system

Abstract

The system according to this embodiment aims to effectively support weight loss even for unmotivated users. [Solution] The system according to the embodiment comprises a registration unit, a setting unit, an instruction unit, a modification unit, and a collection unit. The registration unit registers personal information. The setting unit sets goals based on the information registered by the registration unit. The instruction unit issues instructions based on the goals set by the setting unit. The modification unit issues modified instructions if the instructions issued by the instruction unit are ignored. The collection unit collects user behavior data.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, since the initiative of the user is required in dieting, there is a problem that effective support cannot be obtained for people who lack motivation.

[0005] The system according to the embodiment aims to effectively support dieting even for users who lack motivation.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a registration unit, a setting unit, an instruction unit, a modification unit, and a collection unit. The registration unit registers personal information. The setting unit sets goals based on the information registered by the registration unit. The instruction unit issues instructions based on the goals set by the setting unit. The modification unit issues modified instructions if the instructions issued by the instruction unit are ignored. The collection unit collects user behavior data. [Effects of the Invention]

[0007] The system according to this embodiment can effectively support weight loss even for users who lack motivation. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The diet agent AI system according to an embodiment of the present invention is a system that allows even unmotivated people to lose weight simply by following the agent's instructions. In this system, the user registers and sets personal information (age, physical health information, location information) and sets goals, and the diet agent AI gives specific instructions to the user based on the registered personal information and goal settings. For example, it may give instructions such as, "Exercise at 8 km / h for 60 minutes," "Do 10 sit-ups and 10 push-ups before going to sleep," or "Cabbage is on sale. A coupon is also available. Here are some cabbage recipes." If the user ignores the instructions, the AI ​​will give corrected instructions according to the goal. As a result, the user can proceed with their diet simply by following the agent's instructions without taking any active action. For example, if the user sets "Turn off the 'follow instructions' function in the morning," the AI ​​will not give instructions during that time. Also, if the user sets "Walk two train stops during their commute," the AI ​​will prompt them to take action according to that instruction. Furthermore, the AI ​​collects the user's behavioral data and provides an optimal diet plan. As a result, the user can proceed with their diet at their own pace. For example, if a user is instructed to "eat a banana for breakfast," following that instruction will help them maintain a healthy diet. Similarly, if a user is instructed to "take the stairs instead of the elevator," following that instruction will help them incorporate exercise into their daily life. In this way, the AI ​​diet agent system aims to support the user's health and realize a society where all citizens are healthy. This allows the AI ​​diet agent system to maintain the user's health and enable even those who lack motivation to continue dieting.

[0029] The diet agent AI system according to the embodiment comprises a registration unit, a setting unit, an instruction unit, a modification unit, and a collection unit. The registration unit allows the user to register personal information. Personal information includes, but is not limited to, name, address, age, and health information. The registration unit allows, for example, the user to input personal information through an application. The setting unit sets goals based on the information registered by the registration unit. Goals include, for example, weight loss goals and exercise goals. The setting unit allows, for example, the user to input goals through an application. The instruction unit issues instructions based on the goals set by the setting unit. Instructions include, for example, exercise instructions and dietary instructions. The instruction unit issues, for example, instructions to the user such as "exercise at 8 km / h for 60 minutes." The instruction unit generates instructions using a generation AI. The generation AI generates instructions using, for example, a text generation AI (e.g., LLM). The modification unit issues modified instructions if the instructions issued by the instruction unit are ignored. Corrections include, but are not limited to, changing the content of instructions. The correction unit issues corrective instructions, such as "Do 10 sit-ups and 10 push-ups before going to sleep," if the user ignores the instructions. The correction unit generates corrective instructions using a generative AI. The generative AI generates corrective instructions using, for example, a text generation AI (e.g., LLM). The collection unit collects user behavior data. Behavioral data includes, but is not limited to, the number of steps taken, the content of meals, and the time spent exercising. The collection unit enables, for example, the user to input behavior data through an application. This allows the diet agent AI system according to the embodiment to proceed with dieting without the user having to take active action. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can input user behavior data into a generative AI and have the generative AI perform analysis of the behavior data.

[0030] The registration section allows users to register their personal information. This personal information includes, but is not limited to, name, address, age, and health information. The registration section enables users to input personal information through the application. Specifically, users download the application, and upon first launch, a screen for entering personal information is displayed. Here, users are required to enter detailed information such as name, address, age, gender, height, weight, past medical history, allergy information, and current health status. This information is necessary for the system to provide the user with the most suitable diet plan. The entered information is encrypted and securely stored to protect privacy. Furthermore, the registration section also allows users to update their information later. For example, if a user loses weight or their health status changes, they can easily update their information through the application. This allows the system to always provide the user with the most suitable instructions based on the latest information.

[0031] The settings unit sets goals based on the information registered by the registration unit. Goals include, but are not limited to, weight loss goals and exercise goals. The settings unit allows users to input goals through the application, for example. Specifically, users can input their weight loss target, desired timeframe, exercise frequency and type, and dietary restrictions on the application's settings screen. Based on this information, the settings unit automatically generates an optimal diet plan for the user. For example, if a user aims to lose 5 kg in one month, the settings unit calculates the amount of exercise and dietary restrictions needed to achieve that goal and presents a specific plan. The settings unit can also set realistic goals that are not too strenuous, taking into account the user's health condition and past dieting history. This allows users to continue dieting without undue stress and increases the likelihood of achieving their goals.

[0032] The instruction unit issues instructions based on the goals set by the setting unit. These instructions include, but are not limited to, exercise instructions and dietary instructions. For example, the instruction unit might instruct the user to "exercise at 8 km / h for 60 minutes." The instruction unit generates instructions using a generative AI. The generative AI uses, for example, a text generation AI (e.g., LLM) to generate instructions. Specifically, the generative AI considers the user's goals and current situation to generate optimal exercise and dietary instructions. For example, if the user's goal is weight loss, the generative AI will suggest an optimal exercise plan based on the user's current weight, exercise history, and diet. Furthermore, the generative AI can adjust the type and timing of exercise to suit the user's preferences and lifestyle. For example, if the user is busy, it can suggest short, effective exercises and select exercises that the user can enjoy. Regarding dietary instructions, the generative AI considers the user's nutritional balance and suggests healthy and delicious recipes. This allows the user to continue dieting without difficulty and work effectively towards achieving their goals.

[0033] The correction unit issues corrected instructions if the instructions issued by the instruction unit are ignored. Corrections include, but are not limited to, changing the content of the instructions. For example, if the user ignores the instructions, the correction unit will issue corrected instructions such as "Do 10 sit-ups and 10 push-ups before going to sleep." The correction unit generates corrected instructions using a generative AI. The generative AI generates corrected instructions using, for example, a text generation AI (e.g., LLM). Specifically, the generative AI analyzes the user's behavior data and infers why the instructions were ignored. For example, if the user did not exercise, and the generative AI determines that the reason was fatigue or lack of time, it will generate corrected instructions accordingly. For example, it may issue instructions such as "You seem tired today, so please do some light stretching" or "If you don't have time, please do some exercises that can be done in a short amount of time." This allows the user to follow the instructions without difficulty and maintain their motivation to continue dieting. In addition, the correction unit can collect user feedback and continuously improve the accuracy and effectiveness of the instructions. This allows the correction unit to provide the user with optimal instructions and support the success of their diet.

[0034] The data collection unit collects user behavior data. This behavior data includes, but is not limited to, steps taken, meals eaten, and exercise time. The data collection unit enables users to input behavior data through an application. Specifically, users can input their daily steps, meals eaten, and exercise time on a dedicated screen within the application. Furthermore, the data collection unit can also automatically collect data by linking with external devices such as smartwatches and fitness trackers. For example, it can acquire data such as steps taken, heart rate, and exercise time from a smartwatch in real time and reflect it in the application. This allows users to collect accurate data without any effort. The data collection unit can also input the collected data into a generating AI and have the generating AI perform behavior data analysis. Based on the collected data, the generating AI can analyze the user's behavior patterns and trends and suggest improvements to the diet plan or new instructions. In this way, the data collection unit can efficiently collect user behavior data and improve the overall system performance.

[0035] The time setting instruction unit includes a time setting unit that does not issue instructions during specific time periods. For example, if the user sets "turn off the obedience function in the morning," the time setting unit will not issue instructions during that time period. The time setting unit enables instructions that are tailored to the user's lifestyle. For example, the time setting unit can be set not to issue instructions during the time when the user rests at night. This makes it possible to issue instructions that are tailored to the user's lifestyle by not issuing instructions during specific time periods. Some or all of the above processing in the time setting unit may be performed using AI, for example, or not using AI. For example, the time setting unit can input the user's lifestyle data into a generating AI and have the generating AI execute the setting of time periods when instructions are not issued.

[0036] The specific instruction unit includes a specific instruction unit that issues specific instructions, such as walking two train stations' distance during the commute. For example, if the user sets "walk two train stations' distance during the commute," the specific instruction unit prompts the user to act according to that instruction. The specific instruction unit enables the user to incorporate exercise into their daily life. For example, the specific instruction unit can issue instructions to the user such as "please use the stairs instead of the elevator." In this way, by issuing specific instructions, the user can incorporate exercise into their daily life. Some or all of the above processing in the specific instruction unit may be performed using AI, for example, or without using AI. For example, the specific instruction unit can input user behavior data into a generating AI and have the generating AI execute the generation of specific instructions.

[0037] The data collection unit collects user behavior data, and the instruction unit provides an optimal diet plan based on the collected data. The data collection unit, for example, allows the user to input behavior data through an application. The instruction unit provides an optimal diet plan, such as a meal plan or exercise plan, based on the collected data. This enables effective dieting by providing an optimal diet plan based on the user's behavior data. Some or all of the above processing in the data collection unit and instruction unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user behavior data into a generation AI and have the generation AI perform analysis of the behavior data. The instruction unit can also generate an optimal diet plan based on the data analyzed by the generation AI.

[0038] The instruction unit instructs the user, "Please eat a banana for breakfast." The instruction unit then provides the user with specific dietary instructions. These specific instructions may include, but are not limited to, the types and quantities of food to eat. By providing the user with specific dietary instructions, a healthy diet can be maintained. Some or all of the above-described processes in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input the user's dietary data into a generating AI and have the generating AI generate specific dietary instructions.

[0039] The instruction unit instructs the user to "use the stairs instead of the elevator." The instruction unit also provides specific exercise instructions to the user. These specific instructions may include, but are not limited to, the type and frequency of exercise. This allows the user to incorporate exercise into their daily life by providing specific exercise instructions. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input the user's exercise data into a generating AI and have the generating AI generate specific exercise instructions.

[0040] The registration unit improves the accuracy of registration information by referring to the user's past health data during registration. For example, the registration unit refers to the user's past health checkup data and registers information that reflects the current health status. The registration unit refers to the user's past exercise history and registers information for setting appropriate exercise goals. The registration unit refers to the user's past meal records and registers information based on eating habits. In this way, the accuracy of registration information can be improved by referring to the user's past health data. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the user's past health data into a generating AI and have the generating AI perform the improvement of the accuracy of registration information.

[0041] The registration unit customizes registration information based on the user's lifestyle habits during registration. For example, the registration unit registers appropriate sleep duration based on the user's sleep pattern. The registration unit registers the timing and content of meals based on the user's eating habits. The registration unit registers the frequency and intensity of exercise based on the user's exercise habits. By customizing the registration information based on the user's lifestyle habits, more appropriate information can be registered. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the user's lifestyle data into a generating AI and have the generating AI perform the customization of the registration information.

[0042] The setting unit sets the optimal goal by referring to the user's past diet history when setting a goal. For example, the setting unit sets a realistic goal based on the user's past successful diets. The setting unit sets an achievable goal based on the user's past unsuccessful diets. The setting unit analyzes the user's past diet history and sets the optimal goal. This allows for the setting of realistic goals by referring to the user's past diet history. Some or all of the above processes in the setting unit may be performed using AI, for example, or without AI. For example, the setting unit can input the user's past diet history data into a generating AI and have the generating AI perform optimal goal setting.

[0043] The setting unit customizes the goal based on the user's current health status when setting a goal. For example, the setting unit sets an appropriate goal based on the user's current weight and body fat percentage. The setting unit sets an achievable goal based on the user's current exercise capacity. The setting unit sets a realistic goal based on the user's current eating habits. In this way, achievable goals can be set by customizing the goal based on the user's current health status. Some or all of the above processes in the setting unit may be performed using AI, for example, or without AI. For example, the setting unit can input the user's current health status data into a generating AI and have the generating AI perform the goal customization.

[0044] The instruction unit, when issuing instructions, refers to the user's past behavioral data to provide the most appropriate instructions. For example, the instruction unit provides appropriate exercise instructions based on the user's past exercise history. The instruction unit provides appropriate dietary instructions based on the user's past eating history. The instruction unit provides appropriate sleep instructions based on the user's past sleep history. In this way, more appropriate instructions can be provided by referring to the user's past behavioral data. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input the user's past behavioral data into a generating AI and have the generating AI perform the generation of optimal instructions.

[0045] The instruction unit customizes instructions based on the user's current lifestyle when issuing them. For example, the instruction unit provides appropriate exercise instructions based on the user's current work schedule. The instruction unit provides appropriate dietary instructions based on the user's current eating habits. The instruction unit provides appropriate sleep instructions based on the user's current sleep patterns. By customizing the instructions based on the user's current lifestyle, more realistic instructions can be provided. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input the user's current lifestyle data into a generating AI and have the generating AI perform the instruction customization.

[0046] The correction unit, when issuing correction instructions, refers to the user's past behavioral data to issue the most appropriate correction instructions. For example, the correction unit issues appropriate corrected exercise instructions based on the user's past exercise history. The correction unit issues appropriate corrected dietary instructions based on the user's past eating history. The correction unit issues appropriate corrected sleep instructions based on the user's past sleep history. In this way, more appropriate correction instructions can be issued by referring to the user's past behavioral data. Some or all of the above processing in the correction unit may be performed using AI, for example, or without AI. For example, the correction unit can input the user's past behavioral data into a generating AI and have the generating AI execute the generation of the most appropriate correction instructions.

[0047] The correction unit customizes the correction instructions based on the user's current lifestyle when issuing them. For example, the correction unit issues appropriate corrective exercise instructions based on the user's current work schedule. The correction unit issues appropriate corrective dietary instructions based on the user's current eating habits. The correction unit issues appropriate corrective sleep instructions based on the user's current sleep patterns. By customizing the correction instructions based on the user's current lifestyle, more realistic correction instructions can be issued. Some or all of the above processing in the correction unit may be performed using AI, for example, or without AI. For example, the correction unit can input the user's current lifestyle data into a generating AI and have the generating AI perform the customization of the correction instructions.

[0048] The data collection unit improves the accuracy of data collection by referring to the user's past behavioral data. For example, the data collection unit collects appropriate exercise data based on the user's past exercise history. The data collection unit collects appropriate dietary data based on the user's past eating history. The data collection unit collects appropriate sleep data based on the user's past sleep history. This improves the accuracy of data collection by referring to the user's past behavioral data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral data into a generating AI and have the generating AI perform the task of improving the accuracy of data collection.

[0049] The data collection unit customizes the collected data based on the user's current lifestyle. For example, the data collection unit collects appropriate exercise data based on the user's current work schedule. For example, the data collection unit collects appropriate dietary data based on the user's current eating habits. For example, the data collection unit collects appropriate sleep data based on the user's current sleep patterns. By customizing the collected data based on the user's current lifestyle, more appropriate data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current lifestyle data into a generating AI and have the generating AI perform the customization of the collected data.

[0050] The data collection unit prioritizes the collection of highly relevant data, taking into account the user's geographical location. For example, if the user is in a specific region, the data collection unit collects exercise data appropriate to the environment of that region. If the user is in a specific location, the data collection unit collects dietary data feasible in that location. If the user is traveling, the data collection unit collects sleep data appropriate to the environment of the travel destination. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0051] The data collection unit analyzes the user's social media activity and collects relevant data during data collection. For example, the data collection unit collects appropriate exercise data based on exercise information shared by the user on social media. The data collection unit collects appropriate dietary data based on information from diet-related accounts followed by the user on social media. The data collection unit collects appropriate sleep data based on information from sleep-related groups the user participates in on social media. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0052] The time setting unit sets the optimal time zone by referring to the user's past behavioral data when setting the time. For example, the time setting unit sets an appropriate exercise time zone based on the user's past exercise history. The time setting unit sets an appropriate meal time zone based on the user's past meal history. The time setting unit sets an appropriate sleep time zone based on the user's past sleep history. In this way, the optimal time zone can be set by referring to the user's past behavioral data. Some or all of the above processing in the time setting unit may be performed using AI, for example, or without using AI. For example, the time setting unit can input the user's past behavioral data into a generating AI and have the generating AI perform the setting of the optimal time zone.

[0053] The time setting unit sets the optimal time zone when setting the time, taking into account the user's geographical location information. For example, if the user is in a specific region, the time setting unit sets a time zone suitable for the environment of that region. If the user is in a specific location, the time setting unit sets a time zone that can be performed at that location. If the user is traveling, the time setting unit sets a time zone suitable for the environment of the travel destination. In this way, the optimal time zone can be set by taking into account the user's geographical location information. Some or all of the above processing in the time setting unit may be performed using AI, for example, or without using AI. For example, the time setting unit can input the user's geographical location information data into a generating AI and have the generating AI perform the setting of the optimal time zone.

[0054] The specific instruction unit provides optimal instructions by referring to the user's past behavioral data when issuing specific instructions. For example, the specific instruction unit provides appropriate specific exercise instructions based on the user's past exercise history. For example, the specific instruction unit provides appropriate specific dietary instructions based on the user's past eating history. For example, the specific instruction unit provides appropriate specific sleep instructions based on the user's past sleep history. By referring to the user's past behavioral data, it is possible to provide more appropriate specific instructions. Some or all of the above processing in the specific instruction unit may be performed using AI, for example, or without AI. For example, the specific instruction unit can input the user's past behavioral data into a generating AI and have the generating AI generate optimal specific instructions.

[0055] The specific instruction unit provides optimal instructions by considering the user's geographical location when issuing specific instructions. For example, if the user is in a specific region, the specific instruction unit provides specific exercise instructions suitable for the environment of that region. If the user is in a specific location, the specific instruction unit provides specific meal instructions that can be performed at that location. If the user is traveling, the specific instruction unit provides specific sleep instructions suitable for the environment of the travel destination. In this way, optimal specific instructions can be provided by considering the user's geographical location. Some or all of the above processing in the specific instruction unit may be performed using AI, for example, or without AI. For example, the specific instruction unit can input the user's geographical location data into a generating AI and have the generating AI perform the generation of optimal specific instructions.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The diet agent AI system also includes a rewards section. The rewards section provides rewards to users when they follow instructions. Rewards include, but are not limited to, points, badges, and perks. For example, the rewards section might award points to a user who follows the instruction, "Exercise at 8 km / h for 60 minutes." The rewards section might award a badge to a user who follows the instruction, "Do 10 sit-ups and 10 push-ups before going to sleep." The rewards section might offer perks to a user who follows the instruction, "Cabbage is on sale. A coupon is also available. Here are some cabbage recipes." This makes it easier for users to maintain motivation by earning rewards.

[0058] The diet agent AI system also includes a communication section. This section allows users to share information with other users. For example, it provides a function for users to share their progress with others. It also provides a messaging function for users to encourage each other. Furthermore, it provides a ranking function for users to compete with others. This makes it easier for users to stick to their diet by interacting with others.

[0059] The diet agent AI system also features a customization function. This customization function customizes the instructions according to the user's preferences. For example, it allows the user to select their preferred exercises, their favorite foods, and their preferred time of day. This allows users to enjoy their diet by receiving instructions tailored to their preferences.

[0060] The diet agent AI system also includes a feedback unit. The feedback unit provides feedback on the user's actions. For example, it sends a message of praise when the user follows instructions. It sends a message of encouragement when the user does not follow instructions. The feedback unit provides advice according to the user's progress. As a result, users are more likely to continue their diet by receiving feedback.

[0061] The diet agent AI system also includes a reminder function. The reminder function reminds the user to avoid forgetting instructions. For example, the reminder function sends a notification when it's time for the user to exercise, when it's time for the user to eat, and when it's time for the user to rest. This ensures that the user doesn't forget to follow the instructions by receiving reminders.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The registration section allows users to register their personal information. This includes name, address, age, health information, etc. The registration section enables users to enter their personal information through the application. Step 2: The settings unit sets goals based on the information registered by the registration unit. Goals include weight loss goals, exercise goals, etc. The settings unit allows the user to input goals through the application. Step 3: The instruction unit issues instructions based on the goals set by the settings unit. These instructions include exercise instructions, dietary instructions, etc. The instruction unit will give the user instructions such as "Exercise at 8 km / h for 60 minutes." The instruction unit generates instructions using a generation AI. Step 4: The correction unit issues corrected instructions if the instructions issued by the instruction unit are ignored. Corrections include changing the content of the instructions. If the user ignores the instructions, the correction unit will issue corrected instructions such as "Do 10 sit-ups and 10 push-ups before going to sleep." The correction unit generates corrected instructions using a generation AI. Step 5: The data collection unit collects user behavior data. This behavior data includes steps taken, meals eaten, exercise time, etc. The data collection unit enables users to input behavior data through the application.

[0064] (Example of form 2) The diet agent AI system according to an embodiment of the present invention is a system that allows even unmotivated people to lose weight simply by following the agent's instructions. In this system, the user registers and sets personal information (age, physical health information, location information) and sets goals, and the diet agent AI gives specific instructions to the user based on the registered personal information and goal settings. For example, it may give instructions such as, "Exercise at 8 km / h for 60 minutes," "Do 10 sit-ups and 10 push-ups before going to sleep," or "Cabbage is on sale. A coupon is also available. Here are some cabbage recipes." If the user ignores the instructions, the AI ​​will give corrected instructions according to the goal. As a result, the user can proceed with their diet simply by following the agent's instructions without taking any active action. For example, if the user sets "Turn off the 'follow instructions' function in the morning," the AI ​​will not give instructions during that time. Also, if the user sets "Walk two train stops during their commute," the AI ​​will prompt them to take action according to that instruction. Furthermore, the AI ​​collects the user's behavioral data and provides an optimal diet plan. As a result, the user can proceed with their diet at their own pace. For example, if a user is instructed to "eat a banana for breakfast," following that instruction will help them maintain a healthy diet. Similarly, if a user is instructed to "take the stairs instead of the elevator," following that instruction will help them incorporate exercise into their daily life. In this way, the AI ​​diet agent system aims to support the user's health and realize a society where all citizens are healthy. This allows the AI ​​diet agent system to maintain the user's health and enable even those who lack motivation to continue dieting.

[0065] The diet agent AI system according to the embodiment comprises a registration unit, a setting unit, an instruction unit, a modification unit, and a collection unit. The registration unit allows the user to register personal information. Personal information includes, but is not limited to, name, address, age, and health information. The registration unit allows, for example, the user to input personal information through an application. The setting unit sets goals based on the information registered by the registration unit. Goals include, for example, weight loss goals and exercise goals. The setting unit allows, for example, the user to input goals through an application. The instruction unit issues instructions based on the goals set by the setting unit. Instructions include, for example, exercise instructions and dietary instructions. The instruction unit issues, for example, instructions to the user such as "exercise at 8 km / h for 60 minutes." The instruction unit generates instructions using a generation AI. The generation AI generates instructions using, for example, a text generation AI (e.g., LLM). The modification unit issues modified instructions if the instructions issued by the instruction unit are ignored. Corrections include, but are not limited to, changing the content of instructions. The correction unit issues corrective instructions, such as "Do 10 sit-ups and 10 push-ups before going to sleep," if the user ignores the instructions. The correction unit generates corrective instructions using a generative AI. The generative AI generates corrective instructions using, for example, a text generation AI (e.g., LLM). The collection unit collects user behavior data. Behavioral data includes, but is not limited to, the number of steps taken, the content of meals, and the time spent exercising. The collection unit enables, for example, the user to input behavior data through an application. This allows the diet agent AI system according to the embodiment to proceed with dieting without the user having to take active action. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can input user behavior data into a generative AI and have the generative AI perform analysis of the behavior data.

[0066] The registration section allows users to register their personal information. This personal information includes, but is not limited to, name, address, age, and health information. The registration section enables users to input personal information through the application. Specifically, users download the application, and upon first launch, a screen for entering personal information is displayed. Here, users are required to enter detailed information such as name, address, age, gender, height, weight, past medical history, allergy information, and current health status. This information is necessary for the system to provide the user with the most suitable diet plan. The entered information is encrypted and securely stored to protect privacy. Furthermore, the registration section also allows users to update their information later. For example, if a user loses weight or their health status changes, they can easily update their information through the application. This allows the system to always provide the user with the most suitable instructions based on the latest information.

[0067] The settings unit sets goals based on the information registered by the registration unit. Goals include, but are not limited to, weight loss goals and exercise goals. The settings unit allows users to input goals through the application, for example. Specifically, users can input their weight loss target, desired timeframe, exercise frequency and type, and dietary restrictions on the application's settings screen. Based on this information, the settings unit automatically generates an optimal diet plan for the user. For example, if a user aims to lose 5 kg in one month, the settings unit calculates the amount of exercise and dietary restrictions needed to achieve that goal and presents a specific plan. The settings unit can also set realistic goals that are not too strenuous, taking into account the user's health condition and past dieting history. This allows users to continue dieting without undue stress and increases the likelihood of achieving their goals.

[0068] The instruction unit issues instructions based on the goals set by the setting unit. These instructions include, but are not limited to, exercise instructions and dietary instructions. For example, the instruction unit might instruct the user to "exercise at 8 km / h for 60 minutes." The instruction unit generates instructions using a generative AI. The generative AI uses, for example, a text generation AI (e.g., LLM) to generate instructions. Specifically, the generative AI considers the user's goals and current situation to generate optimal exercise and dietary instructions. For example, if the user's goal is weight loss, the generative AI will suggest an optimal exercise plan based on the user's current weight, exercise history, and diet. Furthermore, the generative AI can adjust the type and timing of exercise to suit the user's preferences and lifestyle. For example, if the user is busy, it can suggest short, effective exercises and select exercises that the user can enjoy. Regarding dietary instructions, the generative AI considers the user's nutritional balance and suggests healthy and delicious recipes. This allows the user to continue dieting without difficulty and work effectively towards achieving their goals.

[0069] The correction unit issues corrected instructions if the instructions issued by the instruction unit are ignored. Corrections include, but are not limited to, changing the content of the instructions. For example, if the user ignores the instructions, the correction unit will issue corrected instructions such as "Do 10 sit-ups and 10 push-ups before going to sleep." The correction unit generates corrected instructions using a generative AI. The generative AI generates corrected instructions using, for example, a text generation AI (e.g., LLM). Specifically, the generative AI analyzes the user's behavior data and infers why the instructions were ignored. For example, if the user did not exercise, and the generative AI determines that the reason was fatigue or lack of time, it will generate corrected instructions accordingly. For example, it may issue instructions such as "You seem tired today, so please do some light stretching" or "If you don't have time, please do some exercises that can be done in a short amount of time." This allows the user to follow the instructions without difficulty and maintain their motivation to continue dieting. In addition, the correction unit can collect user feedback and continuously improve the accuracy and effectiveness of the instructions. This allows the correction unit to provide the user with optimal instructions and support the success of their diet.

[0070] The data collection unit collects user behavior data. This behavior data includes, but is not limited to, steps taken, meals eaten, and exercise time. The data collection unit enables users to input behavior data through an application. Specifically, users can input their daily steps, meals eaten, and exercise time on a dedicated screen within the application. Furthermore, the data collection unit can also automatically collect data by linking with external devices such as smartwatches and fitness trackers. For example, it can acquire data such as steps taken, heart rate, and exercise time from a smartwatch in real time and reflect it in the application. This allows users to collect accurate data without any effort. The data collection unit can also input the collected data into a generating AI and have the generating AI perform behavior data analysis. Based on the collected data, the generating AI can analyze the user's behavior patterns and trends and suggest improvements to the diet plan or new instructions. In this way, the data collection unit can efficiently collect user behavior data and improve the overall system performance.

[0071] The time setting instruction unit includes a time setting unit that does not issue instructions during specific time periods. For example, if the user sets "turn off the obedience function in the morning," the time setting unit will not issue instructions during that time period. The time setting unit enables instructions that are tailored to the user's lifestyle. For example, the time setting unit can be set not to issue instructions during the time when the user rests at night. This makes it possible to issue instructions that are tailored to the user's lifestyle by not issuing instructions during specific time periods. Some or all of the above processing in the time setting unit may be performed using AI, for example, or not using AI. For example, the time setting unit can input the user's lifestyle data into a generating AI and have the generating AI execute the setting of time periods when instructions are not issued.

[0072] The specific instruction unit includes a specific instruction unit that issues specific instructions, such as walking two train stations' distance during the commute. For example, if the user sets "walk two train stations' distance during the commute," the specific instruction unit prompts the user to act according to that instruction. The specific instruction unit enables the user to incorporate exercise into their daily life. For example, the specific instruction unit can issue instructions to the user such as "please use the stairs instead of the elevator." In this way, by issuing specific instructions, the user can incorporate exercise into their daily life. Some or all of the above processing in the specific instruction unit may be performed using AI, for example, or without using AI. For example, the specific instruction unit can input user behavior data into a generating AI and have the generating AI execute the generation of specific instructions.

[0073] The data collection unit collects user behavior data, and the instruction unit provides an optimal diet plan based on the collected data. The data collection unit, for example, allows the user to input behavior data through an application. The instruction unit provides an optimal diet plan, such as a meal plan or exercise plan, based on the collected data. This enables effective dieting by providing an optimal diet plan based on the user's behavior data. Some or all of the above processing in the data collection unit and instruction unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user behavior data into a generation AI and have the generation AI perform analysis of the behavior data. The instruction unit can also generate an optimal diet plan based on the data analyzed by the generation AI.

[0074] The instruction unit instructs the user, "Please eat a banana for breakfast." The instruction unit then provides the user with specific dietary instructions. These specific instructions may include, but are not limited to, the types and quantities of food to eat. By providing the user with specific dietary instructions, a healthy diet can be maintained. Some or all of the above-described processes in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input the user's dietary data into a generating AI and have the generating AI generate specific dietary instructions.

[0075] The instruction unit instructs the user to "use the stairs instead of the elevator." The instruction unit also provides specific exercise instructions to the user. These specific instructions may include, but are not limited to, the type and frequency of exercise. This allows the user to incorporate exercise into their daily life by providing specific exercise instructions. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input the user's exercise data into a generating AI and have the generating AI generate specific exercise instructions.

[0076] The registration unit estimates the user's emotions and adjusts the level of detail of the information to be registered based on the estimated emotions. For example, if the user is stressed, the registration unit prompts them to register only the minimum necessary information. If the user is relaxed, the registration unit prompts them to register detailed health and lifestyle information. If the user is in a hurry, the registration unit prompts them to register information in the form of simple questions. This reduces the burden on the user by adjusting the level of detail of the information to be registered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the registration unit may be performed using AI or not using AI. For example, the registration unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0077] The registration unit improves the accuracy of registration information by referring to the user's past health data during registration. For example, the registration unit refers to the user's past health checkup data and registers information that reflects the current health status. The registration unit refers to the user's past exercise history and registers information for setting appropriate exercise goals. The registration unit refers to the user's past meal records and registers information based on eating habits. In this way, the accuracy of registration information can be improved by referring to the user's past health data. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the user's past health data into a generating AI and have the generating AI perform the improvement of the accuracy of registration information.

[0078] The registration unit customizes registration information based on the user's lifestyle habits during registration. For example, the registration unit registers appropriate sleep duration based on the user's sleep pattern. The registration unit registers the timing and content of meals based on the user's eating habits. The registration unit registers the frequency and intensity of exercise based on the user's exercise habits. By customizing the registration information based on the user's lifestyle habits, more appropriate information can be registered. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the user's lifestyle data into a generating AI and have the generating AI perform the customization of the registration information.

[0079] The settings unit estimates the user's emotions and adjusts the difficulty of goal setting based on the estimated emotions. For example, if the user is stressed, the settings unit lowers the difficulty of goal setting. If the user is relaxed, the settings unit increases the difficulty of goal setting. If the user is in a hurry, the settings unit sets goals that can be achieved in a short period of time. In this way, by adjusting the difficulty of goal setting according to the user's emotions, it becomes possible to set realistic goals. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the settings unit may be performed using AI, for example, or not using AI. For example, the settings unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The setting unit sets the optimal goal by referring to the user's past diet history when setting a goal. For example, the setting unit sets a realistic goal based on the user's past successful diets. The setting unit sets an achievable goal based on the user's past unsuccessful diets. The setting unit analyzes the user's past diet history and sets the optimal goal. This allows for the setting of realistic goals by referring to the user's past diet history. Some or all of the above processes in the setting unit may be performed using AI, for example, or without AI. For example, the setting unit can input the user's past diet history data into a generating AI and have the generating AI perform optimal goal setting.

[0081] The setting unit customizes the goal based on the user's current health status when setting a goal. For example, the setting unit sets an appropriate goal based on the user's current weight and body fat percentage. The setting unit sets an achievable goal based on the user's current exercise capacity. The setting unit sets a realistic goal based on the user's current eating habits. In this way, achievable goals can be set by customizing the goal based on the user's current health status. Some or all of the above processes in the setting unit may be performed using AI, for example, or without AI. For example, the setting unit can input the user's current health status data into a generating AI and have the generating AI perform the goal customization.

[0082] The instruction unit estimates the user's emotions and adjusts the way instructions are expressed based on the estimated emotions. For example, if the user is stressed, the instruction unit will give instructions in gentle language. If the user is relaxed, the instruction unit will give instructions that include detailed explanations. If the user is in a hurry, the instruction unit will give concise and quick instructions. By adjusting the way instructions are expressed according to the user's emotions, the user is more likely to accept the instructions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the instruction unit may be performed using AI or not using AI. For example, the instruction unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The instruction unit, when issuing instructions, refers to the user's past behavioral data to provide the most appropriate instructions. For example, the instruction unit provides appropriate exercise instructions based on the user's past exercise history. The instruction unit provides appropriate dietary instructions based on the user's past eating history. The instruction unit provides appropriate sleep instructions based on the user's past sleep history. In this way, more appropriate instructions can be provided by referring to the user's past behavioral data. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input the user's past behavioral data into a generating AI and have the generating AI perform the generation of optimal instructions.

[0084] The instruction unit customizes instructions based on the user's current lifestyle when issuing them. For example, the instruction unit provides appropriate exercise instructions based on the user's current work schedule. The instruction unit provides appropriate dietary instructions based on the user's current eating habits. The instruction unit provides appropriate sleep instructions based on the user's current sleep patterns. By customizing the instructions based on the user's current lifestyle, more realistic instructions can be provided. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input the user's current lifestyle data into a generating AI and have the generating AI perform the instruction customization.

[0085] The editing unit estimates the user's emotions and adjusts the content of the editing instructions based on the estimated emotions. For example, if the user is stressed, the editing unit will issue editing instructions in gentle language. If the user is relaxed, the editing unit will issue editing instructions that include detailed explanations. If the user is in a hurry, the editing unit will issue concise and quick editing instructions. By adjusting the content of the editing instructions according to the user's emotions, the user is more likely to accept the editing instructions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the editing unit may be performed using AI or not using AI. For example, the editing unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0086] The correction unit, when issuing correction instructions, refers to the user's past behavioral data to issue the most appropriate correction instructions. For example, the correction unit issues appropriate corrected exercise instructions based on the user's past exercise history. The correction unit issues appropriate corrected dietary instructions based on the user's past eating history. The correction unit issues appropriate corrected sleep instructions based on the user's past sleep history. In this way, more appropriate correction instructions can be issued by referring to the user's past behavioral data. Some or all of the above processing in the correction unit may be performed using AI, for example, or without AI. For example, the correction unit can input the user's past behavioral data into a generating AI and have the generating AI execute the generation of the most appropriate correction instructions.

[0087] The correction unit customizes the correction instructions based on the user's current lifestyle when issuing them. For example, the correction unit issues appropriate corrective exercise instructions based on the user's current work schedule. The correction unit issues appropriate corrective dietary instructions based on the user's current eating habits. The correction unit issues appropriate corrective sleep instructions based on the user's current sleep patterns. By customizing the correction instructions based on the user's current lifestyle, more realistic correction instructions can be issued. Some or all of the above processing in the correction unit may be performed using AI, for example, or without AI. For example, the correction unit can input the user's current lifestyle data into a generating AI and have the generating AI perform the customization of the correction instructions.

[0088] The data collection unit estimates the user's emotions and adjusts the type of data collected based on the estimated emotions. For example, if the user is stressed, the data collection unit collects only the minimum necessary data. If the user is relaxed, the data collection unit collects detailed data. If the user is in a hurry, the data collection unit collects simple data. This reduces the user's burden by adjusting the type of data collected according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0089] The data collection unit improves the accuracy of data collection by referring to the user's past behavioral data. For example, the data collection unit collects appropriate exercise data based on the user's past exercise history. The data collection unit collects appropriate dietary data based on the user's past eating history. The data collection unit collects appropriate sleep data based on the user's past sleep history. This improves the accuracy of data collection by referring to the user's past behavioral data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral data into a generating AI and have the generating AI perform the task of improving the accuracy of data collection.

[0090] The data collection unit customizes the collected data based on the user's current lifestyle. For example, the data collection unit collects appropriate exercise data based on the user's current work schedule. For example, the data collection unit collects appropriate dietary data based on the user's current eating habits. For example, the data collection unit collects appropriate sleep data based on the user's current sleep patterns. By customizing the collected data based on the user's current lifestyle, more appropriate data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current lifestyle data into a generating AI and have the generating AI perform the customization of the collected data.

[0091] The data collection unit estimates the user's emotions and determines the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit prioritizes collecting important data. If the user is relaxed, the data collection unit prioritizes collecting detailed data. If the user is in a hurry, the data collection unit prioritizes collecting simple data. This allows for the priority collection of important data by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0092] The data collection unit prioritizes the collection of highly relevant data, taking into account the user's geographical location. For example, if the user is in a specific region, the data collection unit collects exercise data appropriate to the environment of that region. If the user is in a specific location, the data collection unit collects dietary data feasible in that location. If the user is traveling, the data collection unit collects sleep data appropriate to the environment of the travel destination. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0093] The data collection unit analyzes the user's social media activity and collects relevant data during data collection. For example, the data collection unit collects appropriate exercise data based on exercise information shared by the user on social media. The data collection unit collects appropriate dietary data based on information from diet-related accounts followed by the user on social media. The data collection unit collects appropriate sleep data based on information from sleep-related groups the user participates in on social media. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0094] The time setting unit estimates the user's emotions and adjusts the time periods during which it does not issue instructions based on the estimated emotions. For example, if the user is stressed, the time setting unit will not issue instructions during relaxation times. If the user is relaxed, the time setting unit will not issue instructions during rest times. If the user is in a hurry, the time setting unit will not issue instructions during busy times. In this way, the burden on the user can be reduced by adjusting the time periods during which it does not issue instructions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the time setting unit may be performed using AI, for example, or not using AI. For example, the time setting unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0095] The time setting unit sets the optimal time zone by referring to the user's past behavioral data when setting the time. For example, the time setting unit sets an appropriate exercise time zone based on the user's past exercise history. The time setting unit sets an appropriate meal time zone based on the user's past meal history. The time setting unit sets an appropriate sleep time zone based on the user's past sleep history. In this way, the optimal time zone can be set by referring to the user's past behavioral data. Some or all of the above processing in the time setting unit may be performed using AI, for example, or without using AI. For example, the time setting unit can input the user's past behavioral data into a generating AI and have the generating AI perform the setting of the optimal time zone.

[0096] The time setting unit estimates the user's emotions and, based on the estimated emotions, determines the priority of time periods during which it will not issue instructions. For example, if the user is stressed, the time setting unit will prioritize important time periods. If the user is relaxed, the time setting unit will prioritize detailed time periods. If the user is in a hurry, the time setting unit will prioritize time periods that can be acted quickly. In this way, by determining the priority of time periods during which it will not issue instructions according to the user's emotions, it is possible to prioritize important time periods. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the time setting unit may be performed using AI, for example, or not using AI. For example, the time setting unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0097] The time setting unit sets the optimal time zone when setting the time, taking into account the user's geographical location information. For example, if the user is in a specific region, the time setting unit sets a time zone suitable for the environment of that region. If the user is in a specific location, the time setting unit sets a time zone that can be performed at that location. If the user is traveling, the time setting unit sets a time zone suitable for the environment of the travel destination. In this way, the optimal time zone can be set by taking into account the user's geographical location information. Some or all of the above processing in the time setting unit may be performed using AI, for example, or without using AI. For example, the time setting unit can input the user's geographical location information data into a generating AI and have the generating AI perform the setting of the optimal time zone.

[0098] The specific instruction unit estimates the user's emotions and adjusts the content of specific instructions based on the estimated emotions. For example, if the user is stressed, the specific instruction unit will give specific instructions in gentle language. If the user is relaxed, the specific instruction unit will give specific instructions that include detailed explanations. If the user is in a hurry, the specific instruction unit will give concise and quick specific instructions. By adjusting the content of specific instructions according to the user's emotions, the user is more likely to accept the instructions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the specific instruction unit may be performed using AI or not using AI. For example, the specific instruction unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0099] The specific instruction unit provides optimal instructions by referring to the user's past behavioral data when issuing specific instructions. For example, the specific instruction unit provides appropriate specific exercise instructions based on the user's past exercise history. For example, the specific instruction unit provides appropriate specific dietary instructions based on the user's past eating history. For example, the specific instruction unit provides appropriate specific sleep instructions based on the user's past sleep history. By referring to the user's past behavioral data, it is possible to provide more appropriate specific instructions. Some or all of the above processing in the specific instruction unit may be performed using AI, for example, or without AI. For example, the specific instruction unit can input the user's past behavioral data into a generating AI and have the generating AI generate optimal specific instructions.

[0100] The specific instruction unit estimates the user's emotions and determines the priority of specific instructions based on the estimated emotions. For example, if the user is stressed, the specific instruction unit will prioritize important specific instructions. If the user is relaxed, the specific instruction unit will prioritize detailed specific instructions. If the user is in a hurry, the specific instruction unit will prioritize specific instructions that can be executed quickly. In this way, by determining the priority of specific instructions according to the user's emotions, important specific instructions can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the specific instruction unit may be performed using AI, for example, or not using AI. For example, the specific instruction unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0101] The specific instruction unit provides optimal instructions by considering the user's geographical location when issuing specific instructions. For example, if the user is in a specific region, the specific instruction unit provides specific exercise instructions suitable for the environment of that region. If the user is in a specific location, the specific instruction unit provides specific meal instructions that can be performed at that location. If the user is traveling, the specific instruction unit provides specific sleep instructions suitable for the environment of the travel destination. In this way, optimal specific instructions can be provided by considering the user's geographical location. Some or all of the above processing in the specific instruction unit may be performed using AI, for example, or without AI. For example, the specific instruction unit can input the user's geographical location data into a generating AI and have the generating AI perform the generation of optimal specific instructions.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The diet agent AI system also includes a rewards section. The rewards section provides rewards to users when they follow instructions. Rewards include, but are not limited to, points, badges, and perks. For example, the rewards section might award points to a user who follows the instruction, "Exercise at 8 km / h for 60 minutes." The rewards section might award a badge to a user who follows the instruction, "Do 10 sit-ups and 10 push-ups before going to sleep." The rewards section might offer perks to a user who follows the instruction, "Cabbage is on sale. A coupon is also available. Here are some cabbage recipes." This makes it easier for users to maintain motivation by earning rewards.

[0104] The diet agent AI system also includes a communication section. This section allows users to share information with other users. For example, it provides a function for users to share their progress with others. It also provides a messaging function for users to encourage each other. Furthermore, it provides a ranking function for users to compete with others. This makes it easier for users to stick to their diet by interacting with others.

[0105] The diet agent AI system also features a customization function. This customization function customizes the instructions according to the user's preferences. For example, it allows the user to select their preferred exercises, their favorite foods, and their preferred time of day. This allows users to enjoy their diet by receiving instructions tailored to their preferences.

[0106] The diet agent AI system also includes a feedback unit. The feedback unit provides feedback on the user's actions. For example, it sends a message of praise when the user follows instructions. It sends a message of encouragement when the user does not follow instructions. The feedback unit provides advice according to the user's progress. As a result, users are more likely to continue their diet by receiving feedback.

[0107] The diet agent AI system also includes a reminder function. The reminder function reminds the user to avoid forgetting instructions. For example, the reminder function sends a notification when it's time for the user to exercise, when it's time for the user to eat, and when it's time for the user to rest. This ensures that the user doesn't forget to follow the instructions by receiving reminders.

[0108] The diet agent AI system also includes an emotion estimation unit. This unit estimates the user's emotions and adjusts the instructions based on those emotions. For example, if the user is feeling stressed, the emotion estimation unit will instruct them to do relaxing exercises. If the user is relaxed, the emotion estimation unit will instruct them to do challenging exercises. If the user is in a hurry, the emotion estimation unit will instruct them to do short, effective exercises. In this way, by providing instructions that match the user's emotions, dieting can be done smoothly and without undue stress.

[0109] The diet agent AI system also includes an emotion estimation unit. This unit estimates the user's emotions and adjusts the feedback content based on the estimated emotions. For example, if the user is feeling stressed, the emotion estimation unit provides feedback in gentle words. If the user is relaxed, the emotion estimation unit provides detailed feedback. If the user is in a hurry, the emotion estimation unit provides concise feedback. By providing feedback that is tailored to the user's emotions, it makes the user more receptive to the feedback.

[0110] The diet agent AI system also includes an emotion estimation unit. This unit estimates the user's emotions and adjusts the reward content based on the estimated emotions. For example, if the user is feeling stressed, the emotion estimation unit provides a relaxing reward. If the user is relaxed, the emotion estimation unit provides a challenging reward. If the user is in a hurry, the emotion estimation unit provides a reward that can be received quickly. This makes it easier to maintain the user's motivation by providing rewards that match the user's emotions.

[0111] The diet agent AI system also includes an emotion estimation unit. This unit estimates the user's emotions and adjusts the reminder content based on the estimated emotions. For example, if the user is feeling stressed, the emotion estimation unit will remind them in gentle words. If the user is relaxed, the emotion estimation unit will provide a detailed reminder. If the user is in a hurry, the emotion estimation unit will provide a concise reminder. By providing reminders that are tailored to the user's emotions, the system makes it easier for the user to accept the reminders.

[0112] The diet agent AI system also includes an emotion estimation unit. This unit estimates the user's emotions and adjusts the communication content based on the estimated emotions. For example, if the user is feeling stressed, the emotion estimation unit sends an encouraging message. If the user is relaxed, the emotion estimation unit provides detailed advice. If the user is in a hurry, the emotion estimation unit sends a concise message. By providing communication that is tailored to the user's emotions, the system makes it easier for the user to accept the communication.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The registration section allows users to register their personal information. This includes name, address, age, health information, etc. The registration section enables users to enter their personal information through the application. Step 2: The settings unit sets goals based on the information registered by the registration unit. Goals include weight loss goals, exercise goals, etc. The settings unit allows the user to input goals through the application. Step 3: The instruction unit issues instructions based on the goals set by the settings unit. These instructions include exercise instructions, dietary instructions, etc. The instruction unit will give the user instructions such as "Exercise at 8 km / h for 60 minutes." The instruction unit generates instructions using a generation AI. Step 4: The correction unit issues corrected instructions if the instructions issued by the instruction unit are ignored. Corrections include changing the content of the instructions. If the user ignores the instructions, the correction unit will issue corrected instructions such as "Do 10 sit-ups and 10 push-ups before going to sleep." The correction unit generates corrected instructions using a generation AI. Step 5: The data collection unit collects user behavior data. This behavior data includes steps taken, meals eaten, exercise time, etc. The data collection unit enables users to input behavior data through the application.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0117] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the registration unit, setting unit, instruction unit, modification unit, collection unit, time setting unit, and specific instruction unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the registration unit is implemented by the control unit 46A of the smart device 14, enabling the user to input personal information through an application. The setting unit is implemented by the specific processing unit 290 of the data processing device 12, setting goals based on the registered information. The instruction unit is implemented by the control unit 46A of the smart device 14, issuing instructions based on the set goals. The modification unit is implemented by the specific processing unit 290 of the data processing device 12, issuing modified instructions if the instructions are ignored. The collection unit is implemented by the control unit 46A of the smart device 14, collecting user behavior data. The time setting unit is implemented by the specific processing unit 290 of the data processing device 12, setting it not to issue instructions during specific time periods. The specific instruction unit is implemented by the control unit 46A of the smart device 14, issuing specific instructions. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the registration unit, setting unit, instruction unit, modification unit, collection unit, time setting unit, and specific instruction unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the registration unit is implemented by the control unit 46A of the smart glasses 214, enabling the user to input personal information through an application. The setting unit is implemented by the specific processing unit 290 of the data processing device 12, setting goals based on the registered information. The instruction unit is implemented by the control unit 46A of the smart glasses 214, issuing instructions based on the set goals. The modification unit is implemented by the specific processing unit 290 of the data processing device 12, issuing modified instructions if the instructions are ignored. The collection unit is implemented by the control unit 46A of the smart glasses 214, collecting user behavior data. The time setting unit is implemented by the specific processing unit 290 of the data processing device 12, setting it not to issue instructions during specific time periods. The specific instruction unit is implemented by the control unit 46A of the smart glasses 214, issuing specific instructions. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the registration unit, setting unit, instruction unit, modification unit, collection unit, time setting unit, and specific instruction unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the registration unit is implemented by the control unit 46A of the headset terminal 314, enabling the user to input personal information through the application. The setting unit is implemented by the specific processing unit 290 of the data processing device 12, setting goals based on the registered information. The instruction unit is implemented by the control unit 46A of the headset terminal 314, issuing instructions based on the set goals. The modification unit is implemented by the specific processing unit 290 of the data processing device 12, issuing modified instructions if the instructions are ignored. The collection unit is implemented by the control unit 46A of the headset terminal 314, collecting user behavior data. The time setting unit is implemented by the specific processing unit 290 of the data processing device 12, setting it so that no instructions are issued during specific time periods. The specific instruction unit is implemented, for example, by the control unit 46A of the headset terminal 314, which issues specific instructions. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the registration unit, setting unit, instruction unit, modification unit, collection unit, time setting unit, and specific instruction unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the robot 414, enabling the user to input personal information through an application. The setting unit is implemented by the specific processing unit 290 of the data processing unit 12, setting a goal based on the registered information. The instruction unit is implemented by the control unit 46A of the robot 414, issuing instructions based on the set goal. The modification unit is implemented by the specific processing unit 290 of the data processing unit 12, issuing a modified instruction if the instruction is ignored. The collection unit is implemented by the control unit 46A of the robot 414, collecting user behavior data. The time setting unit is implemented by the specific processing unit 290 of the data processing unit 12, setting it not to issue instructions during a specific time period. The specific instruction unit is implemented by the control unit 46A of the robot 414, issuing specific instructions. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0168] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0170] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0171] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0172] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0176] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0177] 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.

[0178] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0179] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0180] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0181] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0185] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0186] (Note 1) The registration section where you register your personal information, A setting unit sets targets based on the information registered by the registration unit, An instruction unit that issues instructions based on the target set by the setting unit, A correction unit that issues a corrected instruction if the instruction issued by the aforementioned instruction unit is ignored, It comprises a collection unit that collects user behavior data. A system characterized by the following features. (Note 2) The indicator unit is, It includes a time setting unit that prevents instructions from being issued during specific time periods. The system described in Appendix 1, characterized by the features described herein. (Note 3) The indicator unit is, It is equipped with a specific instruction unit that gives concrete instructions, such as walking two train stations' distance during your commute. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is We collect user behavior data, The indicator unit is, We provide the optimal diet plan based on the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The indicator unit is, Instruct the user to "eat a banana for breakfast." The system described in Appendix 1, characterized by the features described herein. (Note 6) The indicator unit is, Instruct the user to "use the stairs instead of the elevator." The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned registration unit is Estimate the user's emotions, Adjust the level of detail in the information registered based on the estimated user's sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned registration unit is During registration, we refer to the user's past health data to improve the accuracy of registration information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned registration unit is During registration, registration information is customized based on the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 10) The setting unit is, Estimate the user's emotions, Adjust the difficulty level of goal setting based on estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The setting unit is, When setting goals, the system will refer to the user's past diet history to set the most appropriate goals. The system described in Appendix 1, characterized by the features described herein. (Note 12) The setting unit is, When setting goals, customize them based on the user's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 13) The indicator unit is, Estimate the user's emotions, The way instructions are phrased is adjusted based on the estimated user's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The indicator unit is, When issuing instructions, the system refers to the user's past behavioral data to provide the most appropriate instructions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The indicator unit is, When issuing instructions, customize the instructions based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned modification section is, Estimate the user's emotions, Adjust the content of the correction instructions based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned modification section is, When issuing correction instructions, the system refers to the user's past behavior data to provide the most appropriate instructions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned modification section is, When issuing correction instructions, customize the instructions based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned collection unit is Estimate the user's emotions, Adjust the types of data collected based on estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned collection unit is When collecting data, we improve the accuracy of the collection by referring to the user's past behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned collection unit is During data collection, the collected data is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned collection unit is Estimate the user's emotions, Prioritize the data to collect based on estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned time setting unit is Estimate the user's emotions, Adjust the time periods during which instructions are not issued based on the estimated user's emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned time setting unit is When setting the time, the system uses the user's past behavioral data to determine the optimal time slot. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned time setting unit is Estimate the user's emotions, Prioritize times when instructions will not be issued based on estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned time setting unit is When setting the time, the system will take the user's geographical location into consideration to determine the optimal time zone. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned specific instruction unit is, Estimate the user's emotions, Adjust the specific instructions based on the estimated user's emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned specific instruction unit is, When giving specific instructions, the system refers to the user's past behavioral data to provide the most appropriate instructions. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned specific instruction unit is, Estimate the user's emotions, Prioritize specific instructions based on estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned specific instruction unit is, When issuing specific instructions, the system takes the user's geographical location into consideration to provide the most appropriate instructions. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The registration section where you register your personal information, A setting unit sets targets based on the information registered by the registration unit, An instruction unit that issues instructions based on the target set by the setting unit, A correction unit that issues a corrected instruction if the instruction issued by the aforementioned instruction unit is ignored, It comprises a collection unit that collects user behavior data. A system characterized by the following features.

2. The indicator unit is, It includes a time setting unit that prevents instructions from being issued during specific time periods. The system according to feature 1.

3. The indicator unit is, It is equipped with a specific instruction unit that gives concrete instructions, such as walking two train stations' distance during your commute. The system according to feature 1.

4. The aforementioned collection unit is We collect user behavior data, The indicator unit is, We provide the optimal diet plan based on the collected data. The system according to feature 1.

5. The aforementioned registration unit is To estimate the user's emotions, Adjust the level of detail in the information registered based on the estimated user's sentiment. The system according to feature 1.

6. The aforementioned registration unit is During registration, we refer to the user's past health data to improve the accuracy of registration information. The system according to feature 1.

7. The aforementioned registration unit is During registration, registration information is customized based on the user's lifestyle. The system according to feature 1.

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