System

Abstract

An object of a system according to an embodiment is to support weight loss of a user by providing knowledge about weight loss and checking daily progress.SOLUTION: A system according to an embodiment includes a knowledge providing unit and a progress check unit. The Knowledge Provider provides basic knowledge about weight loss. The progress check unit checks daily progress.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, providing weight loss information and checking progress was often done alone, making it difficult to maintain motivation.

[0005] The system according to the embodiment aims to support a user in losing weight by providing knowledge about weight loss and checking daily progress. [Means for solving the problem]

[0006] The system according to the embodiment includes a knowledge providing unit and a progress checking unit. The knowledge providing unit provides basic knowledge about weight loss. The progress checking unit checks daily progress. [Effects of the Invention]

[0007] The system according to the embodiment can support a user in losing weight by providing knowledge about weight loss and checking daily progress. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The weight loss support AI app according to an embodiment of the present invention is a system designed to help users effectively achieve weight loss without having to struggle alone. This system provides basic knowledge about weight loss and checks daily progress. This allows the weight loss support AI app to help users effectively achieve weight loss without having to struggle alone.

[0029] A weight-loss support AI app according to an embodiment includes a knowledge provision unit and a progress check unit. The knowledge provision unit provides basic knowledge about weight loss. For example, the generation AI teaches the user about proper dietary habits, exercise methods, and calorie calculation methods. The generation AI also provides appropriate information based on prompts containing instructions about the user's desired content. For example, in response to a prompt such as "Tell me a healthy breakfast recipe," the generation AI provides specific advice such as "A breakfast of oatmeal and fruit would be good." The progress check unit checks the user's daily weight loss progress. For example, the generation AI allows the user to enter their daily weight, diet, exercise amount, etc. into the app, and analyzes the data to evaluate their progress. For example, if the user answers "70 kg" to a question such as "How much do you weigh today?", the generation AI provides feedback such as "You've lost 0.5 kg since yesterday. That's amazing!" This allows the weight-loss support AI app to effectively achieve weight loss without the user having to struggle alone.

[0030] The knowledge provision unit can analyze the user's past dietary history and exercise history and provide individually customized weight loss knowledge. For example, the knowledge provision unit uses a generation AI to analyze the user's past dietary history and identify imbalances in nutritional balance. For example, if a specific nutrient is lacking based on dietary data from the past month, the generation AI will suggest meals to supplement that nutrient. The generation AI will analyze the user's exercise history and evaluate the effectiveness of the exercise. For example, it will suggest an effective exercise plan based on past exercise data. This allows the user to be provided with optimal weight loss knowledge.

[0031] The knowledge provider can learn the user's lifestyle habits and preferences and suggest the optimal weight loss plan based on that. For example, the knowledge provider's generation AI learns the user's lifestyle habits and suggests appropriate meal timings. For example, if the user is a nocturnal person, it will suggest meals that are less likely to make you fat even if eaten late at night. The generation AI learns the user's preferences and suggests a weight loss plan that suits their tastes. For example, it will suggest a meal plan that incorporates the user's favorite foods. This makes it possible to provide a weight loss plan based on the user's lifestyle habits and preferences.

[0032] The progress check unit can analyze the user's progress data in real time and provide instant feedback. In the progress check unit, for example, the generation AI analyzes the user's weight data in real time and provides instant feedback. For example, if the user loses weight, it sends a message such as "Great progress!" The generation AI analyzes the user's dietary data in real time and provides instant feedback. For example, if the user enters their dietary information, it provides specific advice such as "You should eat more vegetables." The generation AI analyzes the user's exercise data in real time and provides instant feedback. For example, if the user increases their exercise volume, it sends a message such as "That's a good amount of exercise!" This allows the user's progress data to be analyzed in real time and provide instant feedback.

[0033] The progress check unit can compare the user's progress data with other users and evaluate the relative progress. In the progress check unit, for example, the generation AI compares the user's weight data with other users and evaluates the relative progress. For example, it provides feedback such as, "Compared to other users with the same goal, your progress is good." The generation AI compares the user's diet data with other users and evaluates the balanced diet. For example, it provides feedback such as, "Compared to other users, your diet is balanced." The generation AI compares the user's exercise data with other users and evaluates the amount of exercise. For example, it provides feedback such as, "Compared to other users, your amount of exercise is sufficient." This makes it possible to compare the user's progress data with other users and evaluate the relative progress.

[0034] The progress check unit can visualize the user's progress data and display it visually in graphs and charts. In the progress check unit, for example, the generation AI graphs the user's weight data and displays the progress visually. For example, it may display changes in weight in a line graph, allowing the progress of weight loss to be confirmed at a glance. The generation AI graphs the user's dietary data and displays the progress visually. For example, it may display the intake of each nutrient in a pie chart, allowing the user to confirm a balanced diet. The generation AI graphs the user's exercise data and displays the progress visually. For example, it may display changes in exercise volume in a bar graph, allowing the progress of exercise to be confirmed at a glance. In this way, the user's progress data is displayed visually, allowing the progress to be confirmed at a glance.

[0035] The progress check unit can integrate the user's progress data with other health data to perform a comprehensive health assessment. In the progress check unit, for example, the generation AI integrates the user's weight data and sleep data to perform a comprehensive health assessment. For example, if the user's sleep time is short, the generation AI can provide advice such as, "Getting more sleep will improve your weight loss results." The generation AI can integrate the user's weight data and stress level to perform a comprehensive health assessment. For example, if the user's stress level is high, the generation AI can provide advice such as, "Relaxing will improve your weight loss results." The generation AI can integrate the user's weight data and blood pressure data to perform a comprehensive health assessment. For example, if the user's blood pressure is high, the generation AI can provide advice such as, "Reducing salt intake will improve your weight loss results." In this way, the user's progress data can be integrated with other health data to perform a comprehensive health assessment.

[0036] The knowledge provision unit can incorporate weight loss methods from different cultural spheres and regions to provide the user with a variety of options. For example, the generation AI learns the eating habits of different cultural spheres and suggests a weight loss method that is suitable for the user. For example, it can provide a weight loss plan that incorporates the Mediterranean diet or Japanese food. The generation AI learns the exercise habits of different regions and suggests an exercise method that is suitable for the user. For example, it can provide an exercise plan that incorporates yoga or Pilates. This makes it possible to incorporate weight loss methods from different cultural spheres and regions and provide the user with a variety of options.

[0037] The knowledge provision unit can refer to the weight loss data of the user's family and friends and provide advice for working together to lose weight. For example, the generation AI can refer to the weight loss data of the user's family and propose a weight loss plan that the whole family can work on together. For example, it can provide exercises and meal menus that the family can do together. The generation AI can refer to the weight loss data of the user's friends and propose a weight loss plan that the friends can work on together. For example, it can provide exercises and meal menus that the friends can do together. In this way, it can refer to the weight loss data of the user's family and friends and provide advice for working together to lose weight.

[0038] The progress check unit can visualize the user's progress data and display it visually in graphs and charts. In the progress check unit, for example, the generation AI graphs the user's weight data and displays the progress visually. For example, it may display changes in weight in a line graph, allowing the progress of weight loss to be confirmed at a glance. The generation AI graphs the user's dietary data and displays the progress visually. For example, it may display the intake of each nutrient in a pie chart, allowing the user to confirm a balanced diet. The generation AI graphs the user's exercise data and displays the progress visually. For example, it may display changes in exercise volume in a bar graph, allowing the progress of exercise to be confirmed at a glance. In this way, the user's progress data is displayed visually, allowing the progress to be confirmed at a glance.

[0039] The progress check unit can integrate the user's progress data with other health data to perform a comprehensive health assessment. In the progress check unit, for example, the generation AI integrates the user's weight data and sleep data to perform a comprehensive health assessment. For example, if the user's sleep time is short, the generation AI can provide advice such as, "Getting more sleep will improve your weight loss results." The generation AI can integrate the user's weight data and stress level to perform a comprehensive health assessment. For example, if the user's stress level is high, the generation AI can provide advice such as, "Relaxing will improve your weight loss results." The generation AI can integrate the user's weight data and blood pressure data to perform a comprehensive health assessment. For example, if the user's blood pressure is high, the generation AI can provide advice such as, "Reducing salt intake will improve your weight loss results." In this way, the user's progress data can be integrated with other health data to perform a comprehensive health assessment.

[0040] The knowledge provision unit can analyze the user's past successful experiences and provide encouraging messages based on them. For example, the generation AI analyzes the user's past successful experiences and provides messages that emphasize those experiences. For example, it sends a message such as, "You've worked hard and succeeded before. I'm sure you'll succeed this time too!" The generation AI provides specific advice based on the user's past successful experiences. For example, it provides advice such as, "Try again the method that was successful in the past." This makes it possible to analyze the user's past successful experiences and provide encouraging messages based on them.

[0041] The knowledge provider can periodically evaluate the user's degree of goal achievement and suggest rewards and incentives according to the degree of achievement. For example, the generation AI in the knowledge provider can periodically evaluate the user's degree of goal achievement and suggest rewards according to the degree of achievement. For example, it can make a suggestion such as, "You're getting close to your goal weight, so go see your favorite movie as a reward for yourself." The generation AI can suggest incentives based on the user's degree of goal achievement. For example, it can make a suggestion such as, "If you achieve your goal well, we'll give you a special experience as a gift." This allows the system to periodically evaluate the user's degree of goal achievement and suggest rewards and incentives according to the degree of achievement.

[0042] The knowledge provision unit can work with the user's friends and family to provide support for jointly maintaining motivation. For example, the generation AI works with the user's friends and family to provide support for jointly maintaining motivation. For example, making a plan to exercise together with friends and family. The generation AI works with the user's friends and family to jointly create a meal plan. For example, making a plan to cook healthy meals together with friends and family. In this way, the generation AI can work with the user's friends and family to provide support for jointly maintaining motivation.

[0043] The knowledge providing unit can suggest activities related to weight loss based on the user's hobbies and interests. For example, the generation AI learns the user's hobbies and interests and suggests activities related to weight loss based on the learned hobbies and interests. For example, dance exercises are suggested for a user who likes dancing. The generation AI learns the user's interests and suggests activities related to weight loss based on the learned hobbies and interests. For example, a hiking plan is suggested for a user who likes hiking. In this way, activities related to weight loss can be suggested based on the user's hobbies and interests.

[0044] The knowledge provider can analyze the user's genetic information and health checkup data and provide personalized weight loss advice based on that. For example, the knowledge provider's generation AI analyzes the user's genetic information and suggests a genetically appropriate weight loss method. For example, it provides advice that takes into account the effects of diet and exercise that are influenced by specific genes. The generation AI analyzes the user's health checkup data and suggests a weight loss method based on the user's health condition. For example, it suggests a nutritionally balanced meal plan based on blood test results. This allows the knowledge provider to analyze the user's genetic information and health checkup data and provide personalized weight loss advice based on that.

[0045] The knowledge provision unit can consider the user's living environment and suggest the optimal weight loss method. For example, the generation AI considers the user's work environment and suggests weight loss methods that can be done at work. For example, it suggests stretches and light exercises that can be done while doing desk work. The generation AI considers the user's home environment and suggests weight loss methods that can be done at home. For example, it suggests exercises and healthy meal plans that can be done at home. In this way, the knowledge provision unit can consider the user's living environment and suggest the optimal weight loss method.

[0046] The knowledge providing unit can analyze the user's past weight loss attempt data, identify successful and unsuccessful methods, and provide advice. For example, the generation AI analyzes the user's past weight loss attempt data, identify successful methods, and provide advice. For example, it provides advice such as "Try the method that was successful in the past again." The generation AI analyzes the user's past weight loss attempt data, identify unsuccessful methods, and provide advice. For example, it provides advice such as "Try a different method to avoid the method that failed in the past." In this way, it is possible to analyze the user's past weight loss attempt data, identify successful and unsuccessful methods, and provide advice.

[0047] The knowledge providing unit can refer to the weight loss data of the user's colleagues and friends and provide advice for working together to lose weight. For example, the generation AI in the knowledge providing unit refers to the weight loss data of the user's colleagues and suggests a weight loss plan that can be undertaken together at work. For example, making a plan to go for a walk together at lunchtime. The generation AI refers to the weight loss data of the user's friends and suggests a weight loss plan that can be undertaken together with the friends. For example, making a plan to exercise together with the friends. In this way, the generation AI can refer to the weight loss data of the user's colleagues and friends and provide advice for working together to lose weight.

[0048] The progress check unit can analyze the user's diet and exercise data in real time and provide instant feedback. For example, the generation AI in the progress check unit analyzes the user's diet data in real time and provides instant feedback. For example, when the user inputs the details of their diet, it provides specific advice such as "You should eat more vegetables." The generation AI analyzes the user's exercise data in real time and provides instant feedback. For example, if the amount of exercise increases, it sends a message such as "That's a good amount of exercise!" This allows the user's diet and exercise data to be analyzed in real time and provide instant feedback.

[0049] The progress check unit can compare the user's diet and exercise data with other users and make a relative evaluation. In the progress check unit, for example, the generation AI compares the user's diet data with other users and evaluates the balanced diet. For example, it provides feedback such as, "Compared to other users, your diet is balanced." The generation AI compares the user's exercise data with other users and evaluates the amount of exercise. For example, it provides feedback such as, "Compared to other users, your amount of exercise is sufficient." This allows the user's diet and exercise data to be compared with other users and made a relative evaluation.

[0050] The progress check unit can visualize the user's dietary and exercise data and display it visually in graphs and charts. In the progress check unit, for example, the generation AI graphs the user's dietary data and visually displays the progress. For example, it can display the intake of each nutrient in a pie chart, allowing the user to confirm a balanced diet. The generation AI graphs the user's exercise data and visually displays the progress. For example, it can display changes in exercise volume in a bar graph, allowing the user to check the progress of exercise at a glance. This makes it possible to visualize the user's dietary and exercise data and visually display it in graphs and charts.

[0051] The progress check unit can integrate the user's diet and exercise data with other health data to provide a comprehensive health assessment. For example, the generation AI in the progress check unit integrates the user's weight data and sleep data to provide a comprehensive health assessment. For example, if the user's sleep time is short, the generation AI can provide advice such as, "Getting more sleep will improve your weight loss results." The generation AI can integrate the user's weight data and stress level to provide a comprehensive health assessment. For example, if the user's stress level is high, the generation AI can provide advice such as, "Relaxing will improve your weight loss results." The generation AI can integrate the user's weight data and blood pressure data to provide a comprehensive health assessment. For example, if the user's blood pressure is high, the generation AI can provide advice such as, "Reducing salt intake will improve your weight loss results." This allows the user's diet and exercise data to be integrated with other health data to provide a comprehensive health assessment.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The knowledge provider can periodically evaluate the user's degree of goal achievement and suggest rewards and incentives according to the degree of achievement. For example, the generation AI can periodically evaluate the user's degree of goal achievement and suggest rewards according to the degree of achievement. For example, it can make a suggestion such as, "You're getting close to your goal weight, so go see your favorite movie as a reward for yourself." The generation AI can then suggest incentives based on the user's degree of goal achievement. For example, it can make a suggestion such as, "If you achieve your goal well, we'll give you a special experience as a gift." This allows the system to periodically evaluate the user's degree of goal achievement and suggest rewards and incentives according to the degree of achievement.

[0054] The knowledge provision unit can work with the user's friends and family to provide support for jointly maintaining motivation. For example, the generation AI can work with the user's friends and family to provide support for jointly maintaining motivation. For example, making a plan to exercise together with friends and family. The generation AI can work with the user's friends and family to jointly create a meal plan. For example, making a plan to cook healthy meals together with friends and family. In this way, the generation AI can work with the user's friends and family to provide support for jointly maintaining motivation.

[0055] The knowledge providing unit can suggest weight loss-related activities based on the user's hobbies and interests. For example, the generation AI learns the user's hobbies and interests and suggests weight loss-related activities based on them. For example, it suggests dance exercises for a user who likes dancing. The generation AI learns the user's interests and suggests weight loss-related activities based on them. For example, it suggests a hiking plan for a user who likes hiking. In this way, it is possible to suggest weight loss-related activities based on the user's hobbies and interests.

[0056] The knowledge provider can analyze the user's genetic information and health checkup data and provide personalized weight loss advice based on that. For example, the generation AI can analyze the user's genetic information and suggest a weight loss method that is genetically appropriate. For example, it can provide advice that takes into account the effects of diet and exercise that are influenced by specific genes. The generation AI can analyze the user's health checkup data and suggest a weight loss method based on the user's health condition. For example, it can suggest a nutritionally balanced meal plan based on blood test results. This allows the system to analyze the user's genetic information and health checkup data and provide personalized weight loss advice based on that.

[0057] The knowledge provider can consider the user's living environment and suggest the optimal weight loss method. For example, the generation AI considers the user's work environment and suggests weight loss methods that can be done at work. For example, it suggests stretches and light exercises that can be done while working at a desk. The generation AI considers the user's home environment and suggests weight loss methods that can be done at home. For example, it suggests exercises and healthy meal plans that can be done at home. This makes it possible to consider the user's living environment and suggest the optimal weight loss method.

[0058] The knowledge providing unit can analyze the user's past weight loss attempt data, identify successful and unsuccessful methods, and provide advice. For example, the generation AI can analyze the user's past weight loss attempt data, identify successful methods, and provide advice. For example, it can provide advice such as, "Try the method that was successful in the past again." The generation AI can analyze the user's past weight loss attempt data, identify unsuccessful methods, and provide advice. For example, it can provide advice such as, "Try a different method to avoid the method that failed in the past." This makes it possible to analyze the user's past weight loss attempt data, identify successful and unsuccessful methods, and provide advice.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The knowledge provider provides basic knowledge about weight loss. For example, the generation AI teaches how to eat properly, how to exercise, and how to calculate calories. The generation AI also provides appropriate information based on prompts containing instructions about what the user wants to know. For example, in response to a prompt such as "Tell me a healthy breakfast recipe," the AI ​​provides specific advice such as "A breakfast of oatmeal and fruit would be good." Step 2: The progress checker checks the user's daily weight loss progress. For example, the generation AI inputs the user's daily weight, diet, and exercise amount into the app, and analyzes the data to evaluate the user's progress. If the user answers "70 kg" to a question such as "How much do you weigh today?", the generation AI will provide feedback such as "You've lost 0.5 kg since yesterday. That's great!"

[0061] (Example 2) The weight loss support AI app according to an embodiment of the present invention is a system designed to help users effectively achieve weight loss without having to struggle alone. This system provides basic knowledge about weight loss and checks daily progress. This allows the weight loss support AI app to help users effectively achieve weight loss without having to struggle alone.

[0062] A weight-loss support AI app according to an embodiment includes a knowledge provision unit and a progress check unit. The knowledge provision unit provides basic knowledge about weight loss. For example, the generation AI teaches the user about proper dietary habits, exercise methods, and calorie calculation methods. The generation AI also provides appropriate information based on prompts containing instructions about the user's desired content. For example, in response to a prompt such as "Tell me a healthy breakfast recipe," the generation AI provides specific advice such as "A breakfast of oatmeal and fruit would be good." The progress check unit checks the user's daily weight loss progress. For example, the generation AI allows the user to enter their daily weight, diet, exercise amount, etc. into the app, and analyzes the data to evaluate their progress. For example, if the user answers "70 kg" to a question such as "How much do you weigh today?", the generation AI provides feedback such as "You've lost 0.5 kg since yesterday. That's amazing!" This allows the weight-loss support AI app to effectively achieve weight loss without the user having to struggle alone.

[0063] The knowledge provision unit can analyze the user's past dietary history and exercise history and provide individually customized weight loss knowledge. For example, the knowledge provision unit uses a generation AI to analyze the user's past dietary history and identify imbalances in nutritional balance. For example, if a specific nutrient is lacking based on dietary data from the past month, the generation AI will suggest meals to supplement that nutrient. The generation AI will analyze the user's exercise history and evaluate the effectiveness of the exercise. For example, it will suggest an effective exercise plan based on past exercise data. This allows the user to be provided with optimal weight loss knowledge.

[0064] The knowledge provider can learn the user's lifestyle habits and preferences and suggest the optimal weight loss plan based on that. For example, the knowledge provider's generation AI learns the user's lifestyle habits and suggests appropriate meal timings. For example, if the user is a nocturnal person, it will suggest meals that are less likely to make you fat even if eaten late at night. The generation AI learns the user's preferences and suggests a weight loss plan that suits their tastes. For example, it will suggest a meal plan that incorporates the user's favorite foods. This makes it possible to provide a weight loss plan based on the user's lifestyle habits and preferences.

[0065] The knowledge providing unit uses the emotion estimation function to provide weight loss knowledge that corresponds to the user's emotional state, thereby eliciting positive emotions. For example, the knowledge providing unit uses the emotion estimation function to suggest relaxing meals and exercises when the user is feeling stressed. For example, when stress is high, the knowledge providing unit may suggest relaxing herbal tea or yoga. The emotion estimation function analyzes the user's emotions using facial expression recognition technology. For example, the user's facial expression is captured with a camera and an emotion score is calculated. The emotion estimation function analyzes the user's emotions using voice analysis technology. For example, the tone and speed of the user's voice are analyzed and an emotion score is calculated. This makes it possible to provide weight loss knowledge that corresponds to the user's emotional state, thereby eliciting positive emotions.

[0066] The progress check unit can analyze the user's progress data in real time and provide instant feedback. In the progress check unit, for example, the generation AI analyzes the user's weight data in real time and provides instant feedback. For example, if the user loses weight, it sends a message such as "Great progress!" The generation AI analyzes the user's dietary data in real time and provides instant feedback. For example, if the user enters their dietary information, it provides specific advice such as "You should eat more vegetables." The generation AI analyzes the user's exercise data in real time and provides instant feedback. For example, if the user increases their exercise volume, it sends a message such as "That's a good amount of exercise!" This allows the user's progress data to be analyzed in real time and provide instant feedback.

[0067] The progress check unit can compare the user's progress data with other users and evaluate the relative progress. In the progress check unit, for example, the generation AI compares the user's weight data with other users and evaluates the relative progress. For example, it provides feedback such as, "Compared to other users with the same goal, your progress is good." The generation AI compares the user's diet data with other users and evaluates the balanced diet. For example, it provides feedback such as, "Compared to other users, your diet is balanced." The generation AI compares the user's exercise data with other users and evaluates the amount of exercise. For example, it provides feedback such as, "Compared to other users, your amount of exercise is sufficient." This makes it possible to compare the user's progress data with other users and evaluate the relative progress.

[0068] The progress check unit uses the emotion estimation function to provide progress feedback according to the user's emotional state, thereby maintaining motivation. For example, the progress check unit uses the emotion estimation function to provide progress feedback when the user is feeling positive emotions. For example, it may send a message such as "Great progress! Keep it up!" The emotion estimation function uses facial expression recognition technology to analyze the user's emotions. For example, it may take a photo of the user's facial expression with a camera and calculate an emotion score. The emotion estimation function uses voice analysis technology to analyze the user's emotions. For example, it may analyze the tone and speed of the user's voice and calculate an emotion score. This allows the user to provide progress feedback according to the user's emotional state, thereby maintaining motivation.

[0069] The progress check unit can visualize the user's progress data and display it visually in graphs and charts. In the progress check unit, for example, the generation AI graphs the user's weight data and displays the progress visually. For example, it may display changes in weight in a line graph, allowing the progress of weight loss to be confirmed at a glance. The generation AI graphs the user's dietary data and displays the progress visually. For example, it may display the intake of each nutrient in a pie chart, allowing the user to confirm a balanced diet. The generation AI graphs the user's exercise data and displays the progress visually. For example, it may display changes in exercise volume in a bar graph, allowing the progress of exercise to be confirmed at a glance. In this way, the user's progress data is displayed visually, allowing the progress to be confirmed at a glance.

[0070] The progress check unit can integrate the user's progress data with other health data to perform a comprehensive health assessment. In the progress check unit, for example, the generation AI integrates the user's weight data and sleep data to perform a comprehensive health assessment. For example, if the user's sleep time is short, the generation AI can provide advice such as, "Getting more sleep will improve your weight loss results." The generation AI can integrate the user's weight data and stress level to perform a comprehensive health assessment. For example, if the user's stress level is high, the generation AI can provide advice such as, "Relaxing will improve your weight loss results." The generation AI can integrate the user's weight data and blood pressure data to perform a comprehensive health assessment. For example, if the user's blood pressure is high, the generation AI can provide advice such as, "Reducing salt intake will improve your weight loss results." In this way, the user's progress data can be integrated with other health data to perform a comprehensive health assessment.

[0071] The progress check unit can use the emotion estimation function to analyze the user's emotional response to progress and provide feedback to elicit positive emotions. The progress check unit, for example, uses the emotion estimation function to analyze the user's emotional response to progress in real time. For example, when the user has positive emotions, it can provide feedback such as "Great progress!" The emotion estimation function analyzes the user's emotions using facial expression recognition technology. For example, it can capture the user's facial expression with a camera and calculate an emotion score. The emotion estimation function analyzes the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice and calculate an emotion score. This makes it possible to analyze the user's emotional response to progress and provide feedback to elicit positive emotions.

[0072] The knowledge provision unit can incorporate weight loss methods from different cultural spheres and regions to provide the user with a variety of options. For example, the generation AI learns the eating habits of different cultural spheres and suggests a weight loss method that is suitable for the user. For example, it can provide a weight loss plan that incorporates the Mediterranean diet or Japanese food. The generation AI learns the exercise habits of different regions and suggests an exercise method that is suitable for the user. For example, it can provide an exercise plan that incorporates yoga or Pilates. This makes it possible to incorporate weight loss methods from different cultural spheres and regions and provide the user with a variety of options.

[0073] The knowledge provision unit can refer to the weight loss data of the user's family and friends and provide advice for working together to lose weight. For example, the generation AI can refer to the weight loss data of the user's family and propose a weight loss plan that the whole family can work on together. For example, it can provide exercises and meal menus that the family can do together. The generation AI can refer to the weight loss data of the user's friends and propose a weight loss plan that the friends can work on together. For example, it can provide exercises and meal menus that the friends can do together. In this way, it can refer to the weight loss data of the user's family and friends and provide advice for working together to lose weight.

[0074] The knowledge providing unit can use the emotion estimation function to analyze the emotional reactions of the user when learning weight loss knowledge and provide feedback to improve the learning effect. The knowledge providing unit, for example, uses the emotion estimation function to analyze the emotional reactions of the user when learning weight loss knowledge in real time. For example, it provides positive feedback on topics that the user is interested in. The emotion estimation function analyzes the user's emotions using facial expression recognition technology. For example, it captures the user's facial expression with a camera and calculates an emotion score. The emotion estimation function analyzes the user's emotions using voice analysis technology. For example, it analyzes the tone and speed of the user's voice and calculates an emotion score. This makes it possible to analyze the emotional reactions of the user when learning weight loss knowledge and provide feedback to improve the learning effect.

[0075] The progress check unit can visualize the user's progress data and display it visually in graphs and charts. In the progress check unit, for example, the generation AI graphs the user's weight data and displays the progress visually. For example, it may display changes in weight in a line graph, allowing the progress of weight loss to be confirmed at a glance. The generation AI graphs the user's dietary data and displays the progress visually. For example, it may display the intake of each nutrient in a pie chart, allowing the user to confirm a balanced diet. The generation AI graphs the user's exercise data and displays the progress visually. For example, it may display changes in exercise volume in a bar graph, allowing the progress of exercise to be confirmed at a glance. In this way, the user's progress data is displayed visually, allowing the progress to be confirmed at a glance.

[0076] The progress check unit can integrate the user's progress data with other health data to perform a comprehensive health assessment. In the progress check unit, for example, the generation AI integrates the user's weight data and sleep data to perform a comprehensive health assessment. For example, if the user's sleep time is short, the generation AI can provide advice such as, "Getting more sleep will improve your weight loss results." The generation AI can integrate the user's weight data and stress level to perform a comprehensive health assessment. For example, if the user's stress level is high, the generation AI can provide advice such as, "Relaxing will improve your weight loss results." The generation AI can integrate the user's weight data and blood pressure data to perform a comprehensive health assessment. For example, if the user's blood pressure is high, the generation AI can provide advice such as, "Reducing salt intake will improve your weight loss results." In this way, the user's progress data can be integrated with other health data to perform a comprehensive health assessment.

[0077] The progress check unit can use the emotion estimation function to analyze the user's emotional response to progress and provide feedback to elicit positive emotions. The progress check unit, for example, uses the emotion estimation function to analyze the user's emotional response to progress in real time. For example, when the user has positive emotions, it can provide feedback such as "Great progress!" The emotion estimation function analyzes the user's emotions using facial expression recognition technology. For example, it can capture the user's facial expression with a camera and calculate an emotion score. The emotion estimation function analyzes the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice and calculate an emotion score. This makes it possible to analyze the user's emotional response to progress and provide feedback to elicit positive emotions.

[0078] The knowledge provision unit can analyze the user's past successful experiences and provide encouraging messages based on them. For example, the generation AI analyzes the user's past successful experiences and provides messages that emphasize those experiences. For example, it sends a message such as, "You've worked hard and succeeded before. I'm sure you'll succeed this time too!" The generation AI provides specific advice based on the user's past successful experiences. For example, it provides advice such as, "Try again the method that was successful in the past." This makes it possible to analyze the user's past successful experiences and provide encouraging messages based on them.

[0079] The knowledge provider can periodically evaluate the user's degree of goal achievement and suggest rewards and incentives according to the degree of achievement. For example, the generation AI in the knowledge provider can periodically evaluate the user's degree of goal achievement and suggest rewards according to the degree of achievement. For example, it can make a suggestion such as, "You're getting close to your goal weight, so go see your favorite movie as a reward for yourself." The generation AI can suggest incentives based on the user's degree of goal achievement. For example, it can make a suggestion such as, "If you achieve your goal well, we'll give you a special experience as a gift." This allows the system to periodically evaluate the user's degree of goal achievement and suggest rewards and incentives according to the degree of achievement.

[0080] The knowledge provider can use the emotion estimation function to provide an encouraging message according to the user's emotional state, thereby maintaining motivation. For example, the knowledge provider can use the emotion estimation function to provide an encouraging message when the user is feeling positive emotions. For example, it can send a message such as "Great progress! Keep it up!" The emotion estimation function uses facial expression recognition technology to analyze the user's emotions. For example, it can capture the user's facial expressions with a camera and calculate an emotion score. The emotion estimation function can analyze the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice and calculate an emotion score. This allows the knowledge provider to provide an encouraging message according to the user's emotional state, thereby maintaining motivation.

[0081] The knowledge provision unit can work with the user's friends and family to provide support for jointly maintaining motivation. For example, the generation AI works with the user's friends and family to provide support for jointly maintaining motivation. For example, making a plan to exercise together with friends and family. The generation AI works with the user's friends and family to jointly create a meal plan. For example, making a plan to cook healthy meals together with friends and family. In this way, the generation AI can work with the user's friends and family to provide support for jointly maintaining motivation.

[0082] The knowledge providing unit can suggest activities related to weight loss based on the user's hobbies and interests. For example, the generation AI learns the user's hobbies and interests and suggests activities related to weight loss based on the learned hobbies and interests. For example, dance exercises are suggested for a user who likes dancing. The generation AI learns the user's interests and suggests activities related to weight loss based on the learned hobbies and interests. For example, a hiking plan is suggested for a user who likes hiking. In this way, activities related to weight loss can be suggested based on the user's hobbies and interests.

[0083] The knowledge providing unit can use the emotion estimation function to suggest activities to maintain motivation according to the user's emotional state. For example, the knowledge providing unit uses the emotion estimation function to suggest activities that will further increase motivation when the user is feeling positive emotions. For example, the knowledge providing unit can suggest exercises or activities that the user can enjoy. The emotion estimation function analyzes the user's emotions using facial expression recognition technology. For example, it captures the user's facial expression with a camera and calculates an emotion score. The emotion estimation function analyzes the user's emotions using voice analysis technology. For example, it analyzes the tone and speed of the user's voice and calculates an emotion score. This makes it possible to suggest activities to maintain motivation according to the user's emotional state.

[0084] The knowledge provider can analyze the user's genetic information and health checkup data and provide personalized weight loss advice based on that. For example, the knowledge provider's generation AI analyzes the user's genetic information and suggests a genetically appropriate weight loss method. For example, it provides advice that takes into account the effects of diet and exercise that are influenced by specific genes. The generation AI analyzes the user's health checkup data and suggests a weight loss method based on the user's health condition. For example, it suggests a nutritionally balanced meal plan based on blood test results. This allows the knowledge provider to analyze the user's genetic information and health checkup data and provide personalized weight loss advice based on that.

[0085] The knowledge provision unit can consider the user's living environment and suggest the optimal weight loss method. For example, the generation AI considers the user's work environment and suggests weight loss methods that can be done at work. For example, it suggests stretches and light exercises that can be done while doing desk work. The generation AI considers the user's home environment and suggests weight loss methods that can be done at home. For example, it suggests exercises and healthy meal plans that can be done at home. In this way, the knowledge provision unit can consider the user's living environment and suggest the optimal weight loss method.

[0086] The knowledge provider uses the emotion estimation function to provide personalized advice according to the user's emotional state and draw out positive emotions. For example, the knowledge provider uses the emotion estimation function to provide advice that further motivates the user when the user is feeling positive emotions. For example, the knowledge provider may send a message such as, "Great progress! Keep it up!" The emotion estimation function uses facial expression recognition technology to analyze the user's emotions. For example, it may capture the user's facial expressions with a camera and calculate an emotion score. The emotion estimation function uses voice analysis technology to analyze the user's emotions. For example, it may analyze the tone and speed of the user's voice and calculate an emotion score. This makes it possible to provide personalized advice according to the user's emotional state and draw out positive emotions.

[0087] The knowledge providing unit can analyze the user's past weight loss attempt data, identify successful and unsuccessful methods, and provide advice. For example, the generation AI analyzes the user's past weight loss attempt data, identify successful methods, and provide advice. For example, it provides advice such as "Try the method that was successful in the past again." The generation AI analyzes the user's past weight loss attempt data, identify unsuccessful methods, and provide advice. For example, it provides advice such as "Try a different method to avoid the method that failed in the past." In this way, it is possible to analyze the user's past weight loss attempt data, identify successful and unsuccessful methods, and provide advice.

[0088] The knowledge providing unit can refer to the weight loss data of the user's colleagues and friends and provide advice for working together to lose weight. For example, the generation AI in the knowledge providing unit refers to the weight loss data of the user's colleagues and suggests a weight loss plan that can be undertaken together at work. For example, making a plan to go for a walk together at lunchtime. The generation AI refers to the weight loss data of the user's friends and suggests a weight loss plan that can be undertaken together with the friends. For example, making a plan to exercise together with the friends. In this way, the generation AI can refer to the weight loss data of the user's colleagues and friends and provide advice for working together to lose weight.

[0089] The knowledge provider uses the emotion estimation function to provide personalized advice according to the user's emotional state, thereby maintaining motivation. For example, the knowledge provider uses the emotion estimation function to provide advice that further motivates the user when the user is feeling positive. For example, it may send a message such as, "Great progress! Keep it up!" The emotion estimation function uses facial expression recognition technology to analyze the user's emotions. For example, it may capture the user's facial expressions with a camera and calculate an emotion score. The emotion estimation function uses voice analysis technology to analyze the user's emotions. For example, it may analyze the tone and speed of the user's voice and calculate an emotion score. This allows the knowledge provider to provide personalized advice according to the user's emotional state, thereby maintaining motivation.

[0090] The progress check unit can analyze the user's diet and exercise data in real time and provide instant feedback. For example, the generation AI in the progress check unit analyzes the user's diet data in real time and provides instant feedback. For example, when the user inputs the details of their diet, it provides specific advice such as "You should eat more vegetables." The generation AI analyzes the user's exercise data in real time and provides instant feedback. For example, if the amount of exercise increases, it sends a message such as "That's a good amount of exercise!" This allows the user's diet and exercise data to be analyzed in real time and provide instant feedback.

[0091] The progress check unit can compare the user's diet and exercise data with other users and make a relative evaluation. In the progress check unit, for example, the generation AI compares the user's diet data with other users and evaluates the balanced diet. For example, it provides feedback such as, "Compared to other users, your diet is balanced." The generation AI compares the user's exercise data with other users and evaluates the amount of exercise. For example, it provides feedback such as, "Compared to other users, your amount of exercise is sufficient." This allows the user's diet and exercise data to be compared with other users and made a relative evaluation.

[0092] The progress check unit uses the emotion estimation function to provide diet and exercise advice that corresponds to the user's emotional state, thereby eliciting positive emotions. For example, the progress check unit uses the emotion estimation function to provide diet and exercise advice that further motivates the user when the user is feeling positive emotions. For example, it may send a message such as, "Great progress! Keep it up!" The emotion estimation function uses facial expression recognition technology to analyze the user's emotions. For example, it may capture the user's facial expressions with a camera and calculate an emotion score. The emotion estimation function uses voice analysis technology to analyze the user's emotions. For example, it may analyze the tone and speed of the user's voice and calculate an emotion score. This makes it possible to provide diet and exercise advice that corresponds to the user's emotional state, thereby eliciting positive emotions.

[0093] The progress check unit can visualize the user's dietary and exercise data and display it visually in graphs and charts. In the progress check unit, for example, the generation AI graphs the user's dietary data and visually displays the progress. For example, it can display the intake of each nutrient in a pie chart, allowing the user to confirm a balanced diet. The generation AI graphs the user's exercise data and visually displays the progress. For example, it can display changes in exercise volume in a bar graph, allowing the user to check the progress of exercise at a glance. This makes it possible to visualize the user's dietary and exercise data and visually display it in graphs and charts.

[0094] The progress check unit can integrate the user's diet and exercise data with other health data to provide a comprehensive health assessment. For example, the generation AI in the progress check unit integrates the user's weight data and sleep data to provide a comprehensive health assessment. For example, if the user's sleep time is short, the generation AI can provide advice such as, "Getting more sleep will improve your weight loss results." The generation AI can integrate the user's weight data and stress level to provide a comprehensive health assessment. For example, if the user's stress level is high, the generation AI can provide advice such as, "Relaxing will improve your weight loss results." The generation AI can integrate the user's weight data and blood pressure data to provide a comprehensive health assessment. For example, if the user's blood pressure is high, the generation AI can provide advice such as, "Reducing salt intake will improve your weight loss results." This allows the user's diet and exercise data to be integrated with other health data to provide a comprehensive health assessment.

[0095] The progress check unit uses the emotion estimation function to provide diet and exercise advice according to the user's emotional state, thereby maintaining motivation. For example, when the user is feeling positive, the progress check unit uses the emotion estimation function to provide diet and exercise advice that further motivates the user. For example, it sends a message such as, "Great progress! Keep it up!" The emotion estimation function uses facial expression recognition technology to analyze the user's emotions. For example, it takes a picture of the user's facial expression with a camera and calculates an emotion score. The emotion estimation function uses voice analysis technology to analyze the user's emotions. For example, it analyzes the tone and speed of the user's voice and calculates an emotion score. This allows the unit to provide diet and exercise advice according to the user's emotional state, thereby maintaining motivation.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The knowledge provider can analyze the user's past successful experiences and provide encouraging messages based on them. For example, the generation AI can analyze the user's past successful experiences and provide messages that emphasize those experiences. For example, it can send a message such as, "You've worked hard and succeeded before. I'm sure you'll succeed this time too!" The generation AI can provide specific advice based on the user's past successful experiences. For example, it can provide advice such as, "Try again the method that was successful in the past." This allows the generation AI to analyze the user's past successful experiences and provide encouraging messages based on them.

[0098] The knowledge provider can periodically evaluate the user's degree of goal achievement and suggest rewards and incentives according to the degree of achievement. For example, the generation AI can periodically evaluate the user's degree of goal achievement and suggest rewards according to the degree of achievement. For example, it can make a suggestion such as, "You're getting close to your goal weight, so go see your favorite movie as a reward for yourself." The generation AI can then suggest incentives based on the user's degree of goal achievement. For example, it can make a suggestion such as, "If you achieve your goal well, we'll give you a special experience as a gift." This allows the system to periodically evaluate the user's degree of goal achievement and suggest rewards and incentives according to the degree of achievement.

[0099] The knowledge provision unit can work with the user's friends and family to provide support for jointly maintaining motivation. For example, the generation AI can work with the user's friends and family to provide support for jointly maintaining motivation. For example, making a plan to exercise together with friends and family. The generation AI can work with the user's friends and family to jointly create a meal plan. For example, making a plan to cook healthy meals together with friends and family. In this way, the generation AI can work with the user's friends and family to provide support for jointly maintaining motivation.

[0100] The knowledge providing unit can suggest weight loss-related activities based on the user's hobbies and interests. For example, the generation AI learns the user's hobbies and interests and suggests weight loss-related activities based on them. For example, it suggests dance exercises for a user who likes dancing. The generation AI learns the user's interests and suggests weight loss-related activities based on them. For example, it suggests a hiking plan for a user who likes hiking. In this way, it is possible to suggest weight loss-related activities based on the user's hobbies and interests.

[0101] The knowledge provider can use the emotion estimation function to suggest activities to maintain motivation according to the user's emotional state. For example, the emotion estimation function can be used to suggest activities that will further increase motivation when the user is feeling positive. For example, it can suggest exercises or activities that the user can enjoy. The emotion estimation function uses facial expression recognition technology to analyze the user's emotions. For example, it can capture the user's facial expressions with a camera and calculate an emotion score. The emotion estimation function can analyze the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice and calculate an emotion score. This makes it possible to suggest activities to maintain motivation according to the user's emotional state.

[0102] The knowledge provider can analyze the user's genetic information and health checkup data and provide personalized weight loss advice based on that. For example, the generation AI can analyze the user's genetic information and suggest a weight loss method that is genetically appropriate. For example, it can provide advice that takes into account the effects of diet and exercise that are influenced by specific genes. The generation AI can analyze the user's health checkup data and suggest a weight loss method based on the user's health condition. For example, it can suggest a nutritionally balanced meal plan based on blood test results. This allows the system to analyze the user's genetic information and health checkup data and provide personalized weight loss advice based on that.

[0103] The knowledge provider can consider the user's living environment and suggest the optimal weight loss method. For example, the generation AI considers the user's work environment and suggests weight loss methods that can be done at work. For example, it suggests stretches and light exercises that can be done while working at a desk. The generation AI considers the user's home environment and suggests weight loss methods that can be done at home. For example, it suggests exercises and healthy meal plans that can be done at home. This makes it possible to consider the user's living environment and suggest the optimal weight loss method.

[0104] The knowledge provider can use the emotion estimation function to provide personalized advice according to the user's emotional state and elicit positive emotions. For example, when the user is feeling positive emotions, the emotion estimation function can provide advice that further motivates the user. For example, it can send a message such as, "Great progress! Keep it up!" The emotion estimation function uses facial expression recognition technology to analyze the user's emotions. For example, it can capture the user's facial expressions with a camera and calculate an emotion score. The emotion estimation function can analyze the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice and calculate an emotion score. This makes it possible to provide personalized advice according to the user's emotional state and elicit positive emotions.

[0105] The knowledge providing unit can analyze the user's past weight loss attempt data, identify successful and unsuccessful methods, and provide advice. For example, the generation AI can analyze the user's past weight loss attempt data, identify successful methods, and provide advice. For example, it can provide advice such as, "Try the method that was successful in the past again." The generation AI can analyze the user's past weight loss attempt data, identify unsuccessful methods, and provide advice. For example, it can provide advice such as, "Try a different method to avoid the method that failed in the past." This makes it possible to analyze the user's past weight loss attempt data, identify successful and unsuccessful methods, and provide advice.

[0106] The knowledge provider can use the emotion estimation function to provide an encouraging message according to the user's emotional state and maintain motivation. For example, the emotion estimation function can be used to provide an encouraging message when the user is feeling positive emotions. For example, it can send a message such as "Great progress! Keep it up!" The emotion estimation function uses facial expression recognition technology to analyze the user's emotions. For example, it can capture the user's facial expressions with a camera and calculate an emotion score. The emotion estimation function can analyze the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice and calculate an emotion score. This allows it to provide an encouraging message according to the user's emotional state and maintain motivation.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The knowledge provider provides basic knowledge about weight loss. For example, the generation AI teaches how to eat properly, how to exercise, and how to calculate calories. The generation AI also provides appropriate information based on prompts containing instructions about what the user wants to know. For example, in response to a prompt such as "Tell me a healthy breakfast recipe," the AI ​​provides specific advice such as "A breakfast of oatmeal and fruit would be good." Step 2: The progress checker checks the user's daily weight loss progress. For example, the generation AI inputs the user's daily weight, diet, and exercise amount into the app, and analyzes the data to evaluate the user's progress. If the user answers "70 kg" to a question such as "How much do you weigh today?", the generation AI will provide feedback such as "You've lost 0.5 kg since yesterday. That's great!"

[0109] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 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.

[0114] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0129] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, a 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.

[0144] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0150] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0153] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0154] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0160] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0161] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0166] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0167] 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.

[0168] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0169] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0170] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0171] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0173] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0174] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0175] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The knowledge provision department provides basic knowledge about weight loss, A progress check unit that checks daily progress is provided. A system characterized by:

2. The knowledge providing unit Analyzes the user's past diet and exercise history to provide individually customized weight loss advice 2. The system of claim 1.

3. The progress check unit Analyze user progress data in real time and provide immediate feedback 2. The system of claim 1.

4. The knowledge providing unit Incorporating weight loss methods from different cultures and regions to provide users with a variety of options 2. The system of claim 1.

5. The knowledge providing unit Analyzes users' genetic information and health checkup data and provides personalized weight loss advice based on that information 2. The system of claim 1.

6. The knowledge providing unit Providing weight loss tips tailored to the user's emotional state and eliciting positive emotions 2. The system of claim 1.

7. The progress check unit Provides progress feedback based on the user's emotional state to maintain motivation 2. The system of claim 1.

8. The knowledge providing unit Provides encouraging messages based on the user's emotional state to maintain motivation 2. The system of claim 1.

Images