system

Abstract

The system according to this embodiment aims to provide a sustainable diet plan tailored to the user's lifestyle and emotional state. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, a monitoring unit, and a feedback unit. The reception unit receives input from the user. The analysis unit analyzes the information received by the reception unit. The generation unit generates a meal and exercise plan based on the data analyzed by the analysis unit. The monitoring unit monitors the user's progress based on the plan generated by the generation unit. The feedback unit provides feedback based on the progress monitored by the monitoring unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, a sustainable diet plan according to the user's lifestyle and emotional state has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a sustainable diet plan according to the user's lifestyle and emotional state.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, a monitoring unit, and a feedback unit. The reception unit receives input from the user. The analysis unit analyzes the information received by the reception unit. The generation unit generates a meal and exercise plan based on the data analyzed by the analysis unit. The monitoring unit monitors the user's progress based on the plan generated by the generation unit. The feedback unit provides feedback based on the progress monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide a sustainable diet plan tailored to the user's lifestyle and emotional state. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The diet support AI agent according to an embodiment of the present invention is a system in which a conversational AI communicates with the user to set a target weight and deadline. This system analyzes the user's lifestyle, biometric data, and emotional state to generate a sustainable diet and exercise plan. Furthermore, it provides consistent support from purchasing ingredients to real-time monitoring of consumed meals, and provides immediate feedback according to the user's progress and emotional state. For example, the user interacts with the conversational AI to set a target weight and deadline. Next, the conversational AI collects the user's lifestyle, biometric data, and emotional state. Based on the collected data, the generating AI creates an optimal diet and exercise plan for the user. Furthermore, the identifying AI monitors the user's scale data and photos of meals in real time to understand the progress. In this way, the diet support AI agent provides individualized support tailored to the user's lifestyle and emotional state, enabling sustainable dieting.

[0029] The diet support AI agent according to this embodiment comprises a reception unit, an analysis unit, a generation unit, a monitoring unit, and a feedback unit. The reception unit receives input from the user. User input includes, but is not limited to, text input, voice input, and image input. For example, the user can input their target weight and deadline into the reception unit. The analysis unit analyzes the information received by the reception unit. For example, the analysis unit analyzes the user's lifestyle, biometric data, and emotional state. Lifestyle includes, for example, eating habits, exercise habits, and sleep patterns. Biometric data includes, for example, weight, body fat percentage, and heart rate. Emotional state includes, for example, questionnaires, facial recognition, and voice analysis. The generation unit generates a diet and exercise plan based on the data analyzed by the analysis unit. The generation unit generates a diet and exercise plan using, for example, a generation AI. The generation AI includes, for example, a machine learning model, a neural network, and a rule-based system. The monitoring unit monitors the user's progress based on the plan generated by the generation unit. The monitoring unit monitors, for example, data from the user's scale and photos of meals in real time. Real-time monitoring includes, for example, the frequency of data updates and the sensors and devices used. The feedback unit provides feedback based on the progress monitored by the monitoring unit. The feedback unit provides, for example, feedback tailored to the user's progress and emotional state. Feedback includes, for example, advice, encouraging messages, and suggestions for improvement. Thus, the diet support AI agent according to the embodiment can support sustainable dieting by receiving user input, analyzing it, generating a plan, monitoring progress, and providing feedback.

[0030] The reception desk receives input from users. User input includes, but is not limited to, text input, voice input, and image input. Specifically, users can input information using smartphones or computers through dedicated applications or websites. With text input, users can fill in details such as target weight, deadline, current weight, diet, and exercise. With voice input, users provide information using a microphone, and it is converted to text using speech recognition technology. With image input, users can take pictures of meals or scale readings and upload them. This allows the reception desk to support diverse input methods, enabling users to provide information in the way that is most convenient for them. Furthermore, the reception desk has the function to automatically categorize the entered information, extract the necessary data, and store it in a database. For example, the target weight and deadline entered by the user are stored in separate fields and used for subsequent analysis and plan generation. This allows the reception desk to process user input efficiently and accurately, improving the overall system performance.

[0031] The analysis department analyzes information received by the reception department. For example, the analysis department analyzes the user's lifestyle, biometric data, and emotional state. Lifestyle includes, for example, eating habits, exercise habits, and sleep patterns. Specifically, it classifies the user's entered meal content by nutrient and evaluates calories and nutritional balance. Regarding exercise habits, it analyzes the type, frequency, and intensity of exercise to evaluate the user's exercise volume. Regarding sleep patterns, it analyzes the user's entered sleep duration and quality to evaluate sleep quality. Biometric data includes, for example, weight, body fat percentage, and heart rate. This data is used to comprehensively evaluate the user's health status. Emotional state includes, for example, questionnaires, facial recognition, and voice analysis. In questionnaires, users self-assess and input their current emotional state and stress level. Facial recognition analyzes photos and videos taken by the user to estimate the emotional state from facial expressions. Voice analysis analyzes the user's voice input to estimate the emotional state from voice tone and speaking style. This allows the analytics department to comprehensively analyze users' lifestyles, biometric data, and emotional states, enabling them to accurately understand users' health status and diet progress. Furthermore, the analytics department can utilize historical data and statistical information to predict changes in users' behavioral patterns and health status. This allows the analytics department to provide the foundational data necessary to offer optimal diet plans tailored to each user's individual needs.

[0032] The generation unit generates meal and exercise plans based on data analyzed by the analysis unit. The generation unit generates meal and exercise plans using, for example, generative AI. Generative AI includes, for example, machine learning models, neural networks, and rule-based systems. Specifically, machine learning models are used to learn from the user's past data and data from other users to generate optimal meal and exercise plans. Neural networks are used to generate plans tailored to the user's individual needs and goals. Rule-based systems are used to generate plans based on expert knowledge and experience. This allows the generation unit to provide optimal meal and exercise plans tailored to the user's individual needs. For example, the generative AI receives data such as the user's target weight, deadline, current weight, meal content, and exercise content as input to generate an optimal meal and exercise plan. The meal plan includes specific meal content, recipes, and meal timing. The exercise plan includes specific exercise content, frequency, and intensity. This allows the generation unit to provide a concrete plan for users to achieve sustainable weight loss. Furthermore, the generation unit has a function to continuously improve the plan based on user feedback. For example, when a user inputs the results of their diet and exercise according to a plan, the generating AI learns from that data and incorporates it into the next plan generation. This allows the generation unit to flexibly respond to the user's needs and circumstances, and continue to provide the optimal plan.

[0033] The monitoring unit monitors the user's progress based on the plan generated by the generation unit. For example, the monitoring unit monitors the user's scale data and meal photos in real time. Specifically, each time the user steps on the scale, the scale data is automatically sent to the cloud, and the monitoring unit acquires this data in real time. Also, when the user takes and uploads a photo of their meal, the monitoring unit analyzes the photo and evaluates the meal content. Real-time monitoring includes, for example, the frequency of data updates and the sensors and devices used. This allows the monitoring unit to accurately grasp the user's progress and provide plan modifications and feedback as needed. Furthermore, the monitoring unit has the function to continuously collect the user's biometric and lifestyle data and monitor long-term changes in health status. For example, it monitors the user's heart rate and sleep patterns to detect changes in health status early. The monitoring unit also has the function to detect abnormal data and patterns, and can issue a warning to the user if an abnormality is detected. In this way, the monitoring unit can comprehensively monitor the user's health status and support sustainable dieting.

[0034] The feedback unit provides feedback based on the progress monitored by the monitoring unit. For example, the feedback unit provides feedback tailored to the user's progress and emotional state. Specifically, if the user is approaching their target weight, it sends encouraging messages to maintain motivation. If the user is following the plan for diet and exercise, it sends messages praising their efforts. On the other hand, if the user is not following the plan or their progress is stagnant, it points out areas for improvement and provides specific advice. For example, it suggests reviewing the diet or adjusting the intensity of exercise. This allows the feedback unit to support the user in achieving a sustainable diet. Furthermore, the feedback unit has the functionality to provide feedback that takes the user's emotional state into consideration. For example, if the user is stressed, it suggests advice for relaxation and stress relief methods. If the user has lost motivation, it sends messages to reaffirm their goals and restore motivation. This allows the feedback unit to provide appropriate support tailored to the user's emotional state, supporting a sustainable diet. Additionally, the feedback unit has the functionality to collect user feedback and use it to improve the overall system. For example, it evaluates the feedback provided by users and uses that evaluation to improve the content and method of feedback. This allows the feedback unit to continue providing optimal support tailored to the user's needs.

[0035] The reception desk can receive the user's target weight and deadline. The reception desk can, for example, allow the user to input their target weight and deadline. For example, the reception desk can allow the user to set a target weight and input a deadline. By receiving the user's target weight and deadline, an individual diet plan can be set. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can use AI to analyze the user's input of target weight and deadline and propose an appropriate plan.

[0036] The analysis unit can analyze the user's lifestyle, biometric data, and emotional state. For example, the analysis unit can analyze the user's lifestyle, such as their eating habits, exercise habits, and sleep patterns. The analysis unit can also analyze the user's biometric data, such as their weight, body fat percentage, and heart rate. Furthermore, the analysis unit can analyze the user's emotional state, such as using questionnaires, facial recognition, and voice analysis. This allows the analysis of the user's lifestyle, biometric data, and emotional state to provide a personalized diet plan. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the user's lifestyle, biometric data, and emotional state into AI and obtain analysis results.

[0037] The generation unit can generate meal and exercise plans using generative AI. For example, the generation unit generates meal and exercise plans using generative AI. Generative AI includes, for example, machine learning models, neural networks, and rule-based systems. The generation unit generates meal and exercise plans based, for example, the user's lifestyle, biometric data, and emotional state. For example, the generation unit generates meal and exercise plans considering the user's eating habits, exercise habits, and sleep patterns. The generation unit can also generate meal and exercise plans considering the user's weight, body fat percentage, and heart rate. Furthermore, the generation unit can generate meal and exercise plans considering the user's emotional state. For example, the generation unit adjusts the plan considering the user's stress level and motivation. This allows the generation AI to generate the optimal meal and exercise plan for the user. Some or all of the above-described processes in the generation unit are performed using generative AI. For example, the generation unit inputs user data into the generative AI to generate the optimal plan.

[0038] The monitoring unit can monitor the user's scale data and food photos in real time. For example, the monitoring unit can monitor the user's scale data in real time. For example, the monitoring unit can periodically acquire the user's scale data and understand the progress. The monitoring unit can also monitor the user's food photos in real time. For example, the monitoring unit can monitor the contents of meals when the user takes photos of their meals and uploads them to the system. This allows the monitoring unit to understand the progress by monitoring the user's scale data and food photos in real time. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's scale data and food photos into AI and obtain analysis results.

[0039] The feedback unit can provide feedback tailored to the user's progress and emotional state. For example, the feedback unit can provide feedback based on the user's progress. For instance, it can provide advice based on the user's weight changes and exercise performance. The feedback unit can also provide feedback tailored to the user's emotional state. For example, it can provide encouraging messages based on the user's stress level and motivation. This allows for support of sustainable dieting by providing feedback tailored to the user's progress and emotional state. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's progress data and emotional state into AI to generate appropriate feedback.

[0040] The reception desk can analyze the user's past diet history and suggest an optimal target weight and deadline. For example, the reception desk can analyze the user's past diet history. For example, the reception desk can refer to the user's past successful diet plans and suggest a similar target weight and deadline. The reception desk can also analyze the user's past unsuccessful diet plans and suggest a target weight and deadline that avoids the causes of failure. In this way, by analyzing the user's past diet history, it is possible to suggest an optimal target weight and deadline. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past diet history into AI and suggest an optimal target weight and deadline.

[0041] The reception desk can filter users based on their current health status and lifestyle when setting target weight and deadlines. For example, the reception desk can set realistic target weight and deadlines considering the user's current health status. For example, the reception desk can set target weight and deadlines based on the user's health checkup data and doctor's diagnosis results. The reception desk can also set realistic target weight and deadlines considering the user's lifestyle. For example, the reception desk can set target weight and deadlines considering the user's workload and exercise frequency. This allows for the setting of realistic target weight and deadlines by filtering based on the user's current health status and lifestyle. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the user's health status and lifestyle into AI and suggest appropriate target weight and deadlines.

[0042] The reception desk can prioritize setting highly relevant goals when users set their target weight and deadline, taking into account their geographical location. For example, the reception desk can consider the user's geographical location. For instance, it can set target weight and deadlines considering the climate and topography of the area where the user lives. Furthermore, if the user has access to a nearby gym or fitness facility, the reception desk can set target weight and deadlines utilizing that facility. This allows for the setting of realistic target weights and deadlines by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location into AI and suggest appropriate target weights and deadlines.

[0043] The reception desk can analyze the user's social media activity and set relevant goals when setting target weight and deadlines. For example, the reception desk can analyze the user's social media activity. For example, the reception desk can analyze health and fitness posts that the user shares on social media and set target weight and deadlines. The reception desk can also set target weight and deadlines by referring to the trends of influencers and communities that the user follows. In this way, relevant target weight and deadlines can be set by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into AI and suggest appropriate target weight and deadlines.

[0044] The analysis unit can improve the accuracy of its analysis by referring to the user's past health data during the analysis process. For example, the analysis unit can refer to the user's past health data. For example, the analysis unit can refer to the user's past weight fluctuation data to predict current weight fluctuations. The analysis unit can also analyze the user's past eating records to evaluate current eating patterns. This improves the accuracy of the analysis by referring to the user's past health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past health data into AI to improve the accuracy of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the user's lifestyle and eating patterns during analysis. For example, the analysis unit considers the user's lifestyle. For instance, if the user has a nocturnal lifestyle, the analysis unit can apply an analysis algorithm that corresponds to nighttime eating patterns. Similarly, if the user is a vegetarian, the analysis unit can apply an analysis algorithm specifically for plant-based foods. This allows for the provision of more appropriate analysis results by applying different analysis algorithms according to the user's lifestyle and eating patterns. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the user's lifestyle and eating patterns into AI and apply an appropriate analysis algorithm.

[0046] The analysis unit can prioritize analyses based on user submission timing during the analysis process. For example, the analysis unit considers user submission timing. If a user is in a hurry, the analysis unit can prioritize the analysis based on the submission timing. The analysis unit can also prioritize analyses to coincide with a specific event if the user is participating in that event. By prioritizing analyses based on user submission timing, more appropriate analysis results can be provided. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input user submission timing data into AI to determine analysis priorities.

[0047] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant market data during the analysis process. For example, the analysis unit can refer to the user's relevant market data. For example, the analysis unit can refer to health trends in the area where the user lives to improve the accuracy of its analysis. The analysis unit can also refer to data from fitness apps used by the user to improve the accuracy of its analysis. In this way, the accuracy of the analysis is improved by referring to the user's relevant market data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's relevant market data into AI to improve the accuracy of its analysis.

[0048] The generation unit can optimize its generation algorithm by referencing the user's past diet plans during the generation process. For example, the generation unit can refer to the user's past diet plans. For instance, it can generate a similar plan by referencing a diet plan that the user has successfully completed in the past. It can also analyze diet plans that the user has failed at in the past and generate a plan that avoids the causes of failure. This allows the generation algorithm to be optimized by referencing the user's past diet plans, providing a more effective plan. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's past diet plans into the generation AI to optimize its generation algorithm.

[0049] The generation unit can customize the plan content based on the user's current health status during generation. For example, the generation unit can consider the user's current health status. For example, the generation unit can customize the plan content based on the user's health checkup data or doctor's diagnosis results. The generation unit can also generate a meal plan that is fortified with specific nutrients according to the user's health status. This allows for the provision of a more appropriate diet and exercise plan by customizing the plan content based on the user's current health status. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's health status data into AI to customize the plan content.

[0050] The generation unit can generate an optimal plan by considering the user's geographical location information during the generation process. For example, the generation unit can consider the user's geographical location information. For example, the generation unit can generate an exercise plan by considering the climate and topography of the area where the user lives. The generation unit can also generate a plan that utilizes nearby gyms or fitness facilities if the user has access to them. This allows for the provision of more realistic meal and exercise plans by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into AI and generate an optimal plan.

[0051] The monitoring unit can improve the accuracy of monitoring by referring to the user's past weight data during monitoring. For example, the monitoring unit can refer to the user's past weight data. For example, the monitoring unit can refer to the user's past weight fluctuation data and predict current weight fluctuations. The monitoring unit can also analyze the user's past eating records and evaluate current eating patterns. This improves the accuracy of monitoring by referring to the user's past weight data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's past weight data into AI to improve the accuracy of monitoring.

[0052] The monitoring unit can apply different monitoring methods depending on the user's eating patterns and exercise history during monitoring. For example, the monitoring unit considers the user's eating patterns. For instance, if the user has a nocturnal lifestyle, the monitoring unit can apply a monitoring method that corresponds to nighttime eating patterns. Furthermore, if the user is a vegetarian, the monitoring unit can apply a monitoring method specifically tailored to plant-based foods. This allows for more appropriate monitoring by applying different monitoring methods according to the user's eating patterns and exercise history. Some or all of the above-described processes in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can input data on the user's eating patterns and exercise history into an AI and apply an appropriate monitoring method.

[0053] The monitoring unit can determine monitoring priorities based on the user's submission timing during monitoring. For example, the monitoring unit considers the user's submission timing. For instance, if the user is in a hurry, the monitoring unit can prioritize monitoring based on the submission timing. Furthermore, if the user is participating in a specific event, the monitoring unit can prioritize monitoring accordingly. This allows for more appropriate monitoring by determining monitoring priorities based on the user's submission timing. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user submission timing data into AI to determine monitoring priorities.

[0054] The monitoring unit can improve the accuracy of monitoring by referring to the user's relevant market data during monitoring. For example, the monitoring unit can refer to the user's relevant market data. For example, the monitoring unit can refer to health trends in the area where the user lives to improve the accuracy of monitoring. The monitoring unit can also refer to data from fitness apps used by the user to improve the accuracy of monitoring. In this way, the accuracy of monitoring is improved by referring to the user's relevant market data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's relevant market data into AI to improve the accuracy of monitoring.

[0055] The feedback unit can provide optimal feedback by referring to the user's past feedback history when providing feedback. For example, the feedback unit can refer to the user's past feedback history. For example, the feedback unit can refer to feedback the user has received in the past and provide similar feedback. The feedback unit can also analyze the effect of feedback the user has received in the past and provide optimal feedback. In this way, optimal feedback can be provided by referring to the user's past feedback history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without using AI. For example, the feedback unit can input the user's past feedback history into AI and provide optimal feedback.

[0056] The feedback unit can customize the content of the feedback based on the user's current health status. For example, the feedback unit can consider the user's current health status. For example, the feedback unit can customize the content of the feedback based on the user's health checkup data or the results of a doctor's diagnosis. The feedback unit can also provide feedback that is fortified with specific nutrients depending on the user's health status. This allows for the provision of more appropriate feedback by customizing the content of the feedback based on the user's current health status. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's health status data into AI to customize the content of the feedback.

[0057] The feedback unit can provide optimal feedback by considering the user's geographical location information. For example, the feedback unit can consider the user's geographical location information. For example, the feedback unit can suggest an appropriate exercise plan by considering the climate and topography of the area where the user lives. Also, if the user has access to a nearby gym or fitness facility, the feedback unit can provide feedback utilizing that facility. This allows for the provision of more appropriate feedback by considering the user's geographical location information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's geographical location information into AI to provide optimal feedback.

[0058] The feedback unit can analyze the user's social media activity and suggest feedback content when providing feedback. For example, the feedback unit can analyze the user's social media activity. For example, it can analyze posts related to health and fitness that the user shares on social media and suggest feedback content. The feedback unit can also suggest feedback content by referring to the influencers and community trends that the user follows. This allows for the provision of more appropriate feedback by analyzing the user's social media activity. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the user's social media activity data into AI and suggest feedback content.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The reception section can receive information about the user's dietary preferences and allergies. For example, if a user has an allergy to a specific ingredient, inputting this information allows the generation section to generate a meal plan that takes the allergy into account. Furthermore, inputting the user's dietary preferences allows the generation section to provide a meal plan tailored to those preferences. This enables the provision of personalized support that takes into account the user's dietary preferences and allergy information.

[0061] The analysis department can analyze users' sleep data and adjust diet plans based on sleep quality. For example, by analyzing a user's sleep duration and quality, it can adjust the plan, such as reducing exercise, if sleep deprivation persists. Conversely, if sleep quality improves, it can adjust the plan, such as increasing exercise. This allows the system to provide diet plans that take the user's sleep data into consideration.

[0062] The generation unit can incorporate seasonal ingredients into the user's meal plan. For example, it can provide meal plans featuring fresh vegetables and fruits in spring, and plans emphasizing cold dishes and hydration in summer. It can also offer meal plans incorporating nutritious root vegetables in autumn and warming dishes in winter. This allows the system to support the user's health by providing meal plans tailored to the season.

[0063] The monitoring unit can monitor the user's exercise data in real time and evaluate the effectiveness of the exercise. For example, it can monitor the type of exercise, duration, and calories burned by the user in real time and evaluate the effectiveness of the exercise. Furthermore, if the exercise is not effective, it can adjust the exercise plan. This allows for real-time monitoring of the user's exercise data and the provision of an effective exercise plan.

[0064] The feedback section can offer rewards based on user progress. For example, points or badges can be awarded as rewards when a user reaches their target weight or continues their exercise plan. Furthermore, users can accumulate rewards to receive special benefits or discounts. This can boost user motivation and support sustainable weight loss.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk receives input from the user. User input includes, for example, text input, voice input, and image input. The reception desk allows the user to enter their target weight and deadline. Step 2: The analysis department analyzes the information received by the reception department. The analysis department analyzes the user's lifestyle, biometric data, and emotional state. Lifestyle includes eating habits, exercise habits, sleep patterns, etc., while biometric data includes weight, body fat percentage, heart rate, etc. Emotional state includes questionnaires, facial recognition, voice analysis, etc. Step 3: The generation unit generates meal and exercise plans based on the data analyzed by the analysis unit. The generation unit generates meal and exercise plans using generative AI. Generative AI includes machine learning models, neural networks, rule-based systems, etc. Step 4: The monitoring unit monitors the user's progress based on the plan generated by the generation unit. The monitoring unit monitors the user's weighing scale data and meal photos in real time. Real-time monitoring includes the data update frequency, the sensors and devices used, etc. Step 5: The Feedback Unit provides feedback based on the progress monitored by the Monitoring Unit. The Feedback Unit provides feedback tailored to the user's progress and emotional state. Feedback may include advice, encouraging messages, and suggestions for improvement.

[0067] (Example of form 2) The diet support AI agent according to an embodiment of the present invention is a system in which a conversational AI communicates with the user to set a target weight and deadline. This system analyzes the user's lifestyle, biometric data, and emotional state to generate a sustainable diet and exercise plan. Furthermore, it provides consistent support from purchasing ingredients to real-time monitoring of consumed meals, and provides immediate feedback according to the user's progress and emotional state. For example, the user interacts with the conversational AI to set a target weight and deadline. Next, the conversational AI collects the user's lifestyle, biometric data, and emotional state. Based on the collected data, the generating AI creates an optimal diet and exercise plan for the user. Furthermore, the identifying AI monitors the user's scale data and photos of meals in real time to understand the progress. In this way, the diet support AI agent provides individualized support tailored to the user's lifestyle and emotional state, enabling sustainable dieting.

[0068] The diet support AI agent according to this embodiment comprises a reception unit, an analysis unit, a generation unit, a monitoring unit, and a feedback unit. The reception unit receives input from the user. User input includes, but is not limited to, text input, voice input, and image input. For example, the user can input their target weight and deadline into the reception unit. The analysis unit analyzes the information received by the reception unit. For example, the analysis unit analyzes the user's lifestyle, biometric data, and emotional state. Lifestyle includes, for example, eating habits, exercise habits, and sleep patterns. Biometric data includes, for example, weight, body fat percentage, and heart rate. Emotional state includes, for example, questionnaires, facial recognition, and voice analysis. The generation unit generates a diet and exercise plan based on the data analyzed by the analysis unit. The generation unit generates a diet and exercise plan using, for example, a generation AI. The generation AI includes, for example, a machine learning model, a neural network, and a rule-based system. The monitoring unit monitors the user's progress based on the plan generated by the generation unit. The monitoring unit monitors, for example, data from the user's scale and photos of meals in real time. Real-time monitoring includes, for example, the frequency of data updates and the sensors and devices used. The feedback unit provides feedback based on the progress monitored by the monitoring unit. The feedback unit provides, for example, feedback tailored to the user's progress and emotional state. Feedback includes, for example, advice, encouraging messages, and suggestions for improvement. Thus, the diet support AI agent according to the embodiment can support sustainable dieting by receiving user input, analyzing it, generating a plan, monitoring progress, and providing feedback.

[0069] The reception desk receives input from users. User input includes, but is not limited to, text input, voice input, and image input. Specifically, users can input information using smartphones or computers through dedicated applications or websites. With text input, users can fill in details such as target weight, deadline, current weight, diet, and exercise. With voice input, users provide information using a microphone, and it is converted to text using speech recognition technology. With image input, users can take pictures of meals or scale readings and upload them. This allows the reception desk to support diverse input methods, enabling users to provide information in the way that is most convenient for them. Furthermore, the reception desk has the function to automatically categorize the entered information, extract the necessary data, and store it in a database. For example, the target weight and deadline entered by the user are stored in separate fields and used for subsequent analysis and plan generation. This allows the reception desk to process user input efficiently and accurately, improving the overall system performance.

[0070] The analysis department analyzes information received by the reception department. For example, the analysis department analyzes the user's lifestyle, biometric data, and emotional state. Lifestyle includes, for example, eating habits, exercise habits, and sleep patterns. Specifically, it classifies the user's entered meal content by nutrient and evaluates calories and nutritional balance. Regarding exercise habits, it analyzes the type, frequency, and intensity of exercise to evaluate the user's exercise volume. Regarding sleep patterns, it analyzes the user's entered sleep duration and quality to evaluate sleep quality. Biometric data includes, for example, weight, body fat percentage, and heart rate. This data is used to comprehensively evaluate the user's health status. Emotional state includes, for example, questionnaires, facial recognition, and voice analysis. In questionnaires, users self-assess and input their current emotional state and stress level. Facial recognition analyzes photos and videos taken by the user to estimate the emotional state from facial expressions. Voice analysis analyzes the user's voice input to estimate the emotional state from voice tone and speaking style. This allows the analytics department to comprehensively analyze users' lifestyles, biometric data, and emotional states, enabling them to accurately understand users' health status and diet progress. Furthermore, the analytics department can utilize historical data and statistical information to predict changes in users' behavioral patterns and health status. This allows the analytics department to provide the foundational data necessary to offer optimal diet plans tailored to each user's individual needs.

[0071] The generation unit generates meal and exercise plans based on data analyzed by the analysis unit. The generation unit generates meal and exercise plans using, for example, generative AI. Generative AI includes, for example, machine learning models, neural networks, and rule-based systems. Specifically, machine learning models are used to learn from the user's past data and data from other users to generate optimal meal and exercise plans. Neural networks are used to generate plans tailored to the user's individual needs and goals. Rule-based systems are used to generate plans based on expert knowledge and experience. This allows the generation unit to provide optimal meal and exercise plans tailored to the user's individual needs. For example, the generative AI receives data such as the user's target weight, deadline, current weight, meal content, and exercise content as input to generate an optimal meal and exercise plan. The meal plan includes specific meal content, recipes, and meal timing. The exercise plan includes specific exercise content, frequency, and intensity. This allows the generation unit to provide a concrete plan for users to achieve sustainable weight loss. Furthermore, the generation unit has a function to continuously improve the plan based on user feedback. For example, when a user inputs the results of their diet and exercise according to a plan, the generating AI learns from that data and incorporates it into the next plan generation. This allows the generation unit to flexibly respond to the user's needs and circumstances, and continue to provide the optimal plan.

[0072] The monitoring unit monitors the user's progress based on the plan generated by the generation unit. For example, the monitoring unit monitors the user's scale data and meal photos in real time. Specifically, each time the user steps on the scale, the scale data is automatically sent to the cloud, and the monitoring unit acquires this data in real time. Also, when the user takes and uploads a photo of their meal, the monitoring unit analyzes the photo and evaluates the meal content. Real-time monitoring includes, for example, the frequency of data updates and the sensors and devices used. This allows the monitoring unit to accurately grasp the user's progress and provide plan modifications and feedback as needed. Furthermore, the monitoring unit has the function to continuously collect the user's biometric and lifestyle data and monitor long-term changes in health status. For example, it monitors the user's heart rate and sleep patterns to detect changes in health status early. The monitoring unit also has the function to detect abnormal data and patterns, and can issue a warning to the user if an abnormality is detected. In this way, the monitoring unit can comprehensively monitor the user's health status and support sustainable dieting.

[0073] The feedback unit provides feedback based on the progress monitored by the monitoring unit. For example, the feedback unit provides feedback tailored to the user's progress and emotional state. Specifically, if the user is approaching their target weight, it sends encouraging messages to maintain motivation. If the user is following the plan for diet and exercise, it sends messages praising their efforts. On the other hand, if the user is not following the plan or their progress is stagnant, it points out areas for improvement and provides specific advice. For example, it suggests reviewing the diet or adjusting the intensity of exercise. This allows the feedback unit to support the user in achieving a sustainable diet. Furthermore, the feedback unit has the functionality to provide feedback that takes the user's emotional state into consideration. For example, if the user is stressed, it suggests advice for relaxation and stress relief methods. If the user has lost motivation, it sends messages to reaffirm their goals and restore motivation. This allows the feedback unit to provide appropriate support tailored to the user's emotional state, supporting a sustainable diet. Additionally, the feedback unit has the functionality to collect user feedback and use it to improve the overall system. For example, it evaluates the feedback provided by users and uses that evaluation to improve the content and method of feedback. This allows the feedback unit to continue providing optimal support tailored to the user's needs.

[0074] The reception desk can receive the user's target weight and deadline. The reception desk can, for example, allow the user to input their target weight and deadline. For example, the reception desk can allow the user to set a target weight and input a deadline. By receiving the user's target weight and deadline, an individual diet plan can be set. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can use AI to analyze the user's input of target weight and deadline and propose an appropriate plan.

[0075] The analysis unit can analyze the user's lifestyle, biometric data, and emotional state. For example, the analysis unit can analyze the user's lifestyle, such as their eating habits, exercise habits, and sleep patterns. The analysis unit can also analyze the user's biometric data, such as their weight, body fat percentage, and heart rate. Furthermore, the analysis unit can analyze the user's emotional state, such as using questionnaires, facial recognition, and voice analysis. This allows the analysis of the user's lifestyle, biometric data, and emotional state to provide a personalized diet plan. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the user's lifestyle, biometric data, and emotional state into AI and obtain analysis results.

[0076] The generation unit can generate meal and exercise plans using generative AI. For example, the generation unit generates meal and exercise plans using generative AI. Generative AI includes, for example, machine learning models, neural networks, and rule-based systems. The generation unit generates meal and exercise plans based, for example, the user's lifestyle, biometric data, and emotional state. For example, the generation unit generates meal and exercise plans considering the user's eating habits, exercise habits, and sleep patterns. The generation unit can also generate meal and exercise plans considering the user's weight, body fat percentage, and heart rate. Furthermore, the generation unit can generate meal and exercise plans considering the user's emotional state. For example, the generation unit adjusts the plan considering the user's stress level and motivation. This allows the generation AI to generate the optimal meal and exercise plan for the user. Some or all of the above-described processes in the generation unit are performed using generative AI. For example, the generation unit inputs user data into the generative AI to generate the optimal plan.

[0077] The monitoring unit can monitor the user's scale data and food photos in real time. For example, the monitoring unit can monitor the user's scale data in real time. For example, the monitoring unit can periodically acquire the user's scale data and understand the progress. The monitoring unit can also monitor the user's food photos in real time. For example, the monitoring unit can monitor the contents of meals when the user takes photos of their meals and uploads them to the system. This allows the monitoring unit to understand the progress by monitoring the user's scale data and food photos in real time. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's scale data and food photos into AI and obtain analysis results.

[0078] The feedback unit can provide feedback tailored to the user's progress and emotional state. For example, the feedback unit can provide feedback based on the user's progress. For instance, it can provide advice based on the user's weight changes and exercise performance. The feedback unit can also provide feedback tailored to the user's emotional state. For example, it can provide encouraging messages based on the user's stress level and motivation. This allows for support of sustainable dieting by providing feedback tailored to the user's progress and emotional state. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's progress data and emotional state into AI to generate appropriate feedback.

[0079] The reception desk can estimate the user's emotions and adjust the target weight and deadline based on the estimated emotions. For example, the reception desk can estimate the user's emotions using facial recognition or voice analysis. The reception desk can also adjust the target weight and deadline based on the estimated emotions. For example, if the user is feeling stressed, the reception desk can set a lenient target weight and deadline and propose a manageable plan. Conversely, if the user is highly motivated, the reception desk can set a challenging target weight and deadline and propose a plan that will give them a sense of accomplishment. In this way, by adjusting the target weight and deadline based on the user's emotions, a manageable diet plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can analyze the user's emotional data in real time and instantly grasp changes in their emotions. For instance, the reception desk can monitor the user's facial expressions in real time and instantly detect changes in their emotions. Furthermore, the reception desk can accumulate user emotional data and analyze long-term emotional trends. For example, the reception desk can analyze user emotional data over time and identify patterns of emotional change. This allows for a detailed understanding of the user's emotions, enabling adjustments to target weight and deadlines.

[0080] The reception desk can analyze the user's past diet history and suggest an optimal target weight and deadline. For example, the reception desk can analyze the user's past diet history. For example, the reception desk can refer to the user's past successful diet plans and suggest a similar target weight and deadline. The reception desk can also analyze the user's past unsuccessful diet plans and suggest a target weight and deadline that avoids the causes of failure. In this way, by analyzing the user's past diet history, it is possible to suggest an optimal target weight and deadline. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past diet history into AI and suggest an optimal target weight and deadline.

[0081] The reception desk can filter users based on their current health status and lifestyle when setting target weight and deadlines. For example, the reception desk can set realistic target weight and deadlines considering the user's current health status. For example, the reception desk can set target weight and deadlines based on the user's health checkup data and doctor's diagnosis results. The reception desk can also set realistic target weight and deadlines considering the user's lifestyle. For example, the reception desk can set target weight and deadlines considering the user's workload and exercise frequency. This allows for the setting of realistic target weight and deadlines by filtering based on the user's current health status and lifestyle. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the user's health status and lifestyle into AI and suggest appropriate target weight and deadlines.

[0082] The reception desk can estimate the user's emotions and determine the priority of target weight and deadlines based on the estimated emotions. For example, the reception desk can estimate the user's emotions using facial recognition or voice analysis. The reception desk can also determine the priority of target weight and deadlines based on the estimated emotions. For example, if the user is feeling stressed, the reception desk can prioritize the deadline over the target weight. Conversely, if the user is highly motivated, the reception desk can prioritize the target weight over the deadline. This allows for the provision of a manageable diet plan by determining the priority of target weight and deadlines based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input user emotion data into AI to determine the priority of target weight and deadlines.

[0083] The reception desk can prioritize setting highly relevant goals when users set their target weight and deadline, taking into account their geographical location. For example, the reception desk can consider the user's geographical location. For instance, it can set target weight and deadlines considering the climate and topography of the area where the user lives. Furthermore, if the user has access to a nearby gym or fitness facility, the reception desk can set target weight and deadlines utilizing that facility. This allows for the setting of realistic target weights and deadlines by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location into AI and suggest appropriate target weights and deadlines.

[0084] The reception desk can analyze the user's social media activity and set relevant goals when setting target weight and deadlines. For example, the reception desk can analyze the user's social media activity. For example, the reception desk can analyze health and fitness posts that the user shares on social media and set target weight and deadlines. The reception desk can also set target weight and deadlines by referring to the trends of influencers and communities that the user follows. In this way, relevant target weight and deadlines can be set by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into AI and suggest appropriate target weight and deadlines.

[0085] The analysis unit can estimate the user's emotions and adjust the analysis methods for lifestyle and biometric data based on the estimated emotions. For example, the analysis unit can estimate the user's emotions using facial recognition or voice analysis. The analysis unit can also adjust the analysis methods for lifestyle and biometric data based on the estimated emotions. For example, if the user is stressed, the analysis unit can perform a lifestyle analysis focused on stress reduction. If the user is highly motivated, the analysis unit can perform a biometric data analysis aimed at challenging goals. By adjusting the analysis method based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into AI and adjust the analysis methods for lifestyle and biometric data.

[0086] The analysis unit can improve the accuracy of its analysis by referring to the user's past health data during the analysis process. For example, the analysis unit can refer to the user's past health data. For example, the analysis unit can refer to the user's past weight fluctuation data to predict current weight fluctuations. The analysis unit can also analyze the user's past eating records to evaluate current eating patterns. This improves the accuracy of the analysis by referring to the user's past health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past health data into AI to improve the accuracy of the analysis.

[0087] The analysis unit can apply different analysis algorithms depending on the user's lifestyle and eating patterns during analysis. For example, the analysis unit considers the user's lifestyle. For instance, if the user has a nocturnal lifestyle, the analysis unit can apply an analysis algorithm that corresponds to nighttime eating patterns. Similarly, if the user is a vegetarian, the analysis unit can apply an analysis algorithm specifically for plant-based foods. This allows for the provision of more appropriate analysis results by applying different analysis algorithms according to the user's lifestyle and eating patterns. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the user's lifestyle and eating patterns into AI and apply an appropriate analysis algorithm.

[0088] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions using facial recognition or voice analysis. The analysis unit can also adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. By adjusting the display method based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into AI and adjust the display method of the analysis results.

[0089] The analysis unit can prioritize analyses based on user submission timing during the analysis process. For example, the analysis unit considers user submission timing. If a user is in a hurry, the analysis unit can prioritize the analysis based on the submission timing. The analysis unit can also prioritize analyses to coincide with a specific event if the user is participating in that event. By prioritizing analyses based on user submission timing, more appropriate analysis results can be provided. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input user submission timing data into AI to determine analysis priorities.

[0090] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant market data during the analysis process. For example, the analysis unit can refer to the user's relevant market data. For example, the analysis unit can refer to health trends in the area where the user lives to improve the accuracy of its analysis. The analysis unit can also refer to data from fitness apps used by the user to improve the accuracy of its analysis. In this way, the accuracy of the analysis is improved by referring to the user's relevant market data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's relevant market data into AI to improve the accuracy of its analysis.

[0091] The generation unit can estimate the user's emotions and adjust the method of generating meal and exercise plans based on the estimated emotions. For example, the generation unit can estimate the user's emotions using facial recognition or voice analysis. The generation unit can also adjust the method of generating meal and exercise plans based on the estimated emotions. For example, if the user is feeling stressed, the generation unit can generate a relaxing meal and exercise plan. If the user is highly motivated, the generation unit can generate a challenging meal and exercise plan. By adjusting the generation method based on the user's emotions, a more appropriate meal and exercise plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit can input user emotion data into the generation AI and adjust the method of generating meal and exercise plans.

[0092] The generation unit can optimize its generation algorithm by referencing the user's past diet plans during the generation process. For example, the generation unit can refer to the user's past diet plans. For instance, it can generate a similar plan by referencing a diet plan that the user has successfully completed in the past. It can also analyze diet plans that the user has failed at in the past and generate a plan that avoids the causes of failure. This allows the generation algorithm to be optimized by referencing the user's past diet plans, providing a more effective plan. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's past diet plans into the generation AI to optimize its generation algorithm.

[0093] The generation unit can customize the plan content based on the user's current health status during generation. For example, the generation unit can consider the user's current health status. For example, the generation unit can customize the plan content based on the user's health checkup data or doctor's diagnosis results. The generation unit can also generate a meal plan that is fortified with specific nutrients according to the user's health status. This allows for the provision of a more appropriate diet and exercise plan by customizing the plan content based on the user's current health status. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's health status data into AI to customize the plan content.

[0094] The generation unit can estimate the user's emotions and determine the priority of the plans to generate based on the estimated emotions. For example, the generation unit can estimate the user's emotions using facial recognition or voice analysis. The generation unit can also determine the priority of the plans to generate based on the estimated emotions. For example, if the user is feeling stressed, the generation unit can prioritize generating plans with a relaxing effect. Also, if the user is highly motivated, the generation unit can prioritize generating challenging plans. This allows for the provision of more appropriate diet and exercise plans by prioritizing plans based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit can input user emotion data into the generation AI to determine the priority of plans.

[0095] The generation unit can generate an optimal plan by considering the user's geographical location information during the generation process. For example, the generation unit can consider the user's geographical location information. For example, the generation unit can generate an exercise plan by considering the climate and topography of the area where the user lives. The generation unit can also generate a plan that utilizes nearby gyms or fitness facilities if the user has access to them. This allows for the provision of more realistic meal and exercise plans by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into AI and generate an optimal plan.

[0096] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated user emotions. For example, the monitoring unit can estimate the user's emotions using facial recognition or voice analysis. The monitoring unit can also adjust the monitoring method based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit can focus on monitoring to reduce stress. If the user is highly motivated, the monitoring unit can focus on monitoring towards challenging goals. By adjusting the monitoring method based on the user's emotions, more appropriate monitoring can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into AI and adjust the monitoring method.

[0097] The monitoring unit can improve the accuracy of monitoring by referring to the user's past weight data during monitoring. For example, the monitoring unit can refer to the user's past weight data. For example, the monitoring unit can refer to the user's past weight fluctuation data and predict current weight fluctuations. The monitoring unit can also analyze the user's past eating records and evaluate current eating patterns. This improves the accuracy of monitoring by referring to the user's past weight data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's past weight data into AI to improve the accuracy of monitoring.

[0098] The monitoring unit can apply different monitoring methods depending on the user's eating patterns and exercise history during monitoring. For example, the monitoring unit considers the user's eating patterns. For instance, if the user has a nocturnal lifestyle, the monitoring unit can apply a monitoring method that corresponds to nighttime eating patterns. Furthermore, if the user is a vegetarian, the monitoring unit can apply a monitoring method specifically tailored to plant-based foods. This allows for more appropriate monitoring by applying different monitoring methods according to the user's eating patterns and exercise history. Some or all of the above-described processes in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can input data on the user's eating patterns and exercise history into an AI and apply an appropriate monitoring method.

[0099] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. For example, the monitoring unit can estimate the user's emotions using facial recognition or voice analysis. The monitoring unit can also adjust the display method of the monitoring results based on the estimated user emotions. For example, if the user is tense, the monitoring unit can provide a simple and highly visible display method. If the user is relaxed, the monitoring unit can provide a display method that includes detailed information. By adjusting the display method based on the user's emotions, more appropriate monitoring results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into AI and adjust the display method of the monitoring results.

[0100] The monitoring unit can determine monitoring priorities based on the user's submission timing during monitoring. For example, the monitoring unit considers the user's submission timing. For instance, if the user is in a hurry, the monitoring unit can prioritize monitoring based on the submission timing. Furthermore, if the user is participating in a specific event, the monitoring unit can prioritize monitoring accordingly. This allows for more appropriate monitoring by determining monitoring priorities based on the user's submission timing. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user submission timing data into AI to determine monitoring priorities.

[0101] The monitoring unit can improve the accuracy of monitoring by referring to the user's relevant market data during monitoring. For example, the monitoring unit can refer to the user's relevant market data. For example, the monitoring unit can refer to health trends in the area where the user lives to improve the accuracy of monitoring. The monitoring unit can also refer to data from fitness apps used by the user to improve the accuracy of monitoring. In this way, the accuracy of monitoring is improved by referring to the user's relevant market data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's relevant market data into AI to improve the accuracy of monitoring.

[0102] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, the feedback unit can estimate the user's emotions using facial recognition or voice analysis. The feedback unit can also adjust the content of the feedback based on the estimated emotions. For example, if the user is feeling stressed, the feedback unit can provide relaxing feedback. If the user is highly motivated, the feedback unit can provide challenging feedback. By adjusting the content of the feedback based on the user's emotions, more appropriate feedback can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input user emotion data into AI and adjust the content of the feedback.

[0103] The feedback unit can provide optimal feedback by referring to the user's past feedback history when providing feedback. For example, the feedback unit can refer to the user's past feedback history. For example, the feedback unit can refer to feedback the user has received in the past and provide similar feedback. The feedback unit can also analyze the effect of feedback the user has received in the past and provide optimal feedback. In this way, optimal feedback can be provided by referring to the user's past feedback history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without using AI. For example, the feedback unit can input the user's past feedback history into AI and provide optimal feedback.

[0104] The feedback unit can customize the content of the feedback based on the user's current health status. For example, the feedback unit can consider the user's current health status. For example, the feedback unit can customize the content of the feedback based on the user's health checkup data or the results of a doctor's diagnosis. The feedback unit can also provide feedback that is fortified with specific nutrients depending on the user's health status. This allows for the provision of more appropriate feedback by customizing the content of the feedback based on the user's current health status. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's health status data into AI to customize the content of the feedback.

[0105] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, the feedback unit can estimate the user's emotions using facial recognition or voice analysis. The feedback unit can also determine the priority of feedback based on the estimated emotions. For example, if the user is feeling stressed, the feedback unit can prioritize providing relaxing feedback. Also, if the user is highly motivated, the feedback unit can prioritize providing challenging feedback. This allows for the provision of more appropriate feedback by prioritizing feedback based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input user emotion data into AI to determine the priority of feedback.

[0106] The feedback unit can provide optimal feedback by considering the user's geographical location information. For example, the feedback unit can consider the user's geographical location information. For example, the feedback unit can suggest an appropriate exercise plan by considering the climate and topography of the area where the user lives. Also, if the user has access to a nearby gym or fitness facility, the feedback unit can provide feedback utilizing that facility. This allows for the provision of more appropriate feedback by considering the user's geographical location information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's geographical location information into AI to provide optimal feedback.

[0107] The feedback unit can analyze the user's social media activity and suggest feedback content when providing feedback. For example, the feedback unit can analyze the user's social media activity. For example, it can analyze posts related to health and fitness that the user shares on social media and suggest feedback content. The feedback unit can also suggest feedback content by referring to the influencers and community trends that the user follows. This allows for the provision of more appropriate feedback by analyzing the user's social media activity. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the user's social media activity data into AI and suggest feedback content.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The reception section can receive information about the user's dietary preferences and allergies. For example, if a user has an allergy to a specific ingredient, inputting this information allows the generation section to generate a meal plan that takes the allergy into account. Furthermore, inputting the user's dietary preferences allows the generation section to provide a meal plan tailored to those preferences. This enables the provision of personalized support that takes into account the user's dietary preferences and allergy information.

[0110] The analysis department can analyze users' sleep data and adjust diet plans based on sleep quality. For example, by analyzing a user's sleep duration and quality, it can adjust the plan, such as reducing exercise, if sleep deprivation persists. Conversely, if sleep quality improves, it can adjust the plan, such as increasing exercise. This allows the system to provide diet plans that take the user's sleep data into consideration.

[0111] The generation unit can incorporate seasonal ingredients into the user's meal plan. For example, it can provide meal plans featuring fresh vegetables and fruits in spring, and plans emphasizing cold dishes and hydration in summer. It can also offer meal plans incorporating nutritious root vegetables in autumn and warming dishes in winter. This allows the system to support the user's health by providing meal plans tailored to the season.

[0112] The monitoring unit can monitor the user's exercise data in real time and evaluate the effectiveness of the exercise. For example, it can monitor the type of exercise, duration, and calories burned by the user in real time and evaluate the effectiveness of the exercise. Furthermore, if the exercise is not effective, it can adjust the exercise plan. This allows for real-time monitoring of the user's exercise data and the provision of an effective exercise plan.

[0113] The feedback section can offer rewards based on user progress. For example, points or badges can be awarded as rewards when a user reaches their target weight or continues their exercise plan. Furthermore, users can accumulate rewards to receive special benefits or discounts. This can boost user motivation and support sustainable weight loss.

[0114] The reception desk can estimate the user's emotions and adjust the target weight and deadline based on those estimates. For example, if the user is feeling stressed, it can set a more lenient target weight and deadline, suggesting a manageable plan. Conversely, if the user is highly motivated, it can set a more challenging target weight and deadline, suggesting a plan that will allow them to achieve a sense of accomplishment. In this way, by adjusting the target weight and deadline based on the user's emotions, a manageable diet plan can be provided.

[0115] The analysis unit can estimate the user's emotions and adjust the analysis methods for lifestyle and biometric data based on those estimated emotions. For example, if a user is experiencing stress, the system can perform a lifestyle analysis focused on stress reduction. Similarly, if a user is highly motivated, the system can analyze biometric data in line with challenging goals. By adjusting the analysis methods based on the user's emotions, the system can provide more appropriate analysis results.

[0116] The generation unit can estimate the user's emotions and adjust the method of generating meal and exercise plans based on those estimated emotions. For example, if the user is feeling stressed, it can generate a meal and exercise plan with a relaxing effect. Conversely, if the user is highly motivated, it can generate a challenging meal and exercise plan. By adjusting the generation method based on the user's emotions, it is possible to provide more appropriate meal and exercise plans.

[0117] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on those emotions. For example, if the user is feeling stressed, monitoring can be focused on stress reduction. Conversely, if the user is highly motivated, monitoring can be geared towards challenging goals. This allows for more appropriate monitoring by adjusting the monitoring method based on the user's emotions.

[0118] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on those emotions. For example, if the user is feeling stressed, it can provide relaxing feedback. Conversely, if the user is highly motivated, it can provide challenging feedback. By adjusting the content of the feedback based on the user's emotions, it is possible to provide more appropriate feedback.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The reception desk receives input from the user. User input includes, for example, text input, voice input, and image input. The reception desk allows the user to enter their target weight and deadline. Step 2: The analysis department analyzes the information received by the reception department. The analysis department analyzes the user's lifestyle, biometric data, and emotional state. Lifestyle includes eating habits, exercise habits, sleep patterns, etc., while biometric data includes weight, body fat percentage, heart rate, etc. Emotional state includes questionnaires, facial recognition, voice analysis, etc. Step 3: The generation unit generates meal and exercise plans based on the data analyzed by the analysis unit. The generation unit generates meal and exercise plans using generative AI. Generative AI includes machine learning models, neural networks, rule-based systems, etc. Step 4: The monitoring unit monitors the user's progress based on the plan generated by the generation unit. The monitoring unit monitors the user's weighing scale data and meal photos in real time. Real-time monitoring includes the data update frequency, the sensors and devices used, etc. Step 5: The Feedback Unit provides feedback based on the progress monitored by the Monitoring Unit. The Feedback Unit provides feedback tailored to the user's progress and emotional state. Feedback may include advice, encouraging messages, and suggestions for improvement.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, monitoring unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives input from the user using the touch panel 38A and microphone 38B of the smart device 14. The analysis unit analyzes the user's lifestyle, biometric data, and emotional state using the specific processing unit 290 of the data processing unit 12. The generation unit generates a meal and exercise plan using generation AI via the specific processing unit 290 of the data processing unit 12. The monitoring unit monitors the user's weight data and photos of meals in real time using the camera 42 of the smart device 14. The feedback unit provides feedback according to the user's progress and emotional state via the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] 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.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The 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.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 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.

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the 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.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 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.

[0140] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, monitoring unit, and feedback unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives voice input from the user using the microphone 238 of the smart glasses 214. The analysis unit analyzes the user's lifestyle, biometric data, and emotional state using the specific processing unit 290 of the data processing unit 12. The generation unit generates a meal and exercise plan using generated AI, also using the specific processing unit 290 of the data processing unit 12. The monitoring unit monitors the user's weight data and photos of meals in real time using the camera 42 of the smart glasses 214. The feedback unit provides feedback according to the user's progress and emotional state using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] 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.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The 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.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (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).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, monitoring unit, and feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives voice input from the user using the microphone 238 of the headset terminal 314. The analysis unit analyzes the user's lifestyle, biometric data, and emotional state using the specific processing unit 290 of the data processing unit 12. The generation unit generates a meal and exercise plan using a generation AI via the specific processing unit 290 of the data processing unit 12. The monitoring unit monitors the user's weight data and photos of meals in real time using the camera 42 of the headset terminal 314. The feedback unit provides feedback according to the user's progress and emotional state using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, monitoring unit, and feedback unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives voice input from the user using the microphone 238 of the robot 414. The analysis unit analyzes the user's lifestyle, biometric data, and emotional state using the specific processing unit 290 of the data processing unit 12. The generation unit generates a meal and exercise plan using generated AI via the specific processing unit 290 of the data processing unit 12. The monitoring unit monitors the user's weight data and photos of meals in real time using the camera 42 of the robot 414. The feedback unit provides feedback according to the user's progress and emotional state via the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] (Note 1) A reception area that receives input from users, An analysis unit analyzes the information received by the aforementioned reception unit, A generation unit generates a meal and exercise plan based on the data analyzed by the analysis unit, A monitoring unit monitors the user's progress based on the plan generated by the generation unit, The system includes a feedback unit that provides feedback based on the progress monitored by the monitoring unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is The system accepts the user's target weight and deadline. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Analyzes users' lifestyles, biometric data, and emotional states. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generating meal and exercise plans using AI. The system described in Appendix 1, characterized by the features described herein. (Note 5) The monitoring unit, It monitors the user's weight scale data and photos of their meals in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned feedback unit is Provide feedback tailored to the user's progress and emotional state. 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 target weight and deadline based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past dieting history and suggests the optimal target weight and deadline. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When setting target weight and deadlines, filtering is performed based on the user's current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of target weight and deadlines 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 setting target weight and deadlines, the system prioritizes highly relevant goals by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When setting target weight and deadlines, the system analyzes the user's social media activity and sets relevant goals. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis methods of lifestyle and biometric data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, we refer to the user's past health data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the user's lifestyle and eating patterns. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, the analysis priority is determined based on when the user submitted the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, we improve the accuracy of the analysis by referring to relevant market data for the user. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts how meal and exercise plans are generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the generation algorithm is optimized by referencing the user's past diet plans. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, the plan content is customized based on the user's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and determines the priority of the plans generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the system considers the user's geographical location to generate the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 24) The monitoring unit, We estimate the user's emotions and adjust the monitoring method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The monitoring unit, During monitoring, the system improves monitoring accuracy by referencing the user's past weight data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The monitoring unit, During monitoring, different monitoring methods are applied depending on the user's eating patterns and exercise history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The monitoring unit, It estimates the user's emotions and adjusts how monitoring results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The monitoring unit, During monitoring, the monitoring priority is determined based on when the user submitted the data. The system described in Appendix 1, characterized by the features described herein. (Note 29) The monitoring unit, During monitoring, we improve the accuracy of monitoring by referencing relevant market data from the user. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback unit is It estimates the user's emotions and adjusts the content of the feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned feedback unit is When providing feedback, we refer to the user's past feedback history to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned feedback unit is When providing feedback, customize the content of the feedback based on the user's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned feedback unit is When providing feedback, we take the user's geographical location into consideration to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned feedback unit is When providing feedback, we analyze the user's social media activity and suggest content for the feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that receives input from users, An analysis unit analyzes the information received by the aforementioned reception unit, A generation unit generates a meal and exercise plan based on the data analyzed by the analysis unit, A monitoring unit monitors the user's progress based on the plan generated by the generation unit, The system includes a feedback unit that provides feedback based on the progress monitored by the monitoring unit. A system characterized by the following features.

2. The aforementioned reception unit is The system accepts the user's target weight and deadline. The system according to feature 1.

3. The aforementioned analysis unit is Analyzes users' lifestyles, biometric data, and emotional states. The system according to feature 1.

4. The generating unit is Generating meal and exercise plans using AI. The system according to feature 1.

5. The monitoring unit, It monitors the user's weight scale data and photos of their meals in real time. The system according to feature 1.

6. The aforementioned feedback unit is Provide feedback tailored to the user's progress and emotional state. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the target weight and deadline based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past dieting history and suggests the optimal target weight and deadline. The system according to feature 1.

Images