system

Abstract

The system according to this embodiment aims to learn the user's needs and, based on those needs, provide reminders and optimal suggestions. [Solution] The system according to the embodiment comprises a learning unit, a reminder unit, and a suggestion unit. The learning unit learns the user's needs. The reminder unit makes reminders based on the needs learned by the learning unit. The suggestion unit makes optimal suggestions based on the information reminded by the reminder unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including: 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 prior art, it cannot be said that the needs of users are sufficiently learned and reminders and proposals are made based on them, and there is room for improvement.

[0005] The system according to the embodiment aims to learn the needs of users and make reminders and optimal proposals based on them.

Means for Solving the Problems

[0006] The system according to the embodiment includes a learning unit, a reminder unit, and a proposal unit. The learning unit learns the needs of users. The reminder unit makes a reminder based on the needs learned by the learning unit. The proposal unit makes an optimal proposal based on the information reminded by the reminder unit. [Effects of the Invention]

[0007] The system according to this embodiment can learn the user's needs and, based on that, provide reminders and optimal suggestions. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 "AI Link OS" terminal, a dedicated terminal for generating AI according to an embodiment of the present invention, is a system that provides an environment in which users can naturally interact with AI on a daily basis. This system builds a mechanism that allows users to access the generating AI with intuitive operation and receive information acquisition and support in real time. Furthermore, the AI ​​assistant learns the user's needs and provides reminders and optimal suggestions, thereby utilizing AI as an extension of the body to support daily life. This mechanism accelerates the penetration of AI technology and promotes a smooth transition to the AI ​​era. For example, a user can access the generating AI with intuitive operation using the "AI Link OS" terminal. For example, if the user inputs "Tell me my schedule for today" by voice, the generating AI analyzes that information and displays the user's schedule. At this time, the generating AI learns the user's past behavior and lifestyle and provides optimal information. Next, the AI ​​assistant learns the user's needs. For example, if the user has a habit of drinking coffee every morning, the AI ​​assistant learns that information and provides a reminder every morning at the time to drink coffee. Also, if the user has plans to go to a specific place, the AI ​​assistant provides information about that place. Furthermore, the AI ​​assistant provides the user with optimal suggestions. For example, if a user is interested in health management, the AI ​​assistant will monitor their health status and suggest optimal exercise and dietary recommendations. Similarly, if a user wants to improve their work efficiency, the AI ​​assistant will provide advice. This system will accelerate the adoption of AI technology and facilitate a smooth transition to the AI ​​era. For instance, by using the AI ​​Link OS, a dedicated AI-generating terminal, users can naturally interact with AI in their daily lives, receiving real-time information and support. This allows users to utilize AI as an extension of their body and receive assistance in their daily lives. Furthermore, the AI ​​Link OS limits voice data communication to text only to reduce communication costs. This approach significantly reduces data usage and keeps communication costs low. The goal is to create an affordable model accessible to all citizens, rather than a luxury item.This allows the AI ​​Link OS, a dedicated terminal for generating AI, to provide an environment where users can naturally interact with AI on a daily basis, accelerating the spread of AI technology and facilitating a smooth transition to the AI ​​era.

[0029] The AI-generating terminal "AI Link OS" according to this embodiment comprises a learning unit, a reminder unit, and a suggestion unit. The learning unit learns the user's needs. For example, the learning unit learns the user's behavior and lifestyle. The learning unit can also analyze the user's past behavioral history, detect changes in behavioral patterns, and update the learned content. The learning unit can also learn the user's life events and provide special reminders. The reminder unit provides reminders based on the needs learned by the learning unit. For example, the reminder unit provides reminders based on the user's behavior and lifestyle. The reminder unit can also adjust the frequency of reminders based on the user's schedule. The reminder unit can also estimate the user's emotions and adjust the timing of reminders based on the estimated emotions. The suggestion unit provides optimal suggestions based on the information reminded by the reminder unit. For example, the suggestion unit monitors the user's health status and provides suggestions for optimal exercise and diet. The suggestion unit can also provide advice to improve the user's work efficiency. The suggestion unit can also estimate the user's emotions and adjust the content of the suggestions based on the estimated emotions. As a result, the AI-generating terminal "AI Link OS" according to this embodiment can support daily life by providing reminders and suggestions based on the user's needs.

[0030] The learning unit learns the user's needs. Specifically, it analyzes the user's behavior and lifestyle in detail and collects foundational data to provide information optimized for each individual user. The learning unit can analyze the user's past behavioral history, detect changes in behavioral patterns, and update its learned content. For example, if a user has a habit of jogging every morning, it can record the time and frequency of their jogs and provide appropriate advice according to changes in weather and physical condition. It can also learn the user's life events and provide special reminders. For example, it can record important dates such as birthdays and anniversaries and notify the user in advance to help them not forget important events. Furthermore, the learning unit can collect information about the user's hobbies and interests and provide relevant events and news. In this way, the learning unit can respond to the diverse needs of users and build a foundation for improving the quality of daily life.

[0031] The reminder unit provides reminders based on the needs learned by the learning unit. Specifically, it provides reminders based on the user's behavior and lifestyle. For example, if a user needs to take medication at a specific time each day, the reminder unit will provide a reminder at that time to help the user remember to take their medication. The reminder unit can also adjust the frequency of reminders based on the user's schedule. For example, it can reduce the frequency of reminders on busy days and provide more detailed reminders on days when the user has more time, thereby reducing the user's burden. Furthermore, the reminder unit can estimate the user's emotions and adjust the timing of reminders based on those emotions. For example, if a user is feeling stressed, it can support the user's mental health by providing a reminder that suggests activities to help them relax. In addition, the reminder unit can collect user feedback and continuously improve the content and timing of reminders. This allows the reminder unit to provide flexible reminders tailored to the user's needs, improving the efficiency and comfort of daily life.

[0032] The suggestion department makes optimal suggestions based on information reminded by the reminder department. Specifically, it monitors the user's health status and suggests optimal exercise and diet. For example, it monitors the user's steps and heart rate, suggests walking or stretching if the user is not getting enough exercise, and also provides advice to avoid excessive exercise. It also records the user's diet and suggests nutritionally balanced meals to support a healthy lifestyle. The suggestion department can also provide advice to improve the user's work efficiency. For example, it analyzes the user's schedule and suggests placing important tasks during times when concentration is highest. It can also estimate the user's emotions and adjust the content of suggestions based on those emotions. For example, if the user is tired, it suggests relaxing activities or breaks to help the user refresh. Furthermore, the suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the suggestion department to provide optimal suggestions that meet the user's needs and improve the quality of their daily life.

[0033] The learning unit can learn about the user's behavior and lifestyle. For example, the learning unit can learn about the user's behavior and lifestyle. The learning unit can analyze the user's behavior patterns and understand the user's needs. The learning unit can also collect the user's behavior history and identify behavior patterns. The learning unit can optimize the content of reminders and suggestions based on the user's lifestyle. This makes it possible to provide more appropriate reminders and suggestions by learning about the user's behavior and lifestyle. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's behavior history into a generating AI and have the generating AI perform an analysis of behavior patterns.

[0034] The reminder unit can send reminders based on the user's behavior and lifestyle. For example, the reminder unit can send reminders based on the user's behavior and lifestyle. The reminder unit can analyze the user's behavior patterns and determine the optimal timing for reminders. The reminder unit can also optimize the content of reminders based on the user's lifestyle. The reminder unit can also adjust the frequency of reminders based on the user's schedule. This makes it possible to send more effective reminders by sending them based on the user's behavior and lifestyle. Some or all of the above processes in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's behavior patterns into a generating AI and have the generating AI determine the timing of reminders.

[0035] The suggestion unit can monitor the user's health status and suggest optimal exercise and diet. For example, the suggestion unit can monitor the user's health status and suggest optimal exercise and diet. The suggestion unit can analyze the user's health status and make suggestions for health management. The suggestion unit can also collect the user's health data and optimize the suggestions based on the health status. The suggestion unit can also suggest exercise and diet based on the user's health status. This makes it possible to suggest optimal exercise and diet based on the user's health status. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's health data into a generating AI and have the generating AI perform an analysis of the health status.

[0036] The suggestion unit can provide advice to improve the efficiency of the user's work. For example, the suggestion unit can provide advice to improve the efficiency of the user's work. The suggestion unit can analyze the progress of the user's work and make suggestions for improving efficiency. The suggestion unit can also collect data on the user's work and provide advice for improving efficiency. The suggestion unit can also provide specific advice to improve the efficiency of the user's work. This makes it possible to provide advice to improve the efficiency of the user's work. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input data on the user's work into a generating AI and have the generating AI perform the task of providing advice for improving efficiency.

[0037] A dedicated terminal for generating AI can limit voice data communication to text only in order to reduce communication costs. For example, the dedicated terminal for generating AI can limit voice data communication to text only. The dedicated terminal for generating AI can convert voice data to text data, thereby reducing the amount of communication. The dedicated terminal for generating AI can also reduce the amount of communication by using voice data compression technology. The dedicated terminal for generating AI can convert voice data to text in real time, thereby reducing communication costs. Some or all of the above processing in the dedicated terminal for generating AI may be performed using AI, for example, or without AI. For example, the dedicated terminal for generating AI can input voice data into the generating AI and have the generating AI perform the conversion to text data.

[0038] The learning unit can analyze the user's past behavior history, detect changes in behavior patterns, and update the learning content. For example, the learning unit can analyze the user's past behavior history, detect changes in behavior patterns, and update the learning content. The learning unit can collect the user's behavior history and identify behavior patterns. The learning unit can also detect changes in the user's behavior patterns and optimize the learning content. The learning unit can also update reminders and suggestions based on the user's behavior history. This allows for more appropriate learning by updating the learning content based on changes in the user's behavior patterns. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's behavior history into a generating AI and have the generating AI detect changes in behavior patterns.

[0039] The learning unit can learn the user's life events and provide special reminders. For example, the learning unit can learn the user's life events and provide special reminders. The learning unit can analyze the user's life events and determine the content of the special reminders. The learning unit can also optimize the content of the reminders based on the user's life events. The learning unit can also provide special reminders based on the user's life events. This makes it possible to provide special reminders based on the user's life events. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's life events into a generating AI and have the generating AI determine the content of the special reminders.

[0040] The learning unit can learn region-specific behavioral patterns by considering the user's geographical location information. For example, the learning unit can learn region-specific behavioral patterns by considering the user's geographical location information. The learning unit can analyze the user's geographical location information and identify region-specific behavioral patterns. The learning unit can also optimize the content of reminders and suggestions based on the user's geographical location information. The learning unit can also learn region-specific behavioral patterns based on the user's geographical location information. This allows for more appropriate reminders and suggestions by learning region-specific behavioral patterns. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's geographical location information into a generating AI and have the generating AI perform the learning of region-specific behavioral patterns.

[0041] The learning unit can analyze the user's social media activity and customize learning content based on their interests. For example, the learning unit can analyze the user's social media activity and customize learning content based on their interests. The learning unit can collect the user's social media activity and identify their interests. The learning unit can also optimize learning content based on the user's social media activity. The learning unit can also customize learning content based on the user's social media activity. This allows for more appropriate learning by customizing learning content based on the user's interests. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's social media activity into a generating AI and have the generating AI customize the learning content.

[0042] The reminder unit can analyze the user's past reminder history and select the optimal reminder method. For example, the reminder unit can analyze the user's past reminder history and select the optimal reminder method. The reminder unit can collect the user's reminder history and identify the optimal reminder method. The reminder unit can also optimize the content and timing of reminders based on the user's reminder history. The reminder unit can also select the optimal reminder method based on the user's reminder history. This allows for more effective reminders by selecting the optimal reminder method based on the user's past reminder history. Some or all of the above processing in the reminder unit may be performed using AI, or without AI. For example, the reminder unit can input the user's reminder history into a generating AI and have the generating AI select the optimal reminder method.

[0043] The reminder unit can adjust the frequency of reminders based on the user's schedule. For example, the reminder unit can adjust the frequency of reminders based on the user's schedule. The reminder unit can analyze the user's schedule and optimize the frequency of reminders. The reminder unit can also collect user schedule data and adjust the frequency of reminders based on the schedule. The reminder unit can also adjust the content and timing of reminders based on the user's schedule. This allows for more appropriate reminders by adjusting the frequency of reminders based on the user's schedule. Some or all of the above processes in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input user schedule data into a generating AI and have the generating AI adjust the frequency of reminders.

[0044] The reminder unit can send reminders at specific locations, taking into account the user's geographical location. For example, the reminder unit can send reminders at specific locations, taking into account the user's geographical location. The reminder unit can analyze the user's geographical location and determine the content of the reminder at specific locations. The reminder unit can also optimize the content and timing of the reminder based on the user's geographical location. The reminder unit can also send reminders at specific locations based on the user's geographical location. This makes it possible to send reminders at specific locations based on the user's geographical location. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's geographical location into a generating AI and have the generating AI determine the content of the reminder at specific locations.

[0045] The reminder unit can provide the optimal reminder method by considering the user's device information. For example, the reminder unit can provide the optimal reminder method by considering the user's device information. The reminder unit can analyze the user's device information and identify the optimal reminder method. The reminder unit can also optimize the content and timing of the reminder based on the user's device information. The reminder unit can also provide the optimal reminder method based on the user's device information. This makes it possible to provide more effective reminders by providing the optimal reminder method based on the user's device information. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without using AI. For example, the reminder unit can input the user's device information into a generating AI and have the generating AI perform the task of providing the optimal reminder method.

[0046] The suggestion unit can monitor the user's health status and update the suggested content in accordance with changes in the health status. For example, the suggestion unit can monitor the user's health status and update the suggested content in accordance with changes in the health status. The suggestion unit can analyze the user's health status and optimize the suggested content based on changes in the health status. The suggestion unit can also collect the user's health data and update the suggested content in accordance with changes in the health status. The suggestion unit can also update the suggested content based on the user's health status. This makes it possible to provide more appropriate suggestions by updating the suggested content based on the user's health status. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without using AI. For example, the suggestion unit can input the user's health data into a generating AI and have the generating AI perform the suggested content update.

[0047] The suggestion department can analyze the user's work progress and provide advice on improving efficiency at the optimal time. For example, the suggestion department can analyze the user's work progress and provide advice on improving efficiency at the optimal time. The suggestion department can collect the user's work progress and provide advice at the optimal time. The suggestion department can also optimize the content and timing of advice based on the user's work progress. The suggestion department can also provide advice on improving efficiency based on the user's work progress. This makes it possible to make more effective suggestions by providing advice on improving efficiency based on the user's work progress. Some or all of the above processes in the suggestion department may be performed using AI, for example, or not using AI. For example, the suggestion department can input the user's work progress into a generating AI and have the generating AI provide advice on improving efficiency.

[0048] The suggestion unit can make region-specific health management and job suggestions, taking into account the user's geographical location information. For example, the suggestion unit can make region-specific health management and job suggestions, taking into account the user's geographical location information. The suggestion unit can analyze the user's geographical location information and determine the content of region-specific health management and job suggestions. The suggestion unit can also optimize the content and timing of suggestions based on the user's geographical location information. The suggestion unit can also make region-specific health management and job suggestions based on the user's geographical location information. This makes it possible to make region-specific health management and job suggestions based on the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information into a generating AI and have the generating AI determine the content of region-specific health management and job suggestions.

[0049] The suggestion unit can analyze the user's social media activity and customize the suggestions based on their interests. For example, the suggestion unit can analyze the user's social media activity and customize the suggestions based on their interests. The suggestion unit can collect the user's social media activity and identify their interests. The suggestion unit can also optimize the suggestions based on the user's social media activity. The suggestion unit can also customize the suggestions based on the user's social media activity. This allows for more appropriate suggestions by customizing the suggestions based on the user's interests. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media activity into a generating AI and have the generating AI customize the suggestions.

[0050] The AI-generating terminal "AI Link OS" can reduce communication costs by limiting voice data communication to text only. For example, the AI-generating terminal "AI Link OS" can limit voice data communication to text only. The AI-generating terminal "AI Link OS" can reduce communication volume by converting voice data to text data. The AI-generating terminal "AI Link OS" can also reduce communication volume by using voice data compression technology. The AI-generating terminal "AI Link OS" can also reduce communication costs by converting voice data to text in real time. As a result, it is possible to reduce communication costs by limiting voice data communication to text only. Some or all of the above processes in the AI-generating terminal "AI Link OS" may be performed using AI, for example, or without using AI. For example, the AI-generating terminal "AI Link OS" can input voice data into the generating AI and have the generating AI perform the conversion to text data.

[0051] The AI ​​Link OS, a dedicated terminal for generating AI, can learn the user's operation history and further optimize intuitive operation. For example, the AI ​​Link OS can learn the user's operation history and further optimize intuitive operation. The AI ​​Link OS can analyze the user's operation history and optimize intuitive operation. The AI ​​Link OS can also collect user operation history data and optimize operation. The AI ​​Link OS can also customize the operation interface based on the user's operation history. This provides a more user-friendly system by further optimizing intuitive operation based on the user's operation history. Some or all of the above processes in the AI ​​Link OS may be performed using AI, or not using AI. For example, the AI ​​Link OS can input user operation history data into the generating AI and have the generating AI perform operation optimization.

[0052] The AI-generating terminal "AI Link OS" can provide the optimal display method by considering the user's device information. For example, the AI-generating terminal "AI Link OS" can provide the optimal display method by considering the user's device information. The AI-generating terminal "AI Link OS" can analyze the user's device information and identify the optimal display method. The AI-generating terminal "AI Link OS" can also optimize the display content and timing based on the user's device information. The AI-generating terminal "AI Link OS" can also provide the optimal display method based on the user's device information. This provides a more user-friendly system by providing the optimal display method based on the user's device information. Some or all of the above processing in the AI-generating terminal "AI Link OS" may be performed using AI, for example, or without AI. For example, the AI-generating terminal "AI Link OS" can input the user's device information into the generating AI and have the generating AI perform the task of providing the optimal display method.

[0053] The AI-generating terminal "AI Link OS" can analyze user usage and propose methods to further reduce communication costs. For example, the AI-generating terminal "AI Link OS" can analyze user usage and propose methods to further reduce communication costs. The AI-generating terminal "AI Link OS" can collect user usage data and identify methods to reduce communication costs. The AI-generating terminal "AI Link OS" can also optimize the proposed methods for reducing communication costs based on user usage data. This provides a more economical system by proposing methods to further reduce communication costs based on user usage data. Some or all of the above-described processes in the AI-generating terminal "AI Link OS" may be performed using AI, or not. For example, the AI-generating terminal "AI Link OS" can input user usage data into the generating AI and have the generating AI propose methods to reduce communication costs.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The AI-generating terminal "AI Link OS" can monitor the user's sleep patterns and provide an optimal sleep environment. For example, it can collect the user's sleep data and analyze the quality of their sleep. The reminder unit can remind the user to go to bed at an appropriate time. Specifically, it can suggest relaxation methods before bedtime and provide advice on creating a suitable bedroom environment. The suggestion unit can also suggest the optimal sleep duration and environment based on the user's sleep data. For example, it can suggest how to choose bedding and adjust the room temperature to ensure the user sleeps comfortably. Furthermore, the learning unit can learn changes in the user's sleep patterns and provide optimal methods to improve sleep quality. In this way, by monitoring the user's sleep patterns and providing an optimal sleep environment, it can support the user's health management.

[0056] The AI-generating terminal "AI Link OS" can manage users' meal records and support healthy eating habits. For example, when a user inputs the details of their meals, the learning unit can analyze the data and evaluate the nutritional balance. The reminder unit can remind users to eat at appropriate times. Specifically, it can notify users of meal times and prompt them to prepare meals. Furthermore, the suggestion unit can suggest healthy meal menus based on the user's meal records. For example, it can provide recipes that take into account the user's nutritional balance and advise on how to choose ingredients. In addition, the learning unit can learn from changes in the user's meal records and provide information that helps improve their eating habits. In this way, by managing users' meal records and supporting healthy eating habits, it can assist in the user's health management.

[0057] The AI-generating terminal "AI Link OS" can monitor a user's exercise habits and provide an optimal exercise plan. For example, it can collect the user's exercise data and analyze the frequency and intensity of exercise. The reminder unit can remind the user to exercise at the appropriate time. Specifically, it can notify the user of the time to exercise and prompt them to prepare for exercise. The suggestion unit can propose an optimal exercise plan based on the user's exercise data. For example, it can provide exercise menus tailored to the user's physical fitness and health condition, and offer advice to maximize the effects of exercise. Furthermore, the learning unit can learn changes in the user's exercise habits and provide information useful for improving the exercise plan. In this way, by monitoring the user's exercise habits and providing an optimal exercise plan, it can support the user's health management.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The learning unit learns the user's needs. For example, it learns the user's behavior and lifestyle, analyzes past behavioral history to detect changes in behavioral patterns, and updates its learned content. It can also learn the user's life events and provide special reminders. Step 2: The reminder unit sends reminders based on the needs learned by the learning unit. For example, it sends reminders based on the user's behavior and lifestyle, and adjusts the frequency of reminders based on the user's schedule. It can also estimate the user's emotions and adjust the timing of reminders based on those emotions. Step 3: The suggestion unit makes optimal suggestions based on the information reminded by the reminder unit. For example, it can monitor the user's health status and suggest optimal exercise and diet. It can also provide advice to improve the user's work efficiency and estimate the user's emotions, adjusting the content of the suggestions based on those estimated emotions.

[0060] (Example of form 2) The "AI Link OS" terminal, a dedicated terminal for generating AI according to an embodiment of the present invention, is a system that provides an environment in which users can naturally interact with AI on a daily basis. This system builds a mechanism that allows users to access the generating AI with intuitive operation and receive information acquisition and support in real time. Furthermore, the AI ​​assistant learns the user's needs and provides reminders and optimal suggestions, thereby utilizing AI as an extension of the body to support daily life. This mechanism accelerates the penetration of AI technology and promotes a smooth transition to the AI ​​era. For example, a user can access the generating AI with intuitive operation using the "AI Link OS" terminal. For example, if the user inputs "Tell me my schedule for today" by voice, the generating AI analyzes that information and displays the user's schedule. At this time, the generating AI learns the user's past behavior and lifestyle and provides optimal information. Next, the AI ​​assistant learns the user's needs. For example, if the user has a habit of drinking coffee every morning, the AI ​​assistant learns that information and provides a reminder every morning at the time to drink coffee. Also, if the user has plans to go to a specific place, the AI ​​assistant provides information about that place. Furthermore, the AI ​​assistant provides the user with optimal suggestions. For example, if a user is interested in health management, the AI ​​assistant will monitor their health status and suggest optimal exercise and dietary recommendations. Similarly, if a user wants to improve their work efficiency, the AI ​​assistant will provide advice. This system will accelerate the adoption of AI technology and facilitate a smooth transition to the AI ​​era. For instance, by using the AI ​​Link OS, a dedicated AI-generating terminal, users can naturally interact with AI in their daily lives, receiving real-time information and support. This allows users to utilize AI as an extension of their body and receive assistance in their daily lives. Furthermore, the AI ​​Link OS limits voice data communication to text only to reduce communication costs. This approach significantly reduces data usage and keeps communication costs low. The goal is to create an affordable model accessible to all citizens, rather than a luxury item.This allows the AI ​​Link OS, a dedicated terminal for generating AI, to provide an environment where users can naturally interact with AI on a daily basis, accelerating the spread of AI technology and facilitating a smooth transition to the AI ​​era.

[0061] The AI-generating terminal "AI Link OS" according to this embodiment comprises a learning unit, a reminder unit, and a suggestion unit. The learning unit learns the user's needs. For example, the learning unit learns the user's behavior and lifestyle. The learning unit can also analyze the user's past behavioral history, detect changes in behavioral patterns, and update the learned content. The learning unit can also learn the user's life events and provide special reminders. The reminder unit provides reminders based on the needs learned by the learning unit. For example, the reminder unit provides reminders based on the user's behavior and lifestyle. The reminder unit can also adjust the frequency of reminders based on the user's schedule. The reminder unit can also estimate the user's emotions and adjust the timing of reminders based on the estimated emotions. The suggestion unit provides optimal suggestions based on the information reminded by the reminder unit. For example, the suggestion unit monitors the user's health status and provides suggestions for optimal exercise and diet. The suggestion unit can also provide advice to improve the user's work efficiency. The suggestion unit can also estimate the user's emotions and adjust the content of the suggestions based on the estimated emotions. As a result, the AI-generating terminal "AI Link OS" according to this embodiment can support daily life by providing reminders and suggestions based on the user's needs.

[0062] The learning unit learns the user's needs. Specifically, it analyzes the user's behavior and lifestyle in detail and collects foundational data to provide information optimized for each individual user. The learning unit can analyze the user's past behavioral history, detect changes in behavioral patterns, and update its learned content. For example, if a user has a habit of jogging every morning, it can record the time and frequency of their jogs and provide appropriate advice according to changes in weather and physical condition. It can also learn the user's life events and provide special reminders. For example, it can record important dates such as birthdays and anniversaries and notify the user in advance to help them not forget important events. Furthermore, the learning unit can collect information about the user's hobbies and interests and provide relevant events and news. In this way, the learning unit can respond to the diverse needs of users and build a foundation for improving the quality of daily life.

[0063] The reminder unit provides reminders based on the needs learned by the learning unit. Specifically, it provides reminders based on the user's behavior and lifestyle. For example, if a user needs to take medication at a specific time each day, the reminder unit will provide a reminder at that time to help the user remember to take their medication. The reminder unit can also adjust the frequency of reminders based on the user's schedule. For example, it can reduce the frequency of reminders on busy days and provide more detailed reminders on days when the user has more time, thereby reducing the user's burden. Furthermore, the reminder unit can estimate the user's emotions and adjust the timing of reminders based on those emotions. For example, if a user is feeling stressed, it can support the user's mental health by providing a reminder that suggests activities to help them relax. In addition, the reminder unit can collect user feedback and continuously improve the content and timing of reminders. This allows the reminder unit to provide flexible reminders tailored to the user's needs, improving the efficiency and comfort of daily life.

[0064] The suggestion department makes optimal suggestions based on information reminded by the reminder department. Specifically, it monitors the user's health status and suggests optimal exercise and diet. For example, it monitors the user's steps and heart rate, suggests walking or stretching if the user is not getting enough exercise, and also provides advice to avoid excessive exercise. It also records the user's diet and suggests nutritionally balanced meals to support a healthy lifestyle. The suggestion department can also provide advice to improve the user's work efficiency. For example, it analyzes the user's schedule and suggests placing important tasks during times when concentration is highest. It can also estimate the user's emotions and adjust the content of suggestions based on those emotions. For example, if the user is tired, it suggests relaxing activities or breaks to help the user refresh. Furthermore, the suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the suggestion department to provide optimal suggestions that meet the user's needs and improve the quality of their daily life.

[0065] The learning unit can learn about the user's behavior and lifestyle. For example, the learning unit can learn about the user's behavior and lifestyle. The learning unit can analyze the user's behavior patterns and understand the user's needs. The learning unit can also collect the user's behavior history and identify behavior patterns. The learning unit can optimize the content of reminders and suggestions based on the user's lifestyle. This makes it possible to provide more appropriate reminders and suggestions by learning about the user's behavior and lifestyle. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's behavior history into a generating AI and have the generating AI perform an analysis of behavior patterns.

[0066] The reminder unit can send reminders based on the user's behavior and lifestyle. For example, the reminder unit can send reminders based on the user's behavior and lifestyle. The reminder unit can analyze the user's behavior patterns and determine the optimal timing for reminders. The reminder unit can also optimize the content of reminders based on the user's lifestyle. The reminder unit can also adjust the frequency of reminders based on the user's schedule. This makes it possible to send more effective reminders by sending them based on the user's behavior and lifestyle. Some or all of the above processes in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's behavior patterns into a generating AI and have the generating AI determine the timing of reminders.

[0067] The suggestion unit can monitor the user's health status and suggest optimal exercise and diet. For example, the suggestion unit can monitor the user's health status and suggest optimal exercise and diet. The suggestion unit can analyze the user's health status and make suggestions for health management. The suggestion unit can also collect the user's health data and optimize the suggestions based on the health status. The suggestion unit can also suggest exercise and diet based on the user's health status. This makes it possible to suggest optimal exercise and diet based on the user's health status. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's health data into a generating AI and have the generating AI perform an analysis of the health status.

[0068] The suggestion unit can provide advice to improve the efficiency of the user's work. For example, the suggestion unit can provide advice to improve the efficiency of the user's work. The suggestion unit can analyze the progress of the user's work and make suggestions for improving efficiency. The suggestion unit can also collect data on the user's work and provide advice for improving efficiency. The suggestion unit can also provide specific advice to improve the efficiency of the user's work. This makes it possible to provide advice to improve the efficiency of the user's work. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input data on the user's work into a generating AI and have the generating AI perform the task of providing advice for improving efficiency.

[0069] A dedicated terminal for generating AI can limit voice data communication to text only in order to reduce communication costs. For example, the dedicated terminal for generating AI can limit voice data communication to text only. The dedicated terminal for generating AI can convert voice data to text data, thereby reducing the amount of communication. The dedicated terminal for generating AI can also reduce the amount of communication by using voice data compression technology. The dedicated terminal for generating AI can convert voice data to text in real time, thereby reducing communication costs. Some or all of the above processing in the dedicated terminal for generating AI may be performed using AI, for example, or without AI. For example, the dedicated terminal for generating AI can input voice data into the generating AI and have the generating AI perform the conversion to text data.

[0070] The learning unit can estimate the user's emotions and adjust the learning content based on the estimated emotions. For example, the learning unit can estimate the user's emotions and adjust the learning content based on the estimated emotions. The learning unit can analyze the user's emotions and optimize the learning content. The learning unit can also collect user emotion data and adjust the learning content based on those emotions. The learning unit can also adjust the learning speed and difficulty based on the user's emotions. This allows for more appropriate learning by adjusting the learning content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the learning unit may be performed using AI, or not. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the learning content.

[0071] The learning unit can analyze the user's past behavior history, detect changes in behavior patterns, and update the learning content. For example, the learning unit can analyze the user's past behavior history, detect changes in behavior patterns, and update the learning content. The learning unit can collect the user's behavior history and identify behavior patterns. The learning unit can also detect changes in the user's behavior patterns and optimize the learning content. The learning unit can also update reminders and suggestions based on the user's behavior history. This allows for more appropriate learning by updating the learning content based on changes in the user's behavior patterns. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's behavior history into a generating AI and have the generating AI detect changes in behavior patterns.

[0072] The learning unit can learn the user's life events and provide special reminders. For example, the learning unit can learn the user's life events and provide special reminders. The learning unit can analyze the user's life events and determine the content of the special reminders. The learning unit can also optimize the content of the reminders based on the user's life events. The learning unit can also provide special reminders based on the user's life events. This makes it possible to provide special reminders based on the user's life events. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's life events into a generating AI and have the generating AI determine the content of the special reminders.

[0073] The learning unit can estimate the user's emotions and determine learning priorities based on the estimated emotions. For example, the learning unit can estimate the user's emotions and determine learning priorities based on the estimated emotions. The learning unit can analyze the user's emotions and optimize learning priorities. The learning unit can also collect user emotion data and determine learning priorities based on those emotions. The learning unit can also adjust the learning speed and difficulty based on the user's emotions. This allows for more appropriate learning by determining learning priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the learning unit may be performed using AI, or not. For example, the learning unit can input user emotion data into a generative AI and have the generative AI determine learning priorities.

[0074] The learning unit can learn region-specific behavioral patterns by considering the user's geographical location information. For example, the learning unit can learn region-specific behavioral patterns by considering the user's geographical location information. The learning unit can analyze the user's geographical location information and identify region-specific behavioral patterns. The learning unit can also optimize the content of reminders and suggestions based on the user's geographical location information. The learning unit can also learn region-specific behavioral patterns based on the user's geographical location information. This allows for more appropriate reminders and suggestions by learning region-specific behavioral patterns. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's geographical location information into a generating AI and have the generating AI perform the learning of region-specific behavioral patterns.

[0075] The learning unit can analyze the user's social media activity and customize learning content based on their interests. For example, the learning unit can analyze the user's social media activity and customize learning content based on their interests. The learning unit can collect the user's social media activity and identify their interests. The learning unit can also optimize learning content based on the user's social media activity. The learning unit can also customize learning content based on the user's social media activity. This allows for more appropriate learning by customizing learning content based on the user's interests. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's social media activity into a generating AI and have the generating AI customize the learning content.

[0076] The reminder unit can estimate the user's emotions and adjust the timing of reminders based on those emotions. For example, the reminder unit can estimate the user's emotions and adjust the timing of reminders based on those emotions. The reminder unit can analyze the user's emotions and optimize the timing of reminders. The reminder unit can also collect user emotion data and adjust the timing of reminders based on those emotions. The reminder unit can also adjust the content and timing of reminders based on the user's emotions. This allows for more appropriate reminders by adjusting the timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reminder unit may be performed using AI, or not. For example, the reminder unit can input user emotion data into a generative AI and have the generative AI adjust the timing of reminders.

[0077] The reminder unit can analyze the user's past reminder history and select the optimal reminder method. For example, the reminder unit can analyze the user's past reminder history and select the optimal reminder method. The reminder unit can collect the user's reminder history and identify the optimal reminder method. The reminder unit can also optimize the content and timing of reminders based on the user's reminder history. The reminder unit can also select the optimal reminder method based on the user's reminder history. This allows for more effective reminders by selecting the optimal reminder method based on the user's past reminder history. Some or all of the above processing in the reminder unit may be performed using AI, or without AI. For example, the reminder unit can input the user's reminder history into a generating AI and have the generating AI select the optimal reminder method.

[0078] The reminder unit can adjust the frequency of reminders based on the user's schedule. For example, the reminder unit can adjust the frequency of reminders based on the user's schedule. The reminder unit can analyze the user's schedule and optimize the frequency of reminders. The reminder unit can also collect user schedule data and adjust the frequency of reminders based on the schedule. The reminder unit can also adjust the content and timing of reminders based on the user's schedule. This allows for more appropriate reminders by adjusting the frequency of reminders based on the user's schedule. Some or all of the above processes in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input user schedule data into a generating AI and have the generating AI adjust the frequency of reminders.

[0079] The reminder unit can estimate the user's emotions and adjust the content of the reminder based on the estimated emotions. For example, the reminder unit can estimate the user's emotions and adjust the content of the reminder based on the estimated emotions. The reminder unit can analyze the user's emotions and optimize the content of the reminder. The reminder unit can also collect user emotion data and adjust the content of the reminder based on those emotions. The reminder unit can also adjust the content and timing of the reminder based on the user's emotions. This allows for more appropriate reminders by adjusting the content of the reminder based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the reminder unit may be performed using AI, or not. For example, the reminder unit can input user emotion data into a generative AI and have the generative AI adjust the content of the reminder.

[0080] The reminder unit can send reminders at specific locations, taking into account the user's geographical location. For example, the reminder unit can send reminders at specific locations, taking into account the user's geographical location. The reminder unit can analyze the user's geographical location and determine the content of the reminder at specific locations. The reminder unit can also optimize the content and timing of the reminder based on the user's geographical location. The reminder unit can also send reminders at specific locations based on the user's geographical location. This makes it possible to send reminders at specific locations based on the user's geographical location. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's geographical location into a generating AI and have the generating AI determine the content of the reminder at specific locations.

[0081] The reminder unit can provide the optimal reminder method by considering the user's device information. For example, the reminder unit can provide the optimal reminder method by considering the user's device information. The reminder unit can analyze the user's device information and identify the optimal reminder method. The reminder unit can also optimize the content and timing of the reminder based on the user's device information. The reminder unit can also provide the optimal reminder method based on the user's device information. This makes it possible to provide more effective reminders by providing the optimal reminder method based on the user's device information. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without using AI. For example, the reminder unit can input the user's device information into a generating AI and have the generating AI perform the task of providing the optimal reminder method.

[0082] The suggestion unit can estimate the user's emotions and adjust the content of the suggestions based on the estimated emotions. For example, the suggestion unit can estimate the user's emotions and adjust the content of the suggestions based on the estimated emotions. The suggestion unit can analyze the user's emotions and optimize the content of the suggestions. The suggestion unit can also collect user emotion data and adjust the content of the suggestions based on those emotions. The suggestion unit can also adjust the content and timing of suggestions based on the user's emotions. This allows for more appropriate suggestions by adjusting the content of suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the content of the suggestions.

[0083] The suggestion unit can monitor the user's health status and update the suggested content in accordance with changes in the health status. For example, the suggestion unit can monitor the user's health status and update the suggested content in accordance with changes in the health status. The suggestion unit can analyze the user's health status and optimize the suggested content based on changes in the health status. The suggestion unit can also collect the user's health data and update the suggested content in accordance with changes in the health status. The suggestion unit can also update the suggested content based on the user's health status. This makes it possible to provide more appropriate suggestions by updating the suggested content based on the user's health status. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without using AI. For example, the suggestion unit can input the user's health data into a generating AI and have the generating AI perform the suggested content update.

[0084] The suggestion department can analyze the user's work progress and provide advice on improving efficiency at the optimal time. For example, the suggestion department can analyze the user's work progress and provide advice on improving efficiency at the optimal time. The suggestion department can collect the user's work progress and provide advice at the optimal time. The suggestion department can also optimize the content and timing of advice based on the user's work progress. The suggestion department can also provide advice on improving efficiency based on the user's work progress. This makes it possible to make more effective suggestions by providing advice on improving efficiency based on the user's work progress. Some or all of the above processes in the suggestion department may be performed using AI, for example, or not using AI. For example, the suggestion department can input the user's work progress into a generating AI and have the generating AI provide advice on improving efficiency.

[0085] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, the suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. The suggestion unit can analyze the user's emotions and optimize the priority of suggestions. The suggestion unit can also collect user emotion data and determine the priority of suggestions based on those emotions. The suggestion unit can also adjust the content and timing of suggestions based on the user's emotions. This allows for more appropriate suggestions by determining the priority of suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0086] The suggestion unit can make region-specific health management and job suggestions, taking into account the user's geographical location information. For example, the suggestion unit can make region-specific health management and job suggestions, taking into account the user's geographical location information. The suggestion unit can analyze the user's geographical location information and determine the content of region-specific health management and job suggestions. The suggestion unit can also optimize the content and timing of suggestions based on the user's geographical location information. The suggestion unit can also make region-specific health management and job suggestions based on the user's geographical location information. This makes it possible to make region-specific health management and job suggestions based on the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information into a generating AI and have the generating AI determine the content of region-specific health management and job suggestions.

[0087] The suggestion unit can analyze the user's social media activity and customize the suggestions based on their interests. For example, the suggestion unit can analyze the user's social media activity and customize the suggestions based on their interests. The suggestion unit can collect the user's social media activity and identify their interests. The suggestion unit can also optimize the suggestions based on the user's social media activity. The suggestion unit can also customize the suggestions based on the user's social media activity. This allows for more appropriate suggestions by customizing the suggestions based on the user's interests. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media activity into a generating AI and have the generating AI customize the suggestions.

[0088] The AI ​​Link OS, a dedicated terminal for generative AI, can estimate the user's emotions and adjust the interface display method based on the estimated user emotions. For example, the AI ​​Link OS can estimate the user's emotions and adjust the interface display method based on the estimated user emotions. The AI ​​Link OS can analyze the user's emotions and optimize the interface display method. The AI ​​Link OS can also collect user emotion data and adjust the interface display method based on those emotions. The AI ​​Link OS can also adjust the content and timing of the interface display based on the user's emotions. This allows for a more appropriate display by adjusting the interface display method based on the user's emotions. Emotion estimation is achieved, for example, using an emotion engine or generative AI with an emotion estimation function. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the AI ​​Link OS may be performed using AI, or not using AI. For example, the "AI Link OS" terminal, which is dedicated to generating AI, allows users to input emotional data into the generating AI and have the AI ​​adjust how the interface is displayed.

[0089] The AI-generating terminal "AI Link OS" can reduce communication costs by limiting voice data communication to text only. For example, the AI-generating terminal "AI Link OS" can limit voice data communication to text only. The AI-generating terminal "AI Link OS" can reduce communication volume by converting voice data to text data. The AI-generating terminal "AI Link OS" can also reduce communication volume by using voice data compression technology. The AI-generating terminal "AI Link OS" can also reduce communication costs by converting voice data to text in real time. As a result, it is possible to reduce communication costs by limiting voice data communication to text only. Some or all of the above processes in the AI-generating terminal "AI Link OS" may be performed using AI, for example, or without using AI. For example, the AI-generating terminal "AI Link OS" can input voice data into the generating AI and have the generating AI perform the conversion to text data.

[0090] The AI ​​Link OS, a dedicated terminal for generating AI, can learn the user's operation history and further optimize intuitive operation. For example, the AI ​​Link OS can learn the user's operation history and further optimize intuitive operation. The AI ​​Link OS can analyze the user's operation history and optimize intuitive operation. The AI ​​Link OS can also collect user operation history data and optimize operation. The AI ​​Link OS can also customize the operation interface based on the user's operation history. This provides a more user-friendly system by further optimizing intuitive operation based on the user's operation history. Some or all of the above processes in the AI ​​Link OS may be performed using AI, or not using AI. For example, the AI ​​Link OS can input user operation history data into the generating AI and have the generating AI perform operation optimization.

[0091] The AI ​​Link OS, a dedicated terminal for generative AI, can estimate the user's emotions and adjust the interface's operation procedures based on the estimated emotions. For example, the AI ​​Link OS can estimate the user's emotions and adjust the interface's operation procedures based on the estimated emotions. The AI ​​Link OS can analyze the user's emotions and optimize the interface's operation procedures. The AI ​​Link OS can also collect user emotion data and adjust the interface's operation procedures based on those emotions. The AI ​​Link OS can also adjust the content and timing of the operation procedures based on the user's emotions. This allows for more appropriate operation by adjusting the interface's operation procedures based on the user's emotions. Emotion estimation is achieved, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the AI ​​Link OS may be performed using AI, or not using AI. For example, the "AI Link OS" terminal, which is dedicated to generating AI, allows users to input their emotional data into the generating AI and have the AI ​​adjust the operating procedures.

[0092] The AI-generating terminal "AI Link OS" can provide the optimal display method by considering the user's device information. For example, the AI-generating terminal "AI Link OS" can provide the optimal display method by considering the user's device information. The AI-generating terminal "AI Link OS" can analyze the user's device information and identify the optimal display method. The AI-generating terminal "AI Link OS" can also optimize the display content and timing based on the user's device information. The AI-generating terminal "AI Link OS" can also provide the optimal display method based on the user's device information. This provides a more user-friendly system by providing the optimal display method based on the user's device information. Some or all of the above processing in the AI-generating terminal "AI Link OS" may be performed using AI, for example, or without AI. For example, the AI-generating terminal "AI Link OS" can input the user's device information into the generating AI and have the generating AI perform the task of providing the optimal display method.

[0093] The AI-generating terminal "AI Link OS" can analyze user usage and propose methods to further reduce communication costs. For example, the AI-generating terminal "AI Link OS" can analyze user usage and propose methods to further reduce communication costs. The AI-generating terminal "AI Link OS" can collect user usage data and identify methods to reduce communication costs. The AI-generating terminal "AI Link OS" can also optimize the proposed methods for reducing communication costs based on user usage data. This provides a more economical system by proposing methods to further reduce communication costs based on user usage data. Some or all of the above-described processes in the AI-generating terminal "AI Link OS" may be performed using AI, or not. For example, the AI-generating terminal "AI Link OS" can input user usage data into the generating AI and have the generating AI propose methods to reduce communication costs.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The AI-generating terminal "AI Link OS" can estimate the user's emotions and monitor the user's stress level based on those estimates. For example, if the system estimates that the user is stressed, the reminder unit can offer suggestions for relaxation. Specifically, it can teach the user how to take deep breaths or play relaxing music. The suggestion unit can also suggest adjusting the user's work schedule according to their stress level. For example, if the system estimates that the user is stressed, it can suggest taking a break. Furthermore, the learning unit can learn about changes in the user's stress level and provide the optimal methods for reducing stress. In this way, by monitoring the user's stress level and offering appropriate suggestions, the system can support the user's health management.

[0096] The AI-generating terminal "AI Link OS" can monitor the user's sleep patterns and provide an optimal sleep environment. For example, it can collect the user's sleep data and analyze the quality of their sleep. The reminder unit can remind the user to go to bed at an appropriate time. Specifically, it can suggest relaxation methods before bedtime and provide advice on creating a suitable bedroom environment. The suggestion unit can also suggest the optimal sleep duration and environment based on the user's sleep data. For example, it can suggest how to choose bedding and adjust the room temperature to ensure the user sleeps comfortably. Furthermore, the learning unit can learn changes in the user's sleep patterns and provide optimal methods to improve sleep quality. In this way, by monitoring the user's sleep patterns and providing an optimal sleep environment, it can support the user's health management.

[0097] The AI-generating terminal "AI Link OS" can manage users' meal records and support healthy eating habits. For example, when a user inputs the details of their meals, the learning unit can analyze the data and evaluate the nutritional balance. The reminder unit can remind users to eat at appropriate times. Specifically, it can notify users of meal times and prompt them to prepare meals. Furthermore, the suggestion unit can suggest healthy meal menus based on the user's meal records. For example, it can provide recipes that take into account the user's nutritional balance and advise on how to choose ingredients. In addition, the learning unit can learn from changes in the user's meal records and provide information that helps improve their eating habits. In this way, by managing users' meal records and supporting healthy eating habits, it can assist in the user's health management.

[0098] The AI-generating terminal "AI Link OS" can monitor a user's exercise habits and provide an optimal exercise plan. For example, it can collect the user's exercise data and analyze the frequency and intensity of exercise. The reminder unit can remind the user to exercise at the appropriate time. Specifically, it can notify the user of the time to exercise and prompt them to prepare for exercise. The suggestion unit can propose an optimal exercise plan based on the user's exercise data. For example, it can provide exercise menus tailored to the user's physical fitness and health condition, and offer advice to maximize the effects of exercise. Furthermore, the learning unit can learn changes in the user's exercise habits and provide information useful for improving the exercise plan. In this way, by monitoring the user's exercise habits and providing an optimal exercise plan, it can support the user's health management.

[0099] The AI-generating terminal "AI Link OS" can estimate the user's emotions and improve their motivation based on those estimates. For example, if the system estimates that the user is losing motivation, the reminder unit can make suggestions to boost their motivation. Specifically, it can send encouraging messages to the user or show steps to achieve their goals. The suggestion unit can also suggest activities to maintain motivation according to the user's emotions. For example, it can suggest hobbies the user can enjoy or activities that help them refresh. Furthermore, the learning unit can learn from changes in the user's emotions and provide the optimal methods to improve motivation. In this way, by monitoring the user's emotions and making appropriate suggestions to improve motivation, the system can improve the user's quality of life.

[0100] The AI-generating terminal "AI Link OS" can estimate the user's emotions and support their communication based on those estimates. For example, if the system estimates that the user is feeling lonely, the reminder unit can make suggestions to facilitate communication. Specifically, it can send reminders to encourage the user to contact friends and family, or suggest joining online communities. The suggestion unit can also suggest communication methods according to the user's emotions. For example, it can provide conversation topics that will help the user relax or adjust the timing of communication. Furthermore, the learning unit can learn the user's emotional changes and provide the optimal way to support communication. In this way, by monitoring the user's emotions and making appropriate suggestions to support communication, the system can strengthen the user's social connections.

[0101] The AI-generating terminal "AI Link OS" can estimate the user's emotions and improve the user's learning efficiency based on those emotions. For example, if the system estimates that the user is lacking concentration, the reminder unit can make suggestions to improve concentration. Specifically, it can encourage the user to take a short break or provide advice on creating an environment conducive to concentration. The suggestion unit can also suggest learning methods based on the user's emotions. For example, it can suggest learning methods that help the user relax or adjust the timing of learning. Furthermore, the learning unit can learn changes in the user's emotions and provide the optimal methods to improve learning efficiency. In this way, by monitoring the user's emotions and making appropriate suggestions to improve learning efficiency, the system can improve the user's learning outcomes.

[0102] The AI-generating terminal "AI Link OS" can estimate the user's emotions and suggest relaxation methods based on those estimates. For example, if the system estimates that the user is stressed, the reminder unit can offer suggestions for relaxation. Specifically, it can teach the user how to take deep breaths or play relaxing music. The suggestion unit can also suggest relaxing activities based on the user's emotions. For example, it can suggest hobbies or refreshing activities that will help the user relax. Furthermore, the learning unit can learn about changes in the user's emotions and provide information to help improve relaxation methods. In this way, by monitoring the user's emotions and suggesting relaxation methods, the system can reduce the user's stress and improve their quality of life.

[0103] The AI-generating terminal "AI Link OS" can estimate the user's emotions and improve their entertainment experience based on those emotions. For example, if the system estimates that the user is bored, the reminder unit can suggest entertainment. Specifically, it can recommend movies or music, or suggest playing games. The suggestion unit can also customize the content of the entertainment according to the user's emotions. For example, it can provide content that the user will enjoy or adjust the timing of the entertainment. Furthermore, the learning unit can learn about changes in the user's emotions and provide information that helps improve the entertainment experience. In this way, by monitoring the user's emotions and making appropriate suggestions to improve the entertainment experience, the system can improve the user's quality of life.

[0104] The AI-generating terminal "AI Link OS" can estimate the user's emotions and adjust the content of user reminders based on those emotions. For example, if the system estimates that the user is tired, the reminder unit can issue a reminder to rest. Specifically, it can encourage the user to take a short break or provide advice on creating a relaxing environment. The suggestion unit can also customize the content of reminders according to the user's emotions. For example, it can provide reminders that help the user relax or adjust the timing of reminders. Furthermore, the learning unit can learn about changes in the user's emotions and provide information that helps improve the content of reminders. In this way, by monitoring the user's emotions and adjusting the content of reminders, the system can improve the user's quality of life.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The learning unit learns the user's needs. For example, it learns the user's behavior and lifestyle, analyzes past behavioral history to detect changes in behavioral patterns, and updates its learned content. It can also learn the user's life events and provide special reminders. Step 2: The reminder unit sends reminders based on the needs learned by the learning unit. For example, it sends reminders based on the user's behavior and lifestyle, and adjusts the frequency of reminders based on the user's schedule. It can also estimate the user's emotions and adjust the timing of reminders based on those emotions. Step 3: The suggestion unit makes optimal suggestions based on the information reminded by the reminder unit. For example, it can monitor the user's health status and suggest optimal exercise and diet. It can also provide advice to improve the user's work efficiency and estimate the user's emotions, adjusting the content of the suggestions based on those estimated emotions.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] Each element of the AI-generating terminal "AI Link OS" described above is implemented in at least one of the smart device 14 and the data processing device 12. For example, the learning unit is implemented by the control unit 46A of the smart device 14 and learns the user's behavior and lifestyle. The reminder unit is implemented by the specific processing unit 290 of the data processing device 12 and makes reminders based on learned needs. The suggestion unit is implemented by the control unit 46A of the smart device 14 and monitors the user's health status and makes suggestions for optimal exercise and diet. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each element of the AI-generating terminal "AI Link OS" described above is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the learning unit is implemented by the control unit 46A of the smart glasses 214 and learns the user's behavior and lifestyle. The reminder unit is implemented by the specific processing unit 290 of the data processing device 12 and makes reminders based on learned needs. The suggestion unit is implemented by the control unit 46A of the smart glasses 214 and monitors the user's health status and makes suggestions for optimal exercise and diet. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0142] Each element of the AI-generating terminal "AI Link OS" described above is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the learning unit is implemented by the control unit 46A of the headset terminal 314 and learns the user's behavior and lifestyle. The reminder unit is implemented by the specific processing unit 290 of the data processing device 12 and makes reminders based on learned needs. The suggestion unit is implemented by the control unit 46A of the headset terminal 314 and monitors the user's health status and makes suggestions for optimal exercise and diet. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each element of the AI-generating terminal "AI Link OS" described above is implemented in at least one of the following: the robot 414 and the data processing device 12. For example, the learning unit is implemented by the control unit 46A of the robot 414 and learns the user's behavior and lifestyle. The reminder unit is implemented by the specific processing unit 290 of the data processing device 12 and makes reminders based on learned needs. The suggestion unit is implemented by the control unit 46A of the robot 414 and monitors the user's health status and makes suggestions for optimal exercise and diet. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] (Note 1) A learning unit that learns user needs, A reminder unit that provides reminders based on the needs learned by the learning unit, The system includes a suggestion unit that makes optimal suggestions based on the information reminded by the reminder unit. A system characterized by the following features. (Note 2) The aforementioned learning unit, Learn user behavior and lifestyle The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reminder unit, Send reminders based on user behavior and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, It monitors the user's health status and suggests optimal exercise and dietary recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We provide advice to improve the efficiency of users' work. The system described in Appendix 1, characterized by the features described herein. (Note 6) The dedicated terminal for generating AI is, To reduce communication costs, voice data communication will be limited to text only. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning content based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned learning unit, The system analyzes the user's past behavior history, detects changes in behavior patterns, and updates the learned data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned learning unit, Learn about the user's life events and send special reminders. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned learning unit, It estimates the user's emotions and determines learning priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned learning unit, It learns region-specific behavioral patterns by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned learning unit, Analyze users' social media activity and customize learning content based on their interests. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reminder unit, It estimates the user's emotions and adjusts the timing of reminders based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reminder unit, Analyze the user's past reminder history and select the most suitable reminder method. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reminder unit, Adjust the reminder frequency based on the user's schedule. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned reminder unit, It estimates the user's emotions and adjusts the content of the reminder based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned reminder unit, The system takes the user's geographical location into account to send reminders at specific locations. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned reminder unit, We provide the optimal reminder method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the content of the suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, The system monitors the user's health status and updates recommendations based on changes in their health. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, We analyze the user's work progress and provide advice on improving efficiency at the optimal time. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, We provide region-specific health management and job suggestions, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, Analyze users' social media activity and customize suggestions based on their interests. The system described in Appendix 1, characterized by the features described herein. (Note 25) The dedicated terminal for generating AI is, It estimates the user's emotions and adjusts the interface display based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The dedicated terminal for generating AI is, By limiting voice data communication to text only, communication costs can be reduced. The system described in Appendix 1, characterized by the features described herein. (Note 27) The dedicated terminal for generating AI is, Learn from the user's operation history to further optimize intuitive operation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The dedicated terminal for generating AI is, It estimates the user's emotions and adjusts the interface operation procedures based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The dedicated terminal for generating AI is, Provides the optimal display method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The dedicated terminal for generating AI is, We analyze user usage patterns and propose ways to further reduce communication costs. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A learning unit that learns user needs, A reminder unit that provides reminders based on the needs learned by the learning unit, The system includes a suggestion unit that makes optimal suggestions based on the information reminded by the reminder unit. A system characterized by the following features.

2. The aforementioned learning unit, Learn user behavior and lifestyle The system according to feature 1.

3. The aforementioned reminder unit, Send reminders based on user behavior and lifestyle. The system according to feature 1.

4. The aforementioned proposal section is, It monitors the user's health status and suggests optimal exercise and dietary recommendations. The system according to feature 1.

5. The aforementioned proposal section is, We provide advice to improve the efficiency of users' work. The system according to feature 1.

6. The AI-generating terminal is To reduce communication costs, voice data communication will be limited to text only. The system according to feature 1.

7. The aforementioned learning unit, It estimates the user's emotions and adjusts the learning content based on the estimated user emotions. The system according to feature 1.

8. The aforementioned learning unit, The system analyzes the user's past behavior history, detects changes in behavior patterns, and updates the learned data. The system according to feature 1.

9. The aforementioned learning unit, Learn about the user's life events and send special reminders. The system according to feature 1.

10. The aforementioned learning unit, It estimates the user's emotions and determines learning priorities based on the estimated user emotions. The system according to feature 1.

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