system

Abstract

The system according to this embodiment aims to provide effective health management for individuals at risk of lifestyle-related diseases. [Solution] The system according to the embodiment comprises a confirmation unit, a provision unit, an image analysis unit, a suggestion unit, and an order unit. The confirmation unit confirms the goal setting. The provision unit provides advice based on the goal confirmed by the confirmation unit. The image analysis unit analyzes photos of meals and weight. The suggestion unit suggests a menu based on the data analyzed by the image analysis unit. The order unit orders the menu suggested by the suggestion unit online.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, health management for those at risk of lifestyle-related diseases has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to provide effective health management for those at risk of lifestyle-related diseases.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a confirmation unit, a provision unit, an analysis unit, a proposal unit, and an order unit. The confirmation unit confirms the goal setting. The provision unit provides advice based on the goal confirmed by the confirmation unit. The analysis unit analyzes photos of meals and weight. The proposal unit proposes a menu based on the data analyzed by the analysis unit. The order unit orders the menu proposed by the proposal unit online. [Effects of the Invention]

[0007] The system according to this embodiment can provide effective health management for individuals at risk of lifestyle-related diseases. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The health management system according to an embodiment of the present invention is a system that utilizes a generating AI agent to provide specialized support for individuals at risk of lifestyle-related diseases. Each time the app is launched, the generating AI agent confirms the user's goal settings and provides specific daily advice in voice and text to help them achieve their goals. By simply uploading photos of meals and weight, the AI ​​automatically records the menu and weight through image analysis. Furthermore, it suggests the optimal menu for the next day based on past eating habits, and allows online ordering if necessary. This allows for continuous support, naturally raising health awareness and reducing the risk of lifestyle-related diseases. For example, users can freely input goals during the initial setup, such as "I want to lose 5kg" or "I want to continue exercising for 30 minutes every day." The generating AI agent provides specific daily advice in voice and text based on the user's goals. For example, users may receive advice such as "Let's eat more vegetables today" or "Let's take a 30-minute walk after dinner." When a user uploads photos of meals and weight, the AI ​​analyzes the photos and automatically records the meal contents. Additionally, uploading a photo of weight allows the AI ​​to automatically record the weight. This allows users to easily record their meals and weight. Furthermore, the AI ​​analyzes the user's past meal data and suggests the optimal menu for the next day. For example, users may receive suggestions such as, "Let's focus on fish tomorrow," or "Let's eat more vegetables." If necessary, users can also order the suggested menu online. This allows users to maintain a healthy diet without any hassle. This system naturally raises health awareness through continuous support and reduces the risk of lifestyle-related diseases. Users can maintain a healthy lifestyle without difficulty while receiving advice from the generating AI agent. For example, by improving their diet and exercise based on daily advice, they can reduce the risk of lifestyle-related diseases. In addition, automating the recording of meals and weight reduces the effort required for self-management and supports continuous health management.This allows the health management system to raise users' health awareness and reduce the risk of lifestyle-related diseases.

[0029] The health management system according to this embodiment comprises a confirmation unit, a provision unit, an analysis unit, a proposal unit, and an order unit. The confirmation unit confirms the setting of goals. For example, the confirmation unit accepts the user's free input of goals during initial setup. The confirmation unit records the goals entered by the user and checks the progress toward achieving those goals. The confirmation unit can also support the user's goal setting using a generation AI. The provision unit provides advice based on the goals confirmed by the confirmation unit. For example, the provision unit provides specific daily advice in voice and text based on the user's goals. The provision unit can also generate advice toward achieving the user's goals using a generation AI. The analysis unit analyzes photos of meals and weight. For example, the analysis unit performs image analysis on photos of meals and weight uploaded by the user and automatically records the menu and weight. The analysis unit can also perform image analysis using a generation AI. The proposal unit proposes menus based on the data analyzed by the analysis unit. For example, the proposal unit analyzes past meal data and proposes the optimal menu for the next day. The proposal unit can also make menu suggestions using a generation AI. The ordering unit orders the menu suggested by the suggestion unit online. The ordering unit can, for example, order the suggested menu online. The ordering unit can also place orders using a generation AI. As a result, the health management system according to this embodiment can consistently perform user goal setting, advice provision, recording of meals and weight, menu suggestions, and online ordering.

[0030] The verification unit verifies goal setting. For example, the verification unit accepts the user's freely entered goals during the initial setup. Users can enter specific goals such as weight loss, muscle gain, or maintaining health through the health management application. The verification unit records the goals entered by the user and checks the progress toward achieving those goals. For example, if a user sets weight loss as their goal, the verification unit periodically records the user's weight data and displays the progress toward the goal in graphs and numerical values. The verification unit can also support the user's goal setting using generative AI. Based on the user's past data and general health information, the generative AI suggests realistic and achievable goals. For example, if a user sets an unrealistic weight loss goal, the generative AI will suggest a goal within a healthy range and notify the user. In this way, the verification unit supports the user in achieving their goals without undue strain. Furthermore, the verification unit can send regular reminders and encouraging messages to maintain the user's motivation toward achieving their goals. This allows the user to continue striving towards their goals.

[0031] The service provider provides advice based on goals confirmed by the verification unit. For example, the service provider provides specific daily advice in voice and text based on the user's goals. If the user's goal is weight loss, the service provider provides specific advice on daily meals and exercise. For example, it might advise consuming high-protein foods for breakfast or doing light exercise after dinner. The service provider can also use generative AI to generate advice to help the user achieve their goals. The generative AI analyzes the user's past data and current situation to generate optimal advice in real time. For example, if the user has an allergy to a particular food, the generative AI will provide advice that avoids that food. The service provider can also collect user feedback and continuously improve the content of the advice. This allows the service provider to provide personalized advice tailored to the user's individual needs and support goal achievement. Furthermore, the service provider can check whether the user has followed the advice and adjust the next advice accordingly. This allows the service provider to provide flexible support according to the user's progress.

[0032] The analysis unit analyzes photos of meals and weight. For example, it automatically records menus and weight by performing image analysis on photos of meals and weight uploaded by users. Users use their smartphone cameras to take photos of their daily meals and weight and upload them to the application. The analysis unit can also perform image analysis using generative AI. The generative AI identifies the types and quantities of food from photos of meals and automatically calculates calories and nutrients. For example, from a photo of a salad taken by a user, it identifies ingredients such as lettuce, tomatoes, and cucumbers and calculates the calories and nutrients of each. It also accurately reads and records the user's weight from photos of weight. This allows the analysis unit to automatically record accurate data without requiring users to manually input data. Furthermore, the analysis unit can analyze changes in the user's meals and weight by comparing them with past data. This allows users to continuously monitor their health and make necessary adjustments. The analysis unit can centrally manage user data and share data in cooperation with other departments. This improves the efficiency and accuracy of the entire system and supports users' health management.

[0033] The suggestion unit proposes menus based on data analyzed by the analysis unit. For example, the suggestion unit analyzes past meal data to propose the optimal menu for the next day. Based on data of meals the user has eaten in the past, it proposes menus that take into account nutritional balance and calorie intake. The suggestion unit can also make menu suggestions using generative AI. Generative AI generates optimal menus according to the user's preferences, allergy information, and goals. For example, if the user's goal is weight loss, it will propose low-calorie, high-nutrient menus. Also, if the user likes a particular ingredient, it will prioritize suggesting menus that include that ingredient. The suggestion unit can also collect user feedback and continuously improve its suggestions. This allows the suggestion unit to provide personalized menus that meet the user's individual needs and support them in achieving their goals. Furthermore, the suggestion unit can check whether the user has followed the suggested menu and adjust the next menu accordingly. This allows the suggestion unit to provide flexible support according to the user's progress.

[0034] The ordering department orders menus suggested by the suggestion department online. For example, the ordering department can order suggested menus online. Users can review the suggested menus and easily place orders through the application. The ordering department can also place orders using generative AI. The generative AI analyzes the user's past ordering history and preferences and automatically places the optimal order. For example, if a user prefers a particular restaurant, it will prioritize orders from that restaurant. The ordering department also takes into account the user's allergy information and dietary restrictions to select appropriate menus. This allows users to order meals with peace of mind. Furthermore, the ordering department can check the progress of orders in real time and notify the user. This allows users to understand the status of their orders and make changes or cancellations as needed. The ordering department can also collaborate with multiple restaurants and delivery services to provide users with a variety of options. This allows the ordering department to provide flexible ordering support tailored to the user's needs and support their health management.

[0035] The verification unit accepts user-defined goals during initial setup. For example, when a user first launches the app, the verification unit displays a screen for goal input. The verification unit records the goals entered by the user and checks the progress toward achieving those goals. The verification unit can also support user goal setting using a generative AI. For example, the verification unit prompts the generative AI with "Generate advice on achieving the goals entered by the user," and the generative AI generates advice. This allows users to freely set their goals.

[0036] The service provider delivers specific daily advice in voice and text based on the user's goals. For example, the service provider generates daily advice based on the goals set by the user. The service provider can also use a generative AI to generate advice to help the user achieve their goals. For example, the service provider can input a prompt to the generative AI such as, "Generate today's advice based on the user's goals," and the generative AI will generate the advice. This allows the user to receive specific daily advice.

[0037] The analysis unit automatically records the menu and weight by performing image analysis on photos of meals and weight uploaded by the user. For example, when a user takes a photo of a meal and uploads it to the app, the analysis unit analyzes the photo and automatically records the contents of the meal. The analysis unit can also perform image analysis using a generative AI. For example, the analysis unit can input a prompt to the generative AI such as, "Analyze this photo of the meal and record the contents of the meal," and the generative AI will perform the image analysis. This allows users to record their meals and weight without any effort on their part.

[0038] The suggestion unit analyzes past meal data to propose the optimal menu for the user the following day. For example, the suggestion unit analyzes the user's past meal data and proposes the optimal menu for the next day. The suggestion unit can also use generative AI to make menu suggestions. For example, the suggestion unit inputs a prompt to the generative AI saying, "Based on the user's past meal data, please propose the optimal menu for the next day," and the generative AI makes menu suggestions. As a result, the user is presented with the optimal menu for the next day.

[0039] The ordering unit orders the suggested menu items online. For example, the ordering unit can order the suggested menu items online. The ordering unit can also place orders using a generative AI. For example, the ordering unit can input a prompt to the generative AI such as "Please order the suggested menu items online," and the generative AI will place the order. This allows the user to order the suggested menu items online.

[0040] The verification unit analyzes the user's past goal achievement history and proposes the optimal goal setting method. For example, the verification unit proposes a similar goal setting method based on goals the user has achieved in the past. The verification unit analyzes goals the user has failed to achieve in the past and proposes areas for improvement. The verification unit proposes the most effective goal setting method based on the user's past goal achievement history. The verification unit can also propose goal setting methods using generative AI. For example, the verification unit inputs the prompt "Please propose the optimal goal setting method based on the user's past goal achievement history" to the generative AI, and the generative AI proposes a goal setting method. This allows the system to propose the optimal goal setting method based on the user's past goal achievement history.

[0041] The verification unit filters goals based on the user's current health and lifestyle when confirming goal settings. For example, the verification unit suggests realistic goals considering the user's current health. The verification unit suggests achievable goals considering the user's lifestyle. The verification unit adjusts the priority of goals based on the user's health and lifestyle. The verification unit can also perform filtering using a generative AI. For example, the verification unit can input a prompt to the generative AI such as, "Please filter goals based on the user's current health and lifestyle," and the generative AI will perform the filtering. This makes it possible to set goals that are appropriate for the user's health and lifestyle.

[0042] The verification unit prioritizes the confirmation of highly relevant goals, taking into account the user's geographical location information, when confirming goal settings. For example, if the user is in a specific region, the verification unit prioritizes goals related to that region. If the user is traveling, the verification unit prioritizes goals that can be achieved at their travel destination. If the user is at home, the verification unit prioritizes goals that can be achieved at home. The verification unit can also use a generation AI to perform goal confirmation that takes geographical location information into account. For example, the verification unit can input a prompt to the generation AI saying, "Please prioritize confirming highly relevant goals based on the user's geographical location information," and the generation AI will perform the goal confirmation. This allows for the prioritization of highly relevant goals based on the user's geographical location information.

[0043] The verification unit analyzes the user's social media activity and suggests relevant goals when confirming goal setting. For example, the verification unit suggests relevant goals based on health goals shared by the user on social media. The verification unit suggests goals of interest based on the user's social media activity. The verification unit suggests achievable goals based on the user's frequency of activity on social media. The verification unit can also analyze social media activity using generative AI. For example, the verification unit can input a prompt to the generative AI such as, "Please suggest relevant goals based on the user's social media activity," and the generative AI will then suggest goals. This allows the system to suggest relevant goals based on the user's social media activity.

[0044] The service provider adjusts the level of detail in the advice based on the importance of the goal. For example, it provides detailed advice for high-importance goals and concise advice for low-importance goals. The service provider also adjusts the frequency of advice according to the importance of the goal. The service provider can also adjust the level of detail in the advice using generative AI. For example, the service provider can input a prompt to the generative AI such as "Adjust the level of detail in the advice based on the importance of the goal," and the generative AI will adjust the level of detail in the advice. This allows the level of detail in the advice to be adjusted according to the importance of the goal.

[0045] The service provider applies different advice algorithms depending on the goal category when providing advice. For example, for a weight loss goal, the service provider provides advice on diet and exercise. For a stress management goal, the service provider provides advice on relaxation methods. For a sleep improvement goal, the service provider provides advice on how to create a suitable sleep environment. The service provider can also apply advice algorithms using generative AI. For example, the service provider can input a prompt to the generative AI such as "Apply different advice algorithms depending on the goal category," and the generative AI will apply the advice algorithms. This allows the service provider to provide appropriate advice according to the goal category.

[0046] The service provider prioritizes advice based on when the goals were set. For example, if a significant amount of time has passed since the goals were set, the service provider will prioritize providing advice. If the goals were set recently, the service provider will provide advice on how to move to the next step. The service provider adjusts the frequency of advice depending on when the goals were set. The service provider can also use a generative AI to determine the priority of advice. For example, the service provider can input a prompt to the generative AI such as, "Please determine the priority of advice based on when the goals were set," and the generative AI will determine the priority of advice. This allows the service provider to determine the priority of advice according to when the goals were set.

[0047] The advice delivery system adjusts the order of advice based on the relevance of the goals when providing advice. For example, the system prioritizes providing advice to highly relevant goals. For less relevant goals, the system postpones providing advice. The system adjusts the order of advice according to the relevance of the goals. The system can also adjust the order of advice using generative AI. For example, the system can input a prompt to the generative AI such as "Adjust the order of advice based on the relevance of the goals," and the generative AI will adjust the order of advice. This allows the order of advice to be adjusted according to the relevance of the goals.

[0048] The analysis unit improves the accuracy of image analysis by considering the patterns of diet and weight fluctuations. For example, the analysis unit improves the accuracy of diet content analysis by considering diet fluctuation patterns. The analysis unit improves the accuracy of weight analysis by considering weight fluctuation patterns. The analysis unit improves the accuracy of analysis by comprehensively considering diet and weight fluctuation patterns. The analysis unit can also perform analysis that considers fluctuation patterns using a generation AI. For example, the analysis unit can input a prompt to the generation AI saying, "Please improve the accuracy of the analysis by considering the patterns of diet and weight fluctuations," and the generation AI will improve the accuracy of the analysis. This makes it possible to improve the accuracy of analysis by considering the patterns of diet and weight fluctuations.

[0049] The analysis unit performs image analysis while considering the user's dietary and weight history. For example, the analysis unit analyzes the content of meals based on the user's past meal history. The analysis unit analyzes weight based on the user's past weight history. The analysis unit performs analysis by comprehensively considering the user's dietary and weight history information. The analysis unit can also perform analysis that considers history information using a generation AI. For example, the analysis unit can input a prompt to the generation AI saying, "Please perform analysis considering the user's dietary and weight history information," and the generation AI will perform the analysis. This improves the accuracy of the analysis by considering the user's dietary and weight history information.

[0050] The analysis unit performs image analysis while considering the geographical distribution of meals and weight. For example, the analysis unit analyzes meal content while considering the geographical distribution of the user's meals. The analysis unit analyzes weight while considering the geographical distribution of the user's weight. The analysis unit performs analysis by comprehensively considering the geographical distribution of meals and weight. The analysis unit can also perform analysis that considers geographical distribution using a generative AI. For example, the analysis unit can input a prompt to the generative AI saying, "Please perform analysis while considering the geographical distribution of meals and weight," and the generative AI will perform the analysis. This improves the accuracy of the analysis by considering the geographical distribution of meals and weight.

[0051] The analysis unit improves the accuracy of image analysis by referring to relevant literature on diet and weight. For example, the analysis unit improves the accuracy of diet content analysis by referring to relevant literature on diet. The analysis unit improves the accuracy of weight analysis by referring to relevant literature on weight. The analysis unit improves the accuracy of analysis by comprehensively referring to relevant literature on diet and weight. The analysis unit can also perform analysis that refers to relevant literature using a generating AI. For example, the analysis unit can input a prompt to the generating AI saying, "Please improve the accuracy of the analysis by referring to relevant literature on diet and weight," and the generating AI will improve the accuracy of the analysis. This makes it possible to improve the accuracy of analysis by referring to relevant literature on diet and weight.

[0052] The suggestion unit improves the accuracy of its menu suggestions by considering the interrelationships between meals. For example, the suggestion unit suggests a dinner menu considering the balance between breakfast and lunch. The suggestion unit suggests a menu for the next day considering the content of the previous day's meals. The suggestion unit suggests the optimal menu considering the nutritional balance of meals. The suggestion unit can also use generative AI to make menu suggestions that consider interrelationships. For example, the suggestion unit can input a prompt to the generative AI saying, "Please improve the accuracy of menu suggestions by considering the interrelationships between meals," and the generative AI will improve the accuracy of its menu suggestions. This allows for improved accuracy of suggestions by considering the interrelationships between meals.

[0053] The suggestion function considers the user's eating history when suggesting menus. For example, the suggestion function suggests the optimal menu based on the user's past eating history. The suggestion function suggests a menu that considers nutritional balance based on the user's eating history. The suggestion function analyzes the user's eating history and suggests a menu that is effective for maintaining health. The suggestion function can also use generative AI to suggest menus that take history information into account. For example, the suggestion function can input a prompt to the generative AI saying, "Please suggest a menu considering the user's eating history information," and the generative AI will then suggest a menu. This improves the accuracy of the suggestions by considering the user's eating history information.

[0054] The suggestion unit considers the geographical distribution of food when suggesting menus. For example, the suggestion unit suggests menus considering ingredients in the user's region. The suggestion unit suggests menus considering the food culture in the user's region. The suggestion unit suggests menus considering seasonal ingredients in the user's region. The suggestion unit can also use generative AI to suggest menus that consider geographical distribution. For example, the suggestion unit can input a prompt to the generative AI saying, "Please suggest menus considering the geographical distribution of food," and the generative AI will then suggest menus. This improves the accuracy of suggestions by considering the geographical distribution of food.

[0055] The suggestion unit improves the accuracy of its menu suggestions by referring to relevant literature on diet. For example, the suggestion unit can suggest nutritionally balanced menus by referring to relevant literature on diet. The suggestion unit can suggest menus that are effective for maintaining health by referring to relevant literature on diet. The suggestion unit can suggest menus that are suitable for specific health goals by referring to relevant literature on diet. The suggestion unit can also use generative AI to make menu suggestions that refer to relevant literature. For example, the suggestion unit can input a prompt to the generative AI saying, "Please improve the accuracy of menu suggestions by referring to relevant literature on diet," and the generative AI will improve the accuracy of its menu suggestions. This allows the accuracy of suggestions to be improved by referring to relevant literature on diet.

[0056] The ordering system analyzes the user's past order history to suggest the optimal ordering method. For example, the ordering system suggests the optimal menu based on the user's past order history. The ordering system prioritizes suggesting frequently ordered menu items based on the user's order history. The ordering system analyzes the user's order history to suggest menu items that are effective for maintaining health. The ordering system can also analyze order history using generative AI. For example, the ordering system can input a prompt to the generative AI such as, "Please suggest the optimal ordering method based on the user's past order history," and the generative AI will suggest an ordering method. This allows for optimal ordering for the user by analyzing the user's past order history and suggesting the best ordering method.

[0057] The ordering system customizes the ordering process based on the user's current lifestyle. For example, if the user is busy, the ordering system suggests menus that can be delivered quickly. If the user is relaxed, the ordering system suggests menus that can be prepared at a leisurely pace. The ordering system proposes the most suitable ordering method according to the user's lifestyle. The ordering system can also use generative AI to suggest ordering methods that take the user's lifestyle into account. For example, the ordering system can input a prompt to the generative AI such as, "Please customize the ordering method based on the user's current lifestyle," and the generative AI will then customize the ordering method. This allows for the user to place the most optimal order by customizing the ordering method based on their current lifestyle.

[0058] The ordering system selects the optimal ordering method when an order is placed, taking into account the user's geographical location. For example, the ordering system proposes the optimal ordering method considering the delivery services in the user's area. Based on the user's geographical location, the ordering system proposes menus that can be delivered quickly. The ordering system proposes the optimal ordering method considering the ingredients in the user's area. The ordering system can also use a generation AI to select an ordering method that takes geographical location into account. For example, the ordering system can input a prompt to the generation AI saying, "Please select the optimal ordering method based on the user's geographical location," and the generation AI will select an ordering method. This allows for the user to place the most optimal order by selecting the optimal ordering method that takes the user's geographical location into account.

[0059] The ordering system analyzes the user's social media activity and suggests ordering methods when an order is placed. For example, the ordering system suggests the optimal menu based on the user's social media posts about food. The ordering system suggests menus that the user might be interested in based on their social media activity. The ordering system suggests the optimal ordering method based on the user's frequency of social media activity. The ordering system can also analyze social media activity using generative AI. For example, the ordering system can input a prompt to the generative AI such as, "Please suggest ordering methods based on the user's social media activity," and the generative AI will suggest ordering methods. This allows for optimal ordering for the user by analyzing their social media activity and suggesting ordering methods.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The verification unit can analyze the user's past goal achievement history and propose the optimal goal setting method. For example, it can propose a similar goal setting method based on goals the user has achieved in the past. It can also analyze goals the user has failed to achieve in the past and suggest areas for improvement. Furthermore, it can propose the most effective goal setting method based on the user's past goal achievement history. This allows for the proposal of the optimal goal setting method based on the user's past goal achievement history, thereby increasing the success rate of goal achievement.

[0062] The analysis unit can perform analysis while considering the user's dietary and weight history. For example, it can analyze the content of meals based on the user's past meal history. It can also analyze weight based on the user's past weight history. Furthermore, it can perform analysis by comprehensively considering the user's dietary and weight history. This improves the accuracy of the analysis by considering the user's dietary and weight history.

[0063] The ordering system can analyze a user's past order history and suggest the optimal ordering method. For example, it can suggest the most suitable menu based on the user's past order history. It can also prioritize suggesting frequently ordered menu items based on the user's order history. Furthermore, it can analyze the user's order history and suggest menu items that are effective for maintaining good health. In this way, by analyzing a user's past order history and suggesting the optimal ordering method, it becomes possible to make the best possible order for the user.

[0064] The service provider can apply different advice algorithms depending on the goal category when providing advice. For example, for weight loss goals, it can provide advice on diet and exercise. For stress management goals, it can provide advice on relaxation methods. For sleep improvement goals, it can provide advice on how to create a suitable sleep environment. This allows the service provider to provide appropriate advice according to the goal category and support the user in achieving their goals.

[0065] The ordering system can select the optimal ordering method by considering the user's geographical location. For example, it can suggest the most suitable ordering method by considering the delivery services available in the user's area. It can also suggest menus that can be delivered quickly based on the user's geographical location. Furthermore, it can suggest the most suitable ordering method by considering the ingredients available in the user's area. By selecting the optimal ordering method based on the user's geographical location, the system enables the user to place the most optimal order.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The verification unit verifies the goal setting. The verification unit accepts the user's freely entered goals during the initial setup, records the goals entered by the user, and checks the progress toward achieving those goals. It can also support the user's goal setting using a generation AI. Step 2: The delivery unit provides advice based on the goals confirmed by the verification unit. The delivery unit provides specific daily advice in voice and text based on the user's goals, and can also use generation AI to generate advice to help the user achieve their goals. Step 3: The analysis unit analyzes the photos of meals and weight. The analysis unit can also perform image analysis using generated AI to automatically record the menu and weight by analyzing the photos of meals and weight uploaded by the user. Step 4: The proposal unit proposes menus based on the data analyzed by the analysis unit. The proposal unit can also analyze past meal data to propose the optimal menu for the next day, and can use generative AI to make menu suggestions. Step 5: The ordering department orders the menu suggested by the suggestion department online. The ordering department can order the suggested menu online, and can also place orders using a generation AI.

[0068] (Example of form 2) The health management system according to an embodiment of the present invention is a system that utilizes a generating AI agent to provide specialized support for individuals at risk of lifestyle-related diseases. Each time the app is launched, the generating AI agent confirms the user's goal settings and provides specific daily advice in voice and text to help them achieve their goals. By simply uploading photos of meals and weight, the AI ​​automatically records the menu and weight through image analysis. Furthermore, it suggests the optimal menu for the next day based on past eating habits, and allows online ordering if necessary. This allows for continuous support, naturally raising health awareness and reducing the risk of lifestyle-related diseases. For example, users can freely input goals during the initial setup, such as "I want to lose 5kg" or "I want to continue exercising for 30 minutes every day." The generating AI agent provides specific daily advice in voice and text based on the user's goals. For example, users may receive advice such as "Let's eat more vegetables today" or "Let's take a 30-minute walk after dinner." When a user uploads photos of meals and weight, the AI ​​analyzes the photos and automatically records the meal contents. Additionally, uploading a photo of weight allows the AI ​​to automatically record the weight. This allows users to easily record their meals and weight. Furthermore, the AI ​​analyzes the user's past meal data and suggests the optimal menu for the next day. For example, users may receive suggestions such as, "Let's focus on fish tomorrow," or "Let's eat more vegetables." If necessary, users can also order the suggested menu online. This allows users to maintain a healthy diet without any hassle. This system naturally raises health awareness through continuous support and reduces the risk of lifestyle-related diseases. Users can maintain a healthy lifestyle without difficulty while receiving advice from the generating AI agent. For example, by improving their diet and exercise based on daily advice, they can reduce the risk of lifestyle-related diseases. In addition, automating the recording of meals and weight reduces the effort required for self-management and supports continuous health management.This allows the health management system to raise users' health awareness and reduce the risk of lifestyle-related diseases.

[0069] The health management system according to this embodiment comprises a confirmation unit, a provision unit, an analysis unit, a proposal unit, and an order unit. The confirmation unit confirms the setting of goals. For example, the confirmation unit accepts the user's free input of goals during initial setup. The confirmation unit records the goals entered by the user and checks the progress toward achieving those goals. The confirmation unit can also support the user's goal setting using a generation AI. The provision unit provides advice based on the goals confirmed by the confirmation unit. For example, the provision unit provides specific daily advice in voice and text based on the user's goals. The provision unit can also generate advice toward achieving the user's goals using a generation AI. The analysis unit analyzes photos of meals and weight. For example, the analysis unit performs image analysis on photos of meals and weight uploaded by the user and automatically records the menu and weight. The analysis unit can also perform image analysis using a generation AI. The proposal unit proposes menus based on the data analyzed by the analysis unit. For example, the proposal unit analyzes past meal data and proposes the optimal menu for the next day. The proposal unit can also make menu suggestions using a generation AI. The ordering unit orders the menu suggested by the suggestion unit online. The ordering unit can, for example, order the suggested menu online. The ordering unit can also place orders using a generation AI. As a result, the health management system according to this embodiment can consistently perform user goal setting, advice provision, recording of meals and weight, menu suggestions, and online ordering.

[0070] The verification unit verifies goal setting. For example, the verification unit accepts the user's freely entered goals during the initial setup. Users can enter specific goals such as weight loss, muscle gain, or maintaining health through the health management application. The verification unit records the goals entered by the user and checks the progress toward achieving those goals. For example, if a user sets weight loss as their goal, the verification unit periodically records the user's weight data and displays the progress toward the goal in graphs and numerical values. The verification unit can also support the user's goal setting using generative AI. Based on the user's past data and general health information, the generative AI suggests realistic and achievable goals. For example, if a user sets an unrealistic weight loss goal, the generative AI will suggest a goal within a healthy range and notify the user. In this way, the verification unit supports the user in achieving their goals without undue strain. Furthermore, the verification unit can send regular reminders and encouraging messages to maintain the user's motivation toward achieving their goals. This allows the user to continue striving towards their goals.

[0071] The service provider provides advice based on goals confirmed by the verification unit. For example, the service provider provides specific daily advice in voice and text based on the user's goals. If the user's goal is weight loss, the service provider provides specific advice on daily meals and exercise. For example, it might advise consuming high-protein foods for breakfast or doing light exercise after dinner. The service provider can also use generative AI to generate advice to help the user achieve their goals. The generative AI analyzes the user's past data and current situation to generate optimal advice in real time. For example, if the user has an allergy to a particular food, the generative AI will provide advice that avoids that food. The service provider can also collect user feedback and continuously improve the content of the advice. This allows the service provider to provide personalized advice tailored to the user's individual needs and support goal achievement. Furthermore, the service provider can check whether the user has followed the advice and adjust the next advice accordingly. This allows the service provider to provide flexible support according to the user's progress.

[0072] The analysis unit analyzes photos of meals and weight. For example, it automatically records menus and weight by performing image analysis on photos of meals and weight uploaded by users. Users use their smartphone cameras to take photos of their daily meals and weight and upload them to the application. The analysis unit can also perform image analysis using generative AI. The generative AI identifies the types and quantities of food from photos of meals and automatically calculates calories and nutrients. For example, from a photo of a salad taken by a user, it identifies ingredients such as lettuce, tomatoes, and cucumbers and calculates the calories and nutrients of each. It also accurately reads and records the user's weight from photos of weight. This allows the image unit to automatically record accurate data, eliminating the need for users to manually input data. Furthermore, the analysis unit can analyze changes in the user's meals and weight by comparing them with past data. This allows users to continuously monitor their health and make necessary adjustments. The analysis unit can centrally manage user data and share data in cooperation with other departments. This improves the overall efficiency and accuracy of the system and supports users' health management.

[0073] The suggestion unit proposes menus based on data analyzed by the analysis unit. For example, the suggestion unit analyzes past meal data to propose the optimal menu for the next day. Based on data of meals the user has eaten in the past, it proposes menus that take into account nutritional balance and calorie intake. The suggestion unit can also make menu suggestions using generative AI. Generative AI generates optimal menus according to the user's preferences, allergy information, and goals. For example, if the user's goal is weight loss, it will propose low-calorie, high-nutrient menus. Also, if the user likes a particular ingredient, it will prioritize suggesting menus that include that ingredient. The suggestion unit can also collect user feedback and continuously improve its suggestions. This allows the suggestion unit to provide personalized menus that meet the user's individual needs and support them in achieving their goals. Furthermore, the suggestion unit can check whether the user has followed the suggested menu and adjust the next menu accordingly. This allows the suggestion unit to provide flexible support according to the user's progress.

[0074] The ordering department orders menus suggested by the suggestion department online. For example, the ordering department can order suggested menus online. Users can review the suggested menus and easily place orders through the application. The ordering department can also place orders using generative AI. The generative AI analyzes the user's past ordering history and preferences and automatically places the optimal order. For example, if a user prefers a particular restaurant, it will prioritize orders from that restaurant. The ordering department also takes into account the user's allergy information and dietary restrictions to select appropriate menus. This allows users to order meals with peace of mind. Furthermore, the ordering department can check the progress of orders in real time and notify the user. This allows users to understand the status of their orders and make changes or cancellations as needed. The ordering department can also collaborate with multiple restaurants and delivery services to provide users with a variety of options. This allows the ordering department to provide flexible ordering support tailored to the user's needs and support their health management.

[0075] The verification unit accepts user-defined goals during initial setup. For example, when a user first launches the app, the verification unit displays a screen for goal input. The verification unit records the goals entered by the user and checks the progress toward achieving those goals. The verification unit can also support user goal setting using a generative AI. For example, the verification unit prompts the generative AI with "Generate advice on achieving the goals entered by the user," and the generative AI generates advice. This allows users to freely set their goals.

[0076] The service provider delivers specific daily advice in voice and text based on the user's goals. For example, the service provider generates daily advice based on the goals set by the user. The service provider can also use a generative AI to generate advice to help the user achieve their goals. For example, the service provider can input a prompt to the generative AI such as, "Generate today's advice based on the user's goals," and the generative AI will generate the advice. This allows the user to receive specific daily advice.

[0077] The analysis unit automatically records the menu and weight by performing image analysis on photos of meals and weight uploaded by the user. For example, when a user takes a photo of a meal and uploads it to the app, the analysis unit analyzes the photo and automatically records the contents of the meal. The analysis unit can also perform image analysis using a generative AI. For example, the analysis unit can input a prompt to the generative AI such as, "Analyze this photo of the meal and record the contents of the meal," and the generative AI will perform the image analysis. This allows users to record their meals and weight without any effort on their part.

[0078] The suggestion unit analyzes past meal data to propose the optimal menu for the user the following day. For example, the suggestion unit analyzes the user's past meal data and proposes the optimal menu for the next day. The suggestion unit can also use generative AI to make menu suggestions. For example, the suggestion unit inputs a prompt to the generative AI saying, "Based on the user's past meal data, please propose the optimal menu for the next day," and the generative AI makes menu suggestions. As a result, the user is presented with the optimal menu for the next day.

[0079] The ordering unit orders the suggested menu items online. For example, the ordering unit can order the suggested menu items online. The ordering unit can also place orders using a generative AI. For example, the ordering unit can input a prompt to the generative AI such as "Please order the suggested menu items online," and the generative AI will place the order. This allows the user to order the suggested menu items online.

[0080] The confirmation unit estimates the user's emotions and adjusts the timing of goal setting confirmation based on the estimated emotions. For example, if the user is stressed, the confirmation unit delays the confirmation timing to allow confirmation in a relaxed state. If the user is relaxed, the confirmation unit speeds up the confirmation timing to proactively set goals. If the user is in a hurry, the confirmation unit shortens the confirmation timing to quickly set goals. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the timing of goal setting confirmation to be adjusted according to the user's emotions.

[0081] The verification unit analyzes the user's past goal achievement history and proposes the optimal goal setting method. For example, the verification unit proposes a similar goal setting method based on goals the user has achieved in the past. The verification unit analyzes goals the user has failed to achieve in the past and proposes areas for improvement. The verification unit proposes the most effective goal setting method based on the user's past goal achievement history. The verification unit can also propose goal setting methods using generative AI. For example, the verification unit inputs the prompt "Please propose the optimal goal setting method based on the user's past goal achievement history" to the generative AI, and the generative AI proposes a goal setting method. This allows the system to propose the optimal goal setting method based on the user's past goal achievement history.

[0082] The verification unit filters goals based on the user's current health and lifestyle when confirming goal settings. For example, the verification unit suggests realistic goals considering the user's current health. The verification unit suggests achievable goals considering the user's lifestyle. The verification unit adjusts the priority of goals based on the user's health and lifestyle. The verification unit can also perform filtering using a generative AI. For example, the verification unit can input a prompt to the generative AI such as, "Please filter goals based on the user's current health and lifestyle," and the generative AI will perform the filtering. This makes it possible to set goals that are appropriate for the user's health and lifestyle.

[0083] The verification unit estimates the user's emotions and determines the priority of goal setting based on the estimated emotions. For example, if the user is stressed, the verification unit sets goals that prioritize stress reduction. If the user is relaxed, the verification unit prioritizes long-term goals. If the user is in a hurry, the verification unit prioritizes short-term goals. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the determination of goal setting priorities according to the user's emotions.

[0084] The verification unit prioritizes the confirmation of highly relevant goals, taking into account the user's geographical location information, when confirming goal settings. For example, if the user is in a specific region, the verification unit prioritizes goals related to that region. If the user is traveling, the verification unit prioritizes goals that can be achieved at their travel destination. If the user is at home, the verification unit prioritizes goals that can be achieved at home. The verification unit can also use a generation AI to perform goal confirmation that takes geographical location information into account. For example, the verification unit can input a prompt to the generation AI saying, "Please prioritize confirming highly relevant goals based on the user's geographical location information," and the generation AI will perform the goal confirmation. This allows for the prioritization of highly relevant goals based on the user's geographical location information.

[0085] The verification unit analyzes the user's social media activity and suggests relevant goals when confirming goal setting. For example, the verification unit suggests relevant goals based on health goals shared by the user on social media. The verification unit suggests goals of interest based on the user's social media activity. The verification unit suggests achievable goals based on the user's frequency of activity on social media. The verification unit can also analyze social media activity using generative AI. For example, the verification unit can input a prompt to the generative AI such as, "Please suggest relevant goals based on the user's social media activity," and the generative AI will then suggest goals. This allows the system to suggest relevant goals based on the user's social media activity.

[0086] The service provider estimates the user's emotions and adjusts the way advice is presented based on those emotions. For example, if the user is stressed, the service provider will offer advice in gentle language. If the user is relaxed, the service provider will offer detailed advice. If the user is in a hurry, the service provider will offer concise advice. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the service provider to adjust the way advice is presented according to the user's emotions.

[0087] The service provider adjusts the level of detail in the advice based on the importance of the goal. For example, it provides detailed advice for high-importance goals and concise advice for low-importance goals. The service provider also adjusts the frequency of advice according to the importance of the goal. The service provider can also adjust the level of detail in the advice using generative AI. For example, the service provider can input a prompt to the generative AI such as "Adjust the level of detail in the advice based on the importance of the goal," and the generative AI will adjust the level of detail in the advice. This allows the level of detail in the advice to be adjusted according to the importance of the goal.

[0088] The service provider applies different advice algorithms depending on the goal category when providing advice. For example, for a weight loss goal, the service provider provides advice on diet and exercise. For a stress management goal, the service provider provides advice on relaxation methods. For a sleep improvement goal, the service provider provides advice on how to create a suitable sleep environment. The service provider can also apply advice algorithms using generative AI. For example, the service provider can input a prompt to the generative AI such as "Apply different advice algorithms depending on the goal category," and the generative AI will apply the advice algorithms. This allows the service provider to provide appropriate advice according to the goal category.

[0089] The service provider estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. For example, if the user is stressed, the service provider provides short, concise advice. If the user is relaxed, the service provider provides detailed advice. If the user is in a hurry, the service provider provides brief advice. 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. This allows the length of the advice to be adjusted according to the user's emotions.

[0090] The service provider prioritizes advice based on when the goals were set. For example, if a significant amount of time has passed since the goals were set, the service provider will prioritize providing advice. If the goals were set recently, the service provider will provide advice on how to move to the next step. The service provider adjusts the frequency of advice depending on when the goals were set. The service provider can also use a generative AI to determine the priority of advice. For example, the service provider can input a prompt to the generative AI such as, "Please determine the priority of advice based on when the goals were set," and the generative AI will determine the priority of advice. This allows the service provider to determine the priority of advice according to when the goals were set.

[0091] The advice delivery system adjusts the order of advice based on the relevance of the goals when providing advice. For example, the system prioritizes providing advice to highly relevant goals. For less relevant goals, the system postpones providing advice. The system adjusts the order of advice according to the relevance of the goals. The system can also adjust the order of advice using generative AI. For example, the system can input a prompt to the generative AI such as "Adjust the order of advice based on the relevance of the goals," and the generative AI will adjust the order of advice. This allows the order of advice to be adjusted according to the relevance of the goals.

[0092] The analysis unit estimates the user's emotions and adjusts the accuracy of the image analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit performs a simple analysis and provides results quickly. If the user is relaxed, the analysis unit performs a detailed analysis and provides highly accurate results. If the user is in a hurry, the analysis unit performs a rapid analysis and provides results quickly. 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. This allows the accuracy of the image analysis to be adjusted according to the user's emotions.

[0093] The analysis unit improves the accuracy of image analysis by considering the patterns of diet and weight fluctuations. For example, the analysis unit improves the accuracy of diet content analysis by considering diet fluctuation patterns. The analysis unit improves the accuracy of weight analysis by considering weight fluctuation patterns. The analysis unit improves the accuracy of analysis by comprehensively considering diet and weight fluctuation patterns. The analysis unit can also perform analysis that considers fluctuation patterns using a generation AI. For example, the analysis unit can input a prompt to the generation AI saying, "Please improve the accuracy of the analysis by considering the patterns of diet and weight fluctuations," and the generation AI will improve the accuracy of the analysis. This makes it possible to improve the accuracy of analysis by considering the patterns of diet and weight fluctuations.

[0094] The analysis unit performs image analysis while considering the user's dietary and weight history. For example, the analysis unit analyzes the content of meals based on the user's past meal history. The analysis unit analyzes weight based on the user's past weight history. The analysis unit performs analysis by comprehensively considering the user's dietary and weight history information. The analysis unit can also perform analysis that considers history information using a generation AI. For example, the analysis unit can input a prompt to the generation AI saying, "Please perform analysis considering the user's dietary and weight history information," and the generation AI will perform the analysis. This improves the accuracy of the analysis by considering the user's dietary and weight history information.

[0095] The analysis unit estimates the user's emotions and adjusts the order in which the image analysis results are displayed based on the estimated emotions. For example, if the user is stressed, the analysis unit prioritizes displaying important results. If the user is relaxed, the analysis unit displays detailed results in a sequential manner. If the user is in a hurry, the analysis unit prioritizes displaying results that summarize the key points. 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. This allows for the provision of optimal information to the user by adjusting the order in which the image analysis results are displayed according to the user's emotions.

[0096] The analysis unit performs image analysis while considering the geographical distribution of meals and weight. For example, the analysis unit analyzes meal content while considering the geographical distribution of the user's meals. The analysis unit analyzes weight while considering the geographical distribution of the user's weight. The analysis unit performs analysis by comprehensively considering the geographical distribution of meals and weight. The analysis unit can also perform analysis that considers geographical distribution using a generative AI. For example, the analysis unit can input a prompt to the generative AI saying, "Please perform analysis while considering the geographical distribution of meals and weight," and the generative AI will perform the analysis. This improves the accuracy of the analysis by considering the geographical distribution of meals and weight.

[0097] The analysis unit improves the accuracy of image analysis by referring to relevant literature on diet and weight. For example, the analysis unit improves the accuracy of diet content analysis by referring to relevant literature on diet. The analysis unit improves the accuracy of weight analysis by referring to relevant literature on weight. The analysis unit improves the accuracy of analysis by comprehensively referring to relevant literature on diet and weight. The analysis unit can also perform analysis that refers to relevant literature using a generating AI. For example, the analysis unit can input a prompt to the generating AI saying, "Please improve the accuracy of the analysis by referring to relevant literature on diet and weight," and the generating AI will improve the accuracy of the analysis. This makes it possible to improve the accuracy of analysis by referring to relevant literature on diet and weight.

[0098] The suggestion function estimates the user's emotions and determines the priority of the suggested menu items based on those emotions. For example, if the user is stressed, the suggestion function will prioritize suggesting menu items that are effective in reducing stress. If the user is relaxed, the suggestion function will prioritize suggesting menu items that are effective in maintaining health. If the user is in a hurry, the suggestion function will prioritize suggesting menu items that are easy to prepare. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for optimal menu suggestions for the user by determining the priority of the suggested menu items according to the user's emotions.

[0099] The suggestion unit improves the accuracy of its menu suggestions by considering the interrelationships between meals. For example, the suggestion unit suggests a dinner menu considering the balance between breakfast and lunch. The suggestion unit suggests a menu for the next day considering the content of the previous day's meals. The suggestion unit suggests the optimal menu considering the nutritional balance of meals. The suggestion unit can also use generative AI to make menu suggestions that consider interrelationships. For example, the suggestion unit can input a prompt to the generative AI saying, "Please improve the accuracy of menu suggestions by considering the interrelationships between meals," and the generative AI will improve the accuracy of its menu suggestions. This allows for improved accuracy of suggestions by considering the interrelationships between meals.

[0100] The suggestion function considers the user's eating history when suggesting menus. For example, the suggestion function suggests the optimal menu based on the user's past eating history. The suggestion function suggests a menu that considers nutritional balance based on the user's eating history. The suggestion function analyzes the user's eating history and suggests a menu that is effective for maintaining health. The suggestion function can also use generative AI to suggest menus that take history information into account. For example, the suggestion function can input a prompt to the generative AI saying, "Please suggest a menu considering the user's eating history information," and the generative AI will then suggest a menu. This improves the accuracy of the suggestions by considering the user's eating history information.

[0101] The suggestion section estimates the user's emotions and adjusts the display method of suggested menus based on the estimated emotions. For example, if the user is stressed, the suggestion section provides a simple and highly visible display method. If the user is relaxed, the suggestion section provides a display method that includes detailed information. If the user is in a hurry, the suggestion section provides a display method that gets straight to the point. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the provision of optimal information to the user by adjusting the display method of suggested menus according to the user's emotions.

[0102] The suggestion unit considers the geographical distribution of food when suggesting menus. For example, the suggestion unit suggests menus considering ingredients in the user's region. The suggestion unit suggests menus considering the food culture in the user's region. The suggestion unit suggests menus considering seasonal ingredients in the user's region. The suggestion unit can also use generative AI to suggest menus that consider geographical distribution. For example, the suggestion unit can input a prompt to the generative AI saying, "Please suggest menus considering the geographical distribution of food," and the generative AI will then suggest menus. This improves the accuracy of suggestions by considering the geographical distribution of food.

[0103] The suggestion unit improves the accuracy of its menu suggestions by referring to relevant literature on diet. For example, the suggestion unit can suggest nutritionally balanced menus by referring to relevant literature on diet. The suggestion unit can suggest menus that are effective for maintaining health by referring to relevant literature on diet. The suggestion unit can suggest menus that are suitable for specific health goals by referring to relevant literature on diet. The suggestion unit can also use generative AI to make menu suggestions that refer to relevant literature. For example, the suggestion unit can input a prompt to the generative AI saying, "Please improve the accuracy of menu suggestions by referring to relevant literature on diet," and the generative AI will improve the accuracy of its menu suggestions. This allows the accuracy of suggestions to be improved by referring to relevant literature on diet.

[0104] The ordering system estimates the user's emotions and prioritizes orders based on those emotions. For example, if the user is stressed, the system prioritizes ordering menu items that are effective in reducing stress. If the user is relaxed, the system prioritizes ordering menu items that are effective in maintaining health. If the user is in a hurry, the system prioritizes ordering menu items that can be prepared quickly. 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. This allows for optimal ordering for the user by prioritizing orders according to their emotions.

[0105] The ordering system analyzes the user's past order history to suggest the optimal ordering method. For example, the ordering system suggests the optimal menu based on the user's past order history. The ordering system prioritizes suggesting frequently ordered menu items based on the user's order history. The ordering system analyzes the user's order history to suggest menu items that are effective for maintaining health. The ordering system can also analyze order history using generative AI. For example, the ordering system can input a prompt to the generative AI such as, "Please suggest the optimal ordering method based on the user's past order history," and the generative AI will suggest an ordering method. This allows for optimal ordering for the user by analyzing the user's past order history and suggesting the best ordering method.

[0106] The ordering system customizes the ordering process based on the user's current lifestyle. For example, if the user is busy, the ordering system suggests menus that can be delivered quickly. If the user is relaxed, the ordering system suggests menus that can be prepared at a leisurely pace. The ordering system proposes the most suitable ordering method according to the user's lifestyle. The ordering system can also use generative AI to suggest ordering methods that take the user's lifestyle into account. For example, the ordering system can input a prompt to the generative AI such as, "Please customize the ordering method based on the user's current lifestyle," and the generative AI will then customize the ordering method. This allows for the user to place the most optimal order by customizing the ordering method based on their current lifestyle.

[0107] The ordering system estimates the user's emotions and adjusts the order display method based on the estimated emotions. For example, if the user is stressed, the ordering system provides a simple and highly visible display method. If the user is relaxed, the ordering system provides a display method that includes detailed information. If the user is in a hurry, the ordering system provides a concise display method. 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. This allows for the provision of optimal information to the user by adjusting the order display method according to the user's emotions.

[0108] The ordering system selects the optimal ordering method when an order is placed, taking into account the user's geographical location. For example, the ordering system proposes the optimal ordering method considering the delivery services in the user's area. Based on the user's geographical location, the ordering system proposes menus that can be delivered quickly. The ordering system proposes the optimal ordering method considering the ingredients in the user's area. The ordering system can also use a generation AI to select an ordering method that takes geographical location into account. For example, the ordering system can input a prompt to the generation AI saying, "Please select the optimal ordering method based on the user's geographical location," and the generation AI will select an ordering method. This allows for the user to place the most optimal order by selecting the optimal ordering method that takes the user's geographical location into account.

[0109] The ordering system analyzes the user's social media activity and suggests ordering methods when an order is placed. For example, the ordering system suggests the optimal menu based on the user's social media posts about food. The ordering system suggests menus that the user might be interested in based on their social media activity. The ordering system suggests the optimal ordering method based on the user's frequency of social media activity. The ordering system can also analyze social media activity using generative AI. For example, the ordering system can input a prompt to the generative AI such as, "Please suggest ordering methods based on the user's social media activity," and the generative AI will suggest ordering methods. This allows for optimal ordering for the user by analyzing their social media activity and suggesting ordering methods.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The confirmation unit can estimate the user's emotions and adjust the timing of goal setting confirmation based on those emotions. For example, if the user is feeling stressed, the confirmation timing can be delayed so that they can confirm in a relaxed state. If the user is relaxed, the confirmation timing can be brought forward so that they can set goals proactively. Also, if the user is in a hurry, the confirmation timing can be shortened so that they can set goals quickly. In this way, the timing of goal setting confirmation can be adjusted according to the user's emotions, reducing the burden on the user.

[0112] The verification unit can analyze the user's past goal achievement history and propose the optimal goal setting method. For example, it can propose a similar goal setting method based on goals the user has achieved in the past. It can also analyze goals the user has failed to achieve in the past and suggest areas for improvement. Furthermore, it can propose the most effective goal setting method based on the user's past goal achievement history. This allows for the proposal of the optimal goal setting method based on the user's past goal achievement history, thereby increasing the success rate of goal achievement.

[0113] The service provider can estimate the user's emotions and adjust the way advice is presented based on those emotions. For example, if the user is stressed, the advice can be given in gentle language. If the user is relaxed, detailed advice can be provided. If the user is in a hurry, concise advice can be provided. This allows the service provider to adjust the way advice is presented according to the user's emotions, thereby providing the most appropriate advice for the user.

[0114] The analysis unit can perform analysis while considering the user's dietary and weight history. For example, it can analyze the content of meals based on the user's past meal history. It can also analyze weight based on the user's past weight history. Furthermore, it can perform analysis by comprehensively considering the user's dietary and weight history. This improves the accuracy of the analysis by considering the user's dietary and weight history.

[0115] The suggestion function can estimate the user's emotions and determine the priority of the suggested menu items based on those emotions. For example, if the user is feeling stressed, it can prioritize suggesting menu items that are effective in reducing stress. If the user is relaxed, it can prioritize suggesting menu items that are effective in maintaining health. Also, if the user is in a hurry, it can prioritize suggesting menu items that are easy to prepare. In this way, the system can determine the priority of suggested menu items according to the user's emotions and provide the most suitable menu suggestions for the user.

[0116] The ordering system can analyze a user's past order history and suggest the optimal ordering method. For example, it can suggest the most suitable menu based on the user's past order history. It can also prioritize suggesting frequently ordered menu items based on the user's order history. Furthermore, it can analyze the user's order history and suggest menu items that are effective for maintaining good health. In this way, by analyzing a user's past order history and suggesting the optimal ordering method, it becomes possible to make the best possible order for the user.

[0117] The confirmation unit can estimate the user's emotions and determine the priority of goal setting based on those emotions. For example, if the user is feeling stressed, goals prioritizing stress reduction can be set. If the user is relaxed, long-term goals can be prioritized. Also, if the user is in a hurry, short-term goals can be prioritized. This allows for the prioritization of goal setting according to the user's emotions, enabling the user to set optimal goals.

[0118] The service provider can apply different advice algorithms depending on the goal category when providing advice. For example, for weight loss goals, it can provide advice on diet and exercise. For stress management goals, it can provide advice on relaxation methods. For sleep improvement goals, it can provide advice on how to create a suitable sleep environment. This allows the service provider to provide appropriate advice according to the goal category and support the user in achieving their goals.

[0119] The analysis unit can estimate the user's emotions and adjust the order in which the image analysis results are displayed based on the estimated emotions. For example, if the user is stressed, important results can be displayed first. If the user is relaxed, detailed results can be displayed in a sequential manner. Also, if the user is in a hurry, results that summarize the key points can be displayed first. In this way, by adjusting the order in which the image analysis results are displayed according to the user's emotions, it becomes possible to provide the user with the most optimal information.

[0120] The ordering system can select the optimal ordering method by considering the user's geographical location. For example, it can suggest the most suitable ordering method by considering the delivery services available in the user's area. It can also suggest menus that can be delivered quickly based on the user's geographical location. Furthermore, it can suggest the most suitable ordering method by considering the ingredients available in the user's area. By selecting the optimal ordering method based on the user's geographical location, the system enables the user to place the most optimal order.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The verification unit verifies the goal setting. The verification unit accepts the user's freely entered goals during the initial setup, records the goals entered by the user, and checks the progress toward achieving those goals. It can also support the user's goal setting using a generation AI. Step 2: The delivery unit provides advice based on the goals confirmed by the verification unit. The delivery unit provides specific daily advice in voice and text based on the user's goals, and can also use generation AI to generate advice to help the user achieve their goals. Step 3: The analysis unit analyzes the photos of meals and weight. The analysis unit can also perform image analysis using generated AI to automatically record the menu and weight by analyzing the photos of meals and weight uploaded by the user. Step 4: The proposal unit proposes menus based on the data analyzed by the analysis unit. The proposal unit can also analyze past meal data to propose the optimal menu for the next day, and can use generative AI to make menu suggestions. Step 5: The ordering department orders the menu suggested by the suggestion department online. The ordering department can order the suggested menu online, and can also place orders using a generation AI.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the verification unit, provision unit, analysis unit, proposal unit, and order unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the verification unit is implemented by the control unit 46A of the smart device 14 and accepts the user's goal setting. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates advice based on the user's goals. The image analysis unit analyzes photos of meals and weight using the camera 42 of the smart device 14. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes a menu based on past meal data. The order unit is implemented, for example, by the control unit 46A of the smart device 14, and the proposed menu can be ordered online. 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.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the verification unit, provision unit, analysis unit, proposal unit, and order unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the verification unit is implemented by the control unit 46A of the smart glasses 214 and accepts the user's goal setting. The provision unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates advice based on the user's goals. The analysis unit analyzes photos of meals and weight using the camera 42 of the smart glasses 214. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes a menu based on past meal data. The order unit is implemented by the control unit 46A of the smart glasses 214 and allows the proposed menu to be ordered online. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the confirmation unit, provision unit, analysis unit, suggestion unit, and order unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the confirmation unit is implemented by the control unit 46A of the headset terminal 314 and accepts the user's goal setting. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates advice based on the user's goals. The analysis unit analyzes photos of meals and weight using the camera 42 of the headset terminal 314. The suggestion unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and suggests a menu based on past meal data. The order unit is implemented by, for example, the control unit 46A of the headset terminal 314 and allows the suggested menu to be ordered online. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the verification unit, provision unit, analysis unit, proposal unit, and order unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the verification unit is implemented by the control unit 46A of the robot 414 and accepts the user's goal setting. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates advice based on the user's goal. The analysis unit analyzes photos of meals and weight using, for example, the camera 42 of the robot 414. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes a menu based on past meal data. The order unit is implemented by, for example, the control unit 46A of the robot 414 and allows the proposed menu to be ordered online. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A verification unit that confirms the goal setting, A providing unit that provides advice based on the objectives confirmed by the aforementioned verification unit, An analysis unit that analyzes photos of meals and weight, A proposal unit proposes a menu based on the data analyzed by the aforementioned analysis unit, A system comprising: an ordering unit for ordering menus proposed by the aforementioned proposal unit via the internet. (Note 2) The aforementioned verification unit is The system allows users to freely enter their goals during the initial setup. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Provides daily, specific advice in audio and text format based on the user's goals. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, The system automatically records menus and weight by analyzing photos of meals and weight uploaded by users. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, By analyzing past meal data, we suggest the optimal menu for the user the following day. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned order section is, Order the suggested menu online. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned verification unit is The system estimates the user's emotions and adjusts the timing of goal setting confirmations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned verification unit is We analyze the user's past goal achievement history and suggest the optimal goal setting method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned verification unit is When reviewing goal settings, filtering is performed based on the user's current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned verification unit is The system estimates user emotions and prioritizes goal setting based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned verification unit is When reviewing goal settings, the system prioritizes reviewing highly relevant goals by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned verification unit is When reviewing goal setting, we analyze users' social media activity and suggest relevant goals. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, When providing advice, adjust the level of detail based on the importance of the goal. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, When providing advice, different advice algorithms are applied depending on the goal category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, When providing advice, prioritize the advice based on when the goals were set. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing advice, adjust the order of advice based on the relevance of the goals. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of image analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, When analyzing images, consider dietary and weight fluctuation patterns to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During image analysis, the analysis takes into account the user's dietary and weight history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, It estimates the user's emotions and adjusts the order in which the image analysis results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, When analyzing images, the geographical distribution of diet and body weight is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, When analyzing images, we refer to relevant literature on diet and weight to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggested menus based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When proposing menus, we improve the accuracy of our suggestions by considering the interrelationships between different dishes. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When suggesting menus, the system takes into account the user's meal history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, It estimates the user's emotions and adjusts how menu suggestions are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When proposing menus, consider the geographical distribution of the food. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When proposing menus, we refer to relevant literature on diet to improve the accuracy of the suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned order 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 32) The aforementioned order section is, When you place an order, we analyze your past order history and suggest the most suitable ordering method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned order section is, When placing an order, the ordering process is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned order section is, It estimates the user's emotions and adjusts how orders are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned order 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 36) The aforementioned order section is, When you place an order, we analyze your social media activity and suggest ways to place your order. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 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 verification unit that confirms the goal setting, A providing unit that provides advice based on the objectives confirmed by the aforementioned verification unit, An analysis unit that analyzes photos of meals and weight, A proposal unit proposes a menu based on the data analyzed by the aforementioned analysis unit, A system comprising: an ordering unit for ordering menus proposed by the aforementioned proposal unit via the internet.

2. The aforementioned verification unit is The system allows users to freely enter their goals during the initial setup. The system according to feature 1.

3. The aforementioned supply unit is, Provides daily, specific advice in audio and text format based on the user's goals. The system according to feature 1.

4. The aforementioned analysis unit, The system automatically records menus and weight by analyzing photos of meals and weight uploaded by users. The system according to feature 1.

5. The aforementioned proposal section is, By analyzing past meal data, we suggest the optimal menu for the user the following day. The system according to feature 1.

6. The aforementioned order section is, Order the suggested menu online. The system according to feature 1.

7. The aforementioned verification unit is The system estimates the user's emotions and adjusts the timing of goal setting confirmations based on those estimated emotions. The system according to feature 1.

8. The aforementioned verification unit is We analyze the user's past goal achievement history and suggest the optimal goal setting method. The system according to feature 1.

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