans and edit them while considering nutritional balance. In this way, the optimal editing method can be selected by analyzing the patient's past meal plans. Some or all of the above processing in the editing unit may be performed using AI or not. For example, the editing unit can input the patient's past meal plan data into a generating AI and have the generating AI select the optimal editing method.

[0085] The modification unit can customize the meal plan modifications based on the patient's current health condition during the modification process. For example, if the patient's blood glucose level is high, the modification unit may modify the plan to reduce sugar intake. If the patient's blood pressure is high, the modification unit may also modify the plan to reduce salt intake. For example, if the patient's weight is increasing, the modification unit may modify the plan to reduce calorie intake. This allows for the provision of a more appropriate meal plan by customizing it based on the patient's current health condition. Some or all of the above-described processes in the modification unit may be performed using AI or not. For example, the modification unit can input patient health data into a generating AI and have the generating AI perform the meal plan modifications.

[0086] The modification unit can estimate the patient's emotions and determine the priority of modifications to the meal plan based on the estimated emotions. For example, if the patient is stressed, the modification unit will prioritize modifications that are effective in reducing stress. If the patient is relaxed, the modification unit can also perform normal modifications. For example, if the patient is tired, the modification unit will prioritize modifications that are highly nutritious. This allows for more effective modifications by determining the priority of modifications to the meal plan based on the patient'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 modification unit may be performed using AI or not. For example, the modification unit can input patient emotion data into a generative AI and have the generative AI determine the priority of modifications.

[0087] The correction unit can select the optimal correction method by considering the patient's geographical location information during the correction process. For example, the correction unit may consider the food ingredients of the area where the patient lives. If the patient is traveling, the correction unit may also consider the food ingredients of the travel destination. For example, if the patient is in a specific region, the correction unit may consider the food culture of that region. This allows for the selection of a more appropriate correction method by considering the patient's geographical location information. Some or all of the above-described processes in the correction unit may be performed using AI or not. For example, the correction unit can input the patient's geographical location information into a generating AI and have the generating AI select the optimal correction method.

[0088] The revision unit can analyze the patient's social media activity and suggest revisions to the meal plan during the revision process. For example, the revision unit may revise the meal plan based on the content of meals posted by the patient on social media. The revision unit can also analyze dietary trends from the patient's social media activity and reflect them in the revisions. For example, the revision unit collects information necessary for revising the meal plan based on the patient's social media activity. This allows the revision unit to suggest more appropriate revisions to the meal plan by analyzing the patient's social media activity. Some or all of the above processes in the revision unit may be performed using AI or not. For example, the revision unit can input the patient's social media data into a generating AI and have the generating AI perform the revisions to the meal plan.

[0089] The response unit can estimate the patient's emotions and adjust its chat response method based on the estimated emotions. For example, if the patient is stressed, the response unit will use gentle language. If the patient is relaxed, the response unit can also use normal language. For example, if the patient is in a hurry, the response unit will respond quickly. This allows for a more appropriate response by adjusting the chat response method based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input patient emotion data into a generative AI and have the generative AI adjust the chat response method.

[0090] The support unit can provide the most appropriate answer by referring to the patient's past question history during chat support. For example, the support unit can provide relevant answers based on questions the patient has asked in the past. The support unit can also prioritize providing answers to common questions from the patient's past question history. For example, the support unit can analyze the patient's past question history and provide the most appropriate answer. This allows for the provision of more appropriate answers by referring to the patient's past question history. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support unit can input the patient's past question history into a generating AI and have the generating AI perform the task of providing the most appropriate answer.

[0091] The support unit can customize its responses during chat support based on the patient's current health status. For example, if the patient has high blood sugar levels, the support unit can provide advice on sugar intake. If the patient has high blood pressure, the support unit can also provide advice on salt intake. For example, if the patient has gained weight, the support unit can provide advice on calorie intake. This allows for more appropriate advice to be provided by customizing responses based on the patient's current health status. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support unit can input patient health data into a generating AI and have the generating AI perform the customization of the responses.

[0092] The response unit can estimate the patient's emotions and determine the priority of chats based on the estimated emotions. For example, if the patient is stressed, the response unit will prioritize their response. If the patient is relaxed, the response unit can also provide a normal response. For example, if the patient is in a hurry, the response unit will respond quickly. This allows for more appropriate responses by determining the priority of chats based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input patient emotion data into a generative AI and have the generative AI determine the priority of chats.

[0093] The support unit can provide the most appropriate response method when responding via chat, taking into account the patient's device information. For example, if the patient is using a smartphone, the support unit can provide a response method adapted to the screen size. If the patient is using a tablet, the support unit can also provide a response method optimized for a larger screen. For example, if the patient is using a smartwatch, the support unit can provide a concise and highly visible response method. This allows for the provision of more appropriate responses by considering the patient's device information. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support unit can input the patient's device information into a generating AI and have the generating AI perform the task of providing the most appropriate response method.

[0094] The support unit can analyze the patient's social media activity during chat support and provide relevant answers. For example, the support unit can provide relevant answers based on what the patient has posted on social media. The support unit can also analyze dietary trends from the patient's social media activity and reflect them in its answers. For example, the support unit can provide the best answer to a question about diet based on the patient's social media activity. This allows for the provision of more appropriate answers by analyzing the patient's social media activity. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support unit can input the patient's social media data into a generating AI and have the generating AI provide relevant answers.

[0095] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0096] The health management system can further collect patient exercise data and link it with meal plans. For example, the data collection unit collects exercise data recorded by the patient using a smartwatch or fitness tracker. The analysis unit evaluates the patient's exercise volume and calorie expenditure based on the collected exercise data. The adjustment unit adjusts the meal plan based on the exercise data and suggests calorie intake appropriate to the exercise level. The response unit allows AI to provide immediate answers to questions the patient has about exercise. This makes it possible to provide meal plans that take the patient's exercise data into consideration, enabling more comprehensive health management.

[0097] The health management system can further collect patient sleep data and link it with meal plans. For example, the data collection unit collects sleep data recorded by the patient using a smartwatch or sleep tracker. The analysis unit evaluates the patient's sleep quality and duration based on the collected sleep data. The adjustment unit adjusts the meal plan based on the sleep data and suggests nutrients to improve sleep quality. The response unit allows AI to provide immediate answers to questions the patient has about sleep. This makes it possible to provide meal plans that take the patient's sleep data into consideration, enabling more comprehensive health management.

[0098] The health management system can further monitor patients' stress levels and link them to meal plans. For example, the data collection unit collects stress data recorded by patients using stress monitoring devices. The analysis unit evaluates the patient's stress level based on the collected stress data. The adjustment unit adjusts the meal plan based on the stress data and suggests meals that are effective in reducing stress. The response unit allows AI to provide immediate answers when patients ask questions about stress. This makes it possible to provide meal plans that take the patient's stress data into consideration, enabling more comprehensive health management.

[0099] The health management system can further collect patient allergy data and link it to meal plans. For example, the data collection unit collects data entered by the patient regarding allergy test results. The analysis unit evaluates the patient's allergic reactions based on the collected allergy data. The modification unit adjusts the meal plan based on the allergy data, excluding allergy-causing ingredients. The response unit allows AI to provide immediate answers when the patient asks questions about allergies. This makes it possible to provide meal plans that take the patient's allergy data into consideration, resulting in safer health management.

[0100] The health management system can further estimate the patient's emotions and adjust meal plans based on those estimates. For example, the data collection unit collects emotional data from diaries and social media posts entered by the patient. The analysis unit evaluates the patient's emotional state based on the collected emotional data. The modification unit adjusts the meal plan based on the emotional data and suggests meals appropriate to the emotional state. The response unit allows the AI ​​to provide immediate answers when the patient asks questions about their emotions. This makes it possible to provide meal plans that take the patient's emotional data into consideration, enabling more comprehensive health management.

[0101] The health management system can further collect patients' genetic data and link it to their meal plans. For example, the data collection unit collects data entered by patients based on the results of genetic testing. The analysis unit evaluates the patient's genetic tendencies based on the collected genetic data. The modification unit adjusts the meal plan based on the genetic data and suggests diets to mitigate genetic risks. The response unit allows AI to provide immediate answers to patients' questions about their genes. This makes it possible to provide meal plans that take the patient's genetic data into consideration, enabling more personalized health management.

[0102] The health management system can further estimate the patient's emotions and adjust the exercise plan based on those emotions. For example, the data collection unit collects emotional data from diaries and social media posts entered by the patient. The analysis unit evaluates the patient's emotional state based on the collected emotional data. The modification unit adjusts the exercise plan based on the emotional data and suggests exercises appropriate to the emotional state. The response unit allows the AI ​​to provide immediate answers when the patient asks questions about their emotions. This makes it possible to provide exercise plans that take the patient's emotional data into consideration, enabling more comprehensive health management.

[0103] The health management system can further estimate the patient's emotions and adjust the sleep plan based on those emotions. For example, the data collection unit collects emotional data from diaries and social media posts entered by the patient. The analysis unit evaluates the patient's emotional state based on the collected emotional data. The adjustment unit adjusts the sleep plan based on the emotional data and suggests a sleep environment appropriate to the emotional state. The response unit allows the AI ​​to provide immediate answers when the patient asks questions about their emotions. This makes it possible to provide a sleep plan that takes the patient's emotional data into consideration, enabling more comprehensive health management.

[0104] The health management system can further estimate the patient's emotions and adjust the stress management plan based on those estimates. For example, the data collection unit collects emotional data from diaries and social media posts entered by the patient. The analysis unit evaluates the patient's emotional state based on the collected emotional data. The modification unit adjusts the stress management plan based on the emotional data and proposes stress reduction methods appropriate to the emotional state. The response unit allows the AI ​​to provide immediate answers when the patient asks questions about their emotions. This makes it possible to provide stress management plans that take the patient's emotional data into consideration, enabling more comprehensive health management.

[0105] The health management system can further estimate the patient's emotions and provide health advice based on those estimated emotions. For example, the data collection unit collects emotional data from diaries and social media posts entered by the patient. The analysis unit evaluates the patient's emotional state based on the collected emotional data. The modification unit adjusts health advice based on the emotional data, providing advice appropriate to the emotional state. The response unit allows the AI ​​to provide immediate answers when the patient asks questions about their emotions. This makes it possible to provide health advice that takes the patient's emotional data into consideration, enabling more comprehensive health management.

[0106] The following briefly describes the processing flow for example form 2.

[0107] Step 1: The data collection unit collects the patient's daily meal logs and physical condition data. The data collection unit collects data entered by the patient using, for example, a smartphone or wearable device. The data collection unit can collect the data entered by the patient in real time and store it in a database. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, use AI to analyze the data and evaluate the patient's health status. The analysis unit can analyze the data based on previous patient cases and the latest medical research. Step 3: The modification unit modifies the meal plan based on the analysis results obtained by the analysis unit. For example, the modification unit modifies the meal plan to reduce sugar intake if the patient's blood glucose level is high. The modification unit can modify the meal plan in real time according to the patient's health condition. Step 4: The support unit responds to the patient's questions and concerns. For example, if a patient has questions or concerns about food, the support unit will respond immediately in a chat format. The support unit can use AI to provide answers to the patient's questions.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] Each of the multiple elements described above, including the data collection unit, analysis unit, modification unit, and response unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the smart device 14 and collects data entered by the patient using a smartphone or wearable device. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using AI to evaluate the patient's health status. The modification unit is implemented by the specific processing unit 290 of the data processing unit 12 and modifies the meal plan based on the analysis results. The response unit is implemented by the control unit 46A of the smart device 14 and responds to the patient's questions and concerns in a chat format. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0112] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the data collection unit, analysis unit, modification unit, and response unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the smart glasses 214 and collects data entered by the patient using a smartphone or wearable device. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the data using AI to evaluate the patient's health status. The modification unit is implemented by the identification processing unit 290 of the data processing unit 12 and modifies the meal plan based on the analysis results. The response unit is implemented by the control unit 46A of the smart glasses 214 and responds to the patient's questions and concerns in a chat format. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0128] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0129] 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.

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

[0131] The 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.

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

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

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

[0135] Figure 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.

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

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

[0138] In the 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.

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

[0140] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0142] The data processing system 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.

[0143] Each of the multiple elements described above, including the data collection unit, analysis unit, modification unit, and response unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the headset terminal 314 and collects data entered by the patient using a smartphone or wearable device. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the data using AI to evaluate the patient's health status. The modification unit is implemented by the identification processing unit 290 of the data processing unit 12 and modifies the meal plan based on the analysis results. The response unit is implemented by the control unit 46A of the headset terminal 314 and responds to the patient's questions and concerns in a chat format. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0144] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0145] 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.

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

[0147] The 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.

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

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

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

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the data collection unit, analysis unit, modification unit, and response unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the robot 414 and collects data entered by the patient using a smartphone or wearable device. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using AI to evaluate the patient's health status. The modification unit is implemented by the specific processing unit 290 of the data processing unit 12 and modifies the meal plan based on the analysis results. The response unit is implemented by the control unit 46A of the robot 414 and responds to the patient's questions and concerns in a chat format. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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."

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] (Note 1) A data collection unit that collects patients' daily meal logs and physical condition data, An analysis unit analyzes the data collected by the aforementioned collection unit, A modification unit modifies the meal plan based on the analysis results obtained by the analysis unit, It includes a unit that responds to patients' questions and concerns. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data entered by patients using smartphones or wearable devices. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We analyze data based on previous patient cases and the latest medical research. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned modification section is, If a patient's blood sugar levels are high, modify their meal plan to reduce their sugar intake. The system described in Appendix 1, characterized by the features described herein. (Note 5) The corresponding part is, We provide immediate chat-based support for patients who have questions or concerns about their diet. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the patient's emotions and adjust the timing of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We analyze the patient's past meal logs and physical condition data to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, filtering is performed based on the patient's current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates the patient's emotions and prioritizes the data to collect 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 collecting data, the system prioritizes the collection of highly relevant data, taking into account the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, analyze patients' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the patient's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the patient's emotions and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned modification section is, The system estimates the patient's emotions and adjusts the meal plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned modification section is, When making revisions, the patient's past meal plans are analyzed to select the most appropriate revision method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned modification section is, During revisions, the meal plan is customized based on the patient's current health condition. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned modification section is, The system estimates the patient's emotions and prioritizes modifications to the meal plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned modification section is, During correction, the optimal correction method is selected considering the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned modification section is, During the revision process, we analyze the patient's social media activity and suggest modifications to the meal plan. The system described in Appendix 1, characterized by the features described herein. (Note 24) The corresponding part is, The system estimates the patient's emotions and adjusts the chat response based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The corresponding part is, When providing support via chat, refer to the patient's past question history to provide the most appropriate answer. The system described in Appendix 1, characterized by the features described herein. (Note 26) The corresponding part is, When providing support via chat, the responses are customized based on the patient's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 27) The corresponding part is, The system estimates the patient's emotions and prioritizes chats based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The corresponding part is, When providing support via chat, we consider the patient's device information to provide the most appropriate response. The system described in Appendix 1, characterized by the features described herein. (Note 29) The corresponding part is, When providing support via chat, we analyze the patient's social media activity to provide relevant answers. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0180] 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 data collection unit that collects patients' daily meal logs and physical condition data, An analysis unit analyzes the data collected by the aforementioned collection unit, A modification unit modifies the meal plan based on the analysis results obtained by the analysis unit, It includes a unit that responds to patients' questions and concerns. A system characterized by the following features.

2. The aforementioned collection unit is We collect data entered by patients using smartphones or wearable devices. The system according to feature 1.

3. The aforementioned analysis unit, We analyze data based on previous patient cases and the latest medical research. The system according to feature 1.

4. The aforementioned modification section is, If a patient's blood sugar levels are high, modify their meal plan to reduce their sugar intake. The system according to feature 1.

5. The corresponding part is, We provide immediate chat-based support for patients who have questions or concerns about their diet. The system according to feature 1.

6. The aforementioned collection unit is We estimate the patient's emotions and adjust the timing of data collection based on the estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is We analyze the patient's past meal logs and physical condition data to select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is During data collection, filtering is performed based on the patient's current health status and lifestyle. The system according to feature 1.

9. The aforementioned collection unit is The system estimates the patient's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the patient's geographical location. The system according to feature 1.

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