system

Abstract

The system according to this embodiment aims to propose optimal meal plans and exercise plans based on individual physical information and goals, and to visualize progress. [Solution] The system according to the embodiment comprises a reception unit, a suggestion unit, a recording unit, and a visualization unit. The reception unit receives the user's physical information and goals. The suggestion unit analyzes the information received by the reception unit and proposes the optimal meal menu and exercise plan. The recording unit records the meals the user has eaten. The visualization unit visualizes progress and a sense of accomplishment based on the data recorded by the recording unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been fully done to propose an optimal diet and exercise plan based on individual physical information and goals and visualize the progress, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal diet menu and exercise plan based on individual physical information and goals and visualize the progress.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a suggestion unit, a recording unit, and a visualization unit. The reception unit receives the user's physical information and goals. The suggestion unit analyzes the information received by the reception unit and proposes an optimal meal menu and exercise plan. The recording unit records the meals the user has eaten. The visualization unit visualizes progress and a sense of accomplishment based on the data recorded by the recording unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose optimal meal plans and exercise plans based on individual physical information and goals, and can visualize progress. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a personalized diet plan called "Smart Diet" that uses an AI agent. In this system, the user inputs individual physical information and goals, and the AI ​​agent analyzes this information to propose the optimal meal menu and exercise plan. Furthermore, by recording what the user eats, the AI ​​agent understands the individual's eating habits and makes further customizations. It also provides a nutritionally balanced meal plan that is appropriate for the season by utilizing information on seasonal ingredients. In addition, Smart Diet can be linked with a healthcare app. Based on the data recorded in the healthcare app, it acquires information such as weight and exercise volume to provide a more accurate plan. It also visualizes the user's progress and sense of accomplishment, and contributes to increased motivation by predicting future weight and body shape from weight and meal records. Through this mechanism, it provides meal and exercise plans tailored to individual needs, and by recording what has been eaten and utilizing seasonal ingredients, it realizes a more attractive and effective diet support. For those aiming to establish healthy eating habits and manage their weight, Smart Diet will be a convenient and reliable partner. For example, if a user inputs "I want to lose 5 kg in 3 months," the AI ​​agent will propose calorie restrictions and exercise volume based on that goal. When a user records their meals with photos, an AI agent analyzes the nutrients and calories and suggests adjustments for the next day. Furthermore, based on weight and exercise data recorded in the healthcare app, it simulates future body shape and visually displays progress to the user. This helps users avoid temptations and maintain motivation. In this way, Smart Diet, with its personalized support from the AI ​​agent, shifts dieting from short-term weight management to establishing healthy habits integrated into daily life. Additionally, community features allow users to interact with each other, sharing recipes and exercise ideas, making health management enjoyable and sustainable. Thus, the Smart Diet system provides meal and exercise plans tailored to the user's individual needs, supporting the establishment of healthy lifestyle habits.

[0029] The smart diet system according to this embodiment comprises a reception unit, a suggestion unit, a recording unit, and a visualization unit. The reception unit receives the user's physical information and goals. Physical information includes, but is not limited to, weight, height, and body fat percentage. Goals include, but are not limited to, weight loss, muscle building, and health maintenance. The reception unit, for example, stores the user's weight and height in a database. The reception unit can also record the goals set by the user and send them to the suggestion unit. The suggestion unit analyzes the information entered by the reception unit and proposes an optimal meal plan and exercise plan. The suggestion unit, for example, uses AI to propose calorie restrictions and exercise levels based on the user's physical information and goals. The suggestion unit, for example, calculates the daily calorie intake based on the user's weight and height and proposes an appropriate meal plan. The suggestion unit can also propose an appropriate exercise plan based on the user's exercise habits. The recording unit records the meals the user has eaten. The recording unit, for example, records the meals the user has eaten with photos and analyzes the nutrients and calories using AI. The recording unit can, for example, input the menu items the user has eaten as text and save them to a database. It can also input the menu items the user has eaten as voice and convert them into text data using voice recognition technology. The visualization unit visualizes progress and a sense of accomplishment based on the data recorded by the recording unit. For example, the visualization unit can display the user's weight and meal records in graphs to visually show progress. For example, the visualization unit can display the user's weight changes numerically to provide a sense of accomplishment. Furthermore, the visualization unit can analyze the user's meal records and predict future weight and body shape. As a result, the smart diet system according to this embodiment can propose optimal meal menus and exercise plans based on the user's physical information and goals, and visualize progress and a sense of accomplishment.

[0030] The reception desk inputs the user's physical information and goals. This physical information includes, but is not limited to, weight, height, and body fat percentage. Specifically, users input this information through a dedicated application using devices such as smartphones or tablets. The application saves the user's input data to a database in real time for use in subsequent processing. Goals include, but are not limited to, weight loss, muscle building, and maintaining health. Users can set specific goals through the application interface. For example, they can input specific goals such as aiming to lose 5 kg in one month or exercising three times a week. The reception desk not only collects and saves this information to the database but can also send it to the suggestion desk. This allows the suggestion desk to obtain basic data to make optimal suggestions based on the user's physical information and goals. Furthermore, the reception desk has a feedback function to verify the accuracy of the data entered by the user. For example, if the entered weight or height is an abnormal value, a message prompting the user to reconfirm can be displayed. This improves data accuracy and enhances the overall reliability of the system.

[0031] The suggestion department analyzes the information entered by the reception department and proposes optimal meal plans and exercise plans. For example, the suggestion department uses AI to suggest calorie restrictions and exercise levels based on the user's physical information and goals. Specifically, the AI ​​calculates the daily calorie intake based on data such as the user's weight, height, body fat percentage, age, and gender. Furthermore, it proposes meal plans suitable for the user's goals, such as weight loss, muscle building, or maintaining health. For example, it proposes a low-calorie, nutritionally balanced meal plan for users aiming to lose weight, and a high-protein, energy-replenishing meal plan for users aiming to build muscle. The suggestion department can also propose appropriate exercise plans based on the user's exercise habits. For example, for a user aiming to exercise three times a week, it proposes specific exercise content, time, and intensity to support the user in continuing without difficulty. The suggestion department notifies the user of these suggestions and makes them available for review through the application. In addition, the suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, by inputting user satisfaction levels and implementation status regarding suggested meal menus and exercise plans, the AI ​​can learn from this data and provide more personalized suggestions. This allows the suggestion system to provide optimal suggestions tailored to the user's needs and support their weight loss success.

[0032] The recording unit records the meals the user has eaten. For example, the recording unit can record the meals the user has eaten with photos and analyze the nutrients and calories using AI. Specifically, the user takes photos of their meals using their smartphone camera and uploads them to the application. The AI ​​analyzes the photos using image recognition technology to identify the ingredients and types of dishes, and calculates the nutrients and calories for each. The recording unit can also input the meals the user has eaten as text and save it to the database. Users can use the application's input form to input the contents and quantity of their meals as text and save it. Furthermore, the recording unit can also input the meals the user has eaten as voice and convert it into text data using voice recognition technology. Users input the contents of their meals as voice using their smartphone's microphone, and the application converts the voice into text and saves it to the database. This allows the recording unit to record the meals the user has eaten in a variety of ways and save it to the database. In addition, the recording unit manages the history of meals the user has eaten and allows them to refer to past meal data. This allows users to understand their eating trends and patterns and find areas for improvement. The recording unit can provide comprehensive support for users' diets by coordinating this data with the proposal and visualization units.

[0033] The visualization unit visualizes progress and a sense of accomplishment based on data recorded by the recording unit. For example, the visualization unit displays the user's weight and meal records in graphs to visually show progress. Specifically, it graphs the user's entered weight data over time, allowing users to see weight changes at a glance. Similarly, it displays calorie intake and nutrient balance in graphs and charts for meal records, allowing users to visually understand their diet. Furthermore, the visualization unit displays the user's weight changes numerically, providing a sense of accomplishment. For example, it displays the difference between the current weight and the target weight numerically, showing how close the user is to their goal. The visualization unit can also analyze the user's meal records and predict future weight and body shape. Using AI, it simulates future weight changes based on past data and presents the prediction results to the user. This allows users to concretely visualize the results of their efforts and maintain motivation. In addition, the visualization unit also provides a function to compare the user's progress with other users. For example, it can anonymously display the progress of other users with the same goal, stimulating a sense of competition and increasing user motivation. This allows the visualization unit to visually display the user's progress and sense of accomplishment, thereby supporting their weight loss success.

[0034] The proposal department can utilize seasonal ingredient information to suggest nutritionally balanced meal plans appropriate for the season. For example, the proposal department can refer to seasonal ingredient lists and propose meal menus that include seasonal ingredients. For example, the proposal department can also suggest meal plans that consider the balance of vitamins, minerals, and proteins based on nutritional value information. For example, the proposal department can analyze the nutritional value of seasonal ingredients and provide meal plans tailored to the user's health condition. In this way, the proposal department can propose nutritionally balanced meal plans appropriate for the season by utilizing seasonal ingredient information.

[0035] The integration unit can acquire data recorded by the healthcare app. For example, the integration unit can acquire weight and exercise data from the healthcare app and store it in a database. The proposal unit can provide a more accurate plan based on the data acquired by the integration unit. For example, the proposal unit can analyze the user's weight changes based on weight data recorded in the healthcare app and propose appropriate meal menus and exercise plans. The proposal unit can also understand the user's exercise habits based on exercise data recorded in the healthcare app and propose an appropriate exercise plan. In this way, the integration unit acquires data recorded in the healthcare app, and the proposal unit can provide a more accurate plan based on the acquired data.

[0036] The prediction unit can predict future weight and body shape based on weight and food records. For example, the prediction unit can predict future weight using a statistical model based on the user's weight data. The prediction unit can also predict future body shape using a machine learning algorithm based on the user's food records. For example, the prediction unit can analyze the user's weight changes and diet to simulate future weight and body shape. In this way, the prediction unit can improve user motivation by predicting future weight and body shape from weight and food records.

[0037] The community section can provide features that allow users to interact with each other and share recipes and exercise ideas. For example, it can offer a chat function, enabling real-time communication between users. It can also offer a forum function, allowing users to post and share recipes and exercise ideas with other users. Furthermore, it can offer an event function, allowing users to participate in and interact at health-related events. Through these features, the community section facilitates the sharing of recipes and exercise ideas among users.

[0038] The suggestion unit can propose calorie restrictions and exercise levels based on the user's physical information and goals. For example, the suggestion unit can calculate daily calorie intake based on the user's weight and height and propose an appropriate meal plan. For example, the suggestion unit can also propose an appropriate exercise plan based on the user's exercise habits. For example, the suggestion unit can adjust calorie restrictions and exercise levels based on the user's goals to provide an optimal plan. In this way, the suggestion unit can propose calorie restrictions and exercise levels based on the user's physical information and goals.

[0039] The reception desk can analyze the user's past physical information and goals to select the optimal input method. For example, the reception desk can provide a simplified input form based on data the user has previously entered. For example, the reception desk can analyze the user's past goal achievement status and suggest appropriate goal settings. For example, the reception desk can prioritize suggesting the user's past input methods (voice, text, etc.). In this way, the reception desk can select the optimal input method by analyzing the user's past physical information and goals.

[0040] The reception desk can filter the user's current health status and lifestyle when they input physical information and goals. For example, the reception desk can suggest appropriate goals based on the user's current health status. For example, the reception desk can set realistic goals considering the user's lifestyle. For example, the reception desk can automatically adjust the input content according to the user's health status. As a result, the reception desk can suggest appropriate goals based on the user's current health status and lifestyle.

[0041] The reception system can prioritize inputting highly relevant information by considering the user's geographical location when they input physical information and goals. For example, if a user is in a specific region, the reception system will prioritize inputting health information related to that region. For example, the reception system will provide information on local food products and exercise facilities based on the user's location. For example, the reception system will prompt the user to input information on local health events based on their location. In this way, the reception system can prioritize inputting highly relevant information by considering the user's geographical location.

[0042] The reception desk can analyze the user's social media activity and input relevant information when they input physical information and goals. For example, the reception desk can analyze the user's interests and concerns regarding health from their social media posts and adjust the input content accordingly. For example, the reception desk can refer to the user's past participation history in health events from their social media activity. For example, the reception desk can suggest collaborative goals considering the user's social media friendships. In this way, the reception desk can input relevant information by analyzing the user's social media activity.

[0043] The proposal department can adjust the level of detail in its proposals based on the importance of the meal plan and exercise plan. For example, it can provide detailed nutritional information for important meal plans, or explain specific implementation methods in detail for important exercise plans, while keeping explanations brief for less important suggestions. This allows the proposal department to adjust the level of detail in its proposals based on the importance of the meal plan and exercise plan.

[0044] The suggestion unit can apply different suggestion algorithms depending on the category of meal menu or exercise plan when making suggestions. For example, for meal menus, the suggestion unit can apply an algorithm that emphasizes nutritional balance. For exercise plans, the suggestion unit can apply an algorithm that is appropriate to the user's fitness level. For example, the suggestion unit can apply different suggestion algorithms depending on specific health goals. In this way, the suggestion unit can apply different suggestion algorithms depending on the category of meal menu or exercise plan.

[0045] The proposal department can prioritize proposals based on the submission timing of meal plans and exercise plans. For example, the proposal department will present urgent proposals first. For example, the proposal department will present proposals with approaching submission deadlines early. For example, the proposal department will postpone proposals with distant submission deadlines. This allows the proposal department to prioritize proposals based on the submission timing of meal plans and exercise plans.

[0046] The suggestion function can adjust the order of suggestions based on the relevance of meal menus and exercise plans. For example, it will prioritize highly relevant suggestions and postpone less relevant suggestions. The suggestion function can also adjust the order of suggestions to present them in the most optimal order for the user. This allows the suggestion function to adjust the order of suggestions based on the relevance of meal menus and exercise plans.

[0047] The recording unit can analyze the user's past dietary and exercise history to select the optimal recording method during recording. For example, the recording unit may provide a simplified recording method based on the user's past dietary history. For example, the recording unit may suggest an appropriate recording method based on the user's past exercise history. For example, the recording unit may select the optimal recording method by referring to the user's past recording methods. In this way, the recording unit can select the optimal recording method by analyzing the user's past dietary and exercise history.

[0048] The recording unit can customize the recording method based on the user's current living situation during recording. For example, the recording unit can suggest an appropriate recording method according to the user's current living situation. For example, the recording unit can provide a recording method that is manageable considering the user's lifestyle. For example, the recording unit can automatically adjust the recording content according to the user's living situation. In this way, the recording unit can customize the recording method based on the user's current living situation.

[0049] The recording unit can select the optimal recording method while considering the user's geographical location information. For example, if the user is in a specific region, the recording unit can record information about meals and exercise related to that region. For example, the recording unit can record information about local food products and exercise facilities based on the user's location information. For example, the recording unit can record information about local health events based on the user's location information. In this way, the recording unit can select the optimal recording method while considering the user's geographical location information.

[0050] The recording unit can analyze the user's social media activity during recording and suggest recording methods. For example, the recording unit can analyze the user's interests and concerns regarding health from their social media posts and adjust the recording content accordingly. For example, the recording unit can refer to the user's past participation history in health events from their social media activity. For example, the recording unit can suggest collaborative recording by considering the user's social media friendships. In this way, the recording unit can suggest recording methods by analyzing the user's social media activity.

[0051] The visualization unit can select the optimal visualization method by referring to the user's past progress data during visualization. For example, the visualization unit visually shows progress using graphs and charts based on the user's past progress data. For example, the visualization unit provides visualization methods that enhance motivation by referring to the user's past achievements. For example, the visualization unit analyzes the user's past data and selects the optimal visualization method. In this way, the visualization unit can select the optimal visualization method by referring to the user's past progress data.

[0052] The visualization unit can customize the visualization method based on the user's current health status during visualization. For example, the visualization unit proposes an appropriate visualization method according to the user's current health status. For example, the visualization unit provides a visualization method that is not burdensome, taking the user's health status into consideration. For example, the visualization unit automatically adjusts the visualization content according to the user's health status. This allows the visualization unit to customize the visualization method based on the user's current health status.

[0053] The visualization unit can select the optimal visualization method by considering the user's geographical location information during visualization. For example, if the user is in a specific region, the visualization unit can visualize health information related to that region. For example, the visualization unit can visualize information on local food products and exercise facilities based on the user's location information. For example, the visualization unit can visualize information on local health events based on the user's location information. In this way, the visualization unit can select the optimal visualization method by considering the user's geographical location information.

[0054] The visualization unit can analyze the user's social media activity and propose visualization methods during the visualization process. For example, the visualization unit can analyze the user's interest in health from their social media posts and adjust the visualization content. For example, the visualization unit can refer to the user's past participation history in health events from their social media activity. For example, the visualization unit can propose collaborative visualization by considering the user's social media friendships. In this way, the visualization unit can propose visualization methods by analyzing the user's social media activity.

[0055] The integration unit can optimize its integration algorithm by referring to past integration data during integration. For example, the integration unit selects the optimal integration algorithm based on past integration data. For example, the integration unit analyzes past integration history to improve integration efficiency. For example, the integration unit optimizes the integration method by referring to past integration data. In this way, the integration unit can optimize its integration algorithm by referring to past integration data.

[0056] The integration unit can weight the integrated data based on the submission timing of the healthcare app data during integration. For example, the integration unit will give higher weight to data submitted recently, and lower weight to data submitted later. The integration unit can adjust the weighting of the integrated data according to the submission timing. This allows the integration unit to weight the integrated data based on the submission timing of the healthcare app data.

[0057] The prediction unit can analyze the user's past weight and dietary records to select the optimal prediction method during the prediction process. For example, the prediction unit selects the optimal prediction method based on the user's past weight records. For example, the prediction unit proposes an appropriate prediction method based on the user's past dietary records. For example, the prediction unit selects the optimal prediction method by referring to the user's past records. In this way, the prediction unit can select the optimal prediction method by analyzing the user's past weight and dietary records.

[0058] The prediction unit can customize its prediction methods based on the user's current living situation. For example, the prediction unit proposes an appropriate prediction method according to the user's current living situation. For example, the prediction unit provides a reasonable prediction method that takes into account the user's lifestyle. For example, the prediction unit automatically adjusts the prediction content according to the user's living situation. In this way, the prediction unit can customize its prediction methods based on the user's current living situation.

[0059] The prediction unit can select the optimal prediction method by considering the user's geographical location information during prediction. For example, if the user is in a specific region, the prediction unit can predict health information related to that region. For example, based on the user's location information, the prediction unit can predict information about local food products and exercise facilities. For example, based on the user's location information, the prediction unit can predict information about local health events. In this way, the prediction unit can select the optimal prediction method by considering the user's geographical location information.

[0060] The prediction unit can analyze the user's social media activity and propose prediction methods during the prediction process. For example, the prediction unit can analyze the user's interest in health from their social media posts and adjust the prediction content. For example, the prediction unit can refer to the user's past participation history in health events from their social media activity. For example, the prediction unit can propose collaborative predictions by considering the user's social media friendships. In this way, the prediction unit can propose prediction methods by analyzing the user's social media activity.

[0061] The community department can select the optimal interaction method by referring to the user's past interaction history during community interactions. For example, the community department selects the optimal interaction method based on the user's past interaction history. For example, the community department analyzes the user's past interaction history to improve the efficiency of interactions. For example, the community department selects the optimal interaction method by referring to the user's past interaction history. This allows the community department to select the optimal interaction method by referring to the user's past interaction history.

[0062] The Community Department can select the most appropriate method of interaction during community engagement, taking into account the user's geographical location. For example, if a user is in a specific area, the Community Department can exchange health information related to that area. For example, based on the user's location, the Community Department can exchange information about local food products and exercise facilities. For example, based on the user's location, the Community Department can exchange information about local health events. This allows the Community Department to select the most appropriate method of interaction, taking into account the user's geographical location.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The suggestion unit can take into account the user's allergy information when suggesting meal menus and exercise plans. For example, the suggestion unit can suggest allergen-free meal menus based on the allergy information entered by the user. The suggestion unit can also store the user's allergy information in a database and refer to it when making suggestions. Furthermore, the suggestion unit can also suggest allergen-free exercise plans based on the user's allergy information. In this way, the suggestion unit can provide safer and more appropriate meal menus and exercise plans by taking the user's allergy information into consideration.

[0065] The recording unit can utilize voice input to record the user's meals and exercise. For example, the recording unit allows the user to input the menu they ate and the exercise they performed by voice, and converts it into text data using speech recognition technology. The recording unit can also store the voice-input data in a database for later reference. Furthermore, the recording unit can analyze the voice-input data and automatically calculate nutrients and calories. As a result, the recording unit makes it easy for users to record their meals and exercise using voice input.

[0066] The suggestion function can consider the user's cultural background when suggesting meal menus and exercise plans. For example, the suggestion function can suggest meal menus that include familiar ingredients and dishes based on the user's cultural background. The suggestion function can also suggest traditional exercises and workouts based on the user's cultural background. Furthermore, the suggestion function can provide advice on diet and exercise according to the user's cultural background. This allows the suggestion function to provide more appropriate meal menus and exercise plans by taking the user's cultural background into consideration.

[0067] The recording unit can utilize photo input to record the user's meals and exercise. For example, the recording unit can record the menu the user ate and the exercise they performed with photos, and analyze the content using image recognition technology. The recording unit can, for example, save the photo-input data to a database for later reference. The recording unit can also analyze the photo-input data and automatically calculate nutrients and calories. In this way, the recording unit can easily record the user's meals and exercise using photo input.

[0068] The visualization unit can incorporate gamification elements in visualizing user progress and sense of accomplishment. For example, the visualization unit can award badges or points according to user progress. For example, the visualization unit can display rankings or leaderboards to enhance user sense of accomplishment. For example, the visualization unit can offer rewards or benefits according to user goal achievement. In this way, the visualization unit can improve user motivation by incorporating gamification elements.

[0069] The integration unit can enhance data privacy protection when integrating data with users' healthcare apps. For example, the integration unit can protect data using encryption technology during data integration. For example, the integration unit can integrate only the necessary data after obtaining the user's consent. For example, the integration unit can also record the history of data integration and make it available for the user to review. In this way, the integration unit can enhance user confidence by strengthening data privacy protection.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The reception desk inputs the user's physical information and goals. Physical information includes, for example, weight, height, and body fat percentage. Goals include, for example, weight loss, muscle building, and maintaining health. The reception desk can save the weight and height entered by the user to a database, record the goals set by the user, and send them to the suggestion department. Step 2: The suggestion department analyzes the information entered by the reception department and proposes the optimal meal plan and exercise plan. The suggestion department uses AI to propose calorie restrictions and exercise levels based on the user's physical information and goals. For example, it calculates the daily calorie intake based on the user's weight and height and proposes an appropriate meal plan. It can also propose an appropriate exercise plan based on the user's exercise habits. Step 3: The recording unit records the menu items the user has eaten. The recording unit records the menu items the user has eaten with photos and uses AI to analyze nutrients and calories. Users can also input the menu items they have eaten as text and save them to the database. Furthermore, users can input the menu items they have eaten by voice and convert them into text data using speech recognition technology. Step 4: The visualization unit visualizes progress and a sense of accomplishment based on the data recorded by the recording unit. The visualization unit displays the user's weight and meal records in graphs, visually showing progress. It displays the user's weight changes numerically, providing a sense of accomplishment. It can also analyze the user's meal records and predict future weight and body shape.

[0072] (Example of form 2) The system according to an embodiment of the present invention is a personalized diet plan called "Smart Diet" that uses an AI agent. In this system, the user inputs individual physical information and goals, and the AI ​​agent analyzes this information to propose the optimal meal menu and exercise plan. Furthermore, by recording what the user eats, the AI ​​agent understands the individual's eating habits and makes further customizations. It also provides a nutritionally balanced meal plan that is appropriate for the season by utilizing information on seasonal ingredients. In addition, Smart Diet can be linked with a healthcare app. Based on the data recorded in the healthcare app, it acquires information such as weight and exercise volume to provide a more accurate plan. It also visualizes the user's progress and sense of accomplishment, and contributes to increased motivation by predicting future weight and body shape from weight and meal records. Through this mechanism, it provides meal and exercise plans tailored to individual needs, and by recording what has been eaten and utilizing seasonal ingredients, it realizes a more attractive and effective diet support. For those aiming to establish healthy eating habits and manage their weight, Smart Diet will be a convenient and reliable partner. For example, if a user inputs "I want to lose 5 kg in 3 months," the AI ​​agent will propose calorie restrictions and exercise volume based on that goal. When a user records their meals with photos, an AI agent analyzes the nutrients and calories and suggests adjustments for the next day. Furthermore, based on weight and exercise data recorded in the healthcare app, it simulates future body shape and visually displays progress to the user. This helps users avoid temptations and maintain motivation. In this way, Smart Diet, with its personalized support from the AI ​​agent, shifts dieting from short-term weight management to establishing healthy habits integrated into daily life. Additionally, community features allow users to interact with each other, sharing recipes and exercise ideas, making health management enjoyable and sustainable. Thus, the Smart Diet system provides meal and exercise plans tailored to the user's individual needs, supporting the establishment of healthy lifestyle habits.

[0073] The smart diet system according to this embodiment comprises a reception unit, a suggestion unit, a recording unit, and a visualization unit. The reception unit receives the user's physical information and goals. Physical information includes, but is not limited to, weight, height, and body fat percentage. Goals include, but are not limited to, weight loss, muscle building, and health maintenance. The reception unit, for example, stores the user's weight and height in a database. The reception unit can also record the goals set by the user and send them to the suggestion unit. The suggestion unit analyzes the information entered by the reception unit and proposes an optimal meal plan and exercise plan. The suggestion unit, for example, uses AI to propose calorie restrictions and exercise levels based on the user's physical information and goals. The suggestion unit, for example, calculates the daily calorie intake based on the user's weight and height and proposes an appropriate meal plan. The suggestion unit can also propose an appropriate exercise plan based on the user's exercise habits. The recording unit records the meals the user has eaten. The recording unit, for example, records the meals the user has eaten with photos and analyzes the nutrients and calories using AI. The recording unit can, for example, input the menu items the user has eaten as text and save them to a database. It can also input the menu items the user has eaten as voice and convert them into text data using voice recognition technology. The visualization unit visualizes progress and a sense of accomplishment based on the data recorded by the recording unit. For example, the visualization unit can display the user's weight and meal records in graphs to visually show progress. For example, the visualization unit can display the user's weight changes numerically to provide a sense of accomplishment. Furthermore, the visualization unit can analyze the user's meal records and predict future weight and body shape. As a result, the smart diet system according to this embodiment can propose optimal meal menus and exercise plans based on the user's physical information and goals, and visualize progress and a sense of accomplishment.

[0074] The reception desk inputs the user's physical information and goals. This physical information includes, but is not limited to, weight, height, and body fat percentage. Specifically, users input this information through a dedicated application using devices such as smartphones or tablets. The application saves the user's input data to a database in real time for use in subsequent processing. Goals include, but are not limited to, weight loss, muscle building, and maintaining health. Users can set specific goals through the application interface. For example, they can input specific goals such as aiming to lose 5 kg in one month or exercising three times a week. The reception desk not only collects and saves this information to the database but can also send it to the suggestion desk. This allows the suggestion desk to obtain basic data to make optimal suggestions based on the user's physical information and goals. Furthermore, the reception desk has a feedback function to verify the accuracy of the data entered by the user. For example, if the entered weight or height is an abnormal value, a message prompting the user to reconfirm can be displayed. This improves data accuracy and enhances the overall reliability of the system.

[0075] The suggestion department analyzes the information entered by the reception department and proposes optimal meal plans and exercise plans. For example, the suggestion department uses AI to suggest calorie restrictions and exercise levels based on the user's physical information and goals. Specifically, the AI ​​calculates the daily calorie intake based on data such as the user's weight, height, body fat percentage, age, and gender. Furthermore, it proposes meal plans suitable for the user's goals, such as weight loss, muscle building, or maintaining health. For example, it proposes a low-calorie, nutritionally balanced meal plan for users aiming to lose weight, and a high-protein, energy-replenishing meal plan for users aiming to build muscle. The suggestion department can also propose appropriate exercise plans based on the user's exercise habits. For example, for a user aiming to exercise three times a week, it proposes specific exercise content, time, and intensity to support the user in continuing without difficulty. The suggestion department notifies the user of these suggestions and makes them available for review through the application. In addition, the suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, by inputting user satisfaction levels and implementation status regarding suggested meal menus and exercise plans, the AI ​​can learn from this data and provide more personalized suggestions. This allows the suggestion system to provide optimal suggestions tailored to the user's needs and support their weight loss success.

[0076] The recording unit records the meals the user has eaten. For example, the recording unit can record the meals the user has eaten with photos and analyze the nutrients and calories using AI. Specifically, the user takes photos of their meals using their smartphone camera and uploads them to the application. The AI ​​analyzes the photos using image recognition technology to identify the ingredients and types of dishes, and calculates the nutrients and calories for each. The recording unit can also input the meals the user has eaten as text and save it to the database. Users can use the application's input form to input the contents and quantity of their meals as text and save it. Furthermore, the recording unit can also input the meals the user has eaten as voice and convert it into text data using voice recognition technology. Users input the contents of their meals as voice using their smartphone's microphone, and the application converts the voice into text and saves it to the database. This allows the recording unit to record the meals the user has eaten in a variety of ways and save it to the database. In addition, the recording unit manages the history of meals the user has eaten and allows them to refer to past meal data. This allows users to understand their eating trends and patterns and find areas for improvement. The recording unit can provide comprehensive support for users' diets by coordinating this data with the proposal and visualization units.

[0077] The visualization unit visualizes progress and a sense of accomplishment based on data recorded by the recording unit. For example, the visualization unit displays the user's weight and meal records in graphs to visually show progress. Specifically, it graphs the user's entered weight data over time, allowing users to see weight changes at a glance. Similarly, it displays calorie intake and nutrient balance in graphs and charts for meal records, allowing users to visually understand their diet. Furthermore, the visualization unit displays the user's weight changes numerically, providing a sense of accomplishment. For example, it displays the difference between the current weight and the target weight numerically, showing how close the user is to their goal. The visualization unit can also analyze the user's meal records and predict future weight and body shape. Using AI, it simulates future weight changes based on past data and presents the prediction results to the user. This allows users to concretely visualize the results of their efforts and maintain motivation. In addition, the visualization unit also provides a function to compare the user's progress with other users. For example, it can anonymously display the progress of other users with the same goal, stimulating a sense of competition and increasing user motivation. This allows the visualization unit to visually display the user's progress and sense of accomplishment, thereby supporting their weight loss success.

[0078] The proposal department can utilize seasonal ingredient information to suggest nutritionally balanced meal plans appropriate for the season. For example, the proposal department can refer to seasonal ingredient lists and propose meal menus that include seasonal ingredients. For example, the proposal department can also suggest meal plans that consider the balance of vitamins, minerals, and proteins based on nutritional value information. For example, the proposal department can analyze the nutritional value of seasonal ingredients and provide meal plans tailored to the user's health condition. In this way, the proposal department can propose nutritionally balanced meal plans appropriate for the season by utilizing seasonal ingredient information.

[0079] The integration unit can acquire data recorded by the healthcare app. For example, the integration unit can acquire weight and exercise data from the healthcare app and store it in a database. The proposal unit can provide a more accurate plan based on the data acquired by the integration unit. For example, the proposal unit can analyze the user's weight changes based on weight data recorded in the healthcare app and propose appropriate meal menus and exercise plans. The proposal unit can also understand the user's exercise habits based on exercise data recorded in the healthcare app and propose an appropriate exercise plan. In this way, the integration unit acquires data recorded in the healthcare app, and the proposal unit can provide a more accurate plan based on the acquired data.

[0080] The prediction unit can predict future weight and body shape based on weight and food records. For example, the prediction unit can predict future weight using a statistical model based on the user's weight data. The prediction unit can also predict future body shape using a machine learning algorithm based on the user's food records. For example, the prediction unit can analyze the user's weight changes and diet to simulate future weight and body shape. In this way, the prediction unit can improve user motivation by predicting future weight and body shape from weight and food records.

[0081] The community section can provide features that allow users to interact with each other and share recipes and exercise ideas. For example, it can offer a chat function, enabling real-time communication between users. It can also offer a forum function, allowing users to post and share recipes and exercise ideas with other users. Furthermore, it can offer an event function, allowing users to participate in and interact at health-related events. Through these features, the community section facilitates the sharing of recipes and exercise ideas among users.

[0082] The suggestion unit can propose calorie restrictions and exercise levels based on the user's physical information and goals. For example, the suggestion unit can calculate daily calorie intake based on the user's weight and height and propose an appropriate meal plan. For example, the suggestion unit can also propose an appropriate exercise plan based on the user's exercise habits. For example, the suggestion unit can adjust calorie restrictions and exercise levels based on the user's goals to provide an optimal plan. In this way, the suggestion unit can propose calorie restrictions and exercise levels based on the user's physical information and goals.

[0083] The reception system can estimate the user's emotions and adjust the timing of inputting physical information and goals based on the estimated emotions. For example, if the user is feeling stressed, the reception system will prompt them to input information at a time when they can relax. For example, if the user is highly motivated, the reception system will prompt them to input information immediately and set goals. For example, if the user is tired, the reception system will send a notification prompting them to input information after resting. In this way, the reception system can adjust the timing of inputting physical information and goals according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The reception desk can analyze the user's past physical information and goals to select the optimal input method. For example, the reception desk can provide a simplified input form based on data the user has previously entered. For example, the reception desk can analyze the user's past goal achievement status and suggest appropriate goal settings. For example, the reception desk can prioritize suggesting the user's past input methods (voice, text, etc.). In this way, the reception desk can select the optimal input method by analyzing the user's past physical information and goals.

[0085] The reception desk can filter the user's current health status and lifestyle when they input physical information and goals. For example, the reception desk can suggest appropriate goals based on the user's current health status. For example, the reception desk can set realistic goals considering the user's lifestyle. For example, the reception desk can automatically adjust the input content according to the user's health status. As a result, the reception desk can suggest appropriate goals based on the user's current health status and lifestyle.

[0086] The reception desk can estimate the user's emotions and prioritize the information to be entered based on those emotions. For example, if the user is stressed, the reception desk will prioritize entering only important information. If the user is relaxed, the reception desk will prioritize entering detailed information. If the user is in a hurry, the reception desk will minimize the amount of information that needs to be entered. In this way, the reception desk can prioritize the information to be entered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The reception system can prioritize inputting highly relevant information by considering the user's geographical location when they input physical information and goals. For example, if a user is in a specific region, the reception system will prioritize inputting health information related to that region. For example, the reception system will provide information on local food products and exercise facilities based on the user's location. For example, the reception system will prompt the user to input information on local health events based on their location. In this way, the reception system can prioritize inputting highly relevant information by considering the user's geographical location.

[0088] The reception desk can analyze the user's social media activity and input relevant information when they input physical information and goals. For example, the reception desk can analyze the user's interests and concerns regarding health from their social media posts and adjust the input content accordingly. For example, the reception desk can refer to the user's past participation history in health events from their social media activity. For example, the reception desk can suggest collaborative goals considering the user's social media friendships. In this way, the reception desk can input relevant information by analyzing the user's social media activity.

[0089] The suggestion function can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is stressed, the suggestion function will present simple and easy-to-understand suggestions. If the user is relaxed, the suggestion function will present suggestions with detailed explanations. If the user is in a hurry, the suggestion function will present concise suggestions that get straight to the point. In this way, the suggestion function can adjust the way it presents its suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The proposal department can adjust the level of detail in its proposals based on the importance of the meal plan and exercise plan. For example, it can provide detailed nutritional information for important meal plans, or explain specific implementation methods in detail for important exercise plans, while keeping explanations brief for less important suggestions. This allows the proposal department to adjust the level of detail in its proposals based on the importance of the meal plan and exercise plan.

[0091] The suggestion unit can apply different suggestion algorithms depending on the category of meal menu or exercise plan when making suggestions. For example, for meal menus, the suggestion unit can apply an algorithm that emphasizes nutritional balance. For exercise plans, the suggestion unit can apply an algorithm that is appropriate to the user's fitness level. For example, the suggestion unit can apply different suggestion algorithms depending on specific health goals. In this way, the suggestion unit can apply different suggestion algorithms depending on the category of meal menu or exercise plan.

[0092] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will provide short, concise suggestions. If the user is relaxed, the suggestion unit will provide longer suggestions with detailed explanations. If the user is excited, the suggestion unit will provide suggestions with visually stimulating effects. In this way, the suggestion unit can adjust the length of suggestions according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The proposal department can prioritize proposals based on the submission timing of meal plans and exercise plans. For example, the proposal department will present urgent proposals first. For example, the proposal department will present proposals with approaching submission deadlines early. For example, the proposal department will postpone proposals with distant submission deadlines. This allows the proposal department to prioritize proposals based on the submission timing of meal plans and exercise plans.

[0094] The suggestion function can adjust the order of suggestions based on the relevance of meal menus and exercise plans. For example, it will prioritize highly relevant suggestions and postpone less relevant suggestions. The suggestion function can also adjust the order of suggestions to present them in the most optimal order for the user. This allows the suggestion function to adjust the order of suggestions based on the relevance of meal menus and exercise plans.

[0095] The recording unit can estimate the user's emotions and adjust the recording method based on the estimated emotions. For example, if the user is stressed, the recording unit provides a simple recording method. For example, if the user is relaxed, the recording unit provides a detailed recording method. For example, if the user is in a hurry, the recording unit provides a voice input recording method. In this way, the recording unit can adjust the recording method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The recording unit can analyze the user's past dietary and exercise history to select the optimal recording method during recording. For example, the recording unit may provide a simplified recording method based on the user's past dietary history. For example, the recording unit may suggest an appropriate recording method based on the user's past exercise history. For example, the recording unit may select the optimal recording method by referring to the user's past recording methods. In this way, the recording unit can select the optimal recording method by analyzing the user's past dietary and exercise history.

[0097] The recording unit can customize the recording method based on the user's current living situation during recording. For example, the recording unit can suggest an appropriate recording method according to the user's current living situation. For example, the recording unit can provide a recording method that is manageable considering the user's lifestyle. For example, the recording unit can automatically adjust the recording content according to the user's living situation. In this way, the recording unit can customize the recording method based on the user's current living situation.

[0098] The recording unit can estimate the user's emotions and determine recording priorities based on the estimated emotions. For example, if the user is stressed, the recording unit will prioritize recording only important information. If the user is relaxed, the recording unit will record detailed information. If the user is in a hurry, the recording unit will minimize the amount of recording needed. In this way, the recording unit can determine recording priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The recording unit can select the optimal recording method while considering the user's geographical location information. For example, if the user is in a specific region, the recording unit can record information about meals and exercise related to that region. For example, the recording unit can record information about local food products and exercise facilities based on the user's location information. For example, the recording unit can record information about local health events based on the user's location information. In this way, the recording unit can select the optimal recording method while considering the user's geographical location information.

[0100] The recording unit can analyze the user's social media activity during recording and suggest recording methods. For example, the recording unit can analyze the user's interests and concerns regarding health from their social media posts and adjust the recording content accordingly. For example, the recording unit can refer to the user's past participation history in health events from their social media activity. For example, the recording unit can suggest collaborative recording by considering the user's social media friendships. In this way, the recording unit can suggest recording methods by analyzing the user's social media activity.

[0101] The visualization unit can estimate the user's emotions and adjust the visualization method based on the estimated emotions. For example, if the user is stressed, the visualization unit provides a simple and easy-to-understand visualization method. For example, if the user is relaxed, the visualization unit provides a visualization method that includes detailed information. For example, if the user is in a hurry, the visualization unit provides a visualization method that gets straight to the point. In this way, the visualization unit can adjust the visualization method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The visualization unit can select the optimal visualization method by referring to the user's past progress data during visualization. For example, the visualization unit visually shows progress using graphs and charts based on the user's past progress data. For example, the visualization unit provides visualization methods that enhance motivation by referring to the user's past achievements. For example, the visualization unit analyzes the user's past data and selects the optimal visualization method. In this way, the visualization unit can select the optimal visualization method by referring to the user's past progress data.

[0103] The visualization unit can customize the visualization method based on the user's current health status during visualization. For example, the visualization unit proposes an appropriate visualization method according to the user's current health status. For example, the visualization unit provides a visualization method that is not burdensome, taking the user's health status into consideration. For example, the visualization unit automatically adjusts the visualization content according to the user's health status. This allows the visualization unit to customize the visualization method based on the user's current health status.

[0104] The visualization unit can estimate the user's emotions and determine the visualization priority based on the estimated emotions. For example, if the user is stressed, the visualization unit will prioritize visualizing only important information. If the user is relaxed, the visualization unit will visualize detailed information. If the user is in a hurry, the visualization unit will visualize minimal information. In this way, the visualization unit can determine the visualization priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The visualization unit can select the optimal visualization method by considering the user's geographical location information during visualization. For example, if the user is in a specific region, the visualization unit can visualize health information related to that region. For example, the visualization unit can visualize information on local food products and exercise facilities based on the user's location information. For example, the visualization unit can visualize information on local health events based on the user's location information. In this way, the visualization unit can select the optimal visualization method by considering the user's geographical location information.

[0106] The visualization unit can analyze the user's social media activity and propose visualization methods during the visualization process. For example, the visualization unit can analyze the user's interest in health from their social media posts and adjust the visualization content. For example, the visualization unit can refer to the user's past participation history in health events from their social media activity. For example, the visualization unit can propose collaborative visualization by considering the user's social media friendships. In this way, the visualization unit can propose visualization methods by analyzing the user's social media activity.

[0107] The integration unit can estimate the user's emotions and select the data to integrate based on the estimated emotions. For example, if the user is stressed, the integration unit will integrate only the most important data. If the user is relaxed, the integration unit will integrate detailed data. If the user is in a hurry, the integration unit will integrate minimal data. In this way, the integration unit can select the data to integrate according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The integration unit can optimize its integration algorithm by referring to past integration data during integration. For example, the integration unit selects the optimal integration algorithm based on past integration data. For example, the integration unit analyzes past integration history to improve integration efficiency. For example, the integration unit optimizes the integration method by referring to past integration data. In this way, the integration unit can optimize its integration algorithm by referring to past integration data.

[0109] The interaction unit can estimate the user's emotions and adjust the frequency of interaction based on the estimated emotions. For example, if the user is stressed, the interaction unit will reduce the frequency of interaction. For example, if the user is relaxed, the interaction unit will increase the frequency of interaction. For example, if the user is in a hurry, the interaction unit will minimize the frequency of interaction. In this way, the interaction unit can adjust the frequency of interaction according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The integration unit can weight the integrated data based on the submission timing of the healthcare app data during integration. For example, the integration unit will give higher weight to data submitted recently, and lower weight to data submitted later. The integration unit can adjust the weighting of the integrated data according to the submission timing. This allows the integration unit to weight the integrated data based on the submission timing of the healthcare app data.

[0111] The prediction unit can estimate the user's emotions and adjust its prediction method based on the estimated emotions. For example, if the user is stressed, the prediction unit provides a simple and easy-to-understand prediction method. For example, if the user is relaxed, the prediction unit provides a detailed prediction method. For example, if the user is in a hurry, the prediction unit provides a concise prediction method. In this way, the prediction unit can adjust its prediction method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0112] The prediction unit can analyze the user's past weight and dietary records to select the optimal prediction method during the prediction process. For example, the prediction unit selects the optimal prediction method based on the user's past weight records. For example, the prediction unit proposes an appropriate prediction method based on the user's past dietary records. For example, the prediction unit selects the optimal prediction method by referring to the user's past records. In this way, the prediction unit can select the optimal prediction method by analyzing the user's past weight and dietary records.

[0113] The prediction unit can customize its prediction methods based on the user's current living situation. For example, the prediction unit proposes an appropriate prediction method according to the user's current living situation. For example, the prediction unit provides a reasonable prediction method that takes into account the user's lifestyle. For example, the prediction unit automatically adjusts the prediction content according to the user's living situation. In this way, the prediction unit can customize its prediction methods based on the user's current living situation.

[0114] The prediction unit can estimate the user's emotions and determine the priority of predictions based on the estimated emotions. For example, if the user is stressed, the prediction unit will prioritize only important predictions. If the user is relaxed, the prediction unit will make detailed predictions. If the user is in a hurry, the prediction unit will minimize the number of predictions. In this way, the prediction unit can determine the priority of predictions according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] The prediction unit can select the optimal prediction method by considering the user's geographical location information during prediction. For example, if the user is in a specific region, the prediction unit can predict health information related to that region. For example, based on the user's location information, the prediction unit can predict information about local food products and exercise facilities. For example, based on the user's location information, the prediction unit can predict information about local health events. In this way, the prediction unit can select the optimal prediction method by considering the user's geographical location information.

[0116] The prediction unit can analyze the user's social media activity and propose prediction methods during the prediction process. For example, the prediction unit can analyze the user's interest in health from their social media posts and adjust the prediction content. For example, the prediction unit can refer to the user's past participation history in health events from their social media activity. For example, the prediction unit can propose collaborative predictions by considering the user's social media friendships. In this way, the prediction unit can propose prediction methods by analyzing the user's social media activity.

[0117] The community unit can estimate users' emotions and adjust community interaction methods based on those estimated emotions. For example, if a user is stressed, the community unit provides simple and easy-to-understand interaction methods. If a user is relaxed, the community unit provides interaction methods that include detailed information. If a user is in a hurry, the community unit provides interaction methods that get straight to the point. In this way, the community unit can adjust community interaction methods according to users' emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0118] The community department can select the optimal interaction method by referring to the user's past interaction history during community interactions. For example, the community department selects the optimal interaction method based on the user's past interaction history. For example, the community department analyzes the user's past interaction history to improve the efficiency of interactions. For example, the community department selects the optimal interaction method by referring to the user's past interaction history. This allows the community department to select the optimal interaction method by referring to the user's past interaction history.

[0119] The community department can estimate a user's emotions and prioritize communities based on those emotions. For example, if a user is stressed, the community department will prioritize only important interactions. If a user is relaxed, the community department will engage in more detailed interactions. If a user is in a hurry, the community department will minimize interactions. This allows the community department to prioritize communities according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0120] The Community Department can select the most appropriate method of interaction during community engagement, taking into account the user's geographical location. For example, if a user is in a specific area, the Community Department can exchange health information related to that area. For example, based on the user's location, the Community Department can exchange information about local food products and exercise facilities. For example, based on the user's location, the Community Department can exchange information about local health events. This allows the Community Department to select the most appropriate method of interaction, taking into account the user's geographical location.

[0121] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0122] The suggestion unit can take into account the user's allergy information when suggesting meal menus and exercise plans. For example, the suggestion unit can suggest allergen-free meal menus based on the allergy information entered by the user. The suggestion unit can also store the user's allergy information in a database and refer to it when making suggestions. Furthermore, the suggestion unit can also suggest allergen-free exercise plans based on the user's allergy information. In this way, the suggestion unit can provide safer and more appropriate meal menus and exercise plans by taking the user's allergy information into consideration.

[0123] The suggestion unit can estimate the user's emotions and adjust the suggested meal menus and exercise plans based on those emotions. For example, if the user is feeling stressed, the suggestion unit will suggest relaxing meal menus and light exercise plans. If the user is highly motivated, the suggestion unit will suggest challenging exercise plans. If the user is tired, the suggestion unit will suggest recovery-promoting meal menus and relaxing exercise plans. In this way, the suggestion unit can provide optimal meal menus and exercise plans according to the user's emotions.

[0124] The recording unit can utilize voice input to record the user's meals and exercise. For example, the recording unit allows the user to input the menu they ate and the exercise they performed by voice, and converts it into text data using speech recognition technology. The recording unit can also store the voice-input data in a database for later reference. Furthermore, the recording unit can analyze the voice-input data and automatically calculate nutrients and calories. As a result, the recording unit makes it easy for users to record their meals and exercise using voice input.

[0125] The visualization unit can estimate the user's emotions and adjust the visualization method for progress and sense of accomplishment based on the estimated emotions. For example, if the user is stressed, the visualization unit provides simple and easy-to-understand graphs and charts. For example, if the user is relaxed, the visualization unit provides a visualization method that includes detailed data. For example, if the user is in a hurry, the visualization unit provides a concise visualization method that gets straight to the point. In this way, the visualization unit can provide the optimal visualization method according to the user's emotions.

[0126] The integration unit can estimate the user's emotions and adjust the frequency of data exchange with the healthcare app based on the estimated emotions. For example, if the user is stressed, the integration unit will reduce the frequency of data exchange. For example, if the user is relaxed, the integration unit will increase the frequency of data exchange. For example, if the user is in a hurry, the integration unit will perform minimal data exchange. In this way, the integration unit can adjust the frequency of data exchange with the healthcare app according to the user's emotions.

[0127] The suggestion function can consider the user's cultural background when suggesting meal menus and exercise plans. For example, the suggestion function can suggest meal menus that include familiar ingredients and dishes based on the user's cultural background. The suggestion function can also suggest traditional exercises and workouts based on the user's cultural background. Furthermore, the suggestion function can provide advice on diet and exercise according to the user's cultural background. This allows the suggestion function to provide more appropriate meal menus and exercise plans by taking the user's cultural background into consideration.

[0128] The recording unit can utilize photo input to record the user's meals and exercise. For example, the recording unit can record the menu the user ate and the exercise they performed with photos, and analyze the content using image recognition technology. The recording unit can, for example, save the photo-input data to a database for later reference. The recording unit can also analyze the photo-input data and automatically calculate nutrients and calories. In this way, the recording unit can easily record the user's meals and exercise using photo input.

[0129] The visualization unit can incorporate gamification elements in visualizing user progress and sense of accomplishment. For example, the visualization unit can award badges or points according to user progress. For example, the visualization unit can display rankings or leaderboards to enhance user sense of accomplishment. For example, the visualization unit can offer rewards or benefits according to user goal achievement. In this way, the visualization unit can improve user motivation by incorporating gamification elements.

[0130] The integration unit can enhance data privacy protection when integrating data with users' healthcare apps. For example, the integration unit can protect data using encryption technology during data integration. For example, the integration unit can integrate only the necessary data after obtaining the user's consent. For example, the integration unit can also record the history of data integration and make it available for the user to review. In this way, the integration unit can enhance user confidence by strengthening data privacy protection.

[0131] The prediction unit can estimate the user's emotions when predicting the user's future weight and body shape, and adjust the way the prediction results are presented based on the estimated emotions. For example, if the user is stressed, the prediction unit will present simple and easy-to-understand prediction results. For example, if the user is relaxed, the prediction unit will present detailed prediction results. For example, if the user is in a hurry, the prediction unit will present concise prediction results that get straight to the point. In this way, the prediction unit can provide the most appropriate way to present prediction results according to the user's emotions.

[0132] The following briefly describes the processing flow for example form 2.

[0133] Step 1: The reception desk inputs the user's physical information and goals. Physical information includes, for example, weight, height, and body fat percentage. Goals include, for example, weight loss, muscle building, and maintaining health. The reception desk can save the weight and height entered by the user to a database, record the goals set by the user, and send them to the suggestion department. Step 2: The suggestion department analyzes the information entered by the reception department and proposes the optimal meal plan and exercise plan. The suggestion department uses AI to propose calorie restrictions and exercise levels based on the user's physical information and goals. For example, it calculates the daily calorie intake based on the user's weight and height and proposes an appropriate meal plan. It can also propose an appropriate exercise plan based on the user's exercise habits. Step 3: The recording unit records the menu items the user has eaten. The recording unit records the menu items the user has eaten with photos and uses AI to analyze nutrients and calories. Users can also input the menu items they have eaten as text and save them to the database. Furthermore, users can input the menu items they have eaten by voice and convert them into text data using speech recognition technology. Step 4: The visualization unit visualizes progress and a sense of accomplishment based on the data recorded by the recording unit. The visualization unit displays the user's weight and meal records in graphs, visually showing progress. It displays the user's weight changes numerically, providing a sense of accomplishment. It can also analyze the user's meal records and predict future weight and body shape.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] Each of the multiple elements mentioned above, including the reception unit, proposal unit, recording unit, visualization unit, collaboration unit, prediction unit, and community unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and inputs the user's physical information and goals. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an optimal meal menu and exercise plan. The recording unit is implemented by the control unit 46A of the smart device 14 and records the menus the user has eaten. The visualization unit is implemented by the specific processing unit 290 of the data processing unit 12 and visualizes progress and a sense of accomplishment. The collaboration unit is implemented by the control unit 46A of the smart device 14 and acquires data recorded in the healthcare app. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts future weight and body shape. The community section is implemented, for example, by the control unit 46A of the smart device 14, and provides functions for users to interact with each other and share recipes and exercise ideas. The correspondence between each section and the device or control unit is not limited to the example described above, and various modifications are possible.

[0138] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] Each of the multiple elements mentioned above, including the reception unit, proposal unit, recording unit, visualization unit, collaboration unit, prediction unit, and community unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and takes the user's physical information and goals into input. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an optimal meal menu and exercise plan. The recording unit is implemented by the control unit 46A of the smart glasses 214 and records the menus the user has eaten. The visualization unit is implemented by the specific processing unit 290 of the data processing unit 12 and visualizes progress and a sense of accomplishment. The collaboration unit is implemented by the control unit 46A of the smart glasses 214 and acquires data recorded in the healthcare app. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts future weight and body shape. The community section is implemented, for example, by the control unit 46A of the smart glasses 214, and provides functions for users to interact with each other and share recipes and exercise ideas. The correspondence between each section and the device or control unit is not limited to the example described above, and various modifications are possible.

[0154] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Each of the multiple elements mentioned above, including the reception unit, proposal unit, recording unit, visualization unit, collaboration unit, prediction unit, and community unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and takes the user's physical information and goals. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an optimal meal menu and exercise plan. The recording unit is implemented by the control unit 46A of the headset terminal 314 and records the menus the user has eaten. The visualization unit is implemented by the specific processing unit 290 of the data processing unit 12 and visualizes progress and a sense of accomplishment. The collaboration unit is implemented by the control unit 46A of the headset terminal 314 and acquires data recorded by the healthcare app. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts future weight and body shape. The community section is implemented, for example, by the control unit 46A of the headset terminal 314, and provides functions for users to interact with each other and share recipes and exercise ideas. The correspondence between each section and the device or control unit is not limited to the example described above, and various modifications are possible.

[0170] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.).

[0183] 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.

[0184] 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.

[0185] 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.

[0186] Each of the multiple elements mentioned above, including the reception unit, proposal unit, recording unit, visualization unit, collaboration unit, prediction unit, and community unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and takes the user's physical information and goals as input. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes an optimal meal menu and exercise plan. The recording unit is implemented by, for example, the control unit 46A of the robot 414 and records the menus the user has eaten. The visualization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and visualizes progress and a sense of accomplishment. The collaboration unit is implemented by, for example, the control unit 46A of the robot 414 and acquires data recorded in the healthcare app. The prediction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and predicts future weight and body shape. The community section is implemented, for example, by the control unit 46A of robot 414, and provides functions for users to interact with each other and share recipes and exercise ideas. The correspondence between each section and the device or control unit is not limited to the example described above and can be modified in various ways.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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."

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] (Note 1) A reception area where users input their physical information and goals, The reception unit analyzes the information entered by the reception unit and proposes the optimal meal menu and exercise plan, A recording unit that records the menu items the user has eaten, The system includes a visualization unit that visualizes progress and a sense of accomplishment based on the data recorded by the recording unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We utilize information on seasonal ingredients to propose nutritionally balanced meal plans that are appropriate for the season. The system described in Appendix 1, characterized by the features described herein. (Note 3) A linkage unit that acquires data recorded in the healthcare app, The system includes a proposal unit that provides a more accurate plan based on the data acquired by the aforementioned collaboration unit. The system described in Appendix 1, characterized by the features described herein. (Note 4) It features a prediction unit that predicts future weight and body shape based on weight and food records. The system described in Appendix 1, characterized by the features described herein. (Note 5) It features a community section where users can interact and share recipes and exercise ideas. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Based on the user's physical information and goals, it suggests calorie restrictions and exercise levels. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of inputting physical information and goals based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system analyzes the user's past physical information and goals to select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users input physical information and goals, filtering is performed based on their current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes the information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users input physical information or goals, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users input physical information and goals, the system analyzes their social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the meal plan and exercise plan. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the category of meal menu or exercise plan. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When submitting proposals, we prioritize them based on when they are submitted, including meal plans and exercise plans. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on their relevance to meal menus and exercise plans. The system described in Appendix 1, characterized by the features described herein. (Note 19) The recording unit is, The system estimates the user's emotions and adjusts the recording method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The recording unit is, During recording, the system analyzes the user's past eating and exercise history to select the optimal recording method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The recording unit is, During recording, the recording method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The recording unit is, The system estimates the user's emotions and prioritizes recordings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The recording unit is, During recording, the optimal recording method is selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The recording unit is, During recording, we analyze the user's social media activity and suggest recording methods. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned visualization unit, It estimates the user's emotions and adjusts the visualization method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned visualization unit, During visualization, the system selects the optimal visualization method by referring to the user's past progress data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned visualization unit, When visualizing data, the visualization method is customized based on the user's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned visualization unit, It estimates the user's emotions and determines the visualization priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned visualization unit, When visualizing data, the optimal visualization method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned visualization unit, When visualizing user activity, we analyze it and propose methods for visualization. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned linkage unit is, The system estimates the user's emotions and selects the data to link based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned linkage unit is, During integration, the integration algorithm is optimized by referring to past integration data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned linkage unit is, It estimates the user's emotions and adjusts the frequency of interaction based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned linkage unit is, During integration, the integrated data is weighted based on when the healthcare app submitted the data. The system described in Appendix 1, characterized by the features described herein. (Note 35) The prediction unit, It estimates the user's emotions and adjusts the prediction method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The prediction unit, During the prediction process, the system analyzes the user's past weight and dietary records to select the optimal prediction method. The system described in Appendix 1, characterized by the features described herein. (Note 37) The prediction unit, During the prediction process, the prediction method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 38) The prediction unit, It estimates the user's emotions and determines the priority of predictions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The prediction unit, When making predictions, the optimal prediction method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 40) The prediction unit, When making predictions, we analyze users' social media activity and propose methods for making predictions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned community department, It estimates user emotions and adjusts community interaction methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned community department, During community interactions, the system selects the most suitable interaction method by referring to the user's past interaction history. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned community department, It estimates user sentiment and determines community priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned community department, When users interact within the community, the system selects the most suitable method of interaction by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0206] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where users input their physical information and goals, The reception unit analyzes the information entered by the reception unit and proposes the optimal meal menu and exercise plan, A recording unit that records the menu items the user has eaten, The system includes a visualization unit that visualizes progress and a sense of accomplishment based on the data recorded by the recording unit. A system characterized by the following features.

2. The aforementioned proposal section is, We utilize information on seasonal ingredients to propose nutritionally balanced meal plans that are appropriate for the season. The system according to feature 1.

3. A linkage unit that acquires data recorded in the healthcare app, The system includes a proposal unit that provides a more accurate plan based on the data acquired by the aforementioned collaboration unit. The system according to feature 1.

4. It features a prediction unit that predicts future weight and body shape based on weight and food records. The system according to feature 1.

5. It features a community section where users can interact and share recipes and exercise ideas. The system according to feature 1.

6. The aforementioned proposal section is, Based on the user's physical information and goals, it suggests calorie restrictions and exercise levels. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of inputting physical information and goals based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is The system analyzes the user's past physical information and goals to select the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When users input physical information and goals, filtering is performed based on their current health status and lifestyle. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and prioritizes the information to be entered based on the estimated user emotions. The system according to feature 1.

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