system

Abstract

The system according to this embodiment aims to suggest appropriate foods based on the user's health status and allergy information. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, and a suggestion unit. The reception unit receives information about the user's health status, health goals, and allergies. The analysis unit analyzes the information received by the reception unit. The suggestion unit suggests foods that are suitable for the user based on the results of the analysis performed by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been sufficiently done to propose appropriate foods based on the health condition and allergy information of users, and there is room for improvement.

[0005] The system according to the embodiment aims to propose appropriate foods based on the health condition and allergy information of users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, and a suggestion unit. The reception unit receives information about the user's health status, health goals, and allergies. The analysis unit analyzes the information received by the reception unit. The suggestion unit suggests foods that are suitable for the user based on the results of the analysis performed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest appropriate foods based on the user's health status and allergy information. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​Assistant Advisory System for Health Foods and Supplements, according to an embodiment of the present invention, is a system that recommends appropriate products based on the user's health data, goals, allergy information, etc. The AI ​​Assistant Advisory System for Health Foods and Supplements receives the user's health status, health goals, and allergy information from the reception unit, the analysis unit analyzes it, and based on the results, the suggestion unit proposes foods that match the user. For example, if the user inputs "I'm looking for foods that are high in vitamin C," the reception unit receives this information, and the analysis unit analyzes it considering the user's health status and allergy information. As a result, the suggestion unit proposes foods that are high in vitamin C. Furthermore, the suggestion unit provides detailed information about the food's ingredients, effects, allergens, and side effects. For example, if the suggested food is oranges, which are high in vitamin C, information about its ingredients, effects, allergens, and side effects is also provided. The suggestion unit also provides advice on the dosage and timing of supplement intake. For example, if the user inputs "I want to take a vitamin C supplement," the suggestion unit provides information on the appropriate dosage and timing of intake. Furthermore, the system includes a determination unit that assesses the user's progress toward their health goals, and a suggestion unit that modifies the suggested foods based on that progress. For example, if a user sets the goal of "losing weight," the determination unit assesses their progress, and the suggestion unit suggests appropriate foods. The suggestion unit also estimates the user's emotions and suggests foods based on those emotions. For example, if the user is feeling stressed, the suggestion unit suggests foods with relaxing effects. In addition, the reception unit filters information based on the user's current lifestyle and areas of interest when receiving it. For example, if the user enters "I lead a busy life," the reception unit takes that information into consideration when filtering. Finally, the reception unit estimates the user's emotions and determines the priority of the information to be received based on those emotions. For example, if the user enters "I'm tired," the reception unit prioritizes processing that information. As a result, the AI ​​assistant advisory system for health foods and supplements can suggest appropriate foods based on the user's health status, goals, and allergy information.

[0029] The AI ​​assistant advisory system for health foods and supplements according to this embodiment comprises a reception unit, an analysis unit, and a suggestion unit. The reception unit receives information about the user's health status, health goals, and allergies. For example, the reception unit stores the health status, goals, and allergy information entered by the user in a database. The reception unit can also refer to information previously entered by the user and compare it with the current information. The analysis unit analyzes the information received by the reception unit. For example, the analysis unit executes an algorithm to select appropriate foods based on the user's health status, goals, and allergy information. The analysis unit analyzes the user's health data and generates data for suggesting foods that match the user's health status. The suggestion unit suggests foods that match the user based on the results of the analysis by the analysis unit. For example, the suggestion unit suggests foods that are rich in vitamin C or foods that do not contain allergens, depending on the user's health status and goals. The suggestion unit also provides information about the ingredients, effects, allergens, and side effects of the suggested foods. For example, if the suggested food is an orange rich in vitamin C, information about its components, effects, allergens, and side effects will also be provided. This allows the health food and supplement advisory system AI assistant according to the embodiment to suggest appropriate foods based on the user's health status, goals, and allergy information. Some or all of the above-described processes in the reception unit, analysis unit, and suggestion unit may be performed using AI, or not using AI. For example, the reception unit may receive the user's health status, goals, and allergy information as input, the analysis unit may analyze the received information as input, and the suggestion unit may use an AI model that makes suggestions using the analysis results as input to execute each process.

[0030] The reception department receives information about the user's health status, health goals, and allergies. Specifically, it stores the information entered by the user through a dedicated interface in a database. For example, the user can enter specific symptoms as their health status, such as "easily fatigued" or "concerned about skin problems," and set health goals such as "want to increase energy" or "want clearer skin." They can also enter detailed allergy information, such as "nut allergy" or "dairy allergy." The reception department centrally manages this information and has a function to compare information entered by the user in the past with current information. For example, if a user previously entered "easily fatigued" but now enters "less fatigued," the reception department records this change and provides it to the analysis department. This allows the reception department to accurately understand the user's health status, goals, and allergy information, and provide the necessary data to the analysis and proposal departments. Furthermore, the reception department has a function to periodically update the information entered by the user, updating the database each time the user enters new health status, goals, or allergy information. This ensures that analysis and proposals are always based on the latest information.

[0031] The analysis unit analyzes the information received by the reception unit. Specifically, it executes algorithms to select appropriate foods based on the user's health status, goals, and allergy information. For example, if a user enters "I get tired easily," the analysis unit analyzes data on the user's diet and lifestyle to identify the cause. Furthermore, if the user sets a goal of "I want to increase my energy," the analysis unit identifies foods containing the nutrients necessary to increase energy. Allergy information is also taken into consideration, and for users with nut allergies, foods that do not contain nuts are selected. Based on this information, the analysis unit generates data to suggest foods that are appropriate for the user's health status. For example, it utilizes machine learning algorithms using AI to predict the optimal foods based on past data and data from similar users. The analysis unit analyzes the user's health data in real time and generates data to suggest the optimal foods that are appropriate for the user's health status. This allows the analysis unit to comprehensively analyze the user's health status, goals, and allergy information and select the optimal foods. Furthermore, the analysis unit can continuously improve its algorithms based on user feedback to improve the accuracy of its suggestions.

[0032] The suggestion department proposes foods that match the user based on the results analyzed by the analysis department. Specifically, it suggests foods rich in vitamin C or foods free of allergens, depending on the user's health condition and goals. For example, if a user sets the goal of "wanting clear skin," the suggestion department will suggest foods rich in vitamin C and collagen. Also, for a user with a nut allergy, it will suggest foods that do not contain nuts. The suggestion department provides information on the ingredients, effects, allergens, and side effects of the suggested foods. For example, if the suggested food is oranges, which are rich in vitamin C, information on its ingredients, effects, allergens, and side effects will also be provided. The suggestion department provides the necessary information when the user selects a suggested food, so that the user can choose with confidence. Furthermore, the suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can collect feedback on the user's health condition and effects after consuming the suggested foods and provide it to the analysis department. This allows the suggestion department to propose the most suitable foods according to the user's health condition and goals, supporting the user's health.

[0033] The reception unit can receive health goals for the entire family. For example, the reception unit can receive the health status and goals of all family members as input. The reception unit can also receive allergy information for all family members. The suggestion unit suggests foods that are good for the health of the entire family. For example, the suggestion unit suggests a balanced diet based on the health status and goals of all family members. The suggestion unit can also suggest allergen-free foods, taking into account the allergy information of all family members. This allows for the suggestion of appropriate foods based on the health goals of the entire family. Some or all of the above processing in the reception unit and suggestion unit may be performed using AI, for example, or without AI. For example, the reception unit can receive the health status, goals, and allergy information of all family members as input, and the suggestion unit can perform each process using an AI model that makes suggestions using the received information as input.

[0034] The suggestion unit can provide information on the ingredients, effects, allergens, and side effects of food. For example, the suggestion unit can provide ingredient information for a suggested food. For example, the suggestion unit can provide ingredient information for a food that is rich in vitamin C. The suggestion unit can also provide information on the effects of a suggested food. For example, the suggestion unit can provide information that vitamin C has the effect of boosting immunity. The suggestion unit can also provide allergen information for a suggested food. For example, the suggestion unit can provide information on allergens contained in a suggested food. The suggestion unit can also provide information on side effects for a suggested food. For example, the suggestion unit can provide information on side effects caused by excessive intake of vitamin C. By providing detailed information on the suggested food, users can make appropriate choices. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can perform each process using an AI model that accepts information on the ingredients, effects, allergens, and side effects of a suggested food as input and provides the information.

[0035] The suggestion unit can provide information regarding the dosage and timing of supplement intake. For example, the suggestion unit can provide the appropriate dosage of a suggested supplement. For example, the suggestion unit can provide the recommended daily intake of a vitamin C supplement. The suggestion unit can also provide the appropriate timing for taking a suggested supplement. For example, the suggestion unit can recommend taking a vitamin C supplement after meals. By providing information on the appropriate dosage and timing of supplement intake, users can effectively utilize supplements. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can take information on the dosage and timing of a suggested supplement as input and execute each process using an AI model that provides the information.

[0036] The determination unit can determine the progress of the user's health status. The determination unit uses indicators to evaluate the progress of the user's health status, for example. For example, the determination unit determines the progress based on health indicators such as the user's weight, blood pressure, and blood sugar level. The suggestion unit can change the foods it suggests based on the progress determined by the determination unit. For example, if the user's weight is decreasing, the suggestion unit will suggest low-calorie foods. Also, if the user's blood pressure is high, the suggestion unit may suggest foods with reduced salt content. In this way, by changing the foods suggested according to the progress of the user's health status, it supports the achievement of goals. Some or all of the above processes in the determination unit and the suggestion unit may be performed using AI, for example, or without AI. For example, the determination unit can use an AI model that accepts the user's health indicators as input and determines the progress to execute each process.

[0037] The reception unit can filter information based on the user's current lifestyle and areas of interest when receiving it. For example, if the user leads a busy life, the reception unit can provide an easy-to-use interface. The reception unit can also prioritize receiving information related to a specific health area if the user is interested in that area. For example, if the user is interested in dieting, it will prioritize receiving information related to dieting. This allows for the reception of more appropriate information by filtering information based on the user's lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can receive information about the user's lifestyle and areas of interest as input and execute each process using an AI model that performs filtering.

[0038] The reception desk can analyze the user's past health data and select the optimal reception method. For example, the reception desk may prioritize suggesting reception methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also suggest the optimal reception method for a specific time of day based on the user's past health data. For example, it may analyze the user's past health data and suggest the most efficient reception method. In this way, the optimal reception method can be selected by analyzing the user's past health data. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk may use an AI model that receives the user's past health data as input and selects the optimal reception method to execute each process.

[0039] The reception unit can filter health information received based on the user's current lifestyle and areas of interest. For example, if the user leads a busy life, the reception unit can provide an easy-to-use interface. The reception unit can also prioritize receiving information related to a specific health area if the user is interested in that area. For example, if the user is interested in dieting, it will prioritize receiving information related to dieting. This allows for the reception of more appropriate information by filtering information based on the user's lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can receive information about the user's lifestyle and areas of interest as input and execute each process using an AI model that performs filtering.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit performs a detailed analysis for important health data. It can also perform a simplified analysis for general health data. For example, it selects the optimal analysis method based on the importance of the health data. By adjusting the level of detail of the analysis based on the importance of the health data, it can provide more appropriate analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can take the importance of the health data as input and execute each process using an AI model that adjusts the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply a nutrition analysis algorithm to nutrition data. It can also apply an exercise analysis algorithm to exercise data. For example, it can select the optimal analysis algorithm according to the category of health data. By applying the optimal analysis algorithm according to the category of health data, it can provide more appropriate analysis results. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use an AI model that accepts the category of health data as input and applies an analysis algorithm to execute each process.

[0042] The analysis unit can determine the priority of analysis based on the timing of health data submission during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted health data. Alternatively, the analysis unit may prioritize the analysis of health data submitted regularly. For example, it may determine the optimal analysis order based on the timing of health data submission. This allows for the provision of more appropriate analysis results by prioritizing analysis based on the timing of health data submission. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may use an AI model that accepts the timing of health data submission as input and determines the priority of analysis to execute each process.

[0043] The analysis unit can adjust the order of analysis based on the relevance of health data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant health data. It can also postpone the analysis of less relevant health data. For example, it may determine the optimal analysis order based on the relevance of health data. By adjusting the order of analysis based on the relevance of health data, it is possible to provide more appropriate analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may use an AI model that accepts the relevance of health data as input and adjusts the order of analysis to perform each process.

[0044] The proposal unit can adjust the level of detail in its proposals based on the importance of the food items. For example, it can provide detailed proposals for important food items, and concise proposals for common food items. For instance, it can select the optimal proposal method based on the importance of the food items. By adjusting the level of detail in proposals based on the importance of the food items, it can provide more appropriate proposals. Some or all of the above processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can take the importance of the food items as input and execute each process using an AI model that adjusts the level of detail in the proposals.

[0045] The suggestion unit can apply different suggestion algorithms depending on the food category when making suggestions. For example, the suggestion unit can apply a vitamin suggestion algorithm to vitamin-rich foods, and a mineral suggestion algorithm to mineral-rich foods. For example, it can select the optimal suggestion algorithm according to the food category. By applying the optimal suggestion algorithm according to the food category, it can provide more appropriate suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can take the food category as input and execute each process using an AI model that applies a suggestion algorithm.

[0046] The proposal department can prioritize proposals based on the timing of food submissions. For example, the proposal department may prioritize recently submitted food items. It can also prioritize regularly submitted food items. For example, it may determine the optimal order of proposals based on the timing of food submissions. This allows for the provision of more appropriate proposals by prioritizing proposals based on the timing of food submissions. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department may use an AI model that accepts the timing of food submissions as input and determines the priority of proposals to execute each process.

[0047] The suggestion unit can adjust the order of suggestions based on the relevance of the foods during the suggestion process. For example, the suggestion unit may prioritize suggesting highly relevant foods. It can also postpone suggesting less relevant foods. For example, it can determine the optimal suggestion order based on the relevance of the foods. By adjusting the order of suggestions based on the relevance of the foods, it can provide more appropriate suggestions. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can perform each process using an AI model that takes the relevance of foods as input and adjusts the order of suggestions.

[0048] The determination unit can predict the progress status by referring to past health data when making a determination. For example, the determination unit predicts the current progress status based on the user's past health data. The determination unit can also predict the future progress status from the user's past health data. For example, it provides the optimal progress status by referring to past health data. In this way, both the current and future progress status can be predicted by referring to past health data. Some or all of the above processes in the determination unit may be performed using AI, for example, or without using AI. For example, the determination unit can perform each process using an AI model that accepts past health data as input and predicts the progress status.

[0049] The judgment unit can apply different judgment methods to each category of health data during the judgment process. For example, the judgment unit can apply a nutrition judgment method to nutrition data. It can also apply an exercise judgment method to exercise data. For example, it can select the optimal judgment method according to the category of health data. By applying the optimal judgment method according to the category of health data, it is possible to provide a more appropriate progress report. Some or all of the above-described processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can take the category of health data as input and execute each process using an AI model that applies a judgment method.

[0050] The determination unit can analyze changes in progress based on the timing of health data submission at the time of determination. For example, the determination unit can analyze changes in progress based on recently submitted health data. The determination unit can also analyze changes in progress based on health data submitted periodically. For example, it can analyze the optimal changes in progress based on the timing of health data submission. This allows for the provision of more appropriate progress information by analyzing changes in progress based on the timing of health data submission. Some or all of the above processes in the determination unit may be performed using AI, for example, or without AI. For example, the determination unit can use an AI model that accepts the timing of health data submission as input and analyzes changes in progress to execute each process.

[0051] The determination unit can analyze the progress status by referring to relevant market data for health data when making a determination. For example, the determination unit analyzes the progress status based on relevant market data for health data. The determination unit can also analyze the optimal progress status from relevant market data for health data. For example, it predicts the progress status by referring to relevant market data for health data. This allows for the provision of a more appropriate progress status by referring to relevant market data for health data. Some or all of the above processing in the determination unit may be performed using AI, for example, or without AI. For example, the determination unit can take relevant market data for health data as input and execute each process using an AI model that analyzes the progress status.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The analysis unit can consider the user's past dietary history when analyzing the user's health data. For example, the analysis unit can suggest foods that are most suitable for the user's current health condition based on data of foods the user has consumed in the past. The analysis unit can also detect deficiencies or excesses of specific nutrients from the user's dietary history and make suggestions based on that. Furthermore, the analysis unit can analyze the user's dietary history, detect changes in eating patterns, and predict improvements or deteriorations in health. This allows for more accurate suggestions by considering the user's past dietary history.

[0054] The reception desk can consider the user's lifestyle and daily activity level when receiving their health data. For example, if a user exercises frequently, the reception desk can suggest appropriate foods based on that information. If a user leads a sedentary lifestyle, it can also suggest adjusting their calorie intake based on that information. Furthermore, if a user works night shifts, it can suggest foods that suit their lifestyle. This allows for more personalized suggestions by considering the user's lifestyle and activity level.

[0055] The analysis unit can take seasonal and climatic changes into account when analyzing users' health data. For example, it can suggest foods rich in vitamin D during winter and foods that promote hydration during summer. It can also suggest foods with anti-allergic effects during allergy season. Furthermore, it can suggest foods that help regulate body temperature in response to temperature fluctuations. This allows for more appropriate food recommendations by considering seasonal and climatic changes.

[0056] The reception desk can consider the user's dietary preferences and allergy information when receiving their health data. For example, if the reception desk has a preference for a particular food, it can make suggestions based on that information. If the user has an allergy to a particular food, it can also suggest allergen-free foods based on that information. Furthermore, if the user has a specific dietary style (vegetarian, vegan, etc.), it can suggest appropriate foods based on that information. This allows for more personalized suggestions by considering the user's dietary preferences and allergy information.

[0057] The analysis unit can consider the user's genetic information when analyzing the user's health data. For example, based on the user's genetic information, the analysis unit can evaluate the absorption efficiency and metabolic capacity of specific nutrients and make recommendations accordingly. The analysis unit can also suggest specific foods as preventative measures against genetically high-risk diseases. Furthermore, the analysis unit can create personalized nutrition plans based on the user's genetic information. This allows for more accurate recommendations by considering the user's genetic information.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The reception desk receives information about the user's health status, health goals, and allergies. For example, it stores the health status, goals, and allergy information entered by the user in a database. The reception desk can also refer to information previously entered by the user and compare it with the current information. Step 2: The analysis unit analyzes the information received by the reception unit. For example, it executes an algorithm to select appropriate foods based on the user's health status, goals, and allergy information. The analysis unit analyzes the user's health data and generates data to suggest foods that are appropriate for the user's health status. Step 3: The suggestion unit proposes foods that match the user based on the results analyzed by the analysis unit. For example, it may suggest foods rich in vitamin C or foods free of allergens, depending on the user's health condition and goals. The suggestion unit also provides information on the ingredients, effects, allergens, and side effects of the suggested foods. For example, if the suggested food is oranges, which are rich in vitamin C, information on its ingredients, effects, allergens, and side effects will also be provided.

[0060] (Example of form 2) The AI ​​Assistant Advisory System for Health Foods and Supplements, according to an embodiment of the present invention, is a system that recommends appropriate products based on the user's health data, goals, allergy information, etc. The AI ​​Assistant Advisory System for Health Foods and Supplements receives the user's health status, health goals, and allergy information from the reception unit, the analysis unit analyzes it, and based on the results, the suggestion unit proposes foods that match the user. For example, if the user inputs "I'm looking for foods that are high in vitamin C," the reception unit receives this information, and the analysis unit analyzes it considering the user's health status and allergy information. As a result, the suggestion unit proposes foods that are high in vitamin C. Furthermore, the suggestion unit provides detailed information about the food's ingredients, effects, allergens, and side effects. For example, if the suggested food is oranges, which are high in vitamin C, information about its ingredients, effects, allergens, and side effects is also provided. The suggestion unit also provides advice on the dosage and timing of supplement intake. For example, if the user inputs "I want to take a vitamin C supplement," the suggestion unit provides information on the appropriate dosage and timing of intake. Furthermore, the system includes a determination unit that assesses the user's progress toward their health goals, and a suggestion unit that modifies the suggested foods based on that progress. For example, if a user sets the goal of "losing weight," the determination unit assesses their progress, and the suggestion unit suggests appropriate foods. The suggestion unit also estimates the user's emotions and suggests foods based on those emotions. For example, if the user is feeling stressed, the suggestion unit suggests foods with relaxing effects. In addition, the reception unit filters information based on the user's current lifestyle and areas of interest when receiving it. For example, if the user enters "I lead a busy life," the reception unit takes that information into consideration when filtering. Finally, the reception unit estimates the user's emotions and determines the priority of the information to be received based on those emotions. For example, if the user enters "I'm tired," the reception unit prioritizes processing that information. As a result, the AI ​​assistant advisory system for health foods and supplements can suggest appropriate foods based on the user's health status, goals, and allergy information.

[0061] The AI ​​assistant advisory system for health foods and supplements according to this embodiment comprises a reception unit, an analysis unit, and a suggestion unit. The reception unit receives information about the user's health status, health goals, and allergies. For example, the reception unit stores the health status, goals, and allergy information entered by the user in a database. The reception unit can also refer to information previously entered by the user and compare it with the current information. The analysis unit analyzes the information received by the reception unit. For example, the analysis unit executes an algorithm to select appropriate foods based on the user's health status, goals, and allergy information. The analysis unit analyzes the user's health data and generates data for suggesting foods that match the user's health status. The suggestion unit suggests foods that match the user based on the results of the analysis by the analysis unit. For example, the suggestion unit suggests foods that are rich in vitamin C or foods that do not contain allergens, depending on the user's health status and goals. The suggestion unit also provides information about the ingredients, effects, allergens, and side effects of the suggested foods. For example, if the suggested food is an orange rich in vitamin C, information about its components, effects, allergens, and side effects will also be provided. This allows the health food and supplement advisory system AI assistant according to the embodiment to suggest appropriate foods based on the user's health status, goals, and allergy information. Some or all of the above-described processes in the reception unit, analysis unit, and suggestion unit may be performed using AI, or not using AI. For example, the reception unit may receive the user's health status, goals, and allergy information as input, the analysis unit may analyze the received information as input, and the suggestion unit may use an AI model that makes suggestions using the analysis results as input to execute each process.

[0062] The reception department receives information about the user's health status, health goals, and allergies. Specifically, it stores the information entered by the user through a dedicated interface in a database. For example, the user can enter specific symptoms as their health status, such as "easily fatigued" or "concerned about skin problems," and set health goals such as "want to increase energy" or "want clearer skin." They can also enter detailed allergy information, such as "nut allergy" or "dairy allergy." The reception department centrally manages this information and has a function to compare information entered by the user in the past with current information. For example, if a user previously entered "easily fatigued" but now enters "less fatigued," the reception department records this change and provides it to the analysis department. This allows the reception department to accurately understand the user's health status, goals, and allergy information, and provide the necessary data to the analysis and proposal departments. Furthermore, the reception department has a function to periodically update the information entered by the user, updating the database each time the user enters new health status, goals, or allergy information. This ensures that analysis and proposals are always based on the latest information.

[0063] The analysis unit analyzes the information received by the reception unit. Specifically, it executes algorithms to select appropriate foods based on the user's health status, goals, and allergy information. For example, if a user enters "I get tired easily," the analysis unit analyzes data on the user's diet and lifestyle to identify the cause. Furthermore, if the user sets a goal of "I want to increase my energy," the analysis unit identifies foods containing the nutrients necessary to increase energy. Allergy information is also taken into consideration, and for users with nut allergies, foods that do not contain nuts are selected. Based on this information, the analysis unit generates data to suggest foods that are appropriate for the user's health status. For example, it utilizes machine learning algorithms using AI to predict the optimal foods based on past data and data from similar users. The analysis unit analyzes the user's health data in real time and generates data to suggest the optimal foods that are appropriate for the user's health status. This allows the analysis unit to comprehensively analyze the user's health status, goals, and allergy information and select the optimal foods. Furthermore, the analysis unit can continuously improve its algorithms based on user feedback to improve the accuracy of its suggestions.

[0064] The suggestion department proposes foods that match the user based on the results analyzed by the analysis department. Specifically, it suggests foods rich in vitamin C or foods free of allergens, depending on the user's health condition and goals. For example, if a user sets the goal of "wanting clear skin," the suggestion department will suggest foods rich in vitamin C and collagen. Also, for a user with a nut allergy, it will suggest foods that do not contain nuts. The suggestion department provides information on the ingredients, effects, allergens, and side effects of the suggested foods. For example, if the suggested food is oranges, which are rich in vitamin C, information on its ingredients, effects, allergens, and side effects will also be provided. The suggestion department provides the necessary information when the user selects a suggested food, so that the user can choose with confidence. Furthermore, the suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can collect feedback on the user's health condition and effects after consuming the suggested foods and provide it to the analysis department. This allows the suggestion department to propose the most suitable foods according to the user's health condition and goals, supporting the user's health.

[0065] The reception unit can receive health goals for the entire family. For example, the reception unit can receive the health status and goals of all family members as input. The reception unit can also receive allergy information for all family members. The suggestion unit suggests foods that are good for the health of the entire family. For example, the suggestion unit suggests a balanced diet based on the health status and goals of all family members. The suggestion unit can also suggest allergen-free foods, taking into account the allergy information of all family members. This allows for the suggestion of appropriate foods based on the health goals of the entire family. Some or all of the above processing in the reception unit and suggestion unit may be performed using AI, for example, or without AI. For example, the reception unit can receive the health status, goals, and allergy information of all family members as input, and the suggestion unit can perform each process using an AI model that makes suggestions using the received information as input.

[0066] The suggestion unit can provide information on the ingredients, effects, allergens, and side effects of food. For example, the suggestion unit can provide ingredient information for a suggested food. For example, the suggestion unit can provide ingredient information for a food that is rich in vitamin C. The suggestion unit can also provide information on the effects of a suggested food. For example, the suggestion unit can provide information that vitamin C has the effect of boosting immunity. The suggestion unit can also provide allergen information for a suggested food. For example, the suggestion unit can provide information on allergens contained in a suggested food. The suggestion unit can also provide information on side effects for a suggested food. For example, the suggestion unit can provide information on side effects caused by excessive intake of vitamin C. By providing detailed information on the suggested food, users can make appropriate choices. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can perform each process using an AI model that accepts information on the ingredients, effects, allergens, and side effects of a suggested food as input and provides the information.

[0067] The suggestion unit can provide information regarding the dosage and timing of supplement intake. For example, the suggestion unit can provide the appropriate dosage of a suggested supplement. For example, the suggestion unit can provide the recommended daily intake of a vitamin C supplement. The suggestion unit can also provide the appropriate timing for taking a suggested supplement. For example, the suggestion unit can recommend taking a vitamin C supplement after meals. By providing information on the appropriate dosage and timing of supplement intake, users can effectively utilize supplements. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can take information on the dosage and timing of a suggested supplement as input and execute each process using an AI model that provides the information.

[0068] The determination unit can determine the progress of the user's health status. The determination unit uses indicators to evaluate the progress of the user's health status, for example. For example, the determination unit determines the progress based on health indicators such as the user's weight, blood pressure, and blood sugar level. The suggestion unit can change the foods it suggests based on the progress determined by the determination unit. For example, if the user's weight is decreasing, the suggestion unit will suggest low-calorie foods. Also, if the user's blood pressure is high, the suggestion unit may suggest foods with reduced salt content. In this way, by changing the foods suggested according to the progress of the user's health status, it supports the achievement of goals. Some or all of the above processes in the determination unit and the suggestion unit may be performed using AI, for example, or without AI. For example, the determination unit can use an AI model that accepts the user's health indicators as input and determines the progress to execute each process.

[0069] The suggestion unit can estimate the user's emotions and suggest food based on those emotions. For example, the suggestion unit can estimate emotions by analyzing the user's facial expressions. For instance, if the user is feeling stressed, the suggestion unit can suggest foods that have a relaxing effect. The suggestion unit can also estimate emotions by analyzing the user's voice. For example, if the user is tired, the suggestion unit can suggest foods that replenish energy. The suggestion unit can also estimate emotions by analyzing the user's biometric data. For example, the suggestion unit can estimate emotions by analyzing the user's heart rate or skin electrical activity. This allows for more appropriate suggestions by suggesting foods that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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-described processes in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0070] The reception unit can filter information based on the user's current lifestyle and areas of interest when receiving it. For example, if the user leads a busy life, the reception unit can provide an easy-to-use interface. The reception unit can also prioritize receiving information related to a specific health area if the user is interested in that area. For example, if the user is interested in dieting, it will prioritize receiving information related to dieting. This allows for the reception of more appropriate information by filtering information based on the user's lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can receive information about the user's lifestyle and areas of interest as input and execute each process using an AI model that performs filtering.

[0071] The reception desk can estimate the user's emotions and determine the priority of information to receive based on those emotions. For example, if the user is tired, the reception desk will prioritize receiving important information. Conversely, if the user is relaxed, the reception desk may prioritize receiving detailed information. For example, if the user is in a hurry, it will prioritize receiving information that can be processed quickly. This allows for the prioritization of more relevant information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the reception desk may be performed using AI, or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0072] The reception unit can estimate the user's emotions and adjust the timing of health information reception based on the user's emotions. For example, if the user is feeling stressed, the reception unit will receive health information during a time when the user can relax. Similarly, if the user is tired, the reception unit can receive health information after the user has rested. For example, if the user is excited, the reception unit will receive health information after the user has calmed down. This allows for information to be received at a more appropriate time by adjusting the timing of health information reception based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 reception unit may be performed using AI, or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0073] The reception desk can analyze the user's past health data and select the optimal reception method. For example, the reception desk may prioritize suggesting reception methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also suggest the optimal reception method for a specific time of day based on the user's past health data. For example, it may analyze the user's past health data and suggest the most efficient reception method. In this way, the optimal reception method can be selected by analyzing the user's past health data. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk may use an AI model that receives the user's past health data as input and selects the optimal reception method to execute each process.

[0074] The reception unit can filter health information received based on the user's current lifestyle and areas of interest. For example, if the user leads a busy life, the reception unit can provide an easy-to-use interface. The reception unit can also prioritize receiving information related to a specific health area if the user is interested in that area. For example, if the user is interested in dieting, it will prioritize receiving information related to dieting. This allows for the reception of more appropriate information by filtering information based on the user's lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can receive information about the user's lifestyle and areas of interest as input and execute each process using an AI model that performs filtering.

[0075] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results. For example, if the user is excited, it can provide visually easy-to-understand analysis results. By adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit performs a detailed analysis for important health data. It can also perform a simplified analysis for general health data. For example, it selects the optimal analysis method based on the importance of the health data. By adjusting the level of detail of the analysis based on the importance of the health data, it can provide more appropriate analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can take the importance of the health data as input and execute each process using an AI model that adjusts the level of detail of the analysis.

[0077] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply a nutrition analysis algorithm to nutrition data. It can also apply an exercise analysis algorithm to exercise data. For example, it can select the optimal analysis algorithm according to the category of health data. By applying the optimal analysis algorithm according to the category of health data, it can provide more appropriate analysis results. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use an AI model that accepts the category of health data as input and applies an analysis algorithm to execute each process.

[0078] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the user's emotions. For example, if the user is in a hurry, the analysis unit will provide a short analysis result. Conversely, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is excited, it will provide a visually easy-to-understand analysis result. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The analysis unit can determine the priority of analysis based on the timing of health data submission during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted health data. Alternatively, the analysis unit may prioritize the analysis of health data submitted regularly. For example, it may determine the optimal analysis order based on the timing of health data submission. This allows for the provision of more appropriate analysis results by prioritizing analysis based on the timing of health data submission. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may use an AI model that accepts the timing of health data submission as input and determines the priority of analysis to execute each process.

[0080] The analysis unit can adjust the order of analysis based on the relevance of health data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant health data. It can also postpone the analysis of less relevant health data. For example, it may determine the optimal analysis order based on the relevance of health data. By adjusting the order of analysis based on the relevance of health data, it is possible to provide more appropriate analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may use an AI model that accepts the relevance of health data as input and adjusts the order of analysis to perform each process.

[0081] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can also provide concise suggestions. For example, if the user is excited, it can provide visually easy-to-understand suggestions. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be provided. 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 processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The proposal unit can adjust the level of detail in its proposals based on the importance of the food items. For example, it can provide detailed proposals for important food items, and concise proposals for common food items. For instance, it can select the optimal proposal method based on the importance of the food items. By adjusting the level of detail in proposals based on the importance of the food items, it can provide more appropriate proposals. Some or all of the above processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can take the importance of the food items as input and execute each process using an AI model that adjusts the level of detail in the proposals.

[0083] The suggestion unit can apply different suggestion algorithms depending on the food category when making suggestions. For example, the suggestion unit can apply a vitamin suggestion algorithm to vitamin-rich foods, and a mineral suggestion algorithm to mineral-rich foods. For example, it can select the optimal suggestion algorithm according to the food category. By applying the optimal suggestion algorithm according to the food category, it can provide more appropriate suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can take the food category as input and execute each process using an AI model that applies a suggestion algorithm.

[0084] The proposal department can prioritize proposals based on the timing of food submissions. For example, the proposal department may prioritize recently submitted food items. It can also prioritize regularly submitted food items. For example, it may determine the optimal order of proposals based on the timing of food submissions. This allows for the provision of more appropriate proposals by prioritizing proposals based on the timing of food submissions. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department may use an AI model that accepts the timing of food submissions as input and determines the priority of proposals to execute each process.

[0085] The suggestion unit can adjust the order of suggestions based on the relevance of the foods during the suggestion process. For example, the suggestion unit may prioritize suggesting highly relevant foods. It can also postpone suggesting less relevant foods. For example, it can determine the optimal suggestion order based on the relevance of the foods. By adjusting the order of suggestions based on the relevance of the foods, it can provide more appropriate suggestions. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can perform each process using an AI model that takes the relevance of foods as input and adjusts the order of suggestions.

[0086] The judgment unit can estimate the user's emotions and adjust the progress status determination method based on the user's emotions. For example, if the user is relaxed, the judgment unit can provide detailed progress status. If the user is in a hurry, the judgment unit can also provide concise progress status. For example, if the user is excited, it can provide visually easy-to-understand progress status. By adjusting the progress status determination method based on the user's emotions, a more appropriate progress status 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 judgment unit may be performed using AI, for example, or not using AI. For example, the judgment unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0087] The determination unit can predict the progress status by referring to past health data when making a determination. For example, the determination unit predicts the current progress status based on the user's past health data. The determination unit can also predict the future progress status from the user's past health data. For example, it provides the optimal progress status by referring to past health data. In this way, both the current and future progress status can be predicted by referring to past health data. Some or all of the above processes in the determination unit may be performed using AI, for example, or without using AI. For example, the determination unit can perform each process using an AI model that accepts past health data as input and predicts the progress status.

[0088] The judgment unit can apply different judgment methods to each category of health data during the judgment process. For example, the judgment unit can apply a nutrition judgment method to nutrition data. It can also apply an exercise judgment method to exercise data. For example, it can select the optimal judgment method according to the category of health data. By applying the optimal judgment method according to the category of health data, it is possible to provide a more appropriate progress report. Some or all of the above-described processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can take the category of health data as input and execute each process using an AI model that applies a judgment method.

[0089] The judgment unit can estimate the user's emotions and adjust the importance of the progress status based on the user's emotions. For example, if the user is relaxed, the judgment unit can provide detailed progress status. If the user is in a hurry, the judgment unit can also provide concise progress status. For example, if the user is excited, it can provide visually easy-to-understand progress status. This allows for the provision of more appropriate progress status by adjusting the importance of the progress status based on the user'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 judgment unit may be performed using AI, for example, or not using AI. For example, the judgment unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0090] The determination unit can analyze changes in progress based on the timing of health data submission at the time of determination. For example, the determination unit can analyze changes in progress based on recently submitted health data. The determination unit can also analyze changes in progress based on health data submitted periodically. For example, it can analyze the optimal changes in progress based on the timing of health data submission. This allows for the provision of more appropriate progress information by analyzing changes in progress based on the timing of health data submission. Some or all of the above processes in the determination unit may be performed using AI, for example, or without AI. For example, the determination unit can use an AI model that accepts the timing of health data submission as input and analyzes changes in progress to execute each process.

[0091] The determination unit can analyze the progress status by referring to relevant market data for health data when making a determination. For example, the determination unit analyzes the progress status based on relevant market data for health data. The determination unit can also analyze the optimal progress status from relevant market data for health data. For example, it predicts the progress status by referring to relevant market data for health data. This allows for the provision of a more appropriate progress status by referring to relevant market data for health data. Some or all of the above processing in the determination unit may be performed using AI, for example, or without AI. For example, the determination unit can take relevant market data for health data as input and execute each process using an AI model that analyzes the progress status.

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

[0093] The analysis unit can consider the user's past dietary history when analyzing the user's health data. For example, the analysis unit can suggest foods that are most suitable for the user's current health condition based on data of foods the user has consumed in the past. The analysis unit can also detect deficiencies or excesses of specific nutrients from the user's dietary history and make suggestions based on that. Furthermore, the analysis unit can analyze the user's dietary history, detect changes in eating patterns, and predict improvements or deteriorations in health. This allows for more accurate suggestions by considering the user's past dietary history.

[0094] The suggestion function can estimate the user's emotions and adjust the presentation method of suggested foods based on those emotions. For example, if the user is relaxed, the suggestion function will suggest foods with detailed explanations. If the user is in a hurry, the suggestion function can suggest foods with concise explanations. Furthermore, if the user is excited, the suggestion function can suggest foods using visually easy-to-understand graphics. This allows for more effective suggestions by using presentation methods that match the user's emotions.

[0095] The reception desk can consider the user's lifestyle and daily activity level when receiving their health data. For example, if a user exercises frequently, the reception desk can suggest appropriate foods based on that information. If a user leads a sedentary lifestyle, it can also suggest adjusting their calorie intake based on that information. Furthermore, if a user works night shifts, it can suggest foods that suit their lifestyle. This allows for more personalized suggestions by considering the user's lifestyle and activity level.

[0096] The suggestion function can estimate the user's emotions and adjust the food choices it suggests based on those emotions. For example, if the user is stressed, the suggestion function will prioritize suggesting foods with relaxing effects. If the user is tired, it can also suggest foods that provide energy. Furthermore, if the user is happy, it can suggest foods that help maintain that feeling. This allows for more appropriate suggestions by providing food choices that match the user's emotions.

[0097] The analysis unit can take seasonal and climatic changes into account when analyzing users' health data. For example, it can suggest foods rich in vitamin D during winter and foods that promote hydration during summer. It can also suggest foods with anti-allergic effects during allergy season. Furthermore, it can suggest foods that help regulate body temperature in response to temperature fluctuations. This allows for more appropriate food recommendations by considering seasonal and climatic changes.

[0098] The suggestion unit can estimate the user's emotions and adjust the amount of food it suggests based on those emotions. For example, if the user is stressed, the suggestion unit will suggest a small amount of food to prevent overeating. If the user is relaxed, the suggestion unit can suggest an appropriate amount of food. Furthermore, if the user is tired, the suggestion unit can suggest a slightly larger amount of food to replenish energy. By adjusting the amount of food according to the user's emotions, more appropriate suggestions can be made.

[0099] The reception desk can consider the user's dietary preferences and allergy information when receiving their health data. For example, if the reception desk has a preference for a particular food, it can make suggestions based on that information. If the user has an allergy to a particular food, it can also suggest allergen-free foods based on that information. Furthermore, if the user has a specific dietary style (vegetarian, vegan, etc.), it can suggest appropriate foods based on that information. This allows for more personalized suggestions by considering the user's dietary preferences and allergy information.

[0100] The suggestion function can estimate the user's emotions and adjust the types of food it suggests based on those emotions. For example, if the user is stressed, the suggestion function might suggest relaxing herbal tea or chocolate. If the user is tired, it might suggest nuts or fruit to replenish energy. Furthermore, if the user is happy, it might suggest desserts or snacks to maintain that feeling. By providing food types that match the user's emotions, it becomes possible to make more appropriate suggestions.

[0101] The analysis unit can consider the user's genetic information when analyzing the user's health data. For example, based on the user's genetic information, the analysis unit can evaluate the absorption efficiency and metabolic capacity of specific nutrients and make recommendations accordingly. The analysis unit can also suggest specific foods as preventative measures against genetically high-risk diseases. Furthermore, the analysis unit can create personalized nutrition plans based on the user's genetic information. This allows for more accurate recommendations by considering the user's genetic information.

[0102] The suggestion unit can estimate the user's emotions and adjust the timing of food intake based on those emotions. For example, if the user is stressed, the suggestion unit may suggest relaxing foods at night. If the user is tired, the suggestion unit may suggest energy-replenishing foods at breakfast. Furthermore, if the user is happy, the suggestion unit may suggest foods to maintain that feeling at lunchtime. By adjusting the timing of food intake according to the user's emotions, more appropriate suggestions can be made.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The reception desk receives information about the user's health status, health goals, and allergies. For example, it stores the health status, goals, and allergy information entered by the user in a database. The reception desk can also refer to information previously entered by the user and compare it with the current information. Step 2: The analysis unit analyzes the information received by the reception unit. For example, it executes an algorithm to select appropriate foods based on the user's health status, goals, and allergy information. The analysis unit analyzes the user's health data and generates data to suggest foods that are appropriate for the user's health status. Step 3: The suggestion unit proposes foods that match the user based on the results analyzed by the analysis unit. For example, it may suggest foods rich in vitamin C or foods free of allergens, depending on the user's health condition and goals. The suggestion unit also provides information on the ingredients, effects, allergens, and side effects of the suggested foods. For example, if the suggested food is oranges, which are rich in vitamin C, information on its ingredients, effects, allergens, and side effects will also be provided.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0106] 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.

[0107] 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.

[0108] For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives the user's health status, goals, and allergy information. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes the received information. The suggestion unit is implemented by the control unit 46A of the smart device 14 and suggests appropriate food based on the analysis results. The suggestion unit can also be implemented by the identification processing unit 290 of the data processing device 12 and provides information on the food's ingredients, effects, allergens, and side effects. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

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

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.).

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0122] 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.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0124] For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives the user's health status, goals, and allergy information. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes the received information. The suggestion unit is implemented by the control unit 46A of the smart glasses 214 and suggests appropriate food based on the analysis results. The suggestion unit can also be implemented by the identification processing unit 290 of the data processing device 12 and provides information on the food's ingredients, effects, allergens, and side effects. 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.

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

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0140] For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives the user's health status, goals, and allergy information. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes the received information. The suggestion unit is implemented by the control unit 46A of the headset terminal 314 and suggests appropriate food based on the analysis results. The suggestion unit can also be implemented by the identification processing unit 290 of the data processing device 12 and provides information on the food's ingredients, effects, allergens, and side effects. 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.

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

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0155] 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.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0157] For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives the user's health status, goals, and allergy information. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes the received information. The suggestion unit is implemented by the control unit 46A of the robot 414 and suggests appropriate food based on the analysis results. The suggestion unit can also be implemented by the identification processing unit 290 of the data processing device 12 and provides information on the food's ingredients, effects, allergens, and side effects. 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

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

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] (Note 1) A reception desk that receives information about the user's health status, health goals, and allergies, An analysis unit that analyzes the information received by the reception unit, Based on the results of the analysis performed by the aforementioned analysis unit, a proposal unit proposes food products that match the user, Equipped with A system characterized by the following features. (Note 2) The aforementioned reception unit is We accept health goals for the entire family. The aforementioned proposal section is, Proposing food for the entire family The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Provides information on food ingredients, effects, allergens, and side effects. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Provides information on supplement dosage and timing. The system described in Appendix 1, characterized by the features described herein. (Note 5) It further includes a determination unit that determines the progress of the user's health status, The aforementioned proposal section is, To achieve the aforementioned health goals, we will change the proposed foods. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Estimate the user's emotions, Suggesting food products based on user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Estimate the user's emotions, Prioritize the information received based on the user's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Estimate the user's emotions, Adjust the timing of health information requests based on user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is Analyze the user's past health data to select the appropriate registration method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When health information is submitted, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, Estimate the user's emotions, Adjust the way the analysis is presented based on the user's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the health 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 category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, Estimate the user's emotions, Adjust the length of the analysis based on the user's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on when the health data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, Estimate the user's emotions, Adjust the way suggestions are presented based on the user's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the food items. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the food category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When submitting proposals, prioritize them based on the timing of food submission. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making proposals, adjust the order of suggestions based on the relevance of the food items. The system described in Appendix 1, characterized by the features described herein. (Note 23) The determination unit, Estimate the user's emotions, Adjust the progress status determination method based on user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The determination unit, During the assessment, past health data is referenced to predict the progress. The system described in Appendix 1, characterized by the features described herein. (Note 25) The determination unit, When making a determination, different determination methods are applied to each category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The determination unit, Estimate the user's emotions, Adjust the importance of progress based on user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The determination unit, At the time of assessment, we analyze changes in progress based on when health data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 28) The determination unit, During the assessment, we analyze the progress by referring to relevant market data for health data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0177] 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 reception area that receives information about the user's health status, health goals, and allergies, An analysis unit that analyzes the information received by the reception unit, Based on the results of the analysis performed by the aforementioned analysis unit, a proposal unit proposes food products that match the user, Equipped with A system characterized by the following features.

2. The aforementioned reception unit is We accept health goals for the entire family. The aforementioned proposal section is, Proposing food for the entire family The system according to feature 1.

3. The aforementioned proposal section is, Provides information on food ingredients, effects, allergens, and side effects. The system according to feature 1.

4. The aforementioned proposal section is, Provides information on supplement dosage and timing. The system according to feature 1.

5. The system further includes a determination unit that determines the progress of the user's health status, The aforementioned proposal section is, To achieve the aforementioned health goals, we will change the proposed foods. The system according to feature 1.

6. The aforementioned proposal section is, The system estimates the user's emotions and suggests food items based on those emotions. The system according to feature 1.

7. The aforementioned reception unit is When receiving information, filtering is performed based on the user's current living situation and areas of interest. The system according to feature 1.

8. The aforementioned reception unit is The system estimates the user's emotions and determines the priority of information to be received based on the user's emotions. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of health information reception based on the user's emotions. The system according to feature 1.

10. The aforementioned reception unit is The user's past health data is analyzed to select a registration method. The system according to feature 1.

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