system

Abstract

The system according to this embodiment aims to identify deficient nutrients based on an individual's purchase history and provide information to supplement those nutrients. [Solution] The system according to the embodiment comprises a profile creation unit, a history import unit, a nutrient analysis unit, and a nutrient presentation unit. The profile creation unit creates an individual's profile. The history import unit imports the purchase history from the electronic payment system. The nutrient analysis unit compares the purchase history imported by the history import unit with a nutrient database and identifies any missing nutrients. The nutrient presentation unit displays on a map the locations where the missing nutrients identified by the nutrient analysis unit can be obtained.

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, the method 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 fully carried out to identify nutrient deficiencies based on an individual's purchase history and provide information for supplementing the deficiencies, and there is room for improvement.

[0005] The system according to the embodiment aims to identify deficient nutrients based on an individual's purchase history and provide information for supplementing the nutrients.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a profile creation unit, a history import unit, a nutrient analysis unit, and a nutrient presentation unit. The profile creation unit creates an individual's profile. The history import unit imports the purchase history from the electronic payment system. The nutrient analysis unit compares the purchase history imported by the history import unit with a nutrient database and identifies any deficient nutrients. The nutrient presentation unit displays on a map the locations where the deficient nutrients identified by the nutrient analysis unit can be obtained. [Effects of the Invention]

[0007] The system according to this embodiment can identify deficient nutrients based on an individual's purchase history and provide information to help them supplement those nutrients. [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 applicable 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) A health management system according to an embodiment of the present invention is a system that manages a user's health using an electronic payment system. This health management system creates an individual profile, imports the purchase history from the electronic payment system, and uses AI to cross-reference it with a nutrient database to identify the user's nutritional deficiencies. Furthermore, it uses AI to analyze locations where the missing nutrients can be obtained and displays them on a map. The initial target stores are convenience stores, and by targeting the nutritional content of foods displayed on the convenience store's website, the system suggests foods and beverages containing recommended nutrients. If this service is successful, it is expected that other stores using electronic payment systems will follow suit, leading to a global shift towards health consciousness. For example, the health management system creates an individual profile within the system. This profile includes the user's basic information, health status, and dietary preferences. For example, the user's age, gender, height, weight, and allergy information are registered. Next, the health management system imports the purchase history from the electronic payment system into the system. This allows the system to understand what foods and beverages the user has purchased. For example, information on foods and beverages purchased by the user at a convenience store is imported. Based on the imported purchase history, the health management system uses AI to cross-reference it with a nutrient database. The AI ​​analyzes the nutritional information of food and beverages included in the purchase history to understand the nutrients the user is consuming. For example, it analyzes the amount of vitamins and minerals contained in the food the user has purchased. Next, the health management system uses the AI's analysis results to identify the nutrients the user is lacking. For example, if the user is not getting enough vitamin C, the AI ​​will detect the deficiency. Furthermore, the health management system uses the AI ​​to analyze where the missing nutrients can be obtained and displays them on a map. For example, it will display on the map the locations of convenience stores that sell foods suitable for supplementing the user's vitamin C. The initial target stores are convenience stores, and the nutritional information of the foods listed on the convenience store's website is used as the target for analysis. This allows the system to suggest foods and beverages containing recommended nutrients. For example, it can suggest suitable products to the user based on the nutritional information of salads and juices listed on the convenience store's website.If this service is successful, other stores using electronic payment systems are expected to follow suit, leading to a global shift towards health consciousness. For example, supermarkets and restaurants will adopt this system and begin suggesting healthy meals to their customers. This is expected to expand the use of electronic payment systems and increase sales. In turn, health management systems will be able to efficiently manage users' health.

[0029] The health management system according to this embodiment comprises a profile creation unit, a history import unit, a nutrient analysis unit, and a nutrient presentation unit. The profile creation unit creates an individual profile. The profile creation unit registers, for example, the user's basic information, health status, and dietary preferences. For example, the profile creation unit can register the user's age, gender, height, weight, allergy information, etc. The profile creation unit can also register information related to the user's health status. For example, the profile creation unit can register information such as the user's blood pressure, weight, and medical history. Furthermore, the profile creation unit can also register information related to the user's dietary preferences. For example, the profile creation unit can register the user's favorite foods and allergy information. The history import unit imports the purchase history of an electronic payment system. The history import unit imports, for example, information on food and beverages purchased by the user. For example, the history import unit can import information on food and beverages purchased by the user at a convenience store. Furthermore, the history import unit can also import information on food and beverages purchased by the user at a supermarket or restaurant. Furthermore, the history import unit can also import information on food and beverages purchased by the user online. For example, the history import unit can import information on food and beverages purchased by the user from online stores. The nutrient analysis unit compares the purchase history imported by the history import unit with a nutrient database to identify any missing nutrients. The nutrient analysis unit analyzes the nutrient information of the food and beverages included in the purchase history to understand the nutrients the user is consuming. For example, the nutrient analysis unit can analyze the amount of vitamins and minerals in the food and beverages included in the purchase history. It can also analyze the amount of calories and protein in the food and beverages included in the purchase history. Furthermore, the nutrient analysis unit can analyze the amount of fat and carbohydrates in the food and beverages included in the purchase history. For example, the nutrient analysis unit can understand the nutrients the user is consuming based on the nutrient information of the food and beverages included in the purchase history. The nutrient presentation unit displays on a map the locations where the missing nutrients identified by the nutrient analysis unit can be obtained.The nutrient display unit uses AI to analyze locations where a user can obtain deficient nutrients and displays them on a map. For example, the nutrient display unit can display on a map the locations of convenience stores that sell foods suitable for the user to supplement their vitamin C intake. It can also display on a map the locations of supermarkets that sell foods suitable for the user to supplement their mineral intake. Furthermore, the nutrient display unit can display on a map the locations of restaurants that sell foods suitable for the user to supplement their protein intake. For example, the nutrient display unit can display on a map the locations of stores that sell foods suitable for the user to supplement their deficient nutrients. This allows the health management system according to the embodiment to efficiently manage the user's health.

[0030] The profile creation unit creates individual profiles. For example, it registers the user's basic information, health status, and dietary preferences. Specifically, the profile creation unit can register detailed information such as the user's age, gender, height, weight, and allergy information. This allows for centralized management of the user's basic physical information. The profile creation unit can also register information about the user's health status. For example, by recording detailed information such as the user's blood pressure, weight, medical history, and current medication information, a comprehensive understanding of the user's health status can be obtained. Furthermore, the profile creation unit can register information about the user's dietary preferences. For example, by recording detailed information such as the user's favorite foods, disliked foods, allergy information, and frequency and quantity of meals, optimal nutritional management for each individual user becomes possible. Thus, the profile creation unit provides a foundation for supporting health management tailored to the user's individual needs. Additionally, the profile creation unit can register information about the user's lifestyle and exercise habits. For example, by recording the user's exercise frequency, type of exercise, and daily activity level, more accurate health management becomes possible. This allows the profile creation unit to comprehensively understand the user's overall health status and support health management tailored to their individual needs.

[0031] The history import unit imports purchase history from electronic payment systems. For example, it imports information on food and beverages purchased by users. Specifically, it can import information on food and beverages purchased by users at convenience stores in real time. This automatically records the user's eating history, allowing for effortless data collection. The history import unit can also import information on food and beverages purchased by users at supermarkets and restaurants. For example, importing information on vegetables and fruits purchased at supermarkets and dishes ordered at restaurants allows for a more detailed record of eating history. Furthermore, the history import unit can import information on food and beverages purchased online. For example, importing information on supplements and health foods purchased from online stores allows for centralized management of online purchase history. This enables the history import unit to comprehensively understand the user's eating history and improve the accuracy of nutritional management. Additionally, the history import unit can analyze the user's purchase history to understand eating patterns and trends. For example, if a user frequently purchases certain foods at specific times, this information can be used to provide appropriate advice to the user. This allows the history acquisition unit to gain a detailed understanding of the user's meal history and support nutritional management tailored to individual needs.

[0032] The nutrient analysis unit compares the purchase history imported by the history import unit with the nutrient database to identify any nutrient deficiencies. Specifically, the nutrient analysis unit analyzes the nutrient information of the foods and beverages included in the purchase history in detail to accurately understand the nutrients the user is consuming. For example, it can analyze the amount of vitamins and minerals in the foods and beverages included in the purchase history to understand how much of each nutrient the user is consuming. The nutrient analysis unit can also analyze the amount of calories and protein in the foods and beverages included in the purchase history. This allows for an accurate understanding of the user's energy and protein intake. Furthermore, the nutrient analysis unit can analyze the amount of fat and carbohydrates in the foods and beverages included in the purchase history. This allows for an accurate understanding of the user's fat and carbohydrate intake. Based on this information, the nutrient analysis unit can evaluate the balance of nutrients the user is consuming and identify any nutrient deficiencies. For example, if there is a deficiency in vitamin C or calcium, this information can be provided to the user, and advice can be given on how to supplement with appropriate nutrients. In this way, the nutrient analysis unit can optimize the user's nutritional balance and support their health management. Furthermore, the nutrient analysis unit can utilize historical data and statistical information to analyze long-term fluctuations in nutritional balance. This allows for continuous monitoring of the user's nutritional status and the provision of appropriate advice as needed.

[0033] The nutrient information display unit shows locations on a map where the deficient nutrients identified by the nutrient analysis unit can be obtained. Specifically, the nutrient information display unit uses AI to analyze locations where deficient nutrients can be obtained and visually displays them on a map. For example, the nutrient information display unit can display on a map the locations of convenience stores that sell foods suitable for supplementing vitamin C for the user. It can also display on a map the locations of supermarkets that sell foods suitable for supplementing minerals for the user. Furthermore, the nutrient information display unit can display on a map the locations of restaurants that sell foods suitable for supplementing protein for the user. This allows users to easily obtain information on how to efficiently supplement deficient nutrients. The nutrient information display unit can use AI to consider the user's current location and travel route to suggest the most suitable stores. For example, it can improve convenience by prioritizing the display of stores closest to the user's current location or stores along their commute route. The nutrient information display unit can also provide individually customized suggestions considering the user's past purchase history and preferences. This allows users to easily find the best way to supplement their nutrients. Furthermore, the nutrient information display unit can also provide detailed information on suggested stores and nutritional information for specific foods. This makes it easier for users to understand specific ways to supplement their nutrition, allowing them to manage their health more efficiently.

[0034] The nutrient information display unit can analyze the nutritional content of foods displayed on a convenience store's website and suggest foods and beverages containing recommended nutrients. For example, the nutrient information display unit can suggest products suitable for the user based on the nutritional information of salads and juices listed on the convenience store's website. For example, the nutrient information display unit can analyze the nutritional information of salads listed on the convenience store's website and suggest salads rich in vitamin C. It can also analyze the nutritional information of juices listed on the convenience store's website and suggest juices rich in minerals. Furthermore, the nutrient information display unit can analyze the nutritional information of snacks listed on the convenience store's website and suggest snacks rich in protein. For example, the nutrient information display unit can suggest products suitable for the user based on the nutritional information of foods listed on the convenience store's website. This allows the unit to suggest foods containing nutrients suitable for the user. Some or all of the above processing in the nutrient information display unit may be performed using AI, for example, or without AI. For example, the nutrient information display unit can input the nutritional information of foods listed on the convenience store's website into a generating AI and have the generating AI suggest foods and beverages containing recommended nutrients.

[0035] The profile creation unit can register the user's basic information, health status, and dietary preferences. For example, the profile creation unit can register the user's age, gender, height, weight, and allergy information. For example, the profile creation unit can register the user's age and perform age-appropriate health management. It can also register the user's gender and perform gender-appropriate health management. Furthermore, the profile creation unit can register the user's height and weight and calculate the BMI (Body Mass Index). For example, the profile creation unit can register the user's allergy information and avoid foods that may cause allergies. This allows for the creation of a detailed profile of the user. Some or all of the above processing in the profile creation unit may be performed using AI, for example, or not using AI. For example, the profile creation unit can input the user's basic information, health status, and dietary preferences into a generating AI and have the generating AI create a detailed profile.

[0036] The history import unit can import information about food and beverages purchased by the user. For example, the history import unit can import information about food and beverages purchased by the user at a convenience store. For example, the history import unit can import information about salads and juices purchased by the user at a convenience store. The history import unit can also import information about food and beverages purchased by the user at supermarkets and restaurants. For example, the history import unit can import information about vegetables and fruits purchased by the user at a supermarket. Furthermore, the history import unit can also import information about food and beverages purchased by the user online. For example, the history import unit can import information about snacks and beverages purchased by the user at an online store. This allows for accurate import of the user's purchase history. Some or all of the above processing in the history import unit may be performed using AI, for example, or without AI. For example, the history import unit can input information about food and beverages purchased by the user into a generating AI and have the generating AI import the purchase history.

[0037] The nutrient analysis unit can analyze the nutrient information of foods and beverages included in the purchase history and understand the nutrients the user is consuming. For example, the nutrient analysis unit can analyze the amount of vitamins and minerals in foods and beverages included in the purchase history. For example, the nutrient analysis unit can analyze the amount of vitamin C and vitamin D in foods and beverages included in the purchase history. The nutrient analysis unit can also analyze the amount of calories and protein in foods and beverages included in the purchase history. For example, the nutrient analysis unit can analyze the amount of calories and protein in foods and beverages included in the purchase history and understand the nutrients the user is consuming. Furthermore, the nutrient analysis unit can also analyze the amount of lipids and carbohydrates in foods and beverages included in the purchase history. For example, the nutrient analysis unit can analyze the amount of lipids and carbohydrates in foods and beverages included in the purchase history and understand the nutrients the user is consuming. This allows for an accurate understanding of the user's nutrient intake. Some or all of the above processing in the nutrient analysis unit may be performed using AI, for example, or without AI. For example, the nutrient analysis unit can input the nutrient information of foods and beverages included in the purchase history into a generating AI and have the generating AI perform the nutrient analysis.

[0038] The nutrient display unit can display locations on a map where deficient nutrients can be obtained. For example, the nutrient display unit can use AI to analyze locations where deficient nutrients can be obtained and display them on a map. For example, the nutrient display unit can display on a map the locations of convenience stores that sell foods suitable for the user to supplement their vitamin C intake. It can also display on a map the locations of supermarkets that sell foods suitable for the user to supplement their minerals intake. Furthermore, the nutrient display unit can display on a map the locations of restaurants that sell foods suitable for the user to supplement their protein intake. For example, the nutrient display unit can display on a map the locations of stores that sell foods suitable for the user to supplement their deficient nutrients. This makes it easy for users to find places to obtain deficient nutrients. Some or all of the above processing in the nutrient display unit may be performed using AI, for example, or without AI. For example, the nutrient display unit can input locations where deficient nutrients can be obtained into a generating AI and have the generating AI perform the task of displaying locations on a map.

[0039] The profile creation unit can analyze the user's past health data and select the most suitable data items when creating the profile. For example, the profile creation unit can analyze the user's past health check results and add important data items to the profile. For example, the profile creation unit can refer to the user's past medical history and include relevant data items in the profile. The profile creation unit can also analyze the user's past exercise history and add data items related to exercise habits to the profile. For example, the profile creation unit can add important data items to the profile based on the user's past health check results. This allows for the creation of an optimal profile based on the user's past health data. Some or all of the above processing in the profile creation unit may be performed using AI, for example, or without AI. For example, the profile creation unit can input the user's past health data into a generating AI and have the generating AI select the most suitable data items.

[0040] The profile creation unit can create a detailed profile by taking into account the user's lifestyle and exercise history. For example, the profile creation unit can analyze the user's eating habits and add data items related to eating to the profile. The profile creation unit can also refer to the user's exercise history and include data items related to exercise habits in the profile. Furthermore, the profile creation unit can analyze the user's sleep patterns and add data items related to sleep to the profile. This allows for the creation of a detailed profile that takes into account the user's lifestyle and exercise history. Some or all of the above processing in the profile creation unit may be performed using AI, for example, or without AI. For example, the profile creation unit can input the user's lifestyle and exercise history into a generating AI and have the generating AI create a detailed profile.

[0041] The profile creation unit can add region-specific health information to the profile, taking into account the user's geographical location. For example, the profile creation unit can add climate information of the user's place of residence to the profile to aid in health management. The profile creation unit can also add data items related to diet to the profile, taking into account the food culture of the user's place of residence. Furthermore, the profile creation unit can add information on medical facilities in the user's place of residence to the profile to aid in emergency response. This makes it possible to create a profile that takes into account the user's geographical location. Some or all of the above processing in the profile creation unit may be performed using AI, for example, or without AI. For example, the profile creation unit can input the user's geographical location information into a generating AI and have the generating AI add region-specific health information.

[0042] The profile creation unit can analyze the user's social media activity and add relevant health information to the profile during profile creation. For example, the profile creation unit can add information about diet and exercise from the user's social media posts to the profile. The profile creation unit can also analyze the user's social media friendships and reflect the impact on their health in the profile. Furthermore, the profile creation unit can analyze the user's social media activity time and add data items related to lifestyle habits to the profile. This makes it possible to create a profile that takes the user's social media activity into account. Some or all of the above processing in the profile creation unit may be performed using AI, for example, or without AI. For example, the profile creation unit can input the user's social media activity into a generating AI and have the generating AI add relevant health information.

[0043] The history import unit can analyze the user's past purchase history and select the optimal import method. For example, the history import unit can prioritize importing products that the user frequently purchases. The history import unit can also analyze the user's past purchase patterns and set the optimal import timing. Furthermore, the history import unit can classify the user's purchase history by category and import it efficiently. This allows the system to select the optimal import method based on the user's past purchase history. Some or all of the above processing in the history import unit may be performed using AI, for example, or without AI. For example, the history import unit can input the user's past purchase history into a generating AI and have the generating AI select the optimal import method.

[0044] The history import unit can filter purchase history based on the user's current health status and dietary preferences when importing it. For example, the history import unit can prioritize importing purchase history of products containing specific nutrients according to the user's health status. The history import unit can also import purchase history of related products based on the user's dietary preferences. For example, the history import unit can import purchase history of related products based on the user's dietary preferences. Furthermore, the history import unit can consider the user's allergy information and exclude purchase history of products that may cause allergies. For example, the history import unit can consider the user's allergy information and exclude purchase history of products that may cause allergies. This allows the purchase history to be filtered based on the user's health status and dietary preferences. Some or all of the above processing in the history import unit may be performed using AI, for example, or without AI. For example, the history import unit can input the user's health status and dietary preferences into a generating AI and have the generating AI perform the filtering.

[0045] The history import unit can prioritize importing highly relevant purchase history by considering the user's geographical location information when importing purchase history. For example, the history import unit can prioritize importing purchase history from stores near the user's place of residence. The history import unit can also prioritize importing purchase history from stores in areas the user frequently visits. Furthermore, the history import unit can prioritize importing purchase history from stores near the user's current location. This allows the import of purchase history while considering the user's geographical location information. Some or all of the above processing in the history import unit may be performed using AI, for example, or without AI. For example, the history import unit can input the user's geographical location information into a generating AI and have the generating AI import highly relevant history.

[0046] The history import unit can analyze the user's social media activity and import relevant history when importing purchase history. For example, the history import unit can prioritize importing the purchase history of products mentioned by the user on social media. The history import unit can also import the purchase history of products purchased by the user's social media friends. Furthermore, the history import unit can analyze the content of the user's social media posts and import the purchase history of related products. This allows the purchase history to be imported while taking the user's social media activity into consideration. Some or all of the above processing in the history import unit may be performed using AI, for example, or without AI. For example, the history import unit can input the user's social media activity into a generating AI and have the generating AI import relevant history.

[0047] The nutrient analysis unit can optimize its analysis algorithm by referring to the user's past dietary history during nutrient analysis. For example, the nutrient analysis unit can analyze the user's past dietary history and adjust the nutrient analysis algorithm. Furthermore, the nutrient analysis unit can optimize its analysis algorithm by referring to the user's dietary patterns. In addition, the nutrient analysis unit can identify the intake trends of specific nutrients from the user's past dietary history and reflect this in the analysis algorithm. This allows the optimal analysis algorithm to be applied based on the user's past dietary history. Some or all of the above-described processes in the nutrient analysis unit may be performed using AI, or without AI. For example, the nutrient analysis unit can input the user's past dietary history into a generating AI and have the generating AI optimize the analysis algorithm.

[0048] The nutrient analysis unit can perform nutrient analysis while considering the user's health status and allergy information. For example, the nutrient analysis unit can prioritize the analysis of specific nutrients according to the user's health status. The nutrient analysis unit can also consider the user's allergy information and exclude nutrients that may cause allergies from the analysis. Furthermore, the nutrient analysis unit can customize the analysis results based on the user's health status and allergy information. This allows for analysis that takes the user's health status and allergy information into account. Some or all of the above-described processes in the nutrient analysis unit may be performed using AI, for example, or without AI. For example, the nutrient analysis unit can input the user's health status and allergy information into a generating AI and have the generating AI perform the analysis.

[0049] The nutrient analysis unit can add region-specific nutrient information by considering the user's geographical location during nutrient analysis. For example, the nutrient analysis unit can consider the food culture of the user's place of residence and reflect region-specific nutrient information in the analysis. The nutrient analysis unit can also perform nutrient analysis based on the climate information of the user's place of residence. For example, the nutrient analysis unit can perform nutrient analysis based on the climate information of the user's place of residence. Furthermore, the nutrient analysis unit can refer to food ingredient information of the user's place of residence and reflect it in the nutrient analysis. For example, the nutrient analysis unit can refer to food ingredient information of the user's place of residence and reflect it in the nutrient analysis. This enables nutrient analysis that takes the user's geographical location into account. Some or all of the above processing in the nutrient analysis unit may be performed using AI, for example, or without AI. For example, the nutrient analysis unit can input the user's geographical location information into a generating AI and have the generating AI add region-specific nutrient information.

[0050] The nutrient analysis unit can analyze the user's social media activity and reflect relevant nutrient information in the analysis. For example, the nutrient analysis unit can reflect information about the user's diet from the user's social media posts. The nutrient analysis unit can also analyze the user's social media friendships and reflect the impact on their health in the analysis. Furthermore, the nutrient analysis unit can analyze the user's social media activity time and reflect data on their lifestyle in the analysis. This allows for nutrient analysis that takes the user's social media activity into account. Some or all of the above processing in the nutrient analysis unit may be performed using AI, for example, or without AI. For example, the nutrient analysis unit can input the user's social media activity into a generating AI and have the generating AI perform the analysis of relevant nutrient information.

[0051] The nutrient presentation unit can select the optimal presentation method by referring to the user's past nutrient intake history when presenting nutrients. For example, the nutrient presentation unit can analyze the user's past nutrient intake history and select the optimal presentation method. The nutrient presentation unit can also refer to the user's nutrient intake pattern and present relevant information. For example, the nutrient presentation unit can refer to the user's nutrient intake pattern and present relevant information. Furthermore, the nutrient presentation unit can select a presentation method to compensate for any deficiencies in specific nutrients based on the user's past nutrient intake history. For example, the nutrient presentation unit can select a presentation method to compensate for any deficiencies in specific nutrients based on the user's past nutrient intake history. This allows the optimal presentation method to be selected based on the user's past nutrient intake history. Some or all of the above processing in the nutrient presentation unit may be performed using AI, for example, or without AI. For example, the nutrient presentation unit can input the user's past nutrient intake history into a generating AI and have the generating AI select the optimal presentation method.

[0052] The nutrient presentation unit can provide customized presentations based on the user's current health status and dietary preferences when presenting nutrients. For example, the nutrient presentation unit can prioritize presenting foods containing specific nutrients according to the user's health status. The nutrient presentation unit can also present relevant foods based on the user's dietary preferences. Furthermore, the nutrient presentation unit can consider the user's allergy information and exclude foods that may cause allergies. This enables customized presentations based on the user's health status and dietary preferences. Some or all of the above processing in the nutrient presentation unit may be performed using AI, for example, or without AI. For example, the nutrient presentation unit can input the user's health status and dietary preferences into a generating AI and have the generating AI execute customized presentations.

[0053] The nutrient information display unit can, when displaying nutrients, suggest the optimal location for obtaining nutrients, taking into account the user's geographical location. For example, the nutrient information display unit can suggest locations where nutrients can be obtained at stores near the user's place of residence. The nutrient information display unit can also suggest locations where nutrients can be obtained at stores in areas the user frequently visits. Furthermore, the nutrient information display unit can suggest locations where nutrients can be obtained at stores near the user's current location. This allows the optimal location for obtaining nutrients to be suggested, taking into account the user's geographical location. Some or all of the above processing in the nutrient information display unit may be performed using AI, for example, or without AI. For example, the nutrient information display unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of suggesting the optimal location for obtaining nutrients.

[0054] The nutrient presentation unit can analyze the user's social media activity and present relevant nutrient information when presenting nutrients. For example, the nutrient presentation unit can present information about the user's diet from the user's social media posts. The nutrient presentation unit can also analyze the user's social media friendships and present the impact on their health. Furthermore, the nutrient presentation unit can analyze the user's social media activity time and present data about their lifestyle. This allows the nutrient presentation unit to present nutrient information that takes the user's social media activity into account. Some or all of the above processing in the nutrient presentation unit may be performed using AI, for example, or without AI. For example, the nutrient presentation unit can input the user's social media activity into a generating AI and have the generating AI perform the presentation of relevant nutrient information.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The health management system can also include an exercise history acquisition unit. This unit acquires the user's exercise history and provides it to the profile creation unit. For example, the exercise history acquisition unit can acquire exercise data recorded by the user using a smartwatch or fitness tracker. It can also acquire exercise data recorded by the user at a gym or fitness app. Furthermore, the exercise history acquisition unit can acquire data on sports events and activities the user has participated in. This allows the health management system to perform health management that takes the user's exercise history into consideration.

[0057] The health management system can also include a sleep analysis unit. This unit analyzes the user's sleep data and provides it to the profile creation unit. For example, the sleep analysis unit can analyze sleep data recorded by the user using a smartwatch or sleep tracker. It can also analyze sleep data recorded by the user using a smartphone app. Furthermore, the sleep analysis unit can analyze the user's sleep patterns and sleep quality, which can be used for health management. This allows the health management system to provide health management that takes the user's sleep data into consideration.

[0058] The health management system can also include a fluid intake analysis unit. This unit analyzes the user's fluid intake and provides the results to the profile creation unit. For example, the fluid intake analysis unit can analyze fluid intake data recorded by the user using a smartwatch or fitness tracker. It can also analyze fluid intake data recorded by the user using a smartphone app. Furthermore, the fluid intake analysis unit can analyze the user's fluid intake patterns and quality, which can be used for health management. This allows the health management system to perform health management that takes the user's fluid intake data into consideration.

[0059] The health management system can also be equipped with a food photo analysis unit. This unit analyzes photos of meals taken by users and extracts nutritional data. For example, it can analyze photos of meals taken by users with their smartphones and identify ingredients and nutrients. It can also analyze photos of meals posted by users on social media. Furthermore, it can analyze photos of menus taken by users at restaurants and extract nutritional information. This allows the health management system to more accurately understand the user's diet and use this information to improve their health management.

[0060] The health management system can also include an exercise suggestion unit. This unit suggests appropriate exercises based on the user's health status and exercise history. For example, it can create an appropriate exercise plan based on the user's profile information. It can also analyze the user's exercise history and adjust the intensity and frequency of exercise. Furthermore, it can recommend specific exercises according to the user's health status and goals. This allows the health management system to suggest exercises suitable for the user and support their health management.

[0061] A health management system can also include a meal suggestion function. This function suggests appropriate meals based on the user's health status and dietary history. For example, it can create a balanced meal plan based on the user's profile information. It can also analyze the user's dietary history and suggest meals to address any nutrient deficiencies. Furthermore, it can recommend specific ingredients and recipes according to the user's health status and goals. This allows the health management system to suggest meals suitable for the user and support their health management.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The profile creation section creates the individual's profile. The profile creation section registers the user's basic information, health status, and dietary preferences. For example, it can register the user's age, gender, height, weight, allergy information, blood pressure, medical history, and favorite foods. Step 2: The history import unit imports the purchase history from the electronic payment system. For example, it can import information on food and beverages purchased by the user at convenience stores, supermarkets, restaurants, and online stores. Step 3: The nutrient analysis unit compares the purchase history imported by the history import unit with the nutrient database to identify any missing nutrients. For example, it analyzes the amounts of vitamins, minerals, calories, protein, fat, and carbohydrates in the foods and beverages included in the purchase history. Step 4: The nutrient display unit shows on a map the locations where the deficient nutrients identified by the nutrient analysis unit can be obtained. For example, it displays on the map the locations of convenience stores that sell suitable foods to supplement vitamin C, supermarkets to supplement minerals, and restaurants to supplement protein.

[0064] (Example of form 2) A health management system according to an embodiment of the present invention is a system that manages a user's health using an electronic payment system. This health management system creates an individual profile, imports the purchase history from the electronic payment system, and uses AI to cross-reference it with a nutrient database to identify the user's nutritional deficiencies. Furthermore, it uses AI to analyze locations where the missing nutrients can be obtained and displays them on a map. The initial target stores are convenience stores, and by targeting the nutritional content of foods displayed on the convenience store's website, the system suggests foods and beverages containing recommended nutrients. If this service is successful, it is expected that other stores using electronic payment systems will follow suit, leading to a global shift towards health consciousness. For example, the health management system creates an individual profile within the system. This profile includes the user's basic information, health status, and dietary preferences. For example, the user's age, gender, height, weight, and allergy information are registered. Next, the health management system imports the purchase history from the electronic payment system into the system. This allows the system to understand what foods and beverages the user has purchased. For example, information on foods and beverages purchased by the user at a convenience store is imported. Based on the imported purchase history, the health management system uses AI to cross-reference it with a nutrient database. The AI ​​analyzes the nutritional information of food and beverages included in the purchase history to understand the nutrients the user is consuming. For example, it analyzes the amount of vitamins and minerals contained in the food the user has purchased. Next, the health management system uses the AI's analysis results to identify the nutrients the user is lacking. For example, if the user is not getting enough vitamin C, the AI ​​will detect the deficiency. Furthermore, the health management system uses the AI ​​to analyze where the missing nutrients can be obtained and displays them on a map. For example, it will display on the map the locations of convenience stores that sell foods suitable for supplementing the user's vitamin C. The initial target stores are convenience stores, and the nutritional information of the foods listed on the convenience store's website is used as the target for analysis. This allows the system to suggest foods and beverages containing recommended nutrients. For example, it can suggest suitable products to the user based on the nutritional information of salads and juices listed on the convenience store's website.If this service is successful, other stores using electronic payment systems are expected to follow suit, leading to a global shift towards health consciousness. For example, supermarkets and restaurants will adopt this system and begin suggesting healthy meals to their customers. This is expected to expand the use of electronic payment systems and increase sales. In turn, health management systems will be able to efficiently manage users' health.

[0065] The health management system according to this embodiment comprises a profile creation unit, a history import unit, a nutrient analysis unit, and a nutrient presentation unit. The profile creation unit creates an individual profile. The profile creation unit registers, for example, the user's basic information, health status, and dietary preferences. For example, the profile creation unit can register the user's age, gender, height, weight, allergy information, etc. The profile creation unit can also register information related to the user's health status. For example, the profile creation unit can register information such as the user's blood pressure, weight, and medical history. Furthermore, the profile creation unit can also register information related to the user's dietary preferences. For example, the profile creation unit can register the user's favorite foods and allergy information. The history import unit imports the purchase history of an electronic payment system. The history import unit imports, for example, information on food and beverages purchased by the user. For example, the history import unit can import information on food and beverages purchased by the user at a convenience store. Furthermore, the history import unit can also import information on food and beverages purchased by the user at a supermarket or restaurant. Furthermore, the history import unit can also import information on food and beverages purchased by the user online. For example, the history import unit can import information on food and beverages purchased by the user from online stores. The nutrient analysis unit compares the purchase history imported by the history import unit with a nutrient database to identify any missing nutrients. The nutrient analysis unit analyzes the nutrient information of the food and beverages included in the purchase history to understand the nutrients the user is consuming. For example, the nutrient analysis unit can analyze the amount of vitamins and minerals in the food and beverages included in the purchase history. It can also analyze the amount of calories and protein in the food and beverages included in the purchase history. Furthermore, the nutrient analysis unit can analyze the amount of fat and carbohydrates in the food and beverages included in the purchase history. For example, the nutrient analysis unit can understand the nutrients the user is consuming based on the nutrient information of the food and beverages included in the purchase history. The nutrient presentation unit displays on a map the locations where the missing nutrients identified by the nutrient analysis unit can be obtained.The nutrient display unit uses AI to analyze locations where a user can obtain deficient nutrients and displays them on a map. For example, the nutrient display unit can display on a map the locations of convenience stores that sell foods suitable for the user to supplement their vitamin C intake. It can also display on a map the locations of supermarkets that sell foods suitable for the user to supplement their mineral intake. Furthermore, the nutrient display unit can display on a map the locations of restaurants that sell foods suitable for the user to supplement their protein intake. For example, the nutrient display unit can display on a map the locations of stores that sell foods suitable for the user to supplement their deficient nutrients. This allows the health management system according to the embodiment to efficiently manage the user's health.

[0066] The profile creation unit creates individual profiles. For example, it registers the user's basic information, health status, and dietary preferences. Specifically, the profile creation unit can register detailed information such as the user's age, gender, height, weight, and allergy information. This allows for centralized management of the user's basic physical information. The profile creation unit can also register information about the user's health status. For example, by recording detailed information such as the user's blood pressure, weight, medical history, and current medication information, a comprehensive understanding of the user's health status can be obtained. Furthermore, the profile creation unit can register information about the user's dietary preferences. For example, by recording detailed information such as the user's favorite foods, disliked foods, allergy information, and frequency and quantity of meals, optimal nutritional management for each individual user becomes possible. Thus, the profile creation unit provides a foundation for supporting health management tailored to the user's individual needs. Additionally, the profile creation unit can register information about the user's lifestyle and exercise habits. For example, by recording the user's exercise frequency, type of exercise, and daily activity level, more accurate health management becomes possible. This allows the profile creation unit to comprehensively understand the user's overall health status and support health management tailored to their individual needs.

[0067] The history import unit imports purchase history from electronic payment systems. For example, it imports information on food and beverages purchased by users. Specifically, it can import information on food and beverages purchased by users at convenience stores in real time. This automatically records the user's eating history, allowing for effortless data collection. The history import unit can also import information on food and beverages purchased by users at supermarkets and restaurants. For example, importing information on vegetables and fruits purchased at supermarkets and dishes ordered at restaurants allows for a more detailed record of eating history. Furthermore, the history import unit can import information on food and beverages purchased online. For example, importing information on supplements and health foods purchased from online stores allows for centralized management of online purchase history. This enables the history import unit to comprehensively understand the user's eating history and improve the accuracy of nutritional management. Additionally, the history import unit can analyze the user's purchase history to understand eating patterns and trends. For example, if a user frequently purchases certain foods at specific times, this information can be used to provide appropriate advice to the user. This allows the history acquisition unit to gain a detailed understanding of the user's meal history and support nutritional management tailored to individual needs.

[0068] The nutrient analysis unit compares the purchase history imported by the history import unit with the nutrient database to identify any nutrient deficiencies. Specifically, the nutrient analysis unit analyzes the nutrient information of the foods and beverages included in the purchase history in detail to accurately understand the nutrients the user is consuming. For example, it can analyze the amount of vitamins and minerals in the foods and beverages included in the purchase history to understand how much of each nutrient the user is consuming. The nutrient analysis unit can also analyze the amount of calories and protein in the foods and beverages included in the purchase history. This allows for an accurate understanding of the user's energy and protein intake. Furthermore, the nutrient analysis unit can analyze the amount of fat and carbohydrates in the foods and beverages included in the purchase history. This allows for an accurate understanding of the user's fat and carbohydrate intake. Based on this information, the nutrient analysis unit can evaluate the balance of nutrients the user is consuming and identify any nutrient deficiencies. For example, if there is a deficiency in vitamin C or calcium, this information can be provided to the user, and advice can be given on how to supplement with appropriate nutrients. In this way, the nutrient analysis unit can optimize the user's nutritional balance and support their health management. Furthermore, the nutrient analysis unit can utilize historical data and statistical information to analyze long-term fluctuations in nutritional balance. This allows for continuous monitoring of the user's nutritional status and the provision of appropriate advice as needed.

[0069] The nutrient information display unit shows locations on a map where the deficient nutrients identified by the nutrient analysis unit can be obtained. Specifically, the nutrient information display unit uses AI to analyze locations where deficient nutrients can be obtained and visually displays them on a map. For example, the nutrient information display unit can display on a map the locations of convenience stores that sell foods suitable for supplementing vitamin C for the user. It can also display on a map the locations of supermarkets that sell foods suitable for supplementing minerals for the user. Furthermore, the nutrient information display unit can display on a map the locations of restaurants that sell foods suitable for supplementing protein for the user. This allows users to easily obtain information on how to efficiently supplement deficient nutrients. The nutrient information display unit can use AI to consider the user's current location and travel route to suggest the most suitable stores. For example, it can improve convenience by prioritizing the display of stores closest to the user's current location or stores along their commute route. The nutrient information display unit can also provide individually customized suggestions considering the user's past purchase history and preferences. This allows users to easily find the best way to supplement their nutrients. Furthermore, the nutrient information display unit can also provide detailed information on suggested stores and nutritional information for specific foods. This makes it easier for users to understand specific ways to supplement their nutrition, allowing them to manage their health more efficiently.

[0070] The nutrient information display unit can analyze the nutritional content of foods displayed on a convenience store's website and suggest foods and beverages containing recommended nutrients. For example, the nutrient information display unit can suggest products suitable for the user based on the nutritional information of salads and juices listed on the convenience store's website. For example, the nutrient information display unit can analyze the nutritional information of salads listed on the convenience store's website and suggest salads rich in vitamin C. It can also analyze the nutritional information of juices listed on the convenience store's website and suggest juices rich in minerals. Furthermore, the nutrient information display unit can analyze the nutritional information of snacks listed on the convenience store's website and suggest snacks rich in protein. For example, the nutrient information display unit can suggest products suitable for the user based on the nutritional information of foods listed on the convenience store's website. This allows the unit to suggest foods containing nutrients suitable for the user. Some or all of the above processing in the nutrient information display unit may be performed using AI, for example, or without AI. For example, the nutrient information display unit can input the nutritional information of foods listed on the convenience store's website into a generating AI and have the generating AI suggest foods and beverages containing recommended nutrients.

[0071] The profile creation unit can register the user's basic information, health status, and dietary preferences. For example, the profile creation unit can register the user's age, gender, height, weight, and allergy information. For example, the profile creation unit can register the user's age and perform age-appropriate health management. It can also register the user's gender and perform gender-appropriate health management. Furthermore, the profile creation unit can register the user's height and weight and calculate the BMI (Body Mass Index). For example, the profile creation unit can register the user's allergy information and avoid foods that may cause allergies. This allows for the creation of a detailed profile of the user. Some or all of the above processing in the profile creation unit may be performed using AI, for example, or not using AI. For example, the profile creation unit can input the user's basic information, health status, and dietary preferences into a generating AI and have the generating AI create a detailed profile.

[0072] The history import unit can import information about food and beverages purchased by the user. For example, the history import unit can import information about food and beverages purchased by the user at a convenience store. For example, the history import unit can import information about salads and juices purchased by the user at a convenience store. The history import unit can also import information about food and beverages purchased by the user at supermarkets and restaurants. For example, the history import unit can import information about vegetables and fruits purchased by the user at a supermarket. Furthermore, the history import unit can also import information about food and beverages purchased by the user online. For example, the history import unit can import information about snacks and beverages purchased by the user at an online store. This allows for accurate import of the user's purchase history. Some or all of the above processing in the history import unit may be performed using AI, for example, or without AI. For example, the history import unit can input information about food and beverages purchased by the user into a generating AI and have the generating AI import the purchase history.

[0073] The nutrient analysis unit can analyze the nutrient information of foods and beverages included in the purchase history and understand the nutrients the user is consuming. For example, the nutrient analysis unit can analyze the amount of vitamins and minerals in foods and beverages included in the purchase history. For example, the nutrient analysis unit can analyze the amount of vitamin C and vitamin D in foods and beverages included in the purchase history. The nutrient analysis unit can also analyze the amount of calories and protein in foods and beverages included in the purchase history. For example, the nutrient analysis unit can analyze the amount of calories and protein in foods and beverages included in the purchase history and understand the nutrients the user is consuming. Furthermore, the nutrient analysis unit can also analyze the amount of lipids and carbohydrates in foods and beverages included in the purchase history. For example, the nutrient analysis unit can analyze the amount of lipids and carbohydrates in foods and beverages included in the purchase history and understand the nutrients the user is consuming. This allows for an accurate understanding of the user's nutrient intake. Some or all of the above processing in the nutrient analysis unit may be performed using AI, for example, or without AI. For example, the nutrient analysis unit can input the nutrient information of foods and beverages included in the purchase history into a generating AI and have the generating AI perform the nutrient analysis.

[0074] The nutrient display unit can display locations on a map where deficient nutrients can be obtained. For example, the nutrient display unit can use AI to analyze locations where deficient nutrients can be obtained and display them on a map. For example, the nutrient display unit can display on a map the locations of convenience stores that sell foods suitable for the user to supplement their vitamin C intake. It can also display on a map the locations of supermarkets that sell foods suitable for the user to supplement their minerals intake. Furthermore, the nutrient display unit can display on a map the locations of restaurants that sell foods suitable for the user to supplement their protein intake. For example, the nutrient display unit can display on a map the locations of stores that sell foods suitable for the user to supplement their deficient nutrients. This makes it easy for users to find places to obtain deficient nutrients. Some or all of the above processing in the nutrient display unit may be performed using AI, for example, or without AI. For example, the nutrient display unit can input locations where deficient nutrients can be obtained into a generating AI and have the generating AI perform the task of displaying locations on a map.

[0075] The profile creation unit can estimate the user's emotions and adjust the profile update frequency based on the estimated emotions. For example, if the user is stressed, the profile creation unit can set a lower profile update frequency to reduce the burden. For example, if the user is relaxed, the profile creation unit can set a higher profile update frequency to collect more detailed data. The profile creation unit can also temporarily stop updating the profile if the user is in a hurry and update it all at once later. For example, the profile creation unit can estimate the user's emotions and adjust the profile update frequency based on the estimated emotions. This allows the profile update frequency to be adjusted according to 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 profile creation unit may be performed using AI, or not using AI. For example, the profile creation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The profile creation unit can analyze the user's past health data and select the most suitable data items when creating the profile. For example, the profile creation unit can analyze the user's past health check results and add important data items to the profile. For example, the profile creation unit can refer to the user's past medical history and include relevant data items in the profile. The profile creation unit can also analyze the user's past exercise history and add data items related to exercise habits to the profile. For example, the profile creation unit can add important data items to the profile based on the user's past health check results. This allows for the creation of an optimal profile based on the user's past health data. Some or all of the above processing in the profile creation unit may be performed using AI, for example, or without AI. For example, the profile creation unit can input the user's past health data into a generating AI and have the generating AI select the most suitable data items.

[0077] The profile creation unit can create a detailed profile by taking into account the user's lifestyle and exercise history. For example, the profile creation unit can analyze the user's eating habits and add data items related to eating to the profile. The profile creation unit can also refer to the user's exercise history and include data items related to exercise habits in the profile. Furthermore, the profile creation unit can analyze the user's sleep patterns and add data items related to sleep to the profile. This allows for the creation of a detailed profile that takes into account the user's lifestyle and exercise history. Some or all of the above processing in the profile creation unit may be performed using AI, for example, or without AI. For example, the profile creation unit can input the user's lifestyle and exercise history into a generating AI and have the generating AI create a detailed profile.

[0078] The profile creation unit can estimate the user's emotions and adjust the profile display method based on the estimated emotions. For example, if the user is nervous, the profile creation unit can provide a simple and highly visible display method. For example, if the user is relaxed, the profile creation unit can provide a display method that includes detailed information. Also, if the user is in a hurry, the profile creation unit can provide a concise display method. For example, the profile creation unit can estimate the user's emotions and adjust the profile display method based on the estimated emotions. This allows the profile display method to be adjusted according to 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 profile creation unit may be performed using AI, for example, or not using AI. For example, the profile creation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The profile creation unit can add region-specific health information to the profile, taking into account the user's geographical location. For example, the profile creation unit can add climate information of the user's place of residence to the profile to aid in health management. The profile creation unit can also add data items related to diet to the profile, taking into account the food culture of the user's place of residence. Furthermore, the profile creation unit can add information on medical facilities in the user's place of residence to the profile to aid in emergency response. This makes it possible to create a profile that takes into account the user's geographical location. Some or all of the above processing in the profile creation unit may be performed using AI, for example, or without AI. For example, the profile creation unit can input the user's geographical location information into a generating AI and have the generating AI add region-specific health information.

[0080] The profile creation unit can analyze the user's social media activity and add relevant health information to the profile during profile creation. For example, the profile creation unit can add information about diet and exercise from the user's social media posts to the profile. The profile creation unit can also analyze the user's social media friendships and reflect the impact on their health in the profile. Furthermore, the profile creation unit can analyze the user's social media activity time and add data items related to lifestyle habits to the profile. This makes it possible to create a profile that takes the user's social media activity into account. Some or all of the above processing in the profile creation unit may be performed using AI, for example, or without AI. For example, the profile creation unit can input the user's social media activity into a generating AI and have the generating AI add relevant health information.

[0081] The history acquisition unit can estimate the user's emotions and adjust the timing of purchase history acquisition based on the estimated emotions. For example, if the user is stressed, the history acquisition unit may delay the acquisition of purchase history. For example, if the user is relaxed, the history acquisition unit may acquire purchase history more frequently. Also, if the user is in a hurry, the history acquisition unit may temporarily stop acquiring purchase history and acquire it all at once later. For example, the history acquisition unit can estimate the user's emotions and adjust the timing of purchase history acquisition based on the estimated emotions. This allows the timing of purchase history acquisition to be adjusted according to 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 history acquisition unit may be performed using AI, for example, or without AI. For example, the history acquisition unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The history import unit can analyze the user's past purchase history and select the optimal import method. For example, the history import unit can prioritize importing products that the user frequently purchases. The history import unit can also analyze the user's past purchase patterns and set the optimal import timing. Furthermore, the history import unit can classify the user's purchase history by category and import it efficiently. This allows the system to select the optimal import method based on the user's past purchase history. Some or all of the above processing in the history import unit may be performed using AI, for example, or without AI. For example, the history import unit can input the user's past purchase history into a generating AI and have the generating AI select the optimal import method.

[0083] The history import unit can filter purchase history based on the user's current health status and dietary preferences when importing it. For example, the history import unit can prioritize importing purchase history of products containing specific nutrients according to the user's health status. The history import unit can also import purchase history of related products based on the user's dietary preferences. For example, the history import unit can import purchase history of related products based on the user's dietary preferences. Furthermore, the history import unit can consider the user's allergy information and exclude purchase history of products that may cause allergies. For example, the history import unit can consider the user's allergy information and exclude purchase history of products that may cause allergies. This allows the purchase history to be filtered based on the user's health status and dietary preferences. Some or all of the above processing in the history import unit may be performed using AI, for example, or without AI. For example, the history import unit can input the user's health status and dietary preferences into a generating AI and have the generating AI perform the filtering.

[0084] The history capture unit can estimate the user's emotions and determine the priority of purchase history to capture based on the estimated emotions. For example, if the user is feeling stressed, the history capture unit can prioritize the capture of purchase history of products that help reduce stress. For example, if the user is feeling stressed, the history capture unit can prioritize the capture of purchase history of products that help maintain health. For example, if the user is relaxed, the history capture unit can prioritize the capture of purchase history of products that help maintain health. For example, if the user is in a hurry, the history capture unit can prioritize the capture of purchase history of products that help replenish energy. For example, if the user is in a hurry, the history capture unit can prioritize the capture of purchase history of products that help replenish energy. In this way, the priority of purchase history can be determined according to 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-described processing in the history acquisition unit may be performed using AI, for example, or without AI. For example, the history acquisition unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0085] The history import unit can prioritize importing highly relevant purchase history by considering the user's geographical location information when importing purchase history. For example, the history import unit can prioritize importing purchase history from stores near the user's place of residence. The history import unit can also prioritize importing purchase history from stores in areas the user frequently visits. Furthermore, the history import unit can prioritize importing purchase history from stores near the user's current location. This allows the import of purchase history while considering the user's geographical location information. Some or all of the above processing in the history import unit may be performed using AI, for example, or without AI. For example, the history import unit can input the user's geographical location information into a generating AI and have the generating AI import highly relevant history.

[0086] The history import unit can analyze the user's social media activity and import relevant history when importing purchase history. For example, the history import unit can prioritize importing the purchase history of products mentioned by the user on social media. The history import unit can also import the purchase history of products purchased by the user's social media friends. Furthermore, the history import unit can analyze the content of the user's social media posts and import the purchase history of related products. This allows the purchase history to be imported while taking the user's social media activity into consideration. Some or all of the above processing in the history import unit may be performed using AI, for example, or without AI. For example, the history import unit can input the user's social media activity into a generating AI and have the generating AI import relevant history.

[0087] The nutrient analysis unit can estimate the user's emotions and adjust the accuracy of the nutrient analysis based on the estimated emotions. For example, if the user is stressed, the nutrient analysis unit can increase the accuracy of the nutrient analysis and provide detailed analysis results. For example, if the user is stressed, the nutrient analysis unit can increase the accuracy of the nutrient analysis and provide detailed analysis results. The nutrient analysis unit can also set the accuracy of the nutrient analysis to normal if the user is relaxed. For example, if the user is relaxed, the nutrient analysis unit can set the accuracy of the nutrient analysis to normal. Furthermore, if the user is in a hurry, the nutrient analysis unit can lower the accuracy of the nutrient analysis and provide quick analysis results. For example, if the user is in a hurry, the nutrient analysis unit can lower the accuracy of the nutrient analysis and provide quick analysis results. In this way, the accuracy of the nutrient analysis can be adjusted according to 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-described processes in the nutrient analysis unit may be performed using AI, for example, or without AI. For example, the nutrient analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0088] The nutrient analysis unit can optimize its analysis algorithm by referring to the user's past dietary history during nutrient analysis. For example, the nutrient analysis unit can analyze the user's past dietary history and adjust the nutrient analysis algorithm. Furthermore, the nutrient analysis unit can optimize its analysis algorithm by referring to the user's dietary patterns. In addition, the nutrient analysis unit can identify the intake trends of specific nutrients from the user's past dietary history and reflect this in the analysis algorithm. This allows the optimal analysis algorithm to be applied based on the user's past dietary history. Some or all of the above-described processes in the nutrient analysis unit may be performed using AI, or without AI. For example, the nutrient analysis unit can input the user's past dietary history into a generating AI and have the generating AI optimize the analysis algorithm.

[0089] The nutrient analysis unit can perform nutrient analysis while considering the user's health status and allergy information. For example, the nutrient analysis unit can prioritize the analysis of specific nutrients according to the user's health status. The nutrient analysis unit can also consider the user's allergy information and exclude nutrients that may cause allergies from the analysis. Furthermore, the nutrient analysis unit can customize the analysis results based on the user's health status and allergy information. This allows for analysis that takes the user's health status and allergy information into account. Some or all of the above processing in the nutrient analysis unit may be performed using AI, for example, or without AI. For example, the nutrient analysis unit can input the user's health status and allergy information into a generating AI and have the generating AI perform the analysis.

[0090] The nutrient analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the nutrient analysis unit can provide a simple and highly visible display method. For example, if the user is tense, the nutrient analysis unit can provide a simple and highly visible display method. Also, if the user is relaxed, the nutrient analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the nutrient analysis unit can provide a display method that gets straight to the point. For example, if the user is in a hurry, the nutrient analysis unit can provide a display method that gets straight to the point. This allows the display method of the analysis results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the nutrient analysis unit may be performed using AI, for example, or without AI. For example, the nutrient analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0091] The nutrient analysis unit can add region-specific nutrient information by considering the user's geographical location during nutrient analysis. For example, the nutrient analysis unit can consider the food culture of the user's place of residence and reflect region-specific nutrient information in the analysis. The nutrient analysis unit can also perform nutrient analysis based on the climate information of the user's place of residence. For example, the nutrient analysis unit can perform nutrient analysis based on the climate information of the user's place of residence. Furthermore, the nutrient analysis unit can refer to food ingredient information of the user's place of residence and reflect it in the nutrient analysis. For example, the nutrient analysis unit can refer to food ingredient information of the user's place of residence and reflect it in the nutrient analysis. This enables nutrient analysis that takes the user's geographical location into account. Some or all of the above processing in the nutrient analysis unit may be performed using AI, for example, or without AI. For example, the nutrient analysis unit can input the user's geographical location information into a generating AI and have the generating AI add region-specific nutrient information.

[0092] The nutrient analysis unit can analyze the user's social media activity and reflect relevant nutrient information in the analysis. For example, the nutrient analysis unit can reflect information about the user's diet from the user's social media posts. The nutrient analysis unit can also analyze the user's social media friendships and reflect the impact on their health in the analysis. Furthermore, the nutrient analysis unit can analyze the user's social media activity time and reflect data on their lifestyle in the analysis. This allows for nutrient analysis that takes the user's social media activity into account. Some or all of the above processing in the nutrient analysis unit may be performed using AI, for example, or without AI. For example, the nutrient analysis unit can input the user's social media activity into a generating AI and have the generating AI perform the analysis of relevant nutrient information.

[0093] The nutrient presentation unit can estimate the user's emotions and adjust the method of presenting nutrients based on the estimated emotions. For example, if the user is nervous, the nutrient presentation unit can provide a simple and highly visible presentation method. For example, if the user is nervous, the nutrient presentation unit can provide a simple and highly visible presentation method. Also, if the user is relaxed, the nutrient presentation unit can provide a presentation method that includes detailed information. Furthermore, if the user is in a hurry, the nutrient presentation unit can provide a presentation method that gets straight to the point. For example, if the user is in a hurry, the nutrient presentation unit can provide a presentation method that gets straight to the point. This allows the method of presenting nutrients to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the nutrient presentation unit may be performed using AI, for example, or without AI. For example, the nutrient presentation unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0094] The nutrient presentation unit can select the optimal presentation method by referring to the user's past nutrient intake history when presenting nutrients. For example, the nutrient presentation unit can analyze the user's past nutrient intake history and select the optimal presentation method. The nutrient presentation unit can also refer to the user's nutrient intake pattern and present relevant information. For example, the nutrient presentation unit can refer to the user's nutrient intake pattern and present relevant information. Furthermore, the nutrient presentation unit can select a presentation method to compensate for any deficiencies in specific nutrients based on the user's past nutrient intake history. For example, the nutrient presentation unit can select a presentation method to compensate for any deficiencies in specific nutrients based on the user's past nutrient intake history. This allows the optimal presentation method to be selected based on the user's past nutrient intake history. Some or all of the above processing in the nutrient presentation unit may be performed using AI, for example, or without AI. For example, the nutrient presentation unit can input the user's past nutrient intake history into a generating AI and have the generating AI select the optimal presentation method.

[0095] The nutrient presentation unit can provide customized presentations based on the user's current health status and dietary preferences when presenting nutrients. For example, the nutrient presentation unit can prioritize presenting foods containing specific nutrients according to the user's health status. The nutrient presentation unit can also present relevant foods based on the user's dietary preferences. Furthermore, the nutrient presentation unit can consider the user's allergy information and exclude foods that may cause allergies. This enables customized presentations based on the user's health status and dietary preferences. Some or all of the above processing in the nutrient presentation unit may be performed using AI, for example, or without AI. For example, the nutrient presentation unit can input the user's health status and dietary preferences into a generating AI and have the generating AI execute customized presentations.

[0096] The nutrient presentation unit can estimate the user's emotions and determine the priority of nutrient presentation based on the estimated emotions. For example, if the user is feeling stressed, the nutrient presentation unit can prioritize presenting nutrients that help reduce stress. For example, if the user is feeling stressed, the nutrient presentation unit can prioritize presenting nutrients that help reduce stress. For example, if the user is relaxed, the nutrient presentation unit can prioritize presenting nutrients that help maintain health. For example, if the user is relaxed, the nutrient presentation unit can prioritize presenting nutrients that help maintain health. For example, if the user is in a hurry, the nutrient presentation unit can prioritize presenting nutrients that help replenish energy. For example, if the user is in a hurry, the nutrient presentation unit can prioritize presenting nutrients that help replenish energy. In this way, the priority of nutrient presentation can be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the nutrient presentation unit may be performed using AI, for example, or without AI. For example, the nutrient presentation unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0097] The nutrient information display unit can, when displaying nutrients, suggest the optimal location for obtaining nutrients, taking into account the user's geographical location. For example, the nutrient information display unit can suggest locations where nutrients can be obtained at stores near the user's place of residence. The nutrient information display unit can also suggest locations where nutrients can be obtained at stores in areas the user frequently visits. Furthermore, the nutrient information display unit can suggest locations where nutrients can be obtained at stores near the user's current location. This allows the optimal location for obtaining nutrients to be suggested, taking into account the user's geographical location. Some or all of the above processing in the nutrient information display unit may be performed using AI, for example, or without AI. For example, the nutrient information display unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of suggesting the optimal location for obtaining nutrients.

[0098] The nutrient presentation unit can analyze the user's social media activity and present relevant nutrient information when presenting nutrients. For example, the nutrient presentation unit can present information about the user's diet from the user's social media posts. The nutrient presentation unit can also analyze the user's social media friendships and present the impact on their health. Furthermore, the nutrient presentation unit can analyze the user's social media activity time and present data about their lifestyle. This allows the nutrient presentation unit to present nutrient information that takes the user's social media activity into account. Some or all of the above processing in the nutrient presentation unit may be performed using AI, for example, or without AI. For example, the nutrient presentation unit can input the user's social media activity into a generating AI and have the generating AI perform the presentation of relevant nutrient information.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The health management system can also include an exercise history acquisition unit. This unit acquires the user's exercise history and provides it to the profile creation unit. For example, the exercise history acquisition unit can acquire exercise data recorded by the user using a smartwatch or fitness tracker. It can also acquire exercise data recorded by the user at a gym or fitness app. Furthermore, the exercise history acquisition unit can acquire data on sports events and activities the user has participated in. This allows the health management system to perform health management that takes the user's exercise history into consideration.

[0101] The health management system can also include a sleep analysis unit. This unit analyzes the user's sleep data and provides it to the profile creation unit. For example, the sleep analysis unit can analyze sleep data recorded by the user using a smartwatch or sleep tracker. It can also analyze sleep data recorded by the user using a smartphone app. Furthermore, the sleep analysis unit can analyze the user's sleep patterns and sleep quality, which can be used for health management. This allows the health management system to provide health management that takes the user's sleep data into consideration.

[0102] The health management system can also include a stress level estimation unit. This unit estimates the user's stress level and provides it to the profile creation unit. For example, the stress level estimation unit can estimate the stress level by analyzing the user's heart rate and breathing patterns. It can also estimate the stress level based on data obtained from the user's smartwatch or fitness tracker. Furthermore, it can estimate the stress level based on the user's self-reported data or survey data. This allows the health management system to perform health management that takes the user's stress level into consideration.

[0103] The health management system can also include a fluid intake analysis unit. This unit analyzes the user's fluid intake and provides the results to the profile creation unit. For example, the fluid intake analysis unit can analyze fluid intake data recorded by the user using a smartwatch or fitness tracker. It can also analyze fluid intake data recorded by the user using a smartphone app. Furthermore, the fluid intake analysis unit can analyze the user's fluid intake patterns and quality, which can be used for health management. This allows the health management system to perform health management that takes the user's fluid intake data into consideration.

[0104] The health management system can also be equipped with an emotion estimation unit. This unit estimates the user's emotions and provides them to the profile creation unit. For example, the emotion estimation unit can estimate emotions by analyzing the user's facial expressions and voice. It can also estimate emotions based on data acquired from the user's smartphone or wearable device. Furthermore, it can estimate emotions based on the user's self-reported information and survey data. This allows the health management system to perform health management that takes the user's emotions into consideration.

[0105] The health management system can also be equipped with a food photo analysis unit. This unit analyzes photos of meals taken by users and extracts nutritional data. For example, it can analyze photos of meals taken by users with their smartphones and identify ingredients and nutrients. It can also analyze photos of meals posted by users on social media. Furthermore, it can analyze photos of menus taken by users at restaurants and extract nutritional information. This allows the health management system to more accurately understand the user's diet and use this information to improve their health management.

[0106] The health management system can also include an exercise suggestion unit. This unit suggests appropriate exercises based on the user's health status and exercise history. For example, it can create an appropriate exercise plan based on the user's profile information. It can also analyze the user's exercise history and adjust the intensity and frequency of exercise. Furthermore, it can recommend specific exercises according to the user's health status and goals. This allows the health management system to suggest exercises suitable for the user and support their health management.

[0107] The health management system can also include an emotional feedback unit. This unit estimates the user's emotions and provides feedback based on those emotions. For example, if the user is feeling stressed, the emotional feedback unit can offer advice on how to relax. If the user is relaxed, it can also offer advice on maintaining good health. Furthermore, if the user is in a hurry, the emotional feedback unit can suggest efficient health management methods. In this way, the health management system can provide feedback tailored to the user's emotions and support their health management.

[0108] A health management system can also include a meal suggestion function. This function suggests appropriate meals based on the user's health status and dietary history. For example, it can create a balanced meal plan based on the user's profile information. It can also analyze the user's dietary history and suggest meals to address any nutrient deficiencies. Furthermore, it can recommend specific ingredients and recipes according to the user's health status and goals. This allows the health management system to suggest meals suitable for the user and support their health management.

[0109] The health management system can also include an emotion recording unit. This unit periodically records the user's emotions and provides them to the profile creation unit. For example, the emotion recording unit allows users to record their emotions using smartphones or wearable devices. It can also allow users to record their emotions through self-reports or questionnaires. Furthermore, the emotion recording unit can analyze the user's emotional data to understand patterns of emotional fluctuations. This enables the health management system to provide health management that takes the user's emotional data into consideration.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The profile creation section creates the individual's profile. The profile creation section registers the user's basic information, health status, and dietary preferences. For example, it can register the user's age, gender, height, weight, allergy information, blood pressure, medical history, and favorite foods. Step 2: The history import unit imports the purchase history from the electronic payment system. For example, it can import information on food and beverages purchased by the user at convenience stores, supermarkets, restaurants, and online stores. Step 3: The nutrient analysis unit compares the purchase history imported by the history import unit with the nutrient database to identify any missing nutrients. For example, it analyzes the amounts of vitamins, minerals, calories, protein, fat, and carbohydrates in the foods and beverages included in the purchase history. Step 4: The nutrient display unit shows on a map the locations where the deficient nutrients identified by the nutrient analysis unit can be obtained. For example, it displays on the map the locations of convenience stores that sell suitable foods to supplement vitamin C, supermarkets to supplement minerals, and restaurants to supplement protein.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the profile creation unit, history acquisition unit, nutrient analysis unit, and nutrient presentation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the profile creation unit is implemented by the control unit 46A of the smart device 14 and registers the user's basic information, health status, and dietary preferences. The history acquisition unit is implemented by the identification processing unit 290 of the data processing unit 12 and acquires the purchase history of the electronic payment system. The nutrient analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and compares the nutrient database based on the purchase history to identify any missing nutrients. The nutrient presentation unit is implemented by the control unit 46A of the smart device 14 and displays on a map the locations where the missing nutrients can be obtained. 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.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the profile creation unit, history acquisition unit, nutrient analysis unit, and nutrient presentation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the profile creation unit is implemented by the control unit 46A of the smart glasses 214 and registers the user's basic information, health status, and dietary preferences. The history acquisition unit is implemented by the identification processing unit 290 of the data processing unit 12 and acquires the purchase history of the electronic payment system. The nutrient analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and compares the nutrient database based on the purchase history to identify any missing nutrients. The nutrient presentation unit is implemented by the control unit 46A of the smart glasses 214 and displays on a map the locations where the missing nutrients can be obtained. 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.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the profile creation unit, history acquisition unit, nutrient analysis unit, and nutrient presentation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the profile creation unit is implemented by the control unit 46A of the headset terminal 314 and registers the user's basic information, health status, and dietary preferences. The history acquisition unit is implemented by the identification processing unit 290 of the data processing unit 12 and acquires the purchase history of the electronic payment system. The nutrient analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and compares the nutrient database based on the purchase history to identify any missing nutrients. The nutrient presentation unit is implemented by the control unit 46A of the headset terminal 314 and displays on a map the locations where the missing nutrients can be obtained. 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.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the profile creation unit, history acquisition unit, nutrient analysis unit, and nutrient presentation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the profile creation unit is implemented by the control unit 46A of the robot 414 and registers the user's basic information, health status, and dietary preferences. The history acquisition unit is implemented by the identification processing unit 290 of the data processing unit 12 and acquires the purchase history of the electronic payment system. The nutrient analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and compares the purchase history with a nutrient database to identify any missing nutrients. The nutrient presentation unit is implemented by the control unit 46A of the robot 414 and displays on a map the locations where the missing nutrients can be obtained. 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A profile creation section for creating individual profiles, A history import unit that imports purchase history from an electronic payment system, A nutrient analysis unit compares the purchase history acquired by the aforementioned history acquisition unit with a nutrient database to identify any missing nutrients, The system includes a nutrient display unit that displays on a map the locations where the deficient nutrients identified by the nutrient analysis unit can be obtained. A system characterized by the following features. (Note 2) The nutrient display unit is, We analyze the nutritional content of food items listed on convenience store websites and suggest recommended foods and drinks containing those nutrients. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned profile creation unit, Register the user's basic information, health status, and dietary preferences. The system described in Appendix 1, characterized by the features described herein. (Note 4) The history acquisition unit, The system incorporates information about food and beverages purchased by the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned nutrient analysis unit, By analyzing the nutritional information of food and beverages included in the purchase history, we can understand the nutrients that the user is consuming. The system described in Appendix 1, characterized by the features described herein. (Note 6) The nutrient display unit is, The map shows where you can obtain the nutrients you are lacking. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned profile creation unit, It estimates the user's emotions and adjusts the frequency of profile updates based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned profile creation unit, Analyze the user's past health data and select the most suitable data items when creating a profile. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned profile creation unit, When creating a profile, a detailed profile is created that takes into account the user's lifestyle and exercise history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned profile creation unit, It estimates the user's emotions and adjusts how the profile is displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned profile creation unit, When creating a profile, region-specific health information is added, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned profile creation unit, When creating a profile, we analyze the user's social media activity and add relevant health information to the profile. The system described in Appendix 1, characterized by the features described herein. (Note 13) The history acquisition unit, The system estimates the user's emotions and adjusts the timing of purchase history import based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The history acquisition unit, Analyze the user's past purchase history and select the optimal method for importing that information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The history acquisition unit, When importing purchase history, filtering is performed based on the user's current health status and dietary preferences. The system described in Appendix 1, characterized by the features described herein. (Note 16) The history acquisition unit, It estimates the user's emotions and determines the priority of purchase history to capture based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The history acquisition unit, When importing purchase history, the system prioritizes importing highly relevant history by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The history acquisition unit, When importing purchase history, the system analyzes the user's social media activity and imports relevant history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned nutrient analysis unit, The system estimates the user's emotions and adjusts the accuracy of the nutrient analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned nutrient analysis unit, During nutrient analysis, the analysis algorithm is optimized by referencing the user's past dietary history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned nutrient analysis unit, When analyzing nutrients, the analysis takes into account the user's health status and allergy information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned nutrient analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned nutrient analysis unit, When analyzing nutrients, region-specific nutrient information is added, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned nutrient analysis unit, During nutrient analysis, the system analyzes the user's social media activity and incorporates relevant nutrient information into the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 25) The nutrient display unit is, The system estimates the user's emotions and adjusts the method of presenting nutrients based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The nutrient display unit is, When presenting nutrients, the system selects the optimal presentation method by referring to the user's past nutrient intake history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The nutrient display unit is, When presenting nutrients, the presentation is customized based on the user's current health status and dietary preferences. The system described in Appendix 1, characterized by the features described herein. (Note 28) The nutrient display unit is, The system estimates the user's emotions and prioritizes nutrient presentation based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The nutrient display unit is, When presenting nutrients, the system will consider the user's geographical location to suggest the optimal location for obtaining those nutrients. The system described in Appendix 1, characterized by the features described herein. (Note 30) The nutrient display unit is, When presenting nutrients, the system analyzes the user's social media activity and displays relevant nutrient information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0184] 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 profile creation section for creating individual profiles, A history import unit that imports purchase history from an electronic payment system, A nutrient analysis unit compares the purchase history acquired by the aforementioned history acquisition unit with a nutrient database to identify any missing nutrients, The system includes a nutrient display unit that displays on a map the locations where the deficient nutrients identified by the nutrient analysis unit can be obtained. A system characterized by the following features.

2. The nutrient display unit is, We analyze the nutritional content of food items listed on convenience store websites and suggest recommended foods and drinks containing those nutrients. The system according to feature 1.

3. The aforementioned profile creation unit, Register the user's basic information, health status, and dietary preferences. The system according to feature 1.

4. The history acquisition unit, The system incorporates information about food and beverages purchased by the user. The system according to feature 1.

5. The aforementioned nutrient analysis unit, By analyzing the nutritional information of food and beverages included in the purchase history, we can understand the nutrients that the user is consuming. The system according to feature 1.

6. The nutrient display unit is, The map shows where you can obtain the nutrients you are lacking. The system according to feature 1.

7. The aforementioned profile creation unit, It estimates the user's emotions and adjusts the frequency of profile updates based on the estimated user emotions. The system according to feature 1.

8. The aforementioned profile creation unit, Analyze the user's past health data and select the most suitable data items when creating a profile. The system according to feature 1.

9. The aforementioned profile creation unit, When creating a profile, a detailed profile is created that takes into account the user's lifestyle and exercise history. The system according to feature 1.

10. The aforementioned profile creation unit, It estimates the user's emotions and adjusts how the profile is displayed based on the estimated emotions. The system according to feature 1.

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