system

Abstract

The system according to this embodiment aims to suggest optimal cooking recipes based on individual health conditions and exercise information, and to provide information on ordering ingredients and dining out. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a suggestion unit, an order unit, and a referral unit. The collection unit collects health and exercise information. The analysis unit analyzes the information collected by the collection unit. The suggestion unit proposes cooking recipes based on the analysis results obtained by the analysis unit. The order unit places orders for ingredients based on the recipes proposed by the suggestion unit. The referral unit provides restaurant information based on the recipes proposed by the suggestion unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the proposal of an optimal cooking recipe based on individual health conditions and exercise information, the ordering of ingredients, and the provision of eating-out information are not sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal cooking recipe based on individual health conditions and exercise information, and to provide the ordering of ingredients and eating-out information.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, an order unit, and a referral unit. The collection unit collects health and exercise information. The analysis unit analyzes the information collected by the collection unit. The suggestion unit proposes cooking recipes based on the analysis results obtained by the analysis unit. The order unit places orders for ingredients based on the recipes proposed by the suggestion unit. The referral unit provides restaurant information based on the recipes proposed by the suggestion unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest optimal cooking recipes based on individual health conditions and exercise information, and can also provide information on ordering ingredients and dining out. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that proposes the optimal cooking recipe for a person and at a given time, based on inputs such as various health and exercise information automatically acquired from a wearable device, health checkup results, and photos of daily meals. This system collects and analyzes health and exercise information, proposes the optimal cooking recipe, and can also provide information on ordering ingredients and dining out. For example, it collects data such as heart rate, steps, calories burned, and sleep time from a wearable device. This allows the system to understand the person's health status and exercise status. Next, it collects data such as health checkup results and photos of daily meals. For example, it collects health checkup results such as blood pressure, blood sugar levels, and cholesterol levels, as well as photos of meals. This allows the system to understand the person's health status and eating habits. Next, it analyzes the collected data. The generating AI analyzes the collected data to understand the person's health status, exercise status, and eating habits. For example, it analyzes data such as heart rate, steps, and calories burned to understand the person's exercise status. It also analyzes health checkup results and photos of meals to understand the person's health status and eating habits. This allows the system to propose the optimal cooking recipe for that person. Furthermore, based on the analysis results, it suggests the most suitable cooking recipe for that person. The generating AI suggests cooking recipes tailored to the person's health condition, exercise status, and eating habits based on the analysis results. For example, it suggests low-calorie recipes for people on a diet and nutritionally balanced recipes for people recovering from illness. It also suggests high-protein recipes for professional athletes. This allows it to suggest the most suitable cooking recipe for each individual. Based on the suggested recipe, users can order ingredients from nearby online supermarkets. The generating AI automatically selects the necessary ingredients based on the suggested cooking recipe and places an order with nearby online supermarkets. This allows users to obtain the necessary ingredients without any hassle. In addition, for people who are not good at cooking at home, it selects and introduces restaurant information with menus similar to the suggested recipe. Based on the suggested cooking recipe, the generating AI searches the menus of nearby restaurants and cafes and selects and introduces restaurant information with menus similar to the suggested recipe. This allows even people who are not good at cooking at home to enjoy healthy meals.This allows the system to suggest optimal cooking recipes based on the user's health status, exercise habits, and eating preferences, and to provide information on ordering ingredients and dining out.

[0029] The health management system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, an order unit, and a referral unit. The collection unit collects health and exercise information. Health and exercise information includes, but is not limited to, heart rate, steps taken, calorie consumption, and meal content. The collection unit automatically collects data such as heart rate, steps taken, calories consumed, and sleep time from wearable devices, for example. The collection unit can also collect data such as health checkup results and photos of daily meals. For example, it collects health checkup results such as blood pressure, blood sugar levels, and cholesterol levels, as well as photos of meals. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected data using statistical analysis and machine learning algorithms to understand the user's health status, exercise status, and dietary trends. The proposal unit proposes cooking recipes based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes the optimal cooking recipe based on the user's health status, exercise status, and dietary trends. For example, it suggests low-calorie recipes to people on a diet and nutritionally balanced recipes to people recovering from illness. It also suggests high-protein recipes to professional athletes. The ordering unit places orders for ingredients based on the recipes suggested by the suggestion unit. For example, the ordering unit automatically selects the necessary ingredients based on the suggested recipe and places an order with a nearby online supermarket. The recommendation unit provides restaurant information based on the recipes suggested by the suggestion unit. For example, the recommendation unit searches for menus of nearby restaurants and cafes based on the suggested recipe and selects and introduces restaurant information with menus similar to the recipe. In this way, the health management system according to this embodiment can suggest optimal recipes based on the user's health status, exercise status, and eating habits, and can also order ingredients and provide restaurant information.

[0030] The data collection unit can collect health checkup results or daily meal photos. For example, the data collection unit can collect health checkup results. Health checkup results include, but are not limited to, blood test results and electrocardiogram results. The data collection unit can also collect daily meal photos. Daily meal photos include, but are not limited to, photos of the entire meal or photos of specific dishes. By collecting health checkup results and meal photos, the data collection unit can obtain more detailed health and exercise information.

[0031] The analysis unit can analyze the collected data to understand the user's health status, exercise habits, and dietary tendencies. For example, the analysis unit can analyze the collected data using statistical analysis and machine learning algorithms. Health status includes, but is not limited to, weight, blood pressure, and blood glucose levels. Exercise habits include, but are not limited to, exercise frequency, type of exercise, and exercise intensity. Dietary tendencies include, but are not limited to, calorie intake and nutritional balance. In this way, the analysis unit can understand the user's health status, exercise habits, and dietary tendencies by analyzing the collected data.

[0032] The suggestion department can suggest low-calorie recipes to people on a diet. For example, the suggestion department can suggest low-calorie recipes to people on a diet. Low-calorie means, for example, a small amount of calories per serving, but is not limited to this example. In this way, the suggestion department can support healthy dieting by suggesting low-calorie recipes to people on a diet.

[0033] The proposal department can suggest nutritionally balanced meal recipes for people recovering from illness. For example, the proposal department can suggest nutritionally balanced meal recipes for people recovering from illness. Nutritionally balanced means, for example, a good balance of protein, fat, and carbohydrates, but is not limited to this example. In this way, the proposal department can support healthy eating for people recovering from illness by suggesting nutritionally balanced meal recipes.

[0034] The proposal department can suggest high-protein meal recipes to professional athletes. For example, the proposal department can suggest high-protein meal recipes to professional athletes. High protein means, for example, a large amount of protein per serving, but is not limited to this example. In this way, the proposal department can maximize the effectiveness of training by suggesting high-protein meal recipes to professional athletes.

[0035] The ordering system can automatically select the necessary ingredients based on a suggested recipe and place an order with a nearby online supermarket. For example, the ordering system can automatically select the necessary ingredients based on a suggested recipe and place an order with a nearby online supermarket. Automatic selection means, for example, selecting ingredients based on criteria such as the user's preferences and nutritional balance, but is not limited to such examples. This allows the ordering system to automatically select and order ingredients based on a suggested recipe, saving the user time and effort.

[0036] The recommendation section can search for menus at nearby restaurants and cafes based on a suggested recipe, and select and introduce dining information with menus similar to the recipe. For example, the recommendation section can search for menus at nearby restaurants and cafes based on a suggested recipe, and select and introduce dining information with menus similar to the recipe. Nearby restaurants and cafes refer to, for example, restaurants and cafes close to the user's current location, but are not limited to this example. In this way, by providing dining information based on a suggested recipe, the recommendation section can help people who are not good at cooking to enjoy healthy meals.

[0037] The data collection unit can analyze the user's past health checkup results and select the optimal data collection method. For example, the data collection unit can determine the type of data to collect based on specific health indicators from the user's past health checkup results. The data collection unit can also set a regular data collection schedule based on the user's past health checkup results. Furthermore, the data collection unit can focus on collecting data that emphasizes specific health indicators based on the user's health checkup results. In this way, the data collection unit can select the optimal data collection method by analyzing past health checkup results.

[0038] The data collection unit can filter health and exercise information based on the user's current lifestyle and areas of interest. For example, if the user is on a diet, the unit will prioritize collecting information on calorie consumption. Similarly, if the user is undergoing medical treatment, the unit can focus on collecting information about their health status. Furthermore, if the user is a professional athlete, the unit can prioritize collecting information on their athletic performance. This allows the data collection unit to collect more relevant information by filtering it based on the user's lifestyle and areas of interest.

[0039] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when gathering health and exercise information. For example, if the user is in a specific region, the data collection unit will prioritize the collection of information about the climate and environment of that region. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of information about health risks at their travel destination. Additionally, if the user is at a specific exercise facility, the data collection unit can prioritize the collection of information about their exercise performance at that facility. In this way, the data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location.

[0040] The data collection unit can analyze users' social media activity and collect relevant information when gathering health and exercise information. For example, it can collect information on exercise performance based on exercise records shared by users on social media. It can also collect information on dietary trends based on photos of meals shared by users on social media. Furthermore, it can collect information on health status based on health-related posts shared by users on social media. In this way, the data collection unit can collect relevant information by analyzing users' social media activity.

[0041] The analysis unit can adjust the level of detail in its analysis based on the importance of health and exercise information. For example, it will analyze information on important health indicators in detail. It can also analyze information on exercise performance in detail according to the user's goals. Furthermore, it can analyze information on dietary trends in detail according to the user's health status. In this way, the analysis unit can analyze more important information in detail by adjusting the level of detail in its analysis based on the importance of health and exercise information.

[0042] The analysis unit can apply different analysis algorithms depending on the category of health and exercise information during analysis. For example, it can apply a health indicator analysis algorithm to information about health status. It can also apply an exercise analysis algorithm to information about exercise performance. Furthermore, it can apply a diet analysis algorithm to information about dietary trends. By applying different analysis algorithms depending on the category of health and exercise information, the analysis unit can provide more appropriate analysis results.

[0043] The analysis unit can determine the priority of analysis based on when health and exercise information was collected. For example, the analysis unit may prioritize the analysis of the most recent health checkup results. It can also prioritize the analysis of recent exercise records. Furthermore, it can prioritize the analysis of the most recent meal photos. In this way, the analysis unit can prioritize the analysis of the latest information by determining the priority of analysis based on when health and exercise information was collected.

[0044] The analysis unit can adjust the order of analysis based on the relevance of health and exercise information during the analysis process. For example, the analysis unit can prioritize the analysis of information related to health status. It can also prioritize the analysis of information related to exercise performance. Furthermore, it can prioritize the analysis of information related to dietary trends. In this way, the analysis unit can prioritize the analysis of more relevant information by adjusting the order of analysis based on the relevance of health and exercise information.

[0045] The suggestion department can adjust the level of detail in its suggestions based on the importance of the recipe. For example, it will provide detailed suggestions for recipes containing important nutrients. It can also provide detailed calorie information for people on a diet. Furthermore, it can provide suggestions that prioritize nutritional balance for people recovering from illness. In this way, by adjusting the level of detail in suggestions based on the importance of the recipe, the suggestion department can provide more detailed information.

[0046] The suggestion function can apply different suggestion algorithms depending on the category of the cooking recipe. For example, it can apply a calorie restriction algorithm to diet recipes. It can also apply a nutritional balance algorithm to recipes for medical treatment. Furthermore, it can apply a high-protein algorithm to recipes for professional athletes. In this way, the suggestion function can provide more appropriate suggestions by applying different suggestion algorithms depending on the category of the cooking recipe.

[0047] The proposal team can prioritize proposals based on when the recipes are submitted. For example, the proposal team can make proposals based on the latest health check results. They can also make proposals based on recent exercise records. Furthermore, they can make proposals based on recent meal photos. This allows the proposal team to prioritize proposals based on when the recipes are submitted, thus ensuring that the most up-to-date information is presented first.

[0048] The suggestion function can adjust the order of suggestions based on the relevance of the recipes during the suggestion process. For example, the suggestion function can prioritize suggesting information related to health status. It can also prioritize suggesting information related to exercise performance. Furthermore, it can prioritize suggesting information related to dietary trends. In this way, the suggestion function can prioritize suggesting more relevant information by adjusting the order of suggestions based on the relevance of the recipes.

[0049] The ordering department can analyze a user's past order history to select the optimal ordering method at the time of ordering. For example, the ordering department can automatically select frequently ordered materials based on the user's past order history. It can also set up regular order schedules based on the user's past order history. Furthermore, the ordering department can prioritize ordering specific materials based on the user's past order history. In this way, the ordering department can select the optimal ordering method by analyzing the user's past order history.

[0050] The ordering system can customize the ordering process based on the user's current circumstances. For example, if the user is busy, the system can provide a quick ordering method. If the user is relaxed, the system can also provide detailed ordering options. Furthermore, if the user is traveling, the system can provide ordering methods for their travel destination. This allows the ordering system to provide a more appropriate ordering method by customizing the ordering process based on the user's current circumstances.

[0051] The ordering system can select the optimal ordering method by considering the user's geographical location when an order is placed. For example, if the user is in a specific region, the ordering system will prioritize ordering from an online supermarket in that region. Furthermore, if the user is traveling, the ordering system can also use an online supermarket in their travel destination. Additionally, if the user is at a specific sports facility, the ordering system can use an online supermarket near that facility. In this way, the ordering system can select the optimal ordering method by considering the user's geographical location.

[0052] The ordering system can analyze a user's social media activity and suggest appropriate ordering methods at the time of ordering. For example, the ordering system can order relevant ingredients based on photos of meals shared by the user on social media. It can also order healthy ingredients based on health-related posts shared by the user on social media. Furthermore, it can order ingredients needed after exercise based on exercise records shared by the user on social media. In this way, the ordering system can suggest more appropriate ordering methods by analyzing the user's social media activity.

[0053] The recommendation system can provide optimal recommendations by referencing the user's past dining history when introducing restaurant information. For example, the recommendation system can recommend relevant restaurants based on restaurants the user has visited in the past. It can also recommend restaurants with menus the user prefers based on their past dining history. Furthermore, the recommendation system can analyze the user's past dining history and recommend the restaurant with the highest satisfaction rating. In this way, the recommendation system can provide more appropriate restaurant information by referring to the user's past dining history.

[0054] The recommendation system can provide optimal recommendations for dining out by considering the user's geographical location. For example, if the user is in a specific area, the system will prioritize recommending restaurants in that area. It can also recommend restaurants in the user's travel destination if the user is traveling. Furthermore, if the user is at a specific sports facility, the system can recommend restaurants near that facility. This allows the system to provide more appropriate dining out information by considering the user's geographical location.

[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 analysis unit can analyze the user's past health checkup results and adjust the level of detail of the analysis based on specific health indicators. For example, if past health checkup results indicate a tendency towards high blood pressure, it can analyze information related to blood pressure in detail. Similarly, if past health checkup results indicate a tendency towards high blood sugar levels, it can analyze information related to blood sugar levels in detail. Furthermore, if past health checkup results indicate a tendency towards high cholesterol levels, it can analyze information related to cholesterol in detail. In this way, the analysis unit can provide more appropriate analysis results by adjusting the level of detail of the analysis based on past health checkup results.

[0057] The suggestion function can analyze the user's past eating history and adjust the level of detail in its suggestions based on specific dietary trends. For example, if past eating history indicates a tendency towards low vegetable intake, it can suggest detailed recipes containing plenty of vegetables. Similarly, if past eating history indicates a tendency towards low protein intake, it can suggest detailed recipes containing plenty of protein. Furthermore, if past eating history indicates a tendency towards high calorie intake, it can suggest detailed recipes for low-calorie dishes. In this way, the suggestion function can provide more appropriate suggestions by adjusting the level of detail based on past eating history.

[0058] The ordering system can analyze a user's past order history and prioritize orders based on specific ingredients. For example, it can prioritize ordering ingredients that are frequently ordered based on past order history. It can also prioritize ordering ingredients that are low in stock based on past order history. Furthermore, if a particular ingredient is in high demand based on past order history, it can suggest recipes that prioritize the use of that ingredient. In this way, the ordering system can provide a more appropriate ordering method by prioritizing orders based on past order history.

[0059] The recommendation system can analyze a user's past dining history and prioritize restaurant recommendations based on specific restaurants. For example, it can prioritize recommending restaurants frequently visited based on past dining history. It can also prioritize recommending restaurants that the user has preferred based on past dining history. Furthermore, if a user was dissatisfied with a particular restaurant based on past dining history, it can exclude those restaurants from recommendations. In this way, the recommendation system can provide more appropriate restaurant recommendations by prioritizing information based on past dining history.

[0060] The data collection unit can determine the type of information to collect based on a specific region, taking into account the user's geographical location. For example, if the user is at high altitude, it can prioritize collecting information on health risks at high altitude. Similarly, if the user is near the coast, it can prioritize collecting information on health risks at the coast. Furthermore, if the user is in an urban area, it can prioritize collecting information on health risks in urban areas. This allows the data collection unit to collect more relevant information by considering the user's geographical location.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects health and exercise information. This information includes heart rate, steps taken, calorie consumption, and dietary content. The data collection unit automatically collects data such as heart rate, steps taken, calories burned, and sleep duration from wearable devices. It can also collect data such as health checkup results and photos of daily meals. For example, it can collect health checkup results such as blood pressure, blood sugar levels, and cholesterol levels, as well as photos of meals. Step 2: The analysis unit analyzes the information collected by the data collection unit. The analysis unit analyzes the collected data using statistical analysis and machine learning algorithms to understand the user's health status, exercise habits, and dietary tendencies. Step 3: The suggestion unit proposes cooking recipes based on the analysis results obtained by the analysis unit. The suggestion unit proposes the optimal cooking recipe based on the user's health status, exercise status, and eating habits. For example, it proposes low-calorie recipes for people on a diet and nutritionally balanced recipes for people recovering from illness. It also proposes high-protein recipes for professional athletes. Step 4: The ordering department places an order for ingredients based on the recipe suggested by the suggestion department. The ordering department automatically selects the necessary ingredients based on the suggested recipe and places an order with a nearby online supermarket. Step 5: The Introduction Department provides restaurant information based on the recipes proposed by the Proposal Department. The Introduction Department searches for menus of nearby restaurants and cafes based on the proposed recipes and selects and introduces restaurant information with menus similar to the recipes.

[0063] (Example of form 2) The system according to an embodiment of the present invention is a system that proposes the optimal cooking recipe for a person and at a given time, based on inputs such as various health and exercise information automatically acquired from a wearable device, health checkup results, and photos of daily meals. This system collects and analyzes health and exercise information, proposes the optimal cooking recipe, and can also provide information on ordering ingredients and dining out. For example, it collects data such as heart rate, steps, calories burned, and sleep time from a wearable device. This allows the system to understand the person's health status and exercise status. Next, it collects data such as health checkup results and photos of daily meals. For example, it collects health checkup results such as blood pressure, blood sugar levels, and cholesterol levels, as well as photos of meals. This allows the system to understand the person's health status and eating habits. Next, it analyzes the collected data. The generating AI analyzes the collected data to understand the person's health status, exercise status, and eating habits. For example, it analyzes data such as heart rate, steps, and calories burned to understand the person's exercise status. It also analyzes health checkup results and photos of meals to understand the person's health status and eating habits. This allows the system to propose the optimal cooking recipe for that person. Furthermore, based on the analysis results, it suggests the most suitable cooking recipe for that person. The generating AI suggests cooking recipes tailored to the person's health condition, exercise status, and eating habits based on the analysis results. For example, it suggests low-calorie recipes for people on a diet and nutritionally balanced recipes for people recovering from illness. It also suggests high-protein recipes for professional athletes. This allows it to suggest the most suitable cooking recipe for each individual. Based on the suggested recipe, users can order ingredients from nearby online supermarkets. The generating AI automatically selects the necessary ingredients based on the suggested cooking recipe and places an order with nearby online supermarkets. This allows users to obtain the necessary ingredients without any hassle. In addition, for people who are not good at cooking at home, it selects and introduces restaurant information with menus similar to the suggested recipe. Based on the suggested cooking recipe, the generating AI searches the menus of nearby restaurants and cafes and selects and introduces restaurant information with menus similar to the suggested recipe. This allows even people who are not good at cooking at home to enjoy healthy meals.This allows the system to suggest optimal cooking recipes based on the user's health status, exercise habits, and eating preferences, and to provide information on ordering ingredients and dining out.

[0064] The health management system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, an order unit, and a referral unit. The collection unit collects health and exercise information. Health and exercise information includes, but is not limited to, heart rate, steps taken, calorie consumption, and meal content. The collection unit automatically collects data such as heart rate, steps taken, calories consumed, and sleep time from wearable devices, for example. The collection unit can also collect data such as health checkup results and photos of daily meals. For example, it collects health checkup results such as blood pressure, blood sugar levels, and cholesterol levels, as well as photos of meals. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected data using statistical analysis and machine learning algorithms to understand the user's health status, exercise status, and dietary trends. The proposal unit proposes cooking recipes based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes the optimal cooking recipe based on the user's health status, exercise status, and dietary trends. For example, it suggests low-calorie recipes to people on a diet and nutritionally balanced recipes to people recovering from illness. It also suggests high-protein recipes to professional athletes. The ordering unit places orders for ingredients based on the recipes suggested by the suggestion unit. For example, the ordering unit automatically selects the necessary ingredients based on the suggested recipe and places an order with a nearby online supermarket. The recommendation unit provides restaurant information based on the recipes suggested by the suggestion unit. For example, the recommendation unit searches for menus of nearby restaurants and cafes based on the suggested recipe and selects and introduces restaurant information with menus similar to the recipe. In this way, the health management system according to this embodiment can suggest optimal recipes based on the user's health status, exercise status, and eating habits, and can also order ingredients and provide restaurant information.

[0065] The data collection unit can collect health checkup results or daily meal photos. For example, the data collection unit can collect health checkup results. Health checkup results include, but are not limited to, blood test results and electrocardiogram results. The data collection unit can also collect daily meal photos. Daily meal photos include, but are not limited to, photos of the entire meal or photos of specific dishes. By collecting health checkup results and meal photos, the data collection unit can obtain more detailed health and exercise information.

[0066] The analysis unit can analyze the collected data to understand the user's health status, exercise habits, and dietary tendencies. For example, the analysis unit can analyze the collected data using statistical analysis and machine learning algorithms. Health status includes, but is not limited to, weight, blood pressure, and blood glucose levels. Exercise habits include, but are not limited to, exercise frequency, type of exercise, and exercise intensity. Dietary tendencies include, but are not limited to, calorie intake and nutritional balance. In this way, the analysis unit can understand the user's health status, exercise habits, and dietary tendencies by analyzing the collected data.

[0067] The suggestion department can suggest low-calorie recipes to people on a diet. For example, the suggestion department can suggest low-calorie recipes to people on a diet. Low-calorie means, for example, a small amount of calories per serving, but is not limited to this example. In this way, the suggestion department can support healthy dieting by suggesting low-calorie recipes to people on a diet.

[0068] The proposal department can suggest nutritionally balanced meal recipes for people recovering from illness. For example, the proposal department can suggest nutritionally balanced meal recipes for people recovering from illness. Nutritionally balanced means, for example, a good balance of protein, fat, and carbohydrates, but is not limited to this example. In this way, the proposal department can support healthy eating for people recovering from illness by suggesting nutritionally balanced meal recipes.

[0069] The proposal department can suggest high-protein meal recipes to professional athletes. For example, the proposal department can suggest high-protein meal recipes to professional athletes. High protein means, for example, a large amount of protein per serving, but is not limited to this example. In this way, the proposal department can maximize the effectiveness of training by suggesting high-protein meal recipes to professional athletes.

[0070] The ordering system can automatically select the necessary ingredients based on a suggested recipe and place an order with a nearby online supermarket. For example, the ordering system can automatically select the necessary ingredients based on a suggested recipe and place an order with a nearby online supermarket. Automatic selection means, for example, selecting ingredients based on criteria such as the user's preferences and nutritional balance, but is not limited to such examples. This allows the ordering system to automatically select and order ingredients based on a suggested recipe, saving the user time and effort.

[0071] The recommendation section can search for menus at nearby restaurants and cafes based on a suggested recipe, and select and introduce dining information with menus similar to the recipe. For example, the recommendation section can search for menus at nearby restaurants and cafes based on a suggested recipe, and select and introduce dining information with menus similar to the recipe. Nearby restaurants and cafes refer to, for example, restaurants and cafes close to the user's current location, but are not limited to this example. In this way, by providing dining information based on a suggested recipe, the recommendation section can help people who are not good at cooking to enjoy healthy meals.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of health and exercise information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect health and exercise information during times when the user is relaxed. Alternatively, if the user is relaxed, the data collection unit can collect health and exercise information after exercise. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting only information that can be collected quickly. This allows the data collection unit to collect information at a more appropriate time by adjusting the collection timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The data collection unit can analyze the user's past health checkup results and select the optimal data collection method. For example, the data collection unit can determine the type of data to collect based on specific health indicators from the user's past health checkup results. The data collection unit can also set a regular data collection schedule based on the user's past health checkup results. Furthermore, the data collection unit can focus on collecting data that emphasizes specific health indicators based on the user's health checkup results. In this way, the data collection unit can select the optimal data collection method by analyzing past health checkup results.

[0074] The data collection unit can filter health and exercise information based on the user's current lifestyle and areas of interest. For example, if the user is on a diet, the unit will prioritize collecting information on calorie consumption. Similarly, if the user is undergoing medical treatment, the unit can focus on collecting information about their health status. Furthermore, if the user is a professional athlete, the unit can prioritize collecting information on their athletic performance. This allows the data collection unit to collect more relevant information by filtering it based on the user's lifestyle and areas of interest.

[0075] The data collection unit can estimate the user's emotions and determine the priority of health and exercise information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting information related to relaxation. Similarly, if the user is relaxed, the data collection unit can prioritize collecting information related to exercise performance. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting information that can be gathered quickly. This allows the data collection unit to prioritize the collection of more important information by determining the priority of information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when gathering health and exercise information. For example, if the user is in a specific region, the data collection unit will prioritize the collection of information about the climate and environment of that region. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of information about health risks at their travel destination. Additionally, if the user is at a specific exercise facility, the data collection unit can prioritize the collection of information about their exercise performance at that facility. In this way, the data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location.

[0077] The data collection unit can analyze users' social media activity and collect relevant information when gathering health and exercise information. For example, it can collect information on exercise performance based on exercise records shared by users on social media. It can also collect information on dietary trends based on photos of meals shared by users on social media. Furthermore, it can collect information on health status based on health-related posts shared by users on social media. In this way, the data collection unit can collect relevant information by analyzing users' social media activity.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The analysis unit can adjust the level of detail in its analysis based on the importance of health and exercise information. For example, it will analyze information on important health indicators in detail. It can also analyze information on exercise performance in detail according to the user's goals. Furthermore, it can analyze information on dietary trends in detail according to the user's health status. In this way, the analysis unit can analyze more important information in detail by adjusting the level of detail in its analysis based on the importance of health and exercise information.

[0080] The analysis unit can apply different analysis algorithms depending on the category of health and exercise information during analysis. For example, it can apply a health indicator analysis algorithm to information about health status. It can also apply an exercise analysis algorithm to information about exercise performance. Furthermore, it can apply a diet analysis algorithm to information about dietary trends. By applying different analysis algorithms depending on the category of health and exercise information, the analysis unit can provide more appropriate analysis results.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis. In this way, the analysis unit can provide more appropriate analysis results by adjusting the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The analysis unit can determine the priority of analysis based on when health and exercise information was collected. For example, the analysis unit may prioritize the analysis of the most recent health checkup results. It can also prioritize the analysis of recent exercise records. Furthermore, it can prioritize the analysis of the most recent meal photos. In this way, the analysis unit can prioritize the analysis of the latest information by determining the priority of analysis based on when health and exercise information was collected.

[0083] The analysis unit can adjust the order of analysis based on the relevance of health and exercise information during the analysis process. For example, the analysis unit can prioritize the analysis of information related to health status. It can also prioritize the analysis of information related to exercise performance. Furthermore, it can prioritize the analysis of information related to dietary trends. In this way, the analysis unit can prioritize the analysis of more relevant information by adjusting the order of analysis based on the relevance of health and exercise information.

[0084] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion function will present simple and easily understandable suggestions. If the user is relaxed, it can present more detailed suggestions. Furthermore, if the user is in a hurry, it can present concise suggestions that get straight to the point. In this way, the suggestion function can provide more appropriate suggestions by adjusting the way it presents suggestions based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The suggestion department can adjust the level of detail in its suggestions based on the importance of the recipe. For example, it will provide detailed suggestions for recipes containing important nutrients. It can also provide detailed calorie information for people on a diet. Furthermore, it can provide suggestions that prioritize nutritional balance for people recovering from illness. In this way, by adjusting the level of detail in suggestions based on the importance of the recipe, the suggestion department can provide more detailed information.

[0086] The suggestion function can apply different suggestion algorithms depending on the category of the cooking recipe. For example, it can apply a calorie restriction algorithm to diet recipes. It can also apply a nutritional balance algorithm to recipes for medical treatment. Furthermore, it can apply a high-protein algorithm to recipes for professional athletes. In this way, the suggestion function can provide more appropriate suggestions by applying different suggestion algorithms depending on the category of the cooking recipe.

[0087] The suggestion unit can estimate the user's emotions and adjust the length of its suggestions based on those emotions. For example, if the user is in a hurry, the suggestion unit will provide short, concise suggestions. If the user is relaxed, it can provide more detailed suggestions. Furthermore, if the user is excited, it can provide visually stimulating suggestions. By adjusting the length of suggestions based on the user's emotions, the suggestion unit can provide more appropriate suggestions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The proposal team can prioritize proposals based on when the recipes are submitted. For example, the proposal team can make proposals based on the latest health check results. They can also make proposals based on recent exercise records. Furthermore, they can make proposals based on recent meal photos. This allows the proposal team to prioritize proposals based on when the recipes are submitted, thus ensuring that the most up-to-date information is presented first.

[0089] The suggestion function can adjust the order of suggestions based on the relevance of the recipes during the suggestion process. For example, the suggestion function can prioritize suggesting information related to health status. It can also prioritize suggesting information related to exercise performance. Furthermore, it can prioritize suggesting information related to dietary trends. In this way, the suggestion function can prioritize suggesting more relevant information by adjusting the order of suggestions based on the relevance of the recipes.

[0090] The ordering system can estimate the user's emotions and adjust the ordering method based on those emotions. For example, if the user is stressed, the ordering system can provide a simple ordering interface. If the user is relaxed, it can also provide detailed ordering options. Furthermore, if the user is in a hurry, it can provide a way to order quickly. In this way, the ordering system can provide a more appropriate ordering method by adjusting the ordering method based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The ordering department can analyze a user's past order history to select the optimal ordering method at the time of ordering. For example, the ordering department can automatically select frequently ordered materials based on the user's past order history. It can also set up regular order schedules based on the user's past order history. Furthermore, the ordering department can prioritize ordering specific materials based on the user's past order history. In this way, the ordering department can select the optimal ordering method by analyzing the user's past order history.

[0092] The ordering system can customize the ordering process based on the user's current circumstances. For example, if the user is busy, the system can provide a quick ordering method. If the user is relaxed, the system can also provide detailed ordering options. Furthermore, if the user is traveling, the system can provide ordering methods for their travel destination. This allows the ordering system to provide a more appropriate ordering method by customizing the ordering process based on the user's current circumstances.

[0093] The ordering system can estimate the user's emotions and prioritize orders based on those emotions. For example, if the user is stressed, the system will prioritize ordering ingredients that have a relaxing effect. If the user is relaxed, the system can also prioritize ordering healthy ingredients. Furthermore, if the user is in a hurry, the system can prioritize ordering ingredients that can be cooked quickly. This allows the system to prioritize ordering more appropriate ingredients by prioritizing orders based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The ordering system can select the optimal ordering method by considering the user's geographical location when an order is placed. For example, if the user is in a specific region, the ordering system will prioritize ordering from an online supermarket in that region. Furthermore, if the user is traveling, the ordering system can also use an online supermarket in their travel destination. Additionally, if the user is at a specific sports facility, the ordering system can use an online supermarket near that facility. In this way, the ordering system can select the optimal ordering method by considering the user's geographical location.

[0095] The ordering system can analyze a user's social media activity and suggest appropriate ordering methods at the time of ordering. For example, the ordering system can order relevant ingredients based on photos of meals shared by the user on social media. It can also order healthy ingredients based on health-related posts shared by the user on social media. Furthermore, it can order ingredients needed after exercise based on exercise records shared by the user on social media. In this way, the ordering system can suggest more appropriate ordering methods by analyzing the user's social media activity.

[0096] The recommendation system can estimate the user's emotions and adjust how it presents restaurant information based on those emotions. For example, if the user is feeling stressed, the system can recommend restaurants where the user can relax. It can also recommend restaurants with healthy menus if the user is relaxed. Furthermore, if the user is in a hurry, it can recommend restaurants where they can eat quickly. In this way, the system can provide more appropriate restaurant information by adjusting how it presents restaurant information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The recommendation system can provide optimal recommendations by referencing the user's past dining history when introducing restaurant information. For example, the recommendation system can recommend relevant restaurants based on restaurants the user has visited in the past. It can also recommend restaurants with menus the user prefers based on their past dining history. Furthermore, the recommendation system can analyze the user's past dining history and recommend the restaurant with the highest satisfaction rating. In this way, the recommendation system can provide more appropriate restaurant information by referring to the user's past dining history.

[0098] The recommendation system can estimate the user's emotions and prioritize restaurant recommendations based on those emotions. For example, if the user is stressed, the system will prioritize recommending restaurants where the user can relax. If the user is relaxed, the system can also prioritize recommending restaurants with healthy menu options. Furthermore, if the user is in a hurry, the system can prioritize recommending restaurants where meals can be served quickly. This allows the system to provide more appropriate restaurant recommendations by prioritizing them based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The recommendation system can provide optimal recommendations for dining out by considering the user's geographical location. For example, if the user is in a specific area, the system will prioritize recommending restaurants in that area. It can also recommend restaurants in the user's travel destination if the user is traveling. Furthermore, if the user is at a specific sports facility, the system can recommend restaurants near that facility. This allows the system to provide more appropriate dining out information by considering the user's geographical location.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The analysis unit can estimate the user's emotions and determine the priority of analysis based on those emotions. For example, if the user is stressed, it can prioritize analyzing health information related to stress reduction. If the user is relaxed, it can prioritize analyzing information related to exercise performance. Furthermore, if the user is in a hurry, it can prioritize analyzing information that can be processed quickly. In this way, the analysis unit can provide more appropriate analysis results by determining the priority of analysis based on the user's emotions.

[0102] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is feeling stressed, suggestions can be made during times when they are relaxed. Similarly, if the user is relaxed, suggestions can be made after exercise. Furthermore, if the user is in a hurry, information that can be presented quickly can be prioritized. In this way, the suggestion function can provide more appropriate suggestions by adjusting the timing of suggestions based on the user's emotions.

[0103] The ordering system can estimate the user's emotions and adjust the level of detail in the order based on that estimation. For example, if the user is stressed, it can provide a simple ordering interface. If the user is relaxed, it can provide more detailed ordering options. Furthermore, if the user is in a hurry, it can provide a way to order quickly. In this way, the ordering system can provide a more appropriate ordering method by adjusting the level of detail in the order based on the user's emotions.

[0104] The recommendation system can estimate the user's emotions and adjust the timing of restaurant recommendations based on those emotions. For example, if a user is feeling stressed, it can recommend restaurants during times when they can relax. If the user is relaxed, it can recommend restaurants before they eat. Furthermore, if the user is in a hurry, it can prioritize recommending restaurants where they can eat quickly. In this way, the recommendation system can provide more appropriate restaurant recommendations by adjusting the timing of recommendations based on the user's emotions.

[0105] The data collection unit can estimate the user's emotions and determine the type of health and exercise information to collect based on those emotions. For example, if the user is stressed, it can prioritize collecting information related to stress reduction. If the user is relaxed, it can prioritize collecting information related to exercise performance. Furthermore, if the user is in a hurry, it can prioritize collecting information that can be gathered quickly. In this way, the data collection unit can collect more appropriate information by determining the type of information to collect based on the user's emotions.

[0106] The analysis unit can analyze the user's past health checkup results and adjust the level of detail of the analysis based on specific health indicators. For example, if past health checkup results indicate a tendency towards high blood pressure, it can analyze information related to blood pressure in detail. Similarly, if past health checkup results indicate a tendency towards high blood sugar levels, it can analyze information related to blood sugar levels in detail. Furthermore, if past health checkup results indicate a tendency towards high cholesterol levels, it can analyze information related to cholesterol in detail. In this way, the analysis unit can provide more appropriate analysis results by adjusting the level of detail of the analysis based on past health checkup results.

[0107] The suggestion function can analyze the user's past eating history and adjust the level of detail in its suggestions based on specific dietary trends. For example, if past eating history indicates a tendency towards low vegetable intake, it can suggest detailed recipes containing plenty of vegetables. Similarly, if past eating history indicates a tendency towards low protein intake, it can suggest detailed recipes containing plenty of protein. Furthermore, if past eating history indicates a tendency towards high calorie intake, it can suggest detailed recipes for low-calorie dishes. In this way, the suggestion function can provide more appropriate suggestions by adjusting the level of detail based on past eating history.

[0108] The ordering system can analyze a user's past order history and prioritize orders based on specific ingredients. For example, it can prioritize ordering ingredients that are frequently ordered based on past order history. It can also prioritize ordering ingredients that are low in stock based on past order history. Furthermore, if a particular ingredient is in high demand based on past order history, it can suggest recipes that prioritize the use of that ingredient. In this way, the ordering system can provide a more appropriate ordering method by prioritizing orders based on past order history.

[0109] The recommendation system can analyze a user's past dining history and prioritize restaurant recommendations based on specific restaurants. For example, it can prioritize recommending restaurants frequently visited based on past dining history. It can also prioritize recommending restaurants that the user has preferred based on past dining history. Furthermore, if a user was dissatisfied with a particular restaurant based on past dining history, it can exclude those restaurants from recommendations. In this way, the recommendation system can provide more appropriate restaurant recommendations by prioritizing information based on past dining history.

[0110] The data collection unit can determine the type of information to collect based on a specific region, taking into account the user's geographical location. For example, if the user is at high altitude, it can prioritize collecting information on health risks at high altitude. Similarly, if the user is near the coast, it can prioritize collecting information on health risks at the coast. Furthermore, if the user is in an urban area, it can prioritize collecting information on health risks in urban areas. This allows the data collection unit to collect more relevant information by considering the user's geographical location.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The data collection unit collects health and exercise information. This information includes heart rate, steps taken, calorie consumption, and dietary content. The data collection unit automatically collects data such as heart rate, steps taken, calories burned, and sleep duration from wearable devices. It can also collect data such as health checkup results and photos of daily meals. For example, it can collect health checkup results such as blood pressure, blood sugar levels, and cholesterol levels, as well as photos of meals. Step 2: The analysis unit analyzes the information collected by the data collection unit. The analysis unit analyzes the collected data using statistical analysis and machine learning algorithms to understand the user's health status, exercise habits, and dietary tendencies. Step 3: The suggestion unit proposes cooking recipes based on the analysis results obtained by the analysis unit. The suggestion unit proposes the optimal cooking recipe based on the user's health status, exercise status, and eating habits. For example, it proposes low-calorie recipes for people on a diet and nutritionally balanced recipes for people recovering from illness. It also proposes high-protein recipes for professional athletes. Step 4: The ordering department places an order for ingredients based on the recipe suggested by the suggestion department. The ordering department automatically selects the necessary ingredients based on the suggested recipe and places an order with a nearby online supermarket. Step 5: The Introduction Department provides restaurant information based on the recipes proposed by the Proposal Department. The Introduction Department searches for menus of nearby restaurants and cafes based on the proposed recipes and selects and introduces restaurant information with menus similar to the recipes.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, order unit, and introduction unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects health and exercise information using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A collects the data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes a cooking recipe based on the analysis results. The order unit is implemented in the specific processing unit 46A of the smart device 14 and orders ingredients based on the proposed recipe. The introduction unit is implemented in the specific processing unit 46A of the smart device 14 and provides restaurant information based on the proposed recipe. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, order unit, and introduction unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects health and exercise information using the camera 42 and microphone 238 of the smart glasses 214 and collects the data using the control unit 46A. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and proposes a cooking recipe based on the analysis results. The order unit is implemented, for example, in the control unit 46A of the smart glasses 214 and orders ingredients based on the proposed recipe. The introduction unit is implemented, for example, in the control unit 46A of the smart glasses 214 and provides restaurant information based on the proposed recipe. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, order unit, and introduction unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects health and exercise information using the camera 42 and microphone 238 of the headset terminal 314, and the control unit 46A collects the data. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and proposes a cooking recipe based on the analysis results. The order unit is implemented by, for example, the control unit 46A of the headset terminal 314, and orders ingredients based on the proposed recipe. The introduction unit is implemented by, for example, the control unit 46A of the headset terminal 314, and provides restaurant information based on the proposed recipe. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, ordering unit, and introduction unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects health and exercise information using the camera 42 and microphone 238 of the robot 414, and the control unit 46A collects the data. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and proposes a cooking recipe based on the analysis results. The ordering unit is implemented by, for example, the control unit 46A of the robot 414, and orders ingredients based on the proposed recipe. The introduction unit is implemented by, for example, the control unit 46A of the robot 414, and provides restaurant information based on the proposed recipe. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) A collection department that collects health and exercise information, An analysis unit analyzes the information collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes a cooking recipe, An ordering unit that places orders for ingredients based on the recipe proposed by the aforementioned proposal unit, The system includes a referral unit that provides restaurant information based on the recipe proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect health checkup results or photos of your daily meals. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to understand health status, exercise habits, and dietary trends. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, For people on a diet, we suggest low-calorie recipes. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We suggest nutritionally balanced meal recipes for people recovering from illness. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We suggest high-protein recipes for professional athletes. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned ordering section is, Based on the suggested recipe, the system automatically selects the necessary ingredients and places an order with a nearby online supermarket. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned introduction section is, Based on the suggested recipe, the system searches for menus at nearby restaurants and cafes and selects and presents dining information that offers dishes similar to the recipe. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of health and exercise information collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze the user's past health checkup results and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting health and exercise information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and determines the priority of health and exercise information to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting health and exercise information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting health and exercise information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of health and exercise information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of health and exercise information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the priority of analysis is determined based on when health and exercise information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of health and exercise information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the cooking recipe. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the category of the cooking recipe. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the recipes are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the cooking recipes. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned ordering section is, The system estimates the user's emotions and adjusts how ingredients are ordered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned ordering section is, When an order is placed, the system analyzes the user's past order history to select the optimal ordering method. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned ordering section is, When placing an order, the ordering method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned ordering section is, It estimates the user's emotions and determines order priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned ordering section is, When an order is placed, the system selects the optimal ordering method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned ordering section is, When an order is placed, we analyze the user's social media activity and suggest ways to place the order. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned introduction section is, The system estimates the user's emotions and adjusts how restaurant information is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned introduction section is, When introducing restaurant information, we refer to the user's past dining history to provide the most suitable recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned introduction section is, The system estimates the user's emotions and prioritizes restaurant information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned introduction section is, When introducing restaurant information, we provide optimal recommendations by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects users' health and exercise information, An analysis unit analyzes the information collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes a cooking recipe, An ordering unit that places orders for ingredients based on the recipe proposed by the aforementioned proposal unit, The system includes a referral unit that provides restaurant information based on the recipes proposed by the aforementioned proposal unit, The collection unit estimates the user's emotions and, if the estimated emotions are stressful, collects health and exercise information during times when the user's emotions are relaxed; if the emotions are relaxed, collects health and exercise information after the user's exercise; and if the emotions are urgent, prioritizes collecting only information that can be collected in a short time. The collection unit adjusts the timing or target of health and exercise information collection based on the user's emotions. A system characterized by the following features.

2. The aforementioned collection unit is Collect user health check results or photos of daily meals. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to understand health status, exercise habits, and dietary trends. The system according to feature 1.

4. The aforementioned proposal section is, For people on a diet, we suggest low-calorie recipes. The system according to feature 1.

5. The aforementioned proposal section is, We suggest nutritionally balanced meal recipes for people recovering from illness. The system according to feature 1.

6. The aforementioned proposal section is, We suggest high-protein recipes for professional athletes. The system according to feature 1.

7. The aforementioned ordering section is, Based on the suggested recipe, the system automatically selects the necessary ingredients and places an order with a nearby online supermarket. The system according to feature 1.

8. The aforementioned introduction section is, Based on the suggested recipe, the system searches for menus at nearby restaurants and cafes and selects and presents dining information that offers dishes similar to the recipe. The system according to feature 1.

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