system

Abstract

The system according to this embodiment aims to propose and support the user in implementing an optimal daily meal and exercise plan. [Solution] The system according to the embodiment comprises a success data collection unit, a personal history collection unit, an analysis unit, an execution unit, a reception unit, and an adjustment unit. The success data collection unit collects data on people who have successfully lost weight. The personal history collection unit collects the personal history of the user's exercise and eating habits. The analysis unit analyzes the data collected by the success data collection unit and the personal history collection unit and proposes an optimal daily meal and exercise menu for the user. The execution unit executes the menu proposed by the analysis unit. The reception unit receives the results executed by the execution unit as feedback. The adjustment unit adjusts the proposed content based on the feedback received by the reception unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to individually propose an optimal diet and exercise menu for successful dieting, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal daily diet and exercise menu for the user and support its execution.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a success data collection unit, a personal history collection unit, an analysis unit, an execution unit, a reception unit, and an adjustment unit. The success data collection unit collects data on people who have successfully lost weight. The personal history collection unit collects the personal history of the user's exercise and eating habits. The analysis unit analyzes the data collected by the success data collection unit and the personal history collection unit and proposes an optimal daily meal and exercise menu for the user. The execution unit executes the menu proposed by the analysis unit. The reception unit receives the results executed by the execution unit as feedback. The adjustment unit adjusts the proposed content based on the feedback received by the reception unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest and support the user in implementing an optimal daily meal and exercise plan. [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, etc. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The goal achievement support system according to an embodiment of the present invention is a system that helps users achieve their goals by suggesting and implementing the necessary daily diet and exercise routines to become their desired self (e.g., "I want to lose XX kg"). This goal achievement support system collects past data from people who have successfully lost weight and the user's personal history of exercise and diet, and an AI agent analyzes this data to suggest the user's optimal daily meal and exercise menu. The user executes the suggested menu and provides feedback on the results to the AI ​​agent. The AI ​​agent adjusts the suggestions based on the feedback and continues to support the user toward achieving their goals. For example, it collects detailed data such as the diet, exercise habits, and weight changes of people who have successfully lost weight. It also collects the user's personal history, such as their diet, exercise habits, and weight changes. This allows the AI ​​agent to understand the user's current situation. Next, the AI ​​agent analyzes the collected data. The AI ​​agent compares the data of successful individuals with the user's personal history and suggests the user's optimal daily meal and exercise menu. For example, it calculates the calorie intake and exercise amount necessary for the user to achieve their target weight and suggests a menu based on that. This allows the user to create a concrete action plan. The user executes the suggested menu and provides feedback on the results to the AI ​​agent. For example, the user actually consumes the suggested meal plan and performs the suggested exercise routine. As a result, they report changes in weight, physical condition, etc., to the AI ​​agent. This allows the AI ​​agent to understand the user's progress. Based on the feedback, the AI ​​agent adjusts the suggestions. For example, if the user's weight is approaching their target, it fine-tunes the meal and exercise routine. If the user has not reached their target, it reconsiders the routine and makes more effective suggestions. In this way, the user receives continuous support towards achieving their goals. Thus, the goal achievement support system can effectively support the user in achieving their goals.

[0029] The goal achievement support system according to this embodiment comprises a success data collection unit, a personal history collection unit, an analysis unit, an execution unit, a reception unit, and an adjustment unit. The success data collection unit collects data on people who have successfully lost weight. The success data collection unit can collect detailed data such as the dietary content, exercise habits, and weight changes of people who have successfully lost weight. For example, the success data collection unit records what kind of meals successful people ate and what kind of exercise they did. For example, the success data collection unit periodically records and collects the weight changes of successful people. The personal history collection unit collects the personal history of the user's exercise and eating habits. For example, the personal history collection unit can collect personal history such as the user's dietary content, exercise habits, and weight changes. For example, the personal history collection unit records what kind of meals users ate and what kind of exercise they did. For example, the personal history collection unit periodically records and collects the weight changes of users. The analysis unit analyzes data collected by the success data collection unit and the personal history collection unit and proposes the optimal daily meal and exercise menu for the user. For example, the analysis unit can compare data from successful individuals with the user's personal history to propose the optimal daily meal and exercise menu for the user. For example, the analysis unit can calculate the calorie intake and exercise amount necessary for the user to achieve their target weight and propose a menu based on that. For example, the analysis unit can analyze the user's diet and exercise habits and propose an optimal menu. The execution unit executes the menu proposed by the analysis unit. For example, the execution unit can actually consume the proposed meal menu and perform the proposed exercise menu. For example, the execution unit can cook and consume the proposed meal menu. For example, the execution unit can perform the proposed exercise menu and exercise. The reception unit receives the results executed by the execution unit as feedback. For example, the reception unit can receive feedback such as changes in weight and changes in physical condition. For example, the reception unit reports the results of the meals and exercises performed by the user. The reception department records, for example, changes in the user's weight and physical condition, and accepts this as feedback. The adjustment department then adjusts the proposed solutions based on the feedback received by the reception department.The adjustment unit can, for example, fine-tune the diet and exercise menu if the user's weight is approaching their target. If the user has not reached their target, the adjustment unit can reconsider the menu and make more effective suggestions. The adjustment unit can also adjust the suggestions based on user feedback. In this way, the goal achievement support system according to the embodiment can effectively support the user in achieving their goals.

[0030] The Success Data Collection Unit collects data on people who have successfully lost weight. For example, it can collect detailed data on the dietary habits, exercise routines, and weight changes of successful dieters. Specifically, it records in detail what ingredients successful dieters chose, what cooking methods they used, the timing and frequency of meals, and their calorie intake. Regarding exercise habits, it records the type of exercise performed, its intensity, frequency, and duration. Furthermore, it regularly records and collects data on the weight changes of successful dieters. This allows the Success Data Collection Unit to collect detailed data on the overall lifestyle of successful dieters, enabling a multifaceted analysis of the factors contributing to their success. The collected data is stored on a cloud server and made accessible to the analysis unit. By adjusting the data collection frequency and accuracy, flexible responses to specific situations and conditions are possible. This allows the Success Data Collection Unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The personal history collection unit collects users' personal history of exercise and eating habits. For example, it can collect personal history such as the user's diet, exercise habits, and weight changes. Specifically, it records in detail what ingredients the user chooses, what cooking methods are used, the timing and frequency of meals, and calorie intake. Regarding exercise habits, it records the type of exercise performed, its intensity, frequency, and duration. Furthermore, it regularly records and collects data on the user's weight changes. This allows the personal history collection unit to collect detailed data across the user's entire lifestyle, enabling a multifaceted understanding of the user's current situation. The collected data is stored on a cloud server and made accessible to the analysis unit. By adjusting the data collection frequency and accuracy, flexible responses to specific situations and conditions are possible. This allows the personal history collection unit to collect data efficiently and effectively, improving the overall system performance.

[0032] The analysis unit analyzes data collected by the success data collection unit and the personal history collection unit to propose the optimal daily meal and exercise menu for the user. For example, the analysis unit can compare data from successful individuals with the user's personal history to propose the optimal daily meal and exercise menu for the user. Specifically, it uses AI to compare data from successful individuals with data from the user and analyzes similarities and differences. For example, it calculates the calorie intake and exercise amount necessary for the user to achieve their target weight and proposes a menu based on that. The AI ​​analyzes the user's diet and exercise habits to propose an optimal menu. For example, it suggests the balance of nutrients the user should consume, as well as the type and intensity of exercise. In this way, the analysis unit can effectively support the user in achieving their goals. Furthermore, the analysis unit can also utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict fluctuations in risk over a specific period based on historical data and formulate future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0033] The execution unit executes the menu proposed by the analysis unit. For example, the execution unit can actually consume the proposed meal menu and perform the proposed exercise menu. Specifically, it can cook and consume the proposed meal menu. The meal menu includes specific recipes and cooking methods, and the user can prepare meals according to them. It can also perform the proposed exercise menu and exercise. The exercise menu includes specific types of exercise, intensity, and duration, and the user can exercise according to them. In this way, the execution unit enables the user to execute the proposed menu and take concrete actions toward achieving their goals. Furthermore, the execution unit can monitor the user's execution status and provide support as needed. For example, if the user encounters difficulties while executing the proposed menu, the execution unit can provide appropriate advice and support. In this way, the execution unit enables the user to effectively execute the proposed menu and make steady progress toward achieving their goals.

[0034] The reception unit receives feedback on the results executed by the execution unit. For example, the reception unit can receive feedback on changes in weight or physical condition. Specifically, users report the results of their diet and exercise. Users can record changes in weight and physical condition and report them to the reception unit. This allows the reception unit to understand the user's performance and receive feedback. Furthermore, the reception unit can analyze the user's feedback and provide information to the adjustment unit as needed. For example, if a user loses weight as a result of following a suggested menu, this information can be provided to the adjustment unit to help adjust the suggestions. In this way, the reception unit can not only understand the user's performance and receive feedback, but also provide information to improve the overall performance of the system.

[0035] The adjustment unit adjusts the suggested content based on the feedback received by the reception unit. For example, if the user's weight is approaching its target, the adjustment unit can fine-tune the diet and exercise menu. Specifically, if the user's weight is approaching its target, it may reduce the calorie intake of meals or adjust the intensity of exercise. If the user has not reached their target, the adjustment unit will review the menu and make more effective suggestions. For example, if the user's weight has not decreased, it will review the contents of their meals and suggest reducing their calorie intake. It will also review the type and intensity of exercise and suggest a more effective exercise menu. In this way, the adjustment unit can adjust the suggested content based on user feedback and effectively support the user in achieving their goals. Furthermore, the adjustment unit can analyze user feedback and provide information to improve the overall system performance. For example, it can analyze feedback from multiple users to identify common challenges and problems and improve the overall system based on that. In this way, the adjustment unit can not only effectively support the user in achieving their goals but also improve the overall system performance.

[0036] The success data collection unit can collect detailed data on the dietary habits, exercise routines, and weight changes of people who have successfully lost weight. For example, the success data collection unit records what kind of meals successful people ate and what kind of exercise they did. For example, the success data collection unit regularly records and collects data on the weight changes of successful people. For example, the success data collection unit records the dietary habits of successful people in detail and analyzes their calorie intake and nutritional balance. For example, the success data collection unit records the exercise routines of successful people in detail and analyzes the type, frequency, and intensity of their exercise. By collecting detailed data on successful people, it becomes possible to make more accurate suggestions. Some or all of the above processing in the success data collection unit may be performed using AI, for example, or not using AI. For example, the success data collection unit can input data on the dietary habits and exercise routines of successful people into a generating AI, which can analyze the data and extract detailed information.

[0037] The personal history collection unit can collect personal history such as the user's diet, exercise habits, and weight changes. For example, the personal history collection unit records what the user ate and what kind of exercise they did. For example, the personal history collection unit periodically records the user's weight changes and collects that data. For example, the personal history collection unit records the user's diet in detail and analyzes calorie intake and nutritional balance. For example, the personal history collection unit records the user's exercise habits in detail and analyzes the type, frequency, and intensity of exercise. By collecting the user's personal history in this way, it becomes possible to make optimal suggestions to the user. Some or all of the above processing in the personal history collection unit may be performed using AI, for example, or not using AI. For example, the personal history collection unit can input data on the user's diet and exercise habits into a generating AI, which can analyze the data and extract detailed information.

[0038] The analysis unit can compare data from successful individuals with the user's personal history and propose the optimal daily meal and exercise menu for the user. For example, the analysis unit can compare data from successful individuals with the user's personal history and propose the optimal daily meal and exercise menu for the user. For example, the analysis unit can calculate the calorie intake and exercise amount necessary for the user to achieve their target weight and propose a menu based on that. For example, the analysis unit can analyze the user's diet and exercise habits and propose an optimal menu. For example, the analysis unit can propose an optimal meal menu for the user based on data from successful individuals. For example, the analysis unit can propose an optimal exercise menu based on the user's personal history. In this way, by comparing data from successful individuals with the user's personal history, the optimal menu can be proposed. 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 data from successful individuals and the user's personal history into a generating AI, which can analyze the data and propose an optimal menu.

[0039] The execution unit can actually consume the proposed meal menu and perform the proposed exercise menu. For example, the execution unit can cook and consume the proposed meal menu. For example, the execution unit can perform the proposed exercise menu and exercise. For example, the execution unit can actually consume the proposed meal menu and record the results. For example, the execution unit can perform the proposed exercise menu and record the results. This allows the user to take action toward achieving their goals by performing the proposed menu. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the results of performing the proposed meal menu and exercise menu into a generating AI, which can then analyze the results and provide feedback.

[0040] The reception desk can receive feedback such as changes in weight and physical condition. The reception desk can, for example, report the results of the meals and exercises performed by the user. The reception desk can, for example, record the user's changes in weight and physical condition and receive this as feedback. The reception desk can, for example, record in detail the results of the meals and exercises performed by the user and collect this data. The reception desk can, for example, periodically record the user's changes in weight and physical condition and collect this data. This allows the reception desk to understand the user's progress by receiving feedback. 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 data on the user's changes in weight and physical condition into a generating AI, which can then analyze the data and provide feedback.

[0041] The adjustment unit can adjust the suggested content based on feedback. For example, if the user's weight is approaching its target, the adjustment unit will fine-tune the diet and exercise menu. For example, if the user has not reached their target, the adjustment unit will reconsider the menu and make more effective suggestions. The adjustment unit adjusts the suggested content based on user feedback. For example, the adjustment unit adjusts the suggested content based on changes in the user's weight or physical condition. In this way, by adjusting the suggested content based on feedback, it supports the user in achieving their goals. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input user feedback data into a generating AI, which can analyze the data and adjust the suggested content.

[0042] The Success Data Collection Unit can analyze the past diet history of successful individuals and select the optimal data collection method. For example, the Success Data Collection Unit can select a similar data collection method based on the diet methods that successful individuals have used in the past. For example, the Success Data Collection Unit can eliminate data collection methods that have failed in the past to avoid those methods. For example, the Success Data Collection Unit can select the most effective data collection method from the past diet history of successful individuals. In this way, the optimal data collection method can be selected by analyzing the past diet history of successful individuals. Some or all of the above processing in the Success Data Collection Unit may be performed using AI, for example, or without AI. For example, the Success Data Collection Unit can input the past diet history data of successful individuals into a generating AI, which can then analyze the data and select the optimal data collection method.

[0043] The success data collection unit can filter data based on the current living situation and areas of interest of successful individuals during data collection. For example, the success data collection unit can prioritize the collection of data that is highly relevant to the successful individual's current living situation. For example, the success data collection unit can filter and collect specific data based on the successful individual's areas of interest. For example, the success data collection unit can adjust the scope of data collection according to the successful individual's living situation and areas of interest. This allows for the collection of highly relevant data by filtering the data based on the successful individual's current living situation and areas of interest. Some or all of the above processing in the success data collection unit may be performed using AI, for example, or without AI. For example, the success data collection unit can input data on the successful individual's living situation and areas of interest into a generating AI, which can then analyze and filter the data.

[0044] The success data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the successors during data collection. For example, if a successor lives in a specific region, the success data collection unit will prioritize the collection of data related to that region. For example, if a successor is traveling, the success data collection unit will prioritize the collection of data related to their travel destination. For example, the success data collection unit can filter and collect highly relevant data based on the geographical location information of the successors. This enables effective data collection by collecting highly relevant data based on the geographical location information of the successors. Some or all of the above processing in the success data collection unit may be performed using AI, for example, or without AI. For example, the success data collection unit can input the geographical location information data of the successors into a generating AI, which can then analyze the data and prioritize the collection of highly relevant data.

[0045] The success data collection unit can analyze the social media activities of successful individuals and collect relevant data during data collection. For example, the success data collection unit can analyze the content of successful individuals' social media posts and collect relevant data. For example, the success data collection unit can adjust the timing of data collection based on the frequency of successful individuals' social media activity. For example, the success data collection unit can collect relevant data based on the areas of interest of successful individuals on social media. This allows for the effective collection of relevant data by analyzing the social media activities of successful individuals. Some or all of the above-described processes in the success data collection unit may be performed using AI, for example, or without AI. For example, the success data collection unit can input successful individuals' social media activity data into a generating AI, which can then analyze the data and collect relevant data.

[0046] The personal history collection unit can analyze a user's past eating habits and exercise history and select the optimal collection method. For example, the personal history collection unit can select a similar history collection method based on a diet method the user has successfully used in the past. For example, the personal history collection unit can exclude a history collection method to avoid a diet method the user has failed at in the past. For example, the personal history collection unit can select the most effective history collection method from a user's past eating habits and exercise history. In this way, the optimal collection method can be selected by analyzing the user's past history. Some or all of the above processing in the personal history collection unit may be performed using AI, for example, or without AI. For example, the personal history collection unit can input the user's past eating habits and exercise history data into a generating AI, which can then analyze the data and select the optimal collection method.

[0047] The personal history collection unit can filter personal history based on the user's current living situation and areas of interest when collecting it. For example, the personal history collection unit can prioritize collecting history that is highly relevant to the user's current living situation. For example, the personal history collection unit can filter and collect specific history based on the user's areas of interest. For example, the personal history collection unit can adjust the scope of history collection according to the user's living situation and areas of interest. This allows for the collection of highly relevant history by filtering history based on the user's current living situation and areas of interest. Some or all of the above processing in the personal history collection unit may be performed using AI, for example, or without AI. For example, the personal history collection unit can input data on the user's living situation and areas of interest into a generating AI, which can then analyze and filter the data.

[0048] The personal history collection unit can prioritize the collection of highly relevant history by considering the user's geographical location information when collecting personal history. For example, if the user lives in a specific region, the personal history collection unit will prioritize the collection of history related to that region. For example, if the user is traveling, the personal history collection unit will prioritize the collection of history related to the travel destination. For example, the personal history collection unit will filter and collect highly relevant history based on the user's geographical location information. This enables effective history collection by collecting highly relevant history based on the user's geographical location information. Some or all of the above processing in the personal history collection unit may be performed using AI, for example, or without AI. For example, the personal history collection unit can input the user's geographical location information data into a generating AI, which can then analyze the data and prioritize the collection of highly relevant history.

[0049] The personal history collection unit can analyze a user's social media activity and collect relevant history when collecting personal history. For example, the personal history collection unit can analyze the content of a user's social media posts and collect relevant history. For example, the personal history collection unit can adjust the timing of history collection based on the frequency of the user's activity on social media. For example, the personal history collection unit can collect relevant history based on the user's areas of interest on social media. This allows for the effective collection of relevant history by analyzing the user's social media activity. Some or all of the above processing in the personal history collection unit may be performed using AI, for example, or without AI. For example, the personal history collection unit can input the user's social media activity data into a generating AI, which can then analyze the data and collect relevant history.

[0050] The analysis unit can adjust the level of detail of the analysis based on the importance of the data of successful users and the user's personal history during the analysis. For example, if the data of successful users is important, the analysis unit will perform a detailed analysis. For example, if the user's personal history is important, the analysis unit will perform a detailed analysis. For example, the analysis unit will adjust the level of detail of the analysis by considering the balance between the data of successful users and the user's personal history. This makes effective analysis possible by adjusting the level of detail of the analysis based on the importance of the data of successful users and the user's personal history. 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 data of successful users and the user's personal history into a generating AI, and the generating AI can analyze the data and adjust the level of detail.

[0051] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a nutrition analysis algorithm to dietary data, an exercise analysis algorithm to exercise data, and a weight analysis algorithm to weight change data. This enables effective analysis by applying the most appropriate analysis algorithm for each data category. 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 dietary data, exercise data, and weight change data into a generating AI, which can then apply different analysis algorithms depending on the data category.

[0052] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may analyze the most recent data while referring to past data. For example, the analysis unit may adjust the order of analysis based on the data collection timing. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection timing. 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 data collection timing data into a generating AI, and the generating AI can analyze the data and determine the priority.

[0053] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit adjusts the order of analysis based on the relevance of the data. This allows for more effective analysis by adjusting the order of analysis based on the relevance of the data. 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 relevance data into a generating AI, and the generating AI can analyze the data and adjust the order.

[0054] The execution unit can analyze the user's past execution history and select the optimal execution method at runtime. For example, the execution unit may select a similar method based on the user's past successful execution methods. For example, the execution unit may eliminate methods to avoid the user's past failures. For example, the execution unit may select the most effective execution method from the user's past execution history. In this way, the optimal execution method can be selected by analyzing the user's past execution history. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's past execution history data into a generating AI, which can then analyze the data and select the optimal execution method.

[0055] The execution unit can customize the means of execution based on the user's current living situation during execution. For example, the execution unit adjusts the means of execution according to the user's current living situation. For example, the execution unit customizes the means of execution based on the user's living situation. For example, the execution unit optimizes the means of execution according to the user's living situation. This makes effective execution possible by customizing the means of execution based on the user's current living situation. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input user living situation data into a generating AI, and the generating AI can analyze the data to customize the means of execution.

[0056] The execution unit can select the optimal execution method at runtime, taking into account the user's geographical location information. For example, if the user lives in a specific region, the execution unit will select an execution method relevant to that region. For example, if the user is traveling, the execution unit will select an execution method for the travel destination. For example, the execution unit will select the optimal execution method based on the user's geographical location information. This enables effective execution by selecting the optimal execution method based on the user's geographical location information. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's geographical location information data into a generating AI, which can then analyze the data and select the optimal execution method.

[0057] The execution unit can analyze the user's social media activity at runtime and propose means of execution. For example, the execution unit can analyze the content of the user's social media posts and propose relevant means of execution. For example, the execution unit can propose means of execution based on the frequency of the user's activity on social media. For example, the execution unit can propose means of execution based on the user's areas of interest on social media. In this way, by analyzing the user's social media activity, relevant means of execution can be effectively proposed. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's social media activity data into a generating AI, and the generating AI can analyze the data and propose means of execution.

[0058] The reception unit can select the optimal reception method when receiving feedback by referring to the user's past feedback history. For example, the reception unit may select a similar reception method based on feedback previously provided by the user. For example, the reception unit may select the optimal reception method based on the content of feedback previously provided by the user. For example, the reception unit may select the most effective reception method from the user's past feedback history. In this way, the optimal reception method can be selected by referring to the user's past feedback history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past feedback history data into a generating AI, and the generating AI can analyze the data to select the optimal reception method.

[0059] The reception unit can customize the means of receiving feedback based on the user's current living situation. For example, the reception unit adjusts the means of receiving feedback according to the user's current living situation. For example, the reception unit customizes the means of receiving feedback based on the user's living situation. For example, the reception unit optimizes the means of receiving feedback according to the user's living situation. This makes it possible to receive effective feedback by customizing the means of receiving feedback based on the user's current living situation. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input user living situation data into a generating AI, and the generating AI can analyze the data to customize the means of receiving feedback.

[0060] The reception unit can select the optimal reception method when receiving feedback, taking into account the user's geographical location information. For example, if the user lives in a specific region, the reception unit will select a reception method relevant to that region. For example, if the user is traveling, the reception unit will select a reception method for their travel destination. For example, the reception unit will select the optimal reception method based on the user's geographical location information. This enables effective feedback reception by selecting the optimal reception method based on the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information data into a generating AI, which can then analyze the data and select the optimal reception method.

[0061] The reception unit can analyze the user's social media activity and propose methods for receiving feedback when it is received. For example, the reception unit can analyze the content of the user's social media posts and propose relevant methods for receiving feedback. For example, the reception unit can propose methods for receiving feedback based on the frequency of the user's activity on social media. For example, the reception unit can propose methods for receiving feedback based on the user's areas of interest on social media. In this way, by analyzing the user's social media activity, relevant methods for receiving feedback can be effectively proposed. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI, and the generating AI can analyze the data and propose methods for receiving feedback.

[0062] The adjustment unit can select the optimal adjustment method by referring to the user's past feedback history during the adjustment process. For example, the adjustment unit may select a similar adjustment method based on feedback previously provided by the user. For example, the adjustment unit may select the optimal adjustment method based on the content of feedback previously provided by the user. For example, the adjustment unit may select the most effective adjustment method from the user's past feedback history. In this way, the optimal adjustment method can be selected by referring to the user's past feedback history. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit may input the user's past feedback history data into a generating AI, and the generating AI may analyze the data to select the optimal adjustment method.

[0063] The adjustment unit can customize the means of adjustment based on the user's current living situation during the adjustment process. For example, the adjustment unit adjusts the means of adjustment according to the user's current living situation. For example, the adjustment unit customizes the means of adjustment based on the user's living situation. For example, the adjustment unit optimizes the means of adjustment according to the user's living situation. This makes effective adjustment possible by customizing the means of adjustment based on the user's current living situation. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input user living situation data into a generating AI, and the generating AI can analyze the data to customize the means of adjustment.

[0064] The adjustment unit can select the optimal adjustment method during adjustment, taking into account the user's geographical location information. For example, if the user lives in a specific region, the adjustment unit will select an adjustment method related to that region. For example, if the user is traveling, the adjustment unit will select an adjustment method for the travel destination. For example, the adjustment unit will select the optimal adjustment method based on the user's geographical location information. This enables effective adjustment by selecting the optimal adjustment method based on the user's geographical location information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's geographical location information data into a generating AI, and the generating AI can analyze the data to select the optimal adjustment method.

[0065] The adjustment unit can analyze the user's social media activity and propose adjustment measures during the adjustment process. For example, the adjustment unit can analyze the content of the user's social media posts and propose relevant adjustment measures. For example, the adjustment unit can propose adjustment measures based on the frequency of the user's activity on social media. For example, the adjustment unit can propose adjustment measures based on the user's areas of interest on social media. In this way, by analyzing the user's social media activity, relevant adjustment measures can be effectively proposed. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's social media activity data into a generating AI, which can then analyze the data and propose adjustment measures.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The goal achievement support system can monitor the user's sleep patterns, and the analysis unit can take that data into consideration to suggest meal and exercise menus. For example, if the user is not getting enough sleep, the analysis unit will suggest a lighter exercise menu and recommend a nutritious diet. Conversely, if the user is getting good quality sleep, the analysis unit will suggest a more strenuous exercise menu and recommend a diet that emphasizes calorie control. This allows the system to provide an optimal menu tailored to the user's sleep situation.

[0068] The success data collection unit also collects data on the hobbies and lifestyles of successful individuals, and the analysis unit can use this data to make suggestions to users. For example, if a successful individual enjoys outdoor activities, the analysis unit will suggest outdoor exercises to the user. Also, if a successful individual likes a particular food, the analysis unit will suggest menus that include that food. This makes it possible to make suggestions that are tailored to the user's lifestyle.

[0069] The reception department automatically categorizes user feedback, and the analysis department can adjust the proposed changes based on this categorization. For example, if the user feedback is positive, the analysis department will propose continuing with the current menu. Conversely, if there is a large amount of negative feedback, the analysis department will propose a significant revision of the menu. This enables flexible proposals based on user feedback.

[0070] The success data collection unit can filter data by considering the dietary and exercise preferences of successful individuals. For example, if a successful individual prefers a particular food, the system prioritizes collecting data containing that food. Similarly, if a successful individual prefers a particular exercise, the system prioritizes collecting data related to that exercise. This enables data collection based on the preferences of successful individuals.

[0071] The personal history collection unit can filter data considering the user's dietary and exercise preferences. For example, if a user prefers a particular food, it will prioritize collecting data containing that food. Similarly, if a user prefers a particular exercise, it will prioritize collecting data related to that exercise. This enables data collection based on the user's preferences.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The success data collection unit collects data on people who have successfully lost weight. For example, it collects detailed data such as the dietary habits, exercise routines, and weight changes of successful individuals. Step 2: The personal history collection unit collects the user's personal history regarding exercise and diet. For example, it collects personal history such as the user's diet, exercise habits, and weight changes. Step 3: The analysis unit analyzes the data collected by the success data collection unit and the personal history collection unit and proposes the optimal daily meal and exercise menu for the user. For example, it compares the data of successful individuals with the user's personal history to calculate the calorie intake and exercise amount necessary for the user to achieve their target weight and proposes a menu based on that. Step 4: The execution unit executes the menu proposed by the analysis unit. For example, it actually consumes the proposed meal menu and performs the proposed exercise menu. Step 5: The reception unit receives feedback on the results performed by the execution unit. For example, it may receive feedback on changes in weight or physical condition. Step 6: The adjustment unit adjusts the suggestions based on the feedback received by the reception unit. For example, if the user's weight is approaching the target, it fine-tunes the diet and exercise menu.

[0074] (Example of form 2) The goal achievement support system according to an embodiment of the present invention is a system that helps users achieve their goals by suggesting and implementing the necessary daily diet and exercise routines to become their desired self (e.g., "I want to lose XX kg"). This goal achievement support system collects past data from people who have successfully lost weight and the user's personal history of exercise and diet, and an AI agent analyzes this data to suggest the user's optimal daily meal and exercise menu. The user executes the suggested menu and provides feedback on the results to the AI ​​agent. The AI ​​agent adjusts the suggestions based on the feedback and continues to support the user toward achieving their goals. For example, it collects detailed data such as the diet, exercise habits, and weight changes of people who have successfully lost weight. It also collects the user's personal history, such as their diet, exercise habits, and weight changes. This allows the AI ​​agent to understand the user's current situation. Next, the AI ​​agent analyzes the collected data. The AI ​​agent compares the data of successful individuals with the user's personal history and suggests the user's optimal daily meal and exercise menu. For example, it calculates the calorie intake and exercise amount necessary for the user to achieve their target weight and suggests a menu based on that. This allows the user to create a concrete action plan. The user executes the suggested menu and provides feedback on the results to the AI ​​agent. For example, the user actually consumes the suggested meal plan and performs the suggested exercise routine. As a result, they report changes in weight, physical condition, etc., to the AI ​​agent. This allows the AI ​​agent to understand the user's progress. Based on the feedback, the AI ​​agent adjusts the suggestions. For example, if the user's weight is approaching their target, it fine-tunes the meal and exercise routine. If the user has not reached their target, it reconsiders the routine and makes more effective suggestions. In this way, the user receives continuous support towards achieving their goals. Thus, the goal achievement support system can effectively support the user in achieving their goals.

[0075] The goal achievement support system according to this embodiment comprises a success data collection unit, a personal history collection unit, an analysis unit, an execution unit, a reception unit, and an adjustment unit. The success data collection unit collects data on people who have successfully lost weight. The success data collection unit can collect detailed data such as the dietary content, exercise habits, and weight changes of people who have successfully lost weight. For example, the success data collection unit records what kind of meals successful people ate and what kind of exercise they did. For example, the success data collection unit periodically records and collects the weight changes of successful people. The personal history collection unit collects the personal history of the user's exercise and eating habits. For example, the personal history collection unit can collect personal history such as the user's dietary content, exercise habits, and weight changes. For example, the personal history collection unit records what kind of meals users ate and what kind of exercise they did. For example, the personal history collection unit periodically records and collects the weight changes of users. The analysis unit analyzes data collected by the success data collection unit and the personal history collection unit and proposes the optimal daily meal and exercise menu for the user. For example, the analysis unit can compare data from successful individuals with the user's personal history to propose the optimal daily meal and exercise menu for the user. For example, the analysis unit can calculate the calorie intake and exercise amount necessary for the user to achieve their target weight and propose a menu based on that. For example, the analysis unit can analyze the user's diet and exercise habits and propose an optimal menu. The execution unit executes the menu proposed by the analysis unit. For example, the execution unit can actually consume the proposed meal menu and perform the proposed exercise menu. For example, the execution unit can cook and consume the proposed meal menu. For example, the execution unit can perform the proposed exercise menu and exercise. The reception unit receives the results executed by the execution unit as feedback. For example, the reception unit can receive feedback such as changes in weight and changes in physical condition. For example, the reception unit reports the results of the meals and exercises performed by the user. The reception department records, for example, changes in the user's weight and physical condition, and accepts this as feedback. The adjustment department then adjusts the proposed solutions based on the feedback received by the reception department.The adjustment unit can, for example, fine-tune the diet and exercise menu if the user's weight is approaching their target. If the user has not reached their target, the adjustment unit can reconsider the menu and make more effective suggestions. The adjustment unit can also adjust the suggestions based on user feedback. In this way, the goal achievement support system according to the embodiment can effectively support the user in achieving their goals.

[0076] The Success Data Collection Unit collects data on people who have successfully lost weight. For example, it can collect detailed data on the dietary habits, exercise routines, and weight changes of successful dieters. Specifically, it records in detail what ingredients successful dieters chose, what cooking methods they used, the timing and frequency of meals, and their calorie intake. Regarding exercise habits, it records the type of exercise performed, its intensity, frequency, and duration. Furthermore, it regularly records and collects data on the weight changes of successful dieters. This allows the Success Data Collection Unit to collect detailed data on the overall lifestyle of successful dieters, enabling a multifaceted analysis of the factors contributing to their success. The collected data is stored on a cloud server and made accessible to the analysis unit. By adjusting the data collection frequency and accuracy, flexible responses to specific situations and conditions are possible. This allows the Success Data Collection Unit to collect data efficiently and effectively, improving the overall system performance.

[0077] The personal history collection unit collects users' personal history of exercise and eating habits. For example, it can collect personal history such as the user's diet, exercise habits, and weight changes. Specifically, it records in detail what ingredients the user chooses, what cooking methods are used, the timing and frequency of meals, and calorie intake. Regarding exercise habits, it records the type of exercise performed, its intensity, frequency, and duration. Furthermore, it regularly records and collects data on the user's weight changes. This allows the personal history collection unit to collect detailed data across the user's entire lifestyle, enabling a multifaceted understanding of the user's current situation. The collected data is stored on a cloud server and made accessible to the analysis unit. By adjusting the data collection frequency and accuracy, flexible responses to specific situations and conditions are possible. This allows the personal history collection unit to collect data efficiently and effectively, improving the overall system performance.

[0078] The analysis unit analyzes data collected by the success data collection unit and the personal history collection unit to propose the optimal daily meal and exercise menu for the user. For example, the analysis unit can compare data from successful individuals with the user's personal history to propose the optimal daily meal and exercise menu for the user. Specifically, it uses AI to compare data from successful individuals with data from the user and analyzes similarities and differences. For example, it calculates the calorie intake and exercise amount necessary for the user to achieve their target weight and proposes a menu based on that. The AI ​​analyzes the user's diet and exercise habits to propose an optimal menu. For example, it suggests the balance of nutrients the user should consume, as well as the type and intensity of exercise. In this way, the analysis unit can effectively support the user in achieving their goals. Furthermore, the analysis unit can also utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict fluctuations in risk over a specific period based on historical data and formulate future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0079] The execution unit executes the menu proposed by the analysis unit. For example, the execution unit can actually consume the proposed meal menu and perform the proposed exercise menu. Specifically, it can cook and consume the proposed meal menu. The meal menu includes specific recipes and cooking methods, and the user can prepare meals according to them. It can also perform the proposed exercise menu and exercise. The exercise menu includes specific types of exercise, intensity, and duration, and the user can exercise according to them. In this way, the execution unit enables the user to execute the proposed menu and take concrete actions toward achieving their goals. Furthermore, the execution unit can monitor the user's execution status and provide support as needed. For example, if the user encounters difficulties while executing the proposed menu, the execution unit can provide appropriate advice and support. In this way, the execution unit enables the user to effectively execute the proposed menu and make steady progress toward achieving their goals.

[0080] The reception unit receives feedback on the results executed by the execution unit. For example, the reception unit can receive feedback on changes in weight or physical condition. Specifically, users report the results of their diet and exercise. Users can record changes in weight and physical condition and report them to the reception unit. This allows the reception unit to understand the user's performance and receive feedback. Furthermore, the reception unit can analyze the user's feedback and provide information to the adjustment unit as needed. For example, if a user loses weight as a result of following a suggested menu, this information can be provided to the adjustment unit to help adjust the suggestions. In this way, the reception unit can not only understand the user's performance and receive feedback, but also provide information to improve the overall performance of the system.

[0081] The adjustment unit adjusts the suggested content based on the feedback received by the reception unit. For example, if the user's weight is approaching its target, the adjustment unit can fine-tune the diet and exercise menu. Specifically, if the user's weight is approaching its target, it may reduce the calorie intake of meals or adjust the intensity of exercise. If the user has not reached their target, the adjustment unit will review the menu and make more effective suggestions. For example, if the user's weight has not decreased, it will review the contents of their meals and suggest reducing their calorie intake. It will also review the type and intensity of exercise and suggest a more effective exercise menu. In this way, the adjustment unit can adjust the suggested content based on user feedback and effectively support the user in achieving their goals. Furthermore, the adjustment unit can analyze user feedback and provide information to improve the overall system performance. For example, it can analyze feedback from multiple users to identify common challenges and problems and improve the overall system based on that. In this way, the adjustment unit can not only effectively support the user in achieving their goals but also improve the overall system performance.

[0082] The success data collection unit can collect detailed data on the dietary habits, exercise routines, and weight changes of people who have successfully lost weight. For example, the success data collection unit records what kind of meals successful people ate and what kind of exercise they did. For example, the success data collection unit regularly records and collects data on the weight changes of successful people. For example, the success data collection unit records the dietary habits of successful people in detail and analyzes their calorie intake and nutritional balance. For example, the success data collection unit records the exercise routines of successful people in detail and analyzes the type, frequency, and intensity of their exercise. By collecting detailed data on successful people, it becomes possible to make more accurate suggestions. Some or all of the above processing in the success data collection unit may be performed using AI, for example, or not using AI. For example, the success data collection unit can input data on the dietary habits and exercise routines of successful people into a generating AI, which can analyze the data and extract detailed information.

[0083] The personal history collection unit can collect personal history such as the user's diet, exercise habits, and weight changes. For example, the personal history collection unit records what the user ate and what kind of exercise they did. For example, the personal history collection unit periodically records the user's weight changes and collects that data. For example, the personal history collection unit records the user's diet in detail and analyzes calorie intake and nutritional balance. For example, the personal history collection unit records the user's exercise habits in detail and analyzes the type, frequency, and intensity of exercise. By collecting the user's personal history in this way, it becomes possible to make optimal suggestions to the user. Some or all of the above processing in the personal history collection unit may be performed using AI, for example, or not using AI. For example, the personal history collection unit can input data on the user's diet and exercise habits into a generating AI, which can analyze the data and extract detailed information.

[0084] The analysis unit can compare data from successful individuals with the user's personal history and propose the optimal daily meal and exercise menu for the user. For example, the analysis unit can compare data from successful individuals with the user's personal history and propose the optimal daily meal and exercise menu for the user. For example, the analysis unit can calculate the calorie intake and exercise amount necessary for the user to achieve their target weight and propose a menu based on that. For example, the analysis unit can analyze the user's diet and exercise habits and propose an optimal menu. For example, the analysis unit can propose an optimal meal menu for the user based on data from successful individuals. For example, the analysis unit can propose an optimal exercise menu based on the user's personal history. In this way, by comparing data from successful individuals with the user's personal history, the optimal menu can be proposed. 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 data from successful individuals and the user's personal history into a generating AI, which can analyze the data and propose an optimal menu.

[0085] The execution unit can actually consume the proposed meal menu and perform the proposed exercise menu. For example, the execution unit can cook and consume the proposed meal menu. For example, the execution unit can perform the proposed exercise menu and exercise. For example, the execution unit can actually consume the proposed meal menu and record the results. For example, the execution unit can perform the proposed exercise menu and record the results. This allows the user to take action toward achieving their goals by performing the proposed menu. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the results of performing the proposed meal menu and exercise menu into a generating AI, which can then analyze the results and provide feedback.

[0086] The reception desk can receive feedback such as changes in weight and physical condition. The reception desk can, for example, report the results of the meals and exercises performed by the user. The reception desk can, for example, record the user's changes in weight and physical condition and receive this as feedback. The reception desk can, for example, record in detail the results of the meals and exercises performed by the user and collect this data. The reception desk can, for example, periodically record the user's changes in weight and physical condition and collect this data. This allows the reception desk to understand the user's progress by receiving feedback. 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 data on the user's changes in weight and physical condition into a generating AI, which can then analyze the data and provide feedback.

[0087] The adjustment unit can adjust the suggested content based on feedback. For example, if the user's weight is approaching its target, the adjustment unit will fine-tune the diet and exercise menu. For example, if the user has not reached their target, the adjustment unit will reconsider the menu and make more effective suggestions. The adjustment unit adjusts the suggested content based on user feedback. For example, the adjustment unit adjusts the suggested content based on changes in the user's weight or physical condition. In this way, by adjusting the suggested content based on feedback, it supports the user in achieving their goals. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input user feedback data into a generating AI, which can analyze the data and adjust the suggested content.

[0088] The success data collection unit can estimate the emotions of successful individuals and adjust the timing of data collection based on the estimated emotions. For example, if a successful individual is stressed, the success data collection unit will refrain from collecting data and collect it when they are relaxed. For example, if a successful individual is highly motivated, the success data collection unit will collect detailed data. For example, if a successful individual is tired, the success data collection unit will collect simple data and then collect detailed data at a later date. This allows for more effective data collection by adjusting the timing of data collection based on the emotions of successful individuals. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the success data collection unit may be performed using AI or not using AI. For example, the success data collection unit can input successful individuals' emotional data into a generative AI, which can estimate emotions and adjust the timing of data collection.

[0089] The Success Data Collection Unit can analyze the past diet history of successful individuals and select the optimal data collection method. For example, the Success Data Collection Unit can select a similar data collection method based on the diet methods that successful individuals have used in the past. For example, the Success Data Collection Unit can eliminate data collection methods that have failed in the past to avoid those methods. For example, the Success Data Collection Unit can select the most effective data collection method from the past diet history of successful individuals. In this way, the optimal data collection method can be selected by analyzing the past diet history of successful individuals. Some or all of the above processing in the Success Data Collection Unit may be performed using AI, for example, or without AI. For example, the Success Data Collection Unit can input the past diet history data of successful individuals into a generating AI, which can then analyze the data and select the optimal data collection method.

[0090] The success data collection unit can filter data based on the current living situation and areas of interest of successful individuals during data collection. For example, the success data collection unit can prioritize the collection of data that is highly relevant to the successful individual's current living situation. For example, the success data collection unit can filter and collect specific data based on the successful individual's areas of interest. For example, the success data collection unit can adjust the scope of data collection according to the successful individual's living situation and areas of interest. This allows for the collection of highly relevant data by filtering the data based on the successful individual's current living situation and areas of interest. Some or all of the above processing in the success data collection unit may be performed using AI, for example, or without AI. For example, the success data collection unit can input data on the successful individual's living situation and areas of interest into a generating AI, which can then analyze and filter the data.

[0091] The success data collection unit can estimate the emotions of successful individuals and determine the priority of data to collect based on the estimated emotions. For example, if a successful individual is relaxed, the success data collection unit will prioritize collecting detailed data. For example, if a successful individual is stressed, the success data collection unit will prioritize collecting basic data. For example, if a successful individual is highly motivated, the success data collection unit will prioritize collecting specific data. This enables effective data collection by prioritizing data based on the emotions of successful individuals. 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 success data collection unit may be performed using AI or not using AI. For example, the success data collection unit can input successful individuals' emotional data into a generative AI, which can estimate emotions and determine the priority of data.

[0092] The success data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the successors during data collection. For example, if a successor lives in a specific region, the success data collection unit will prioritize the collection of data related to that region. For example, if a successor is traveling, the success data collection unit will prioritize the collection of data related to their travel destination. For example, the success data collection unit can filter and collect highly relevant data based on the geographical location information of the successors. This enables effective data collection by collecting highly relevant data based on the geographical location information of the successors. Some or all of the above processing in the success data collection unit may be performed using AI, for example, or without AI. For example, the success data collection unit can input the geographical location information data of the successors into a generating AI, which can then analyze the data and prioritize the collection of highly relevant data.

[0093] The success data collection unit can analyze the social media activities of successful individuals and collect relevant data during data collection. For example, the success data collection unit can analyze the content of successful individuals' social media posts and collect relevant data. For example, the success data collection unit can adjust the timing of data collection based on the frequency of successful individuals' social media activity. For example, the success data collection unit can collect relevant data based on the areas of interest of successful individuals on social media. This allows for the effective collection of relevant data by analyzing the social media activities of successful individuals. Some or all of the above-described processes in the success data collection unit may be performed using AI, for example, or without AI. For example, the success data collection unit can input successful individuals' social media activity data into a generating AI, which can then analyze the data and collect relevant data.

[0094] The personal history collection unit can estimate the user's emotions and adjust the timing of personal history collection based on the estimated emotions. For example, if the user is stressed, the personal history collection unit will refrain from collecting data and collect it when the user is relaxed. For example, if the user is highly motivated, the personal history collection unit will collect detailed history. For example, if the user is tired, the personal history collection unit will collect a simplified history and collect a detailed history at a later date. This allows for effective personal history collection by adjusting the collection timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 personal history collection unit may be performed using AI or not. For example, the personal history collection unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the collection timing.

[0095] The personal history collection unit can analyze a user's past eating habits and exercise history and select the optimal collection method. For example, the personal history collection unit can select a similar history collection method based on a diet method the user has successfully used in the past. For example, the personal history collection unit can exclude a history collection method to avoid a diet method the user has failed at in the past. For example, the personal history collection unit can select the most effective history collection method from a user's past eating habits and exercise history. In this way, the optimal collection method can be selected by analyzing the user's past history. Some or all of the above processing in the personal history collection unit may be performed using AI, for example, or without AI. For example, the personal history collection unit can input the user's past eating habits and exercise history data into a generating AI, which can then analyze the data and select the optimal collection method.

[0096] The personal history collection unit can filter personal history based on the user's current living situation and areas of interest when collecting it. For example, the personal history collection unit can prioritize collecting history that is highly relevant to the user's current living situation. For example, the personal history collection unit can filter and collect specific history based on the user's areas of interest. For example, the personal history collection unit can adjust the scope of history collection according to the user's living situation and areas of interest. This allows for the collection of highly relevant history by filtering history based on the user's current living situation and areas of interest. Some or all of the above processing in the personal history collection unit may be performed using AI, for example, or without AI. For example, the personal history collection unit can input data on the user's living situation and areas of interest into a generating AI, which can then analyze and filter the data.

[0097] The personal history collection unit can estimate the user's emotions and determine the priority of personal history to collect based on the estimated emotions. For example, if the user is relaxed, the personal history collection unit will prioritize collecting detailed history. For example, if the user is stressed, the personal history collection unit will prioritize collecting basic history. For example, if the user is highly motivated, the personal history collection unit will prioritize collecting specific history. This enables effective history collection by prioritizing history 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 processing in the personal history collection unit may be performed using AI or not using AI. For example, the personal history collection unit can input the user's emotion data into a generative AI, which can estimate the emotions and determine the priority of history.

[0098] The personal history collection unit can prioritize the collection of highly relevant history by considering the user's geographical location information when collecting personal history. For example, if the user lives in a specific region, the personal history collection unit will prioritize the collection of history related to that region. For example, if the user is traveling, the personal history collection unit will prioritize the collection of history related to the travel destination. For example, the personal history collection unit will filter and collect highly relevant history based on the user's geographical location information. This enables effective history collection by collecting highly relevant history based on the user's geographical location information. Some or all of the above processing in the personal history collection unit may be performed using AI, for example, or without AI. For example, the personal history collection unit can input the user's geographical location information data into a generating AI, which can then analyze the data and prioritize the collection of highly relevant history.

[0099] The personal history collection unit can analyze a user's social media activity and collect relevant history when collecting personal history. For example, the personal history collection unit can analyze the content of a user's social media posts and collect relevant history. For example, the personal history collection unit can adjust the timing of history collection based on the frequency of the user's activity on social media. For example, the personal history collection unit can collect relevant history based on the user's areas of interest on social media. This allows for the effective collection of relevant history by analyzing the user's social media activity. Some or all of the above processing in the personal history collection unit may be performed using AI, for example, or without AI. For example, the personal history collection unit can input the user's social media activity data into a generating AI, which can then analyze the data and collect relevant history.

[0100] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit provides a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, if the user is in a hurry, the analysis unit provides a concise analysis result. In this way, by adjusting the presentation of the analysis based on the user's emotions, the analysis results can be provided that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a 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 the user's emotion data into a generative AI, which can estimate the emotions and adjust the presentation of the analysis.

[0101] The analysis unit can adjust the level of detail of the analysis based on the importance of the data of successful users and the user's personal history during the analysis. For example, if the data of successful users is important, the analysis unit will perform a detailed analysis. For example, if the user's personal history is important, the analysis unit will perform a detailed analysis. For example, the analysis unit will adjust the level of detail of the analysis by considering the balance between the data of successful users and the user's personal history. This makes effective analysis possible by adjusting the level of detail of the analysis based on the importance of the data of successful users and the user's personal history. 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 data of successful users and the user's personal history into a generating AI, and the generating AI can analyze the data and adjust the level of detail.

[0102] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a nutrition analysis algorithm to dietary data, an exercise analysis algorithm to exercise data, and a weight analysis algorithm to weight change data. This enables effective analysis by applying the most appropriate analysis algorithm for each data category. 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 dietary data, exercise data, and weight change data into a generating AI, which can then apply different analysis algorithms depending on the data category.

[0103] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will perform a short, concise analysis. For example, if the user is relaxed, the analysis unit will perform a detailed analysis. For example, if the user is excited, the analysis unit will perform a visually stimulating analysis. By adjusting the length of the analysis based on the user's emotions, the analysis unit can provide the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's emotion data into a generative AI, which can then estimate the emotions and adjust the length of the analysis.

[0104] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may analyze the most recent data while referring to past data. For example, the analysis unit may adjust the order of analysis based on the data collection timing. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection timing. 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 data collection timing data into a generating AI, and the generating AI can analyze the data and determine the priority.

[0105] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit adjusts the order of analysis based on the relevance of the data. This allows for more effective analysis by adjusting the order of analysis based on the relevance of the data. 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 relevance data into a generating AI, and the generating AI can analyze the data and adjust the order.

[0106] The execution unit can estimate the user's emotions and adjust the execution method based on the estimated emotions. For example, if the user is relaxed, the execution unit may suggest an execution method at a relaxed pace. For example, if the user is in a hurry, the execution unit may suggest an execution method at a rapid pace. For example, if the user is excited, the execution unit may execute in a visually stimulating way. In this way, by adjusting the execution method based on the user's emotions, the execution unit can provide the user with the optimal execution method. 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 execution unit may be performed using AI, for example, or not using AI. For example, the execution unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the execution method.

[0107] The execution unit can analyze the user's past execution history and select the optimal execution method at runtime. For example, the execution unit may select a similar method based on the user's past successful execution methods. For example, the execution unit may eliminate methods to avoid the user's past failures. For example, the execution unit may select the most effective execution method from the user's past execution history. In this way, the optimal execution method can be selected by analyzing the user's past execution history. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's past execution history data into a generating AI, which can then analyze the data and select the optimal execution method.

[0108] The execution unit can customize the means of execution based on the user's current living situation during execution. For example, the execution unit adjusts the means of execution according to the user's current living situation. For example, the execution unit customizes the means of execution based on the user's living situation. For example, the execution unit optimizes the means of execution according to the user's living situation. This makes effective execution possible by customizing the means of execution based on the user's current living situation. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input user living situation data into a generating AI, and the generating AI can analyze the data to customize the means of execution.

[0109] The execution unit can estimate the user's emotions and determine the priority of executions based on the estimated emotions. For example, if the user is relaxed, the execution unit will prioritize detailed executions. For example, if the user is stressed, the execution unit will prioritize basic executions. For example, if the user is highly motivated, the execution unit will prioritize specific executions. This enables effective execution by determining the priority of executions 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 processing in the execution unit may be performed using AI, for example, or not using AI. For example, the execution unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of executions.

[0110] The execution unit can select the optimal execution method at runtime, taking into account the user's geographical location information. For example, if the user lives in a specific region, the execution unit will select an execution method relevant to that region. For example, if the user is traveling, the execution unit will select an execution method for the travel destination. For example, the execution unit will select the optimal execution method based on the user's geographical location information. This enables effective execution by selecting the optimal execution method based on the user's geographical location information. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's geographical location information data into a generating AI, which can then analyze the data and select the optimal execution method.

[0111] The execution unit can analyze the user's social media activity at runtime and propose means of execution. For example, the execution unit can analyze the content of the user's social media posts and propose relevant means of execution. For example, the execution unit can propose means of execution based on the frequency of the user's activity on social media. For example, the execution unit can propose means of execution based on the user's areas of interest on social media. In this way, by analyzing the user's social media activity, relevant means of execution can be effectively proposed. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's social media activity data into a generating AI, and the generating AI can analyze the data and propose means of execution.

[0112] The reception unit can estimate the user's emotions and adjust the feedback reception method based on the estimated emotions. For example, if the user is relaxed, the reception unit will accept detailed feedback. For example, if the user is stressed, the reception unit will accept simple feedback. For example, if the user is highly motivated, the reception unit will accept specific feedback. This allows for effective feedback reception by adjusting the feedback reception method 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 processing in the reception unit may be performed using AI or not using AI. For example, the reception unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the feedback reception method.

[0113] The reception unit can select the optimal reception method when receiving feedback by referring to the user's past feedback history. For example, the reception unit may select a similar reception method based on feedback previously provided by the user. For example, the reception unit may select the optimal reception method based on the content of feedback previously provided by the user. For example, the reception unit may select the most effective reception method from the user's past feedback history. In this way, the optimal reception method can be selected by referring to the user's past feedback history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past feedback history data into a generating AI, and the generating AI can analyze the data to select the optimal reception method.

[0114] The reception unit can customize the means of receiving feedback based on the user's current living situation. For example, the reception unit adjusts the means of receiving feedback according to the user's current living situation. For example, the reception unit customizes the means of receiving feedback based on the user's living situation. For example, the reception unit optimizes the means of receiving feedback according to the user's living situation. This makes it possible to receive effective feedback by customizing the means of receiving feedback based on the user's current living situation. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input user living situation data into a generating AI, and the generating AI can analyze the data to customize the means of receiving feedback.

[0115] The reception unit can estimate the user's emotions and prioritize feedback based on the estimated emotions. For example, if the user is relaxed, the reception unit will prioritize detailed feedback. If the user is stressed, the reception unit will prioritize basic feedback. If the user is highly motivated, the reception unit will prioritize specific feedback. This enables effective feedback reception by prioritizing feedback 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 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 unit may be performed using AI or not. For example, the reception unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of feedback.

[0116] The reception unit can select the optimal reception method when receiving feedback, taking into account the user's geographical location information. For example, if the user lives in a specific region, the reception unit will select a reception method relevant to that region. For example, if the user is traveling, the reception unit will select a reception method for their travel destination. For example, the reception unit will select the optimal reception method based on the user's geographical location information. This enables effective feedback reception by selecting the optimal reception method based on the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information data into a generating AI, which can then analyze the data and select the optimal reception method.

[0117] The reception unit can analyze the user's social media activity and propose methods for receiving feedback when it is received. For example, the reception unit can analyze the content of the user's social media posts and propose relevant methods for receiving feedback. For example, the reception unit can propose methods for receiving feedback based on the frequency of the user's activity on social media. For example, the reception unit can propose methods for receiving feedback based on the user's areas of interest on social media. In this way, by analyzing the user's social media activity, relevant methods for receiving feedback can be effectively proposed. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI, and the generating AI can analyze the data and propose methods for receiving feedback.

[0118] The adjustment unit can estimate the user's emotions and determine how to adjust the suggested content based on the estimated emotions. For example, if the user is relaxed, the adjustment unit will provide detailed suggestions. For example, if the user is stressed, the adjustment unit will provide simple suggestions. For example, if the user is highly motivated, the adjustment unit will provide specific suggestions. This allows for effective adjustment of suggestions by determining how to adjust them based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI or not using AI. For example, the adjustment unit can input user emotion data into a generative AI, which can estimate the emotions and determine how to adjust the suggested content.

[0119] The adjustment unit can select the optimal adjustment method by referring to the user's past feedback history during the adjustment process. For example, the adjustment unit may select a similar adjustment method based on feedback previously provided by the user. For example, the adjustment unit may select the optimal adjustment method based on the content of feedback previously provided by the user. For example, the adjustment unit may select the most effective adjustment method from the user's past feedback history. In this way, the optimal adjustment method can be selected by referring to the user's past feedback history. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit may input the user's past feedback history data into a generating AI, and the generating AI may analyze the data to select the optimal adjustment method.

[0120] The adjustment unit can customize the means of adjustment based on the user's current living situation during the adjustment process. For example, the adjustment unit adjusts the means of adjustment according to the user's current living situation. For example, the adjustment unit customizes the means of adjustment based on the user's living situation. For example, the adjustment unit optimizes the means of adjustment according to the user's living situation. This makes effective adjustment possible by customizing the means of adjustment based on the user's current living situation. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input user living situation data into a generating AI, and the generating AI can analyze the data to customize the means of adjustment.

[0121] The adjustment unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is relaxed, the adjustment unit will prioritize providing detailed suggestions. For example, if the user is stressed, the adjustment unit will prioritize providing basic suggestions. For example, if the user is highly motivated, the adjustment unit will prioritize providing specific suggestions. This allows for effective adjustment of suggestions by determining the priority of suggestions 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 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 adjustment unit may be performed using AI or not using AI. For example, the adjustment unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of suggestions.

[0122] The adjustment unit can select the optimal adjustment method during adjustment, taking into account the user's geographical location information. For example, if the user lives in a specific region, the adjustment unit will select an adjustment method related to that region. For example, if the user is traveling, the adjustment unit will select an adjustment method for the travel destination. For example, the adjustment unit will select the optimal adjustment method based on the user's geographical location information. This enables effective adjustment by selecting the optimal adjustment method based on the user's geographical location information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's geographical location information data into a generating AI, and the generating AI can analyze the data to select the optimal adjustment method.

[0123] The adjustment unit can analyze the user's social media activity and propose adjustment measures during the adjustment process. For example, the adjustment unit can analyze the content of the user's social media posts and propose relevant adjustment measures. For example, the adjustment unit can propose adjustment measures based on the frequency of the user's activity on social media. For example, the adjustment unit can propose adjustment measures based on the user's areas of interest on social media. In this way, by analyzing the user's social media activity, relevant adjustment measures can be effectively proposed. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's social media activity data into a generating AI, which can then analyze the data and propose adjustment measures.

[0124] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0125] The goal achievement support system can monitor the user's sleep patterns, and the analysis unit can take that data into consideration to suggest meal and exercise menus. For example, if the user is not getting enough sleep, the analysis unit will suggest a lighter exercise menu and recommend a nutritious diet. Conversely, if the user is getting good quality sleep, the analysis unit will suggest a more strenuous exercise menu and recommend a diet that emphasizes calorie control. This allows the system to provide an optimal menu tailored to the user's sleep situation.

[0126] The success data collection unit also collects data on the hobbies and lifestyles of successful individuals, and the analysis unit can use this data to make suggestions to users. For example, if a successful individual enjoys outdoor activities, the analysis unit will suggest outdoor exercises to the user. Also, if a successful individual likes a particular food, the analysis unit will suggest menus that include that food. This makes it possible to make suggestions that are tailored to the user's lifestyle.

[0127] The personal history collection unit can monitor the user's stress level, and the analysis unit can then suggest diet and exercise menus based on that data. For example, if the user's stress level is high, the analysis unit will suggest relaxing exercises and meals. Conversely, if the stress level is low, the analysis unit will suggest challenging exercise menus. This allows the system to provide optimal menus tailored to the user's stress level.

[0128] The analysis unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is feeling down, the analysis unit will add an encouraging message to the suggestion. Conversely, if the user is excited, the analysis unit will provide calm advice. This enables the system to offer optimal suggestions tailored to the user's emotions.

[0129] The execution unit can estimate the user's emotions and adjust the timing of execution based on those emotions. For example, if the user is relaxed, the execution unit will prompt execution immediately. Conversely, if the user is stressed, the execution unit will prompt execution after a short delay. This allows for the provision of optimal execution timing tailored to the user's emotions.

[0130] The reception department automatically categorizes user feedback, and the analysis department can adjust the proposed changes based on this categorization. For example, if the user feedback is positive, the analysis department will propose continuing with the current menu. Conversely, if there is a large amount of negative feedback, the analysis department will propose a significant revision of the menu. This enables flexible proposals based on user feedback.

[0131] The adjustment unit can estimate the user's emotions and adjust the level of detail in the suggestions based on those emotions. For example, if the user is relaxed, it will provide detailed suggestions. Conversely, if the user is stressed, it will provide concise suggestions. This allows the system to provide optimal suggestions tailored to the user's emotions.

[0132] The success data collection unit can filter data by considering the dietary and exercise preferences of successful individuals. For example, if a successful individual prefers a particular food, the system prioritizes collecting data containing that food. Similarly, if a successful individual prefers a particular exercise, the system prioritizes collecting data related to that exercise. This enables data collection based on the preferences of successful individuals.

[0133] The personal history collection unit can filter data considering the user's dietary and exercise preferences. For example, if a user prefers a particular food, it will prioritize collecting data containing that food. Similarly, if a user prefers a particular exercise, it will prioritize collecting data related to that exercise. This enables data collection based on the user's preferences.

[0134] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated emotions. For example, if the user is relaxed, it will provide detailed analysis results. Conversely, if the user is stressed, it will provide concise analysis results. This allows the system to provide optimal analysis results tailored to the user's emotions.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: The success data collection unit collects data on people who have successfully lost weight. For example, it collects detailed data such as the dietary habits, exercise routines, and weight changes of successful individuals. Step 2: The personal history collection unit collects the user's personal history regarding exercise and diet. For example, it collects personal history such as the user's diet, exercise habits, and weight changes. Step 3: The analysis unit analyzes the data collected by the success data collection unit and the personal history collection unit and proposes the optimal daily meal and exercise menu for the user. For example, it compares the data of successful individuals with the user's personal history to calculate the calorie intake and exercise amount necessary for the user to achieve their target weight and proposes a menu based on that. Step 4: The execution unit executes the menu proposed by the analysis unit. For example, it actually consumes the proposed meal menu and performs the proposed exercise menu. Step 5: The reception unit receives feedback on the results performed by the execution unit. For example, it may receive feedback on changes in weight or physical condition. Step 6: The adjustment unit adjusts the suggestions based on the feedback received by the reception unit. For example, if the user's weight is approaching the target, it fine-tunes the diet and exercise menu.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the success data collection unit, personal history collection unit, analysis unit, execution unit, reception unit, and adjustment unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the success data collection unit collects data on successful users using the camera 42 and communication I / F 44 of the smart device 14 and analyzes it using the specific processing unit 290 of the data processing unit 12. The personal history collection unit collects the user's personal history using the reception device 38 of the smart device 14 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and compares the data on successful users with the user's personal history to propose an optimal menu. The execution unit is implemented by the control unit 46A of the smart device 14 and executes the proposed menu. The reception unit receives the execution results as feedback using the reception device 38 of the smart device 14. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the proposed content based on the feedback. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0141] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0142] 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.

[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 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.

[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 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.

[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 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.

[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 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.

[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 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.

[0156] Each of the multiple elements described above, including the success data collection unit, personal history collection unit, analysis unit, execution unit, reception unit, and adjustment unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the success data collection unit collects data on successful users using the camera 42 and communication I / F 44 of the smart glasses 214 and analyzes it using the identification processing unit 290 of the data processing unit 12. The personal history collection unit collects the user's personal history using the microphone 238 of the smart glasses 214 and analyzes it using the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and compares the data on successful users with the user's personal history to propose an optimal menu. The execution unit is implemented by the control unit 46A of the smart glasses 214 and executes the proposed menu. The reception unit receives the execution results as feedback using the microphone 238 of the smart glasses 214. The adjustment unit is implemented by the identification processing unit 290 of the data processing unit 12 and adjusts the proposed content based on the feedback. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0157] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0158] 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.

[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 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.

[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 (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).

[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] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements described above, including the success data collection unit, personal history collection unit, analysis unit, execution unit, reception unit, and adjustment unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the success data collection unit collects data on successful users using the camera 42 and communication I / F 44 of the headset terminal 314 and analyzes it using the specific processing unit 290 of the data processing unit 12. The personal history collection unit collects the user's personal history using the microphone 238 of the headset terminal 314 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and compares the data on successful users with the user's personal history to propose an optimal menu. The execution unit is implemented by the control unit 46A of the headset terminal 314 and executes the proposed menu. The reception unit receives the execution results as feedback using the microphone 238 of the headset terminal 314. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the proposed content based on the feedback. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0173] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.).

[0186] 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.

[0187] 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.

[0188] 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.

[0189] Each of the multiple elements described above, including the success data collection unit, personal history collection unit, analysis unit, execution unit, reception unit, and adjustment unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the success data collection unit collects data on successful users using the camera 42 and communication I / F 44 of the robot 414 and analyzes it using the specific processing unit 290 of the data processing unit 12. The personal history collection unit collects the user's personal history using the microphone 238 of the robot 414 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and compares the data on successful users with the user's personal history to propose an optimal menu. The execution unit is implemented by the controlled object 443 of the robot 414 and executes the proposed menu. The reception unit receives the execution results as feedback using the microphone 238 of the robot 414. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the proposed content based on the feedback. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] (Note 1) The Success Data Collection Department collects data on people who have successfully lost weight, A personal history collection unit that collects the user's personal exercise and dietary history, The analysis unit analyzes the data collected by the aforementioned success data collection unit and the aforementioned personal history collection unit and proposes the optimal daily meal and exercise menu for the user, An execution unit that executes the menu proposed by the analysis unit, A receiving unit that receives the results executed by the execution unit as feedback, The system includes an adjustment unit that adjusts the proposed content based on the feedback received by the reception unit. A system characterized by the following features. (Note 2) The aforementioned success data collection unit is: We collect detailed data on the dietary habits, exercise routines, and weight changes of people who have successfully lost weight. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned personal history collection unit, Collects personal history such as the user's diet, exercise habits, and weight changes. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, By comparing data from successful individuals with the user's personal history, we propose the most suitable daily meal and exercise plan for each user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The execution unit is, I will actually consume the suggested meal plan and perform the suggested exercise routine. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is We accept feedback regarding changes in weight, physical condition, etc. The system described in Appendix 1, characterized by the features described herein. (Note 7) The adjustment unit is, We will adjust the proposal based on the feedback. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned success data collection unit is: We estimate the emotions of successful individuals and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 9) The aforementioned success data collection unit is: We analyze the past diet histories of successful dieters and select the optimal data collection method. The system described in Appendix 2, characterized by the features described herein. (Note 10) The aforementioned success data collection unit is: During data collection, filtering is performed based on the current lifestyle and areas of interest of successful individuals. The system described in Appendix 2, characterized by the features described herein. (Note 11) The aforementioned success data collection unit is: We estimate the emotions of successful people and prioritize the data to collect based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 12) The aforementioned success data collection unit is: When collecting data, the geographical location information of successful individuals is taken into consideration to prioritize the collection of highly relevant data. The system described in Appendix 2, characterized by the features described herein. (Note 13) The aforementioned success data collection unit is: During data collection, analyze the social media activities of successful individuals and collect relevant data. The system described in Appendix 2, characterized by the features described herein. (Note 14) The aforementioned personal history collection unit, We estimate the user's emotions and adjust the timing of personal history collection based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 15) The aforementioned personal history collection unit, The system analyzes the user's past eating habits and exercise history to select the optimal data collection method. The system described in Appendix 3, characterized by the features described herein. (Note 16) The aforementioned personal history collection unit, When collecting personal history, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 3, characterized by the features described herein. (Note 17) The aforementioned personal history collection unit, It estimates the user's emotions and determines the priority of personal history to collect based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 18) The aforementioned personal history collection unit, When collecting personal history, the system prioritizes collecting highly relevant history by considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 19) The aforementioned personal history collection unit, When collecting personal history, the system analyzes the user's social media activity and collects relevant history. The system described in Appendix 3, characterized by the features described herein. (Note 20) The aforementioned analysis unit, It estimates the user's emotions and adjusts the way the analysis is presented based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of data from successful users and the user's personal history. The system described in Appendix 4, characterized by the features described herein. (Note 22) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 4, characterized by the features described herein. (Note 23) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 24) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 4, characterized by the features described herein. (Note 25) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 4, characterized by the features described herein. (Note 26) The execution unit is, It estimates the user's emotions and adjusts the execution method based on the estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 27) The execution unit is, During execution, the system analyzes the user's past execution history to select the optimal execution method. The system described in Appendix 5, characterized by the features described herein. (Note 28) The execution unit is, At runtime, the execution method is customized based on the user's current living situation. The system described in Appendix 5, characterized by the features described herein. (Note 29) The execution unit is, It estimates the user's emotions and determines the priority of actions based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 30) The execution unit is, During execution, the system selects the optimal execution method, taking into account the user's geographical location. The system described in Appendix 5, characterized by the features described herein. (Note 31) The execution unit is, During execution, the system analyzes the user's social media activity and suggests implementation methods. The system described in Appendix 5, characterized by the features described herein. (Note 32) The aforementioned reception unit is It estimates the user's emotions and adjusts how feedback is received based on those estimated emotions. The system described in Appendix 6, characterized by the features described herein. (Note 33) The aforementioned reception unit is When receiving feedback, the system will refer to the user's past feedback history to select the most suitable method for receiving it. The system described in Appendix 6, characterized by the features described herein. (Note 34) The aforementioned reception unit is When receiving feedback, customize the submission method based on the user's current living situation. The system described in Appendix 6, characterized by the features described herein. (Note 35) The aforementioned reception unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 6, characterized by the features described herein. (Note 36) The aforementioned reception unit is When receiving feedback, the system will select the most appropriate method of receiving feedback, taking into account the user's geographical location. The system described in Appendix 6, characterized by the features described herein. (Note 37) The aforementioned reception unit is When receiving feedback, we analyze the user's social media activity and suggest methods for receiving feedback. The system described in Appendix 6, characterized by the features described herein. (Note 38) The adjustment unit is, The system estimates the user's emotions and determines how to adjust the suggested content based on those emotions. The system described in Appendix 7, characterized by the features described herein. (Note 39) The adjustment unit is, During the adjustment process, the system will refer to the user's past feedback history to select the optimal adjustment method. The system described in Appendix 7, characterized by the features described herein. (Note 40) The adjustment unit is, During the adjustment process, the adjustment methods are customized based on the user's current living situation. The system described in Appendix 7, characterized by the features described herein. (Note 41) The adjustment unit is, It estimates the user's emotions and prioritizes suggestions based on those emotions. The system described in Appendix 7, characterized by the features described herein. (Note 42) The adjustment unit is, During the adjustment process, the optimal adjustment method is selected by considering the user's geographical location. The system described in Appendix 7, characterized by the features described herein. (Note 43) The adjustment unit is, During the adjustment process, we analyze users' social media activity and propose adjustment methods. The system described in Appendix 7, characterized by the features described herein. [Explanation of symbols]

[0209] 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. The Success Data Collection Department collects data on people who have successfully lost weight, A personal history collection unit that collects users' personal exercise and dietary history, The analysis unit analyzes the data collected by the aforementioned success data collection unit and the aforementioned personal history collection unit and proposes the optimal daily meal and exercise menu for the user, An execution unit that executes the menu proposed by the analysis unit, A receiving unit that receives the results executed by the execution unit as feedback, The system includes an adjustment unit that adjusts the proposed content based on the feedback received by the reception unit. A system characterized by the following features.

2. The aforementioned success data collection unit is: We collect detailed data on the dietary habits, exercise routines, and weight changes of people who have successfully lost weight. The system according to feature 1.

3. The aforementioned personal history collection unit, Collects personal history such as the user's diet, exercise habits, and weight changes. The system according to feature 1.

4. The aforementioned analysis unit, By comparing data from successful individuals with the user's personal history, we propose the most suitable daily meal and exercise plan for each user. The system according to feature 1.

5. The execution unit is, I will actually consume the suggested meal plan and perform the suggested exercise routine. The system according to feature 1.

6. The aforementioned reception unit is We accept feedback regarding changes in weight, physical condition, etc. The system according to feature 1.

7. The adjustment unit is, We will adjust the proposal based on the feedback. The system according to feature 1.

8. The aforementioned success data collection unit is: We estimate the emotions of successful individuals and adjust the timing of data collection based on those estimated emotions. The system according to feature 2.

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