system

Abstract

The system according to this embodiment aims to provide effective support to users who have difficulty managing their time on their own due to developmental disabilities or other reasons. [Solution] The system according to the embodiment comprises a schedule management unit, a voice reminder unit, a task confirmation unit, an action recognition unit, an emotion analysis unit, and a response unit. The schedule management unit manages the user's daily schedule. The voice reminder unit provides voice reminders based on the schedule managed by the schedule management unit. The task confirmation unit confirms the completion of tasks reminded by the voice reminder unit. The action recognition unit recognizes the user's actions based on the progress of tasks confirmed by the task confirmation unit. The emotion analysis unit analyzes the user's emotions based on the actions recognized by the action recognition unit. The response unit provides appropriate responses based on the emotions analyzed by the emotion analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, effective support has not been sufficiently provided for users who have difficulty managing time alone due to reasons such as developmental disabilities, and there is room for improvement.

[0005] The system according to the embodiment aims to provide effective support for users who have difficulty managing time alone due to reasons such as developmental disabilities.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a schedule management unit, a voice reminder unit, a task confirmation unit, an action recognition unit, an emotion analysis unit, and a response unit. The schedule management unit manages the user's daily schedule. The voice reminder unit provides voice reminders based on the schedule managed by the schedule management unit. The task confirmation unit confirms the completion of tasks reminded by the voice reminder unit. The action recognition unit recognizes the user's actions based on the progress of tasks confirmed by the task confirmation unit. The emotion analysis unit analyzes the user's emotions based on the actions recognized by the action recognition unit. The response unit provides appropriate responses based on the emotions analyzed by the emotion analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide effective support to users who have difficulty managing their time on their own due to developmental disabilities or other reasons. [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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. 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 tagged communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 290 (see FIG. 2) acquires 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 ​​agent system according to an embodiment of the present invention is a system that provides total daily support for individuals who have difficulty preparing for their day on their own due to developmental disabilities or other reasons. This AI agent system manages and visually displays the user's daily schedule. Next, it provides voice reminders at appropriate times. For example, it reminds the user of the start and end times of each task, such as getting ready in the morning, getting ready for school, and managing the time for extracurricular activities. It also enables two-way communication, understands voice responses from the user, and takes appropriate action. Furthermore, it uses a camera to recognize the user's actions and provides real-time verbal prompts to encourage appropriate actions. For example, when a user is getting ready in the morning, the camera recognizes their actions and prompts them to check necessary items. It also analyzes the user's emotions from their voice and facial expressions and takes appropriate action as needed. This AI agent system utilizes the following technologies: 1. Natural Language Generation (NLG): Generates appropriate words when the agent provides voice reminders to the target. 2. Natural Language Understanding (NLU): Understands voice responses from the target and enables two-way communication. 3. Task Management AI: Manages the daily schedule and provides reminders at appropriate times. 4. Computer Vision: Uses a camera to recognize the subject's actions. 5. Emotion Analysis: Analyzes the subject's emotions from their voice and facial expressions and takes appropriate action as needed. 6. Generating Checklists: Automatically generates a checklist of necessary items for the day based on the child's or adult's daily activities. This system enables individuals with developmental disabilities or other reasons who have difficulty preparing independently to manage their time, allowing them to spend their day autonomously and improving the quality of life and work for the entire family and workplace. As a result, the AI ​​agent system can manage the user's daily schedule, provide reminders, check tasks, recognize actions, analyze emotions, and respond accordingly.

[0029] The AI ​​agent system according to this embodiment comprises a schedule management unit, a voice reminder unit, a task confirmation unit, an action recognition unit, an emotion analysis unit, and a response unit. The schedule management unit manages the user's daily schedule. The schedule management unit can, for example, display the schedule in a calendar format. The schedule management unit can also prioritize tasks. For example, the schedule management unit can display important tasks at the top and prompt the user to perform them as a priority. Furthermore, the schedule management unit can analyze the user's past schedule history and select the optimal schedule management method. For example, the schedule management unit can automatically incorporate tasks that the user has frequently performed in the past into the schedule. The voice reminder unit provides voice reminders based on the schedule managed by the schedule management unit. The voice reminder unit can, for example, provide voice reminders at appropriate times. For example, the voice reminder unit can remind the user to "start getting ready in the morning" at the time of getting ready in the morning. Furthermore, the voice reminder unit can estimate the user's emotions and adjust the way the reminder is expressed based on the estimated emotions. For example, the voice reminder unit will provide a gentle reminder if the user is feeling stressed. The task confirmation unit confirms the completion of the task reminded by the voice reminder unit. The task confirmation unit can, for example, understand the user's voice response and enable two-way communication. For example, if the user responds "I have completed the task," the task confirmation unit will confirm that the task has been completed. The task confirmation unit can also estimate the user's emotions and adjust the task confirmation method based on the estimated emotions. For example, if the user is feeling stressed, the task confirmation unit will provide a simple and easy-to-understand task confirmation method. The behavior recognition unit recognizes the user's actions based on the progress of the task confirmed by the task confirmation unit. The behavior recognition unit can, for example, recognize the user's actions using a camera while the user is getting ready in the morning and prompt them to check the necessary items.Furthermore, the behavior recognition unit can estimate the user's emotions and adjust the behavior recognition method based on the estimated emotions. For example, if the behavior recognition unit is feeling stressed, it will recognize the behavior and give instructions in a calm voice. The emotion analysis unit analyzes the user's emotions based on the behavior recognized by the behavior recognition unit. The emotion analysis unit can analyze emotions from the user's voice and facial expressions, for example. For example, the emotion analysis unit uses voice analysis technology to analyze the tone and speed of the user's voice and estimate the emotions. The emotion analysis unit can also use facial expression recognition technology to analyze the user's facial expressions and estimate the emotions. The response unit takes appropriate action based on the emotions analyzed by the emotion analysis unit. For example, the response unit can provide feedback according to the user's emotions. For example, if the user is feeling stressed, the response unit will provide an encouraging message. The response unit also has an algorithm for taking appropriate action according to the user's emotions. For example, the response unit will provide encouraging messages and advice according to the user's emotional state. As a result, the AI ​​agent system according to this embodiment can manage the user's daily schedule, provide reminders, check tasks, recognize behavior, analyze emotions, and respond accordingly.

[0030] The Schedule Management Unit manages the user's daily schedule. For example, it can display the schedule in a calendar format. Specifically, it organizes and displays tasks and appointments by date and time slot so that users can visually check them on their smartphone or computer screen. The Schedule Management Unit can also prioritize tasks. For example, it displays important tasks at the top, prompting users to prioritize their execution. This allows users to efficiently manage their schedules without overlooking important tasks. Furthermore, the Schedule Management Unit can analyze the user's past schedule history and select the optimal schedule management method. For example, it automatically incorporates tasks that the user has frequently performed in the past into the schedule. This saves the user the trouble of manually entering the same tasks each time. The Schedule Management Unit can also learn the user's behavior patterns and habits and suggest tasks at the optimal time. For example, if a user has a habit of jogging every morning, the Schedule Management Unit will automatically add a jogging task to that time slot. This allows users to easily manage a schedule that suits their lifestyle.

[0031] The voice reminder unit provides voice reminders based on the schedule managed by the schedule management unit. The voice reminder unit can, for example, provide voice reminders at appropriate times. Specifically, it provides voice reminders based on the time and place set by the user. For example, the voice reminder unit will remind the user to "start getting ready in the morning" at the time of getting ready in the morning. The voice reminder unit can also estimate the user's emotions and adjust the way the reminder is expressed based on those emotions. For example, if the voice reminder unit is feeling stressed, it will provide a calm voice reminder. This allows the user to proceed with tasks smoothly without feeling uncomfortable when receiving reminders. Furthermore, the voice reminder unit can analyze the user's past reminder history and select the optimal reminder method. For example, if the user has shown a positive response to a particular reminder method in the past, it will prioritize using that method. This allows the voice reminder unit to provide reminders tailored to the user's individual needs and improve the task completion rate.

[0032] The task confirmation unit verifies the completion of tasks reminded by the voice reminder unit. The task confirmation unit can, for example, understand voice responses from the user and enable two-way communication. Specifically, if the user responds with "I have completed the task," it verifies that the task has been completed. The task confirmation unit can also estimate the user's emotions and adjust the task confirmation method based on the estimated emotions. For example, if the task confirmation unit is feeling stressed, it provides a concise and easy-to-understand task confirmation method. This allows the user to report task completion smoothly. Furthermore, the task confirmation unit can analyze the user's past task completion history and select the optimal task confirmation method. For example, if the user has shown a positive response to a particular task confirmation method in the past, it will prioritize using that method. This allows the task confirmation unit to provide task confirmation tailored to the user's individual needs and improve the task completion rate.

[0033] The behavior recognition unit recognizes user behavior based on the progress of tasks confirmed by the task confirmation unit. The behavior recognition unit can recognize user behavior using, for example, a camera. Specifically, when a user is getting ready in the morning, the behavior recognition unit recognizes their actions using the camera and prompts them to check necessary items. The behavior recognition unit can also estimate the user's emotions and adjust the behavior recognition method based on the estimated emotions. For example, if the behavior recognition unit is feeling stressed, it will recognize the user's actions and give instructions in a calm voice. This allows the user to proceed with their actions smoothly. Furthermore, the behavior recognition unit can analyze the user's past behavior history and select the optimal behavior recognition method. For example, if a user has shown a positive response to a particular behavior recognition method in the past, that method will be used preferentially. In this way, the behavior recognition unit can provide behavior recognition tailored to the user's individual needs and improve the efficiency of their actions.

[0034] The emotion analysis unit analyzes the user's emotions based on the actions recognized by the action recognition unit. For example, the emotion analysis unit can analyze emotions from the user's voice and facial expressions. Specifically, the emotion analysis unit uses voice analysis technology to analyze the tone and speed of the user's voice and estimate their emotions. It can also use facial expression recognition technology to analyze the user's facial expressions and estimate their emotions. This allows the emotion analysis unit to accurately grasp the user's emotional state. Furthermore, the emotion analysis unit can analyze the user's past emotional history and select the optimal emotion analysis method. For example, if the user has shown a positive response to a particular emotion analysis method in the past, that method will be used preferentially. This allows the emotion analysis unit to provide emotion analysis tailored to the user's individual needs and to quickly detect changes in emotions.

[0035] The response unit provides appropriate responses based on the emotions analyzed by the emotion analysis unit. For example, the response unit can provide feedback according to the user's emotions. Specifically, if the user is feeling stressed, the response unit will provide encouraging messages. The response unit also has an algorithm for providing appropriate responses according to the user's emotions. For example, the response unit will provide encouraging messages and advice according to the user's emotional state. This allows the user to receive appropriate feedback that matches their emotions. Furthermore, the response unit can analyze the user's past emotional history and select the optimal response method. For example, if the user has shown a positive response to a particular response method in the past, that method will be used preferentially. This allows the response unit to provide responses tailored to the user's individual needs and improve user satisfaction.

[0036] The schedule management unit can visually display the user's daily schedule. For example, the schedule management unit displays the schedule in a calendar format. The schedule management unit uses a graphical user interface (GUI) to enable the user to visually understand the schedule. For example, the schedule management unit can color-code tasks on the calendar to highlight important tasks. Furthermore, the schedule management unit can estimate the user's emotions and adjust the schedule display method based on the estimated emotions. For example, if the user is feeling stressed, the schedule management unit provides a simple and visually easy-to-understand schedule display. This makes it easier for the user to understand their schedule by visually displaying their daily schedule. Some or all of the above processes in the schedule management unit may be performed using AI, or not. For example, the schedule management unit can input the user's schedule data into a generating AI and have the generating AI execute the visual schedule display.

[0037] The voice reminder unit can provide voice reminders at appropriate times. For example, the voice reminder unit can determine the timing of reminders based on the user's schedule. The voice reminder unit can also analyze the user's past behavior patterns and select the optimal reminder timing. For example, the voice reminder unit can provide reminders during times when the user has tended to forget tasks in the past. Furthermore, the voice reminder unit can estimate the user's emotions and adjust the way the reminder is expressed based on the estimated emotions. For example, if the voice reminder unit is feeling stressed, it will provide a calm voice reminder. This ensures that users remember to complete tasks by providing voice reminders at appropriate times. Some or all of the above processes in the voice reminder unit may be performed using AI, for example, or not. For example, the voice reminder unit can input the user's schedule data into a generating AI and have the generating AI execute the reminder timing.

[0038] The task confirmation unit can understand voice responses from the user and enable two-way communication. For example, the task confirmation unit analyzes the user's voice responses using speech recognition technology. The task confirmation unit can also provide appropriate feedback based on the user's responses. For example, if the user responds "I have completed the task," the task confirmation unit will confirm that the task has been completed and remind the user of the next task. The task confirmation unit can also estimate the user's emotions and adjust the task confirmation method based on the estimated emotions. For example, if the task confirmation unit is feeling stressed, it will provide a simple and easy-to-understand task confirmation method. This allows for accurate tracking of task progress by understanding voice responses from the user and enabling two-way communication. Some or all of the above-described processes in the task confirmation unit may be performed using AI, for example, or without AI. For example, the task confirmation unit can input the user's voice data into a generating AI and have the generating AI perform the analysis of the voice responses.

[0039] The behavior recognition unit can recognize user behavior using a camera. For example, the behavior recognition unit can appropriately select the camera's installation location and monitor user behavior in real time. The behavior recognition unit can also recognize user behavior using an image analysis algorithm. For example, when a user is getting ready in the morning, the behavior recognition unit can recognize their actions using the camera and prompt them to check necessary items. The behavior recognition unit can also estimate the user's emotions and adjust the behavior recognition method based on the estimated emotions. For example, if the behavior recognition unit is feeling stressed, it can recognize the user's actions in a calm voice and give instructions. This allows for real-time prompting of appropriate actions by recognizing user behavior using a camera. Some or all of the above processing in the behavior recognition unit may be performed using AI, for example, or without AI. For example, the behavior recognition unit can input image data acquired by the camera into a generating AI and have the generating AI perform behavior recognition.

[0040] The emotion analysis unit can analyze emotions from the user's voice and facial expressions. For example, the emotion analysis unit can use voice analysis technology to analyze the tone and speed of the user's voice and estimate their emotions. The emotion analysis unit can also use facial recognition technology to analyze the user's facial expressions and estimate their emotions. For example, the emotion analysis unit can analyze the tone and speed of the user's voice while they are speaking and estimate whether the user is feeling stressed. The emotion analysis unit can also capture the user's facial expressions with a camera and analyze the changes in those expressions to estimate their emotions. This allows for appropriate responses based on the user's state by analyzing emotions from the user's voice and facial expressions. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input the user's voice data and image data into a generating AI and have the generating AI perform the emotion analysis.

[0041] The response unit can take appropriate action as needed. For example, the response unit can provide feedback according to the user's emotions. If the user is feeling stressed, the response unit can provide encouraging messages. The response unit also has an algorithm for taking appropriate action according to the user's emotions. For example, the response unit can provide encouraging messages and advice according to the user's emotional state. This enables support that meets the user's needs by taking appropriate action as needed. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input user emotion data into a generating AI and have the generating AI execute an appropriate response.

[0042] The system includes a packing checklist generation unit. This unit can automatically generate a packing checklist based on the user's daily activities. For example, it can list items based on the user's schedule. The packing checklist generation unit can also analyze the user's past packing history to generate an optimal checklist. For example, it can add items the user has forgotten in the past to the list. Furthermore, the packing checklist generation unit can estimate the user's emotions and adjust the contents of the checklist based on the estimated emotions. For example, if the user is feeling stressed, the packing checklist generation unit will list only the bare minimum of necessary items. This makes packing easier by automatically generating a packing checklist based on the user's daily activities. Some or all of the above-described processes in the packing checklist generation unit may be performed using AI, or not. For example, the packing checklist generation unit can input the user's schedule data into a generating AI and have the generating AI generate the packing checklist.

[0043] The schedule management unit can analyze the user's past schedule history and select the optimal schedule management method. For example, the schedule management unit can automatically incorporate tasks that the user has frequently performed in the past into the schedule. The schedule management unit can also suggest tasks that are optimal for a specific time period based on the user's past schedule history. For example, the schedule management unit analyzes the user's past schedule history and suggests an efficient schedule management method. In this way, the optimal schedule management method can be selected by analyzing the user's past schedule history. Some or all of the above processes in the schedule management unit may be performed using AI, for example, or without AI. For example, the schedule management unit can input the user's schedule history data into a generating AI and have the generating AI select the optimal schedule management method.

[0044] The schedule management unit can customize the schedule based on the user's current lifestyle and areas of interest. For example, the schedule management unit can incorporate appropriate break times into the schedule based on the user's current lifestyle. The schedule management unit can also add relevant tasks and events to the schedule based on the user's areas of interest. For example, the schedule management unit can flexibly adjust the schedule to match the user's daily rhythm. This provides the user with an optimal schedule by customizing it based on the user's current lifestyle and areas of interest. Some or all of the above processes in the schedule management unit may be performed using AI, for example, or without AI. For example, the schedule management unit can input user lifestyle data and areas of interest data into a generating AI and have the generating AI perform the schedule customization.

[0045] The schedule management unit can prioritize displaying schedules that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific location, the schedule management unit will prioritize displaying tasks related to that location. The schedule management unit can also prioritize incorporating tasks that are performed in locations close to the user's current location into the schedule. For example, the schedule management unit will suggest an efficient schedule based on the user's travel route. This enables efficient schedule management by prioritizing the display of highly relevant schedules, taking into account the user's geographical location. Some or all of the above processing in the schedule management unit may be performed using AI, for example, or without AI. For example, the schedule management unit can input the user's geographical location data into a generating AI and have the generating AI prioritize the display of highly relevant schedules.

[0046] The schedule management unit can analyze a user's social media activity and suggest relevant schedules when managing schedules. For example, the schedule management unit can incorporate information about events a user has attended on social media into the schedule. The schedule management unit can also suggest relevant tasks based on a user's interests on social media. For example, the schedule management unit can reflect a user's appointments with friends on social media in the schedule. In this way, by analyzing a user's social media activity, it can suggest relevant schedules. Some or all of the above processes in the schedule management unit may be performed using AI, for example, or not using AI. For example, the schedule management unit can input user social media activity data into a generating AI and have the generating AI suggest relevant schedules.

[0047] The voice reminder unit can adjust the level of detail in reminders based on the importance of the task. For example, the voice reminder unit can provide detailed reminders for high-importance tasks, and concise reminders for low-importance tasks. For example, the voice reminder unit can adjust the frequency of reminders according to the importance of the task. This allows for efficient reminders by adjusting the level of detail based on the importance of the task. Some or all of the above processing in the voice reminder unit may be performed using AI, for example, or without AI. For example, the voice reminder unit can input task importance data into a generating AI and have the generating AI adjust the level of detail in the reminders.

[0048] The voice reminder unit can apply different reminder algorithms depending on the task category when a reminder is given. For example, for tasks related to preparing for school, the voice reminder unit can provide an educational reminder. For tasks related to extracurricular activities, the voice reminder unit can also provide a motivational reminder. For example, for tasks related to daily life, the voice reminder unit can provide a relaxing reminder. By applying different reminder algorithms depending on the task category, optimal reminders can be achieved. Some or all of the above processing in the voice reminder unit may be performed using AI, for example, or without AI. For example, the voice reminder unit can input task category data into a generating AI and have the generating AI execute the application of the reminder algorithm.

[0049] The voice reminder unit can determine the priority of reminders based on the task submission date. For example, the voice reminder unit will prioritize reminders for tasks with approaching deadlines. It can also postpone reminders for tasks with distant deadlines. For example, the voice reminder unit can adjust the frequency of reminders according to the submission deadline. This enables efficient reminders by determining the priority of reminders based on the task submission date. Some or all of the above processing in the voice reminder unit may be performed using AI, for example, or without AI. For example, the voice reminder unit can input task submission date data into a generating AI and have the generating AI determine the priority of reminders.

[0050] The voice reminder unit can adjust the order of reminders based on the relevance of tasks. For example, the voice reminder unit can prioritize reminders for highly relevant tasks. It can also postpone reminders for less relevant tasks. For example, the voice reminder unit adjusts the order of reminders according to the relevance of tasks. This allows for efficient reminders by adjusting the order of reminders based on the relevance of tasks. Some or all of the above processing in the voice reminder unit may be performed using AI, for example, or without AI. For example, the voice reminder unit can input task relevance data into a generating AI and have the generating AI perform the adjustment of the reminder order.

[0051] The task confirmation unit can analyze the user's past task history to select the optimal confirmation method when confirming a task. For example, the task confirmation unit may prioritize suggesting task confirmation methods that the user has frequently used in the past. The task confirmation unit can also suggest efficient confirmation methods based on the user's past task history. For example, the task confirmation unit analyzes the user's past task history and selects the optimal confirmation method. This allows the optimal task confirmation method to be selected by analyzing the user's past task history. Some or all of the above processing in the task confirmation unit may be performed using AI, for example, or without AI. For example, the task confirmation unit can input the user's task history data into a generating AI and have the generating AI select the optimal confirmation method.

[0052] The task verification unit can select the optimal verification method when verifying tasks, taking into account the user's geographical location information. For example, if the user is in a specific location, the task verification unit will prioritize verifying tasks related to that location. The task verification unit can also prioritize verifying tasks that are performed in locations close to the user's current location. For example, the task verification unit can propose an efficient task verification method based on the user's travel route. This enables efficient task verification by selecting the optimal verification method while considering the user's geographical location information. Some or all of the above processing in the task verification unit may be performed using AI, for example, or without AI. For example, the task verification unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal verification method.

[0053] The behavior recognition unit can analyze the user's past behavior history and select the optimal recognition method during behavior recognition. For example, the behavior recognition unit selects the optimal recognition method based on actions the user has frequently performed in the past. The behavior recognition unit can also propose an efficient recognition method from the user's past behavior history. For example, the behavior recognition unit analyzes the user's past behavior history and selects the optimal recognition method. In this way, the optimal behavior recognition method can be selected by analyzing the user's past behavior history. Some or all of the above processing in the behavior recognition unit may be performed using AI, for example, or without using AI. For example, the behavior recognition unit can input the user's behavior history data into a generating AI and have the generating AI perform the selection of the optimal recognition method.

[0054] The behavior recognition unit can customize the means of behavior recognition based on the user's current living situation during behavior recognition. For example, the behavior recognition unit can select an appropriate behavior recognition means based on the user's current living situation. The behavior recognition unit can also adjust the timing of behavior recognition to match the user's daily rhythm. For example, the behavior recognition unit can customize the method of behavior recognition based on the user's living environment. By customizing the means of behavior recognition based on the user's current living situation, optimal behavior recognition for the user becomes possible. Some or all of the above processing in the behavior recognition unit may be performed using AI, for example, or without using AI. For example, the behavior recognition unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of the behavior recognition means.

[0055] The behavior recognition unit can select the optimal recognition method when recognizing an action, taking into account the user's geographical location information. For example, if the user is in a specific location, the behavior recognition unit will prioritize recognizing actions related to that location. The behavior recognition unit can also prioritize recognizing actions performed in locations close to the user's current location. For example, the behavior recognition unit will propose an efficient behavior recognition method based on the user's travel route. This enables efficient behavior recognition by selecting the optimal recognition method while considering the user's geographical location information. Some or all of the above processing in the behavior recognition unit may be performed using AI, for example, or without AI. For example, the behavior recognition unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal recognition method.

[0056] The behavior recognition unit can analyze the user's social media activity and propose methods for behavior recognition during behavior recognition. For example, the behavior recognition unit proposes methods for behavior recognition based on the user's social media activity information. The behavior recognition unit can also recognize relevant behaviors based on the user's interests on social media. For example, the behavior recognition unit proposes methods for behavior recognition based on the user's activities with friends on social media. In this way, by analyzing the user's social media activity, the optimal method for behavior recognition can be proposed. Some or all of the above processing in the behavior recognition unit may be performed using AI, for example, or without AI. For example, the behavior recognition unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of methods for behavior recognition.

[0057] The emotion analysis unit can analyze the user's past emotional history to select the optimal analysis method during emotion analysis. For example, the emotion analysis unit can select the optimal analysis method based on emotions the user has frequently felt in the past. The emotion analysis unit can also propose an efficient analysis method based on the user's past emotional history. For example, the emotion analysis unit can analyze the user's past emotional history and select the optimal analysis method. In this way, the optimal emotion analysis method can be selected by analyzing the user's past emotional history. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without using AI. For example, the emotion analysis unit can input the user's emotional history data into a generating AI and have the generating AI perform the selection of the optimal analysis method.

[0058] The emotion analysis unit can customize the means of emotion analysis based on the user's current living situation. For example, the emotion analysis unit can select an appropriate emotion analysis method based on the user's current living situation. The emotion analysis unit can also adjust the timing of emotion analysis to match the user's daily rhythm. For example, the emotion analysis unit can customize the method of emotion analysis based on the user's living environment. By customizing the means of emotion analysis based on the user's current living situation, it becomes possible to perform optimal emotion analysis for the user. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without using AI. For example, the emotion analysis unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of the emotion analysis means.

[0059] The emotion analysis unit can select the optimal analysis method by considering the user's geographical location information during emotion analysis. For example, if the user is in a specific location, the emotion analysis unit will prioritize analyzing emotions associated with that location. The emotion analysis unit can also prioritize analyzing emotions felt in locations close to the user's current location. For example, the emotion analysis unit can propose an efficient emotion analysis method based on the user's travel route. This enables efficient emotion analysis by selecting the optimal analysis method while considering the user's geographical location information. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal analysis method.

[0060] The sentiment analysis unit can analyze the user's social media activity and propose methods for sentiment analysis during sentiment analysis. For example, the sentiment analysis unit proposes methods for sentiment analysis based on the user's social media activity information. The sentiment analysis unit can also analyze relevant emotions based on the user's interests on social media. For example, the sentiment analysis unit proposes methods for sentiment analysis based on the user's activities with friends on social media. In this way, by analyzing the user's social media activity, the optimal method for sentiment analysis can be proposed. Some or all of the above processing in the sentiment analysis unit may be performed using AI, for example, or without AI. For example, the sentiment analysis unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of methods for sentiment analysis.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The AI ​​agent system can monitor the user's health status and adjust their schedule based on that status. For example, if a user reports feeling unwell, the scheduling system will increase rest time and postpone less important tasks. It can also analyze the user's health data and provide reminders for regular health checks and exercise. Furthermore, it can offer meal suggestions and hydration reminders based on the user's health status. This enables optimal schedule management tailored to the user's health condition.

[0063] The AI ​​agent system can suggest schedules based on the user's hobbies and interests. For example, if the user is interested in music, the schedule management unit will suggest music-related events and lessons. If the user enjoys reading, reading time can be incorporated into the schedule. Furthermore, it can remind the user of new activities and events related to their hobbies. This provides a fulfilling schedule tailored to the user's interests.

[0064] The AI ​​agent system can monitor the user's sleep patterns and suggest an optimal sleep schedule. For example, if the user is staying up late, the schedule management unit will send reminders to encourage earlier bedtimes and wake-up times. It can also analyze the user's sleep data and provide advice to improve sleep quality. Furthermore, it can adjust the daytime activity schedule based on the user's sleep patterns. This supports the user's healthy sleep habits.

[0065] The AI ​​agent system can suggest study schedules to support the user's learning activities. For example, if a user is studying for an exam, the schedule management unit will suggest an efficient study schedule. It can also monitor the user's learning progress and provide reminders as needed. Furthermore, it can suggest the optimal learning method based on the user's learning style. This supports the user's learning activities.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The schedule management unit manages the user's daily schedule. The schedule management unit displays the schedule in a calendar format and allows the user to prioritize tasks. It can also analyze the user's past schedule history and select the optimal schedule management method. Step 2: The voice reminder unit provides voice reminders based on the schedule managed by the schedule management unit. The voice reminder unit provides voice reminders at appropriate times and can also estimate the user's emotions and adjust the way the reminder is expressed. Step 3: The task confirmation unit confirms the completion of the task reminded by the voice reminder unit. The task confirmation unit understands the user's voice response and enables two-way communication. It can also estimate the user's emotions and adjust the task confirmation method accordingly. Step 4: The behavior recognition unit recognizes the user's actions based on the progress of the task confirmed by the task confirmation unit. The behavior recognition unit can use the camera to recognize the user's actions and prompt the user to check necessary items. It can also estimate the user's emotions and adjust the behavior recognition method accordingly. Step 5: The emotion analysis unit analyzes the user's emotions based on the actions recognized by the action recognition unit. The emotion analysis unit analyzes emotions from the user's voice and facial expressions and estimates the emotions using voice analysis technology and facial expression recognition technology. Step 6: The response unit takes appropriate action based on the emotions analyzed by the emotion analysis unit. The response unit provides feedback according to the user's emotions, offering encouraging messages and advice.

[0068] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that provides total daily support for individuals who have difficulty preparing for their day on their own due to developmental disabilities or other reasons. This AI agent system manages and visually displays the user's daily schedule. Next, it provides voice reminders at appropriate times. For example, it reminds the user of the start and end times of each task, such as getting ready in the morning, getting ready for school, and managing the time for extracurricular activities. It also enables two-way communication, understands voice responses from the user, and takes appropriate action. Furthermore, it uses a camera to recognize the user's actions and provides real-time verbal prompts to encourage appropriate actions. For example, when a user is getting ready in the morning, the camera recognizes their actions and prompts them to check necessary items. It also analyzes the user's emotions from their voice and facial expressions and takes appropriate action as needed. This AI agent system utilizes the following technologies: 1. Natural Language Generation (NLG): Generates appropriate words when the agent provides voice reminders to the target. 2. Natural Language Understanding (NLU): Understands voice responses from the target and enables two-way communication. 3. Task Management AI: Manages the daily schedule and provides reminders at appropriate times. 4. Computer Vision: Uses a camera to recognize the subject's actions. 5. Emotion Analysis: Analyzes the subject's emotions from their voice and facial expressions and takes appropriate action as needed. 6. Generating Checklists: Automatically generates a checklist of necessary items for the day based on the child's or adult's daily activities. This system enables individuals with developmental disabilities or other reasons who have difficulty preparing independently to manage their time, allowing them to spend their day autonomously and improving the quality of life and work for the entire family and workplace. As a result, the AI ​​agent system can manage the user's daily schedule, provide reminders, check tasks, recognize actions, analyze emotions, and respond accordingly.

[0069] The AI ​​agent system according to this embodiment comprises a schedule management unit, a voice reminder unit, a task confirmation unit, an action recognition unit, an emotion analysis unit, and a response unit. The schedule management unit manages the user's daily schedule. The schedule management unit can, for example, display the schedule in a calendar format. The schedule management unit can also prioritize tasks. For example, the schedule management unit can display important tasks at the top and prompt the user to perform them as a priority. Furthermore, the schedule management unit can analyze the user's past schedule history and select the optimal schedule management method. For example, the schedule management unit can automatically incorporate tasks that the user has frequently performed in the past into the schedule. The voice reminder unit provides voice reminders based on the schedule managed by the schedule management unit. The voice reminder unit can, for example, provide voice reminders at appropriate times. For example, the voice reminder unit can remind the user to "start getting ready in the morning" at the time of getting ready in the morning. Furthermore, the voice reminder unit can estimate the user's emotions and adjust the way the reminder is expressed based on the estimated emotions. For example, the voice reminder unit will provide a gentle reminder if the user is feeling stressed. The task confirmation unit confirms the completion of the task reminded by the voice reminder unit. The task confirmation unit can, for example, understand the user's voice response and enable two-way communication. For example, if the user responds "I have completed the task," the task confirmation unit will confirm that the task has been completed. The task confirmation unit can also estimate the user's emotions and adjust the task confirmation method based on the estimated emotions. For example, if the user is feeling stressed, the task confirmation unit will provide a simple and easy-to-understand task confirmation method. The behavior recognition unit recognizes the user's actions based on the progress of the task confirmed by the task confirmation unit. The behavior recognition unit can, for example, recognize the user's actions using a camera while the user is getting ready in the morning and prompt them to check the necessary items.Furthermore, the behavior recognition unit can estimate the user's emotions and adjust the behavior recognition method based on the estimated emotions. For example, if the behavior recognition unit is feeling stressed, it will recognize the behavior and give instructions in a calm voice. The emotion analysis unit analyzes the user's emotions based on the behavior recognized by the behavior recognition unit. The emotion analysis unit can analyze emotions from the user's voice and facial expressions, for example. For example, the emotion analysis unit uses voice analysis technology to analyze the tone and speed of the user's voice and estimate the emotions. The emotion analysis unit can also use facial expression recognition technology to analyze the user's facial expressions and estimate the emotions. The response unit takes appropriate action based on the emotions analyzed by the emotion analysis unit. For example, the response unit can provide feedback according to the user's emotions. For example, if the user is feeling stressed, the response unit will provide an encouraging message. The response unit also has an algorithm for taking appropriate action according to the user's emotions. For example, the response unit will provide encouraging messages and advice according to the user's emotional state. As a result, the AI ​​agent system according to this embodiment can manage the user's daily schedule, provide reminders, check tasks, recognize behavior, analyze emotions, and respond accordingly.

[0070] The Schedule Management Unit manages the user's daily schedule. For example, it can display the schedule in a calendar format. Specifically, it organizes and displays tasks and appointments by date and time slot so that users can visually check them on their smartphone or computer screen. The Schedule Management Unit can also prioritize tasks. For example, it displays important tasks at the top, prompting users to prioritize their execution. This allows users to efficiently manage their schedules without overlooking important tasks. Furthermore, the Schedule Management Unit can analyze the user's past schedule history and select the optimal schedule management method. For example, it automatically incorporates tasks that the user has frequently performed in the past into the schedule. This saves the user the trouble of manually entering the same tasks each time. The Schedule Management Unit can also learn the user's behavior patterns and habits and suggest tasks at the optimal time. For example, if a user has a habit of jogging every morning, the Schedule Management Unit will automatically add a jogging task to that time slot. This allows users to easily manage a schedule that suits their lifestyle.

[0071] The voice reminder unit provides voice reminders based on the schedule managed by the schedule management unit. The voice reminder unit can, for example, provide voice reminders at appropriate times. Specifically, it provides voice reminders based on the time and place set by the user. For example, the voice reminder unit will remind the user to "start getting ready in the morning" at the time of getting ready in the morning. The voice reminder unit can also estimate the user's emotions and adjust the way the reminder is expressed based on those emotions. For example, if the voice reminder unit is feeling stressed, it will provide a calm voice reminder. This allows the user to proceed with tasks smoothly without feeling uncomfortable when receiving reminders. Furthermore, the voice reminder unit can analyze the user's past reminder history and select the optimal reminder method. For example, if the user has shown a positive response to a particular reminder method in the past, it will prioritize using that method. This allows the voice reminder unit to provide reminders tailored to the user's individual needs and improve the task completion rate.

[0072] The task confirmation unit verifies the completion of tasks reminded by the voice reminder unit. The task confirmation unit can, for example, understand voice responses from the user and enable two-way communication. Specifically, if the user responds with "I have completed the task," it verifies that the task has been completed. The task confirmation unit can also estimate the user's emotions and adjust the task confirmation method based on the estimated emotions. For example, if the task confirmation unit is feeling stressed, it provides a concise and easy-to-understand task confirmation method. This allows the user to report task completion smoothly. Furthermore, the task confirmation unit can analyze the user's past task completion history and select the optimal task confirmation method. For example, if the user has shown a positive response to a particular task confirmation method in the past, it will prioritize using that method. This allows the task confirmation unit to provide task confirmation tailored to the user's individual needs and improve the task completion rate.

[0073] The behavior recognition unit recognizes user behavior based on the progress of tasks confirmed by the task confirmation unit. The behavior recognition unit can recognize user behavior using, for example, a camera. Specifically, when a user is getting ready in the morning, the behavior recognition unit recognizes their actions using the camera and prompts them to check necessary items. The behavior recognition unit can also estimate the user's emotions and adjust the behavior recognition method based on the estimated emotions. For example, if the behavior recognition unit is feeling stressed, it will recognize the user's actions and give instructions in a calm voice. This allows the user to proceed with their actions smoothly. Furthermore, the behavior recognition unit can analyze the user's past behavior history and select the optimal behavior recognition method. For example, if a user has shown a positive response to a particular behavior recognition method in the past, that method will be used preferentially. In this way, the behavior recognition unit can provide behavior recognition tailored to the user's individual needs and improve the efficiency of their actions.

[0074] The emotion analysis unit analyzes the user's emotions based on the actions recognized by the action recognition unit. For example, the emotion analysis unit can analyze emotions from the user's voice and facial expressions. Specifically, the emotion analysis unit uses voice analysis technology to analyze the tone and speed of the user's voice and estimate their emotions. It can also use facial expression recognition technology to analyze the user's facial expressions and estimate their emotions. This allows the emotion analysis unit to accurately grasp the user's emotional state. Furthermore, the emotion analysis unit can analyze the user's past emotional history and select the optimal emotion analysis method. For example, if the user has shown a positive response to a particular emotion analysis method in the past, that method will be used preferentially. This allows the emotion analysis unit to provide emotion analysis tailored to the user's individual needs and to quickly detect changes in emotions.

[0075] The response unit provides appropriate responses based on the emotions analyzed by the emotion analysis unit. For example, the response unit can provide feedback according to the user's emotions. Specifically, if the user is feeling stressed, the response unit will provide encouraging messages. The response unit also has an algorithm for providing appropriate responses according to the user's emotions. For example, the response unit will provide encouraging messages and advice according to the user's emotional state. This allows the user to receive appropriate feedback that matches their emotions. Furthermore, the response unit can analyze the user's past emotional history and select the optimal response method. For example, if the user has shown a positive response to a particular response method in the past, that method will be used preferentially. This allows the response unit to provide responses tailored to the user's individual needs and improve user satisfaction.

[0076] The schedule management unit can visually display the user's daily schedule. For example, the schedule management unit displays the schedule in a calendar format. The schedule management unit uses a graphical user interface (GUI) to enable the user to visually understand the schedule. For example, the schedule management unit can color-code tasks on the calendar to highlight important tasks. Furthermore, the schedule management unit can estimate the user's emotions and adjust the schedule display method based on the estimated emotions. For example, if the user is feeling stressed, the schedule management unit provides a simple and visually easy-to-understand schedule display. This makes it easier for the user to understand their schedule by visually displaying their daily schedule. Some or all of the above processes in the schedule management unit may be performed using AI, or not. For example, the schedule management unit can input the user's schedule data into a generating AI and have the generating AI execute the visual schedule display.

[0077] The voice reminder unit can provide voice reminders at appropriate times. For example, the voice reminder unit can determine the timing of reminders based on the user's schedule. The voice reminder unit can also analyze the user's past behavior patterns and select the optimal reminder timing. For example, the voice reminder unit can provide reminders during times when the user has tended to forget tasks in the past. Furthermore, the voice reminder unit can estimate the user's emotions and adjust the way the reminder is expressed based on the estimated emotions. For example, if the voice reminder unit is feeling stressed, it will provide a calm voice reminder. This ensures that users remember to complete tasks by providing voice reminders at appropriate times. Some or all of the above processes in the voice reminder unit may be performed using AI, for example, or not. For example, the voice reminder unit can input the user's schedule data into a generating AI and have the generating AI execute the reminder timing.

[0078] The task confirmation unit can understand voice responses from the user and enable two-way communication. For example, the task confirmation unit analyzes the user's voice responses using speech recognition technology. The task confirmation unit can also provide appropriate feedback based on the user's responses. For example, if the user responds "I have completed the task," the task confirmation unit will confirm that the task has been completed and remind the user of the next task. The task confirmation unit can also estimate the user's emotions and adjust the task confirmation method based on the estimated emotions. For example, if the task confirmation unit is feeling stressed, it will provide a simple and easy-to-understand task confirmation method. This allows for accurate tracking of task progress by understanding voice responses from the user and enabling two-way communication. Some or all of the above-described processes in the task confirmation unit may be performed using AI, for example, or without AI. For example, the task confirmation unit can input the user's voice data into a generating AI and have the generating AI perform the analysis of the voice responses.

[0079] The behavior recognition unit can recognize user behavior using a camera. For example, the behavior recognition unit can appropriately select the camera's installation location and monitor user behavior in real time. The behavior recognition unit can also recognize user behavior using an image analysis algorithm. For example, when a user is getting ready in the morning, the behavior recognition unit can recognize their actions using the camera and prompt them to check necessary items. The behavior recognition unit can also estimate the user's emotions and adjust the behavior recognition method based on the estimated emotions. For example, if the behavior recognition unit is feeling stressed, it can recognize the user's actions in a calm voice and give instructions. This allows for real-time prompting of appropriate actions by recognizing user behavior using a camera. Some or all of the above processing in the behavior recognition unit may be performed using AI, for example, or without AI. For example, the behavior recognition unit can input image data acquired by the camera into a generating AI and have the generating AI perform behavior recognition.

[0080] The emotion analysis unit can analyze emotions from the user's voice and facial expressions. For example, the emotion analysis unit can use voice analysis technology to analyze the tone and speed of the user's voice and estimate their emotions. The emotion analysis unit can also use facial recognition technology to analyze the user's facial expressions and estimate their emotions. For example, the emotion analysis unit can analyze the tone and speed of the user's voice while they are speaking and estimate whether the user is feeling stressed. The emotion analysis unit can also capture the user's facial expressions with a camera and analyze the changes in those expressions to estimate their emotions. This allows for appropriate responses based on the user's state by analyzing emotions from the user's voice and facial expressions. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input the user's voice data and image data into a generating AI and have the generating AI perform the emotion analysis.

[0081] The response unit can take appropriate action as needed. For example, the response unit can provide feedback according to the user's emotions. If the user is feeling stressed, the response unit can provide encouraging messages. The response unit also has an algorithm for taking appropriate action according to the user's emotions. For example, the response unit can provide encouraging messages and advice according to the user's emotional state. This enables support that meets the user's needs by taking appropriate action as needed. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input user emotion data into a generating AI and have the generating AI execute an appropriate response.

[0082] The system includes a packing checklist generation unit. This unit can automatically generate a packing checklist based on the user's daily activities. For example, it can list items based on the user's schedule. The packing checklist generation unit can also analyze the user's past packing history to generate an optimal checklist. For example, it can add items the user has forgotten in the past to the list. Furthermore, the packing checklist generation unit can estimate the user's emotions and adjust the contents of the checklist based on the estimated emotions. For example, if the user is feeling stressed, the packing checklist generation unit will list only the bare minimum of necessary items. This makes packing easier by automatically generating a packing checklist based on the user's daily activities. Some or all of the above-described processes in the packing checklist generation unit may be performed using AI, or not. For example, the packing checklist generation unit can input the user's schedule data into a generating AI and have the generating AI generate the packing checklist.

[0083] The schedule management unit can estimate the user's emotions and adjust how the schedule is displayed based on those emotions. For example, if the user is stressed, the schedule management unit can provide a simple and visually easy-to-understand schedule display. If the user is relaxed, the schedule management unit can also display detailed schedule information and provide customizable options. For example, if the user is in a hurry, the schedule management unit can highlight only important tasks for quick review. This allows for an optimal schedule display for the user by adjusting how the schedule is displayed according to their 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 processing in the schedule management unit may be performed using AI or not. For example, the schedule management unit can input user emotion data into a generative AI and have the generative AI adjust how the schedule is displayed.

[0084] The schedule management unit can analyze the user's past schedule history and select the optimal schedule management method. For example, the schedule management unit can automatically incorporate tasks that the user has frequently performed in the past into the schedule. The schedule management unit can also suggest tasks that are optimal for a specific time period based on the user's past schedule history. For example, the schedule management unit analyzes the user's past schedule history and suggests an efficient schedule management method. In this way, the optimal schedule management method can be selected by analyzing the user's past schedule history. Some or all of the above processes in the schedule management unit may be performed using AI, for example, or without AI. For example, the schedule management unit can input the user's schedule history data into a generating AI and have the generating AI select the optimal schedule management method.

[0085] The schedule management unit can customize the schedule based on the user's current lifestyle and areas of interest. For example, the schedule management unit can incorporate appropriate break times into the schedule based on the user's current lifestyle. The schedule management unit can also add relevant tasks and events to the schedule based on the user's areas of interest. For example, the schedule management unit can flexibly adjust the schedule to match the user's daily rhythm. This provides the user with an optimal schedule by customizing it based on the user's current lifestyle and areas of interest. Some or all of the above processes in the schedule management unit may be performed using AI, for example, or without AI. For example, the schedule management unit can input user lifestyle data and areas of interest data into a generating AI and have the generating AI perform the schedule customization.

[0086] The schedule management unit can estimate the user's emotions and determine schedule priorities based on those emotions. For example, if the user is stressed, the schedule management unit will postpone less important tasks and prioritize relaxation time. If the user is relaxed, the schedule management unit can also prioritize scheduling high-importance tasks. For example, if the user is in a hurry, the schedule management unit will prioritize the most important tasks and postpone other tasks. This allows for optimal schedule management for the user by determining schedule priorities according to their 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 processing in the schedule management unit may be performed using AI or not. For example, the schedule management unit can input user emotion data into a generative AI and have the generative AI determine schedule priorities.

[0087] The schedule management unit can prioritize displaying schedules that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific location, the schedule management unit will prioritize displaying tasks related to that location. The schedule management unit can also prioritize incorporating tasks that are performed in locations close to the user's current location into the schedule. For example, the schedule management unit will suggest an efficient schedule based on the user's travel route. This enables efficient schedule management by prioritizing the display of highly relevant schedules, taking into account the user's geographical location. Some or all of the above processing in the schedule management unit may be performed using AI, for example, or without AI. For example, the schedule management unit can input the user's geographical location data into a generating AI and have the generating AI prioritize the display of highly relevant schedules.

[0088] The schedule management unit can analyze a user's social media activity and suggest relevant schedules when managing schedules. For example, the schedule management unit can incorporate information about events a user has attended on social media into the schedule. The schedule management unit can also suggest relevant tasks based on a user's interests on social media. For example, the schedule management unit can reflect a user's appointments with friends on social media in the schedule. In this way, by analyzing a user's social media activity, it can suggest relevant schedules. Some or all of the above processes in the schedule management unit may be performed using AI, for example, or not using AI. For example, the schedule management unit can input user social media activity data into a generating AI and have the generating AI suggest relevant schedules.

[0089] The voice reminder unit can estimate the user's emotions and adjust the way the reminder is delivered based on the estimated emotions. For example, if the user is stressed, the voice reminder unit can deliver the reminder in a calm voice. If the user is relaxed, the voice reminder unit can deliver the reminder in a cheerful voice. For example, if the user is in a hurry, the voice reminder unit can deliver a quick and concise reminder. By adjusting the way the reminder is delivered according to the user's emotions, it becomes possible to provide the most optimal reminder for the user. Emotion estimation is achieved using an emotion estimation function, 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 processing in the voice reminder unit may be performed using AI, or not using AI. For example, the voice reminder unit can input user emotion data into the generative AI and have the generative AI adjust the way the reminder is delivered.

[0090] The voice reminder unit can adjust the level of detail in reminders based on the importance of the task. For example, the voice reminder unit can provide detailed reminders for high-importance tasks, and concise reminders for low-importance tasks. For example, the voice reminder unit can adjust the frequency of reminders according to the importance of the task. This allows for efficient reminders by adjusting the level of detail based on the importance of the task. Some or all of the above processing in the voice reminder unit may be performed using AI, for example, or without AI. For example, the voice reminder unit can input task importance data into a generating AI and have the generating AI adjust the level of detail in the reminders.

[0091] The voice reminder unit can apply different reminder algorithms depending on the task category when a reminder is given. For example, for tasks related to preparing for school, the voice reminder unit can provide an educational reminder. For tasks related to extracurricular activities, the voice reminder unit can also provide a motivational reminder. For example, for tasks related to daily life, the voice reminder unit can provide a relaxing reminder. By applying different reminder algorithms depending on the task category, optimal reminders can be achieved. Some or all of the above processing in the voice reminder unit may be performed using AI, for example, or without AI. For example, the voice reminder unit can input task category data into a generating AI and have the generating AI execute the application of the reminder algorithm.

[0092] The voice reminder unit can estimate the user's emotions and adjust the length of the reminder based on the estimated emotions. For example, if the user is stressed, the voice reminder unit can provide a short, concise reminder. If the user is relaxed, the voice reminder unit can also provide a detailed reminder. For example, if the user is in a hurry, the voice reminder unit can provide a quick and concise reminder. By adjusting the length of the reminder according to the user's emotions, it is possible to provide the most optimal reminder for the user. Emotion estimation is achieved using an emotion estimation function, such as 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 processing in the voice reminder unit may be performed using AI or not. For example, the voice reminder unit can input user emotion data into the generative AI and have the generative AI adjust the length of the reminder.

[0093] The voice reminder unit can determine the priority of reminders based on the task submission date. For example, the voice reminder unit will prioritize reminders for tasks with approaching deadlines. It can also postpone reminders for tasks with distant deadlines. For example, the voice reminder unit can adjust the frequency of reminders according to the submission deadline. This enables efficient reminders by determining the priority of reminders based on the task submission date. Some or all of the above processing in the voice reminder unit may be performed using AI, for example, or without AI. For example, the voice reminder unit can input task submission date data into a generating AI and have the generating AI determine the priority of reminders.

[0094] The voice reminder unit can adjust the order of reminders based on the relevance of tasks. For example, the voice reminder unit can prioritize reminders for highly relevant tasks. It can also postpone reminders for less relevant tasks. For example, the voice reminder unit adjusts the order of reminders according to the relevance of tasks. This allows for efficient reminders by adjusting the order of reminders based on the relevance of tasks. Some or all of the above processing in the voice reminder unit may be performed using AI, for example, or without AI. For example, the voice reminder unit can input task relevance data into a generating AI and have the generating AI perform the adjustment of the reminder order.

[0095] The task confirmation unit can estimate the user's emotions and adjust the task confirmation method based on the estimated emotions. For example, if the user is stressed, the task confirmation unit can provide a concise and easy-to-understand task confirmation method. If the user is relaxed, the task confirmation unit can also provide a detailed task confirmation method. For example, if the user is in a hurry, the task confirmation unit can provide a method for quick task confirmation. By adjusting the task confirmation method according to the user's emotions, it becomes possible to perform task confirmation optimally for the user. Emotion estimation is achieved using an emotion estimation function, 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 processing in the task confirmation unit may be performed using AI, for example, or without AI. For example, the task confirmation unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the task confirmation method.

[0096] The task confirmation unit can analyze the user's past task history to select the optimal confirmation method when confirming a task. For example, the task confirmation unit may prioritize suggesting task confirmation methods that the user has frequently used in the past. The task confirmation unit can also suggest efficient confirmation methods based on the user's past task history. For example, the task confirmation unit analyzes the user's past task history and selects the optimal confirmation method. This allows the optimal task confirmation method to be selected by analyzing the user's past task history. Some or all of the above processing in the task confirmation unit may be performed using AI, for example, or without AI. For example, the task confirmation unit can input the user's task history data into a generating AI and have the generating AI select the optimal confirmation method.

[0097] The task confirmation unit can estimate the user's emotions and determine the priority of task confirmation based on the estimated emotions. For example, if the user is stressed, the task confirmation unit will postpone less important tasks and prioritize time for relaxation. If the user is relaxed, the task confirmation unit can also prioritize high-importance tasks. For example, if the user is in a hurry, the task confirmation unit will prioritize the most important tasks and postpone other tasks. This allows for optimal task confirmation for the user by determining the priority of task confirmation according to 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 processing in the task confirmation unit may be performed using AI or not. For example, the task confirmation unit can input user emotion data into a generative AI and have the generative AI determine the priority of task confirmation.

[0098] The task verification unit can select the optimal verification method when verifying tasks, taking into account the user's geographical location information. For example, if the user is in a specific location, the task verification unit will prioritize verifying tasks related to that location. The task verification unit can also prioritize verifying tasks that are performed in locations close to the user's current location. For example, the task verification unit can propose an efficient task verification method based on the user's travel route. This enables efficient task verification by selecting the optimal verification method while considering the user's geographical location information. Some or all of the above processing in the task verification unit may be performed using AI, for example, or without AI. For example, the task verification unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal verification method.

[0099] The behavior recognition unit can estimate the user's emotions and adjust the behavior recognition method based on the estimated emotions. For example, if the user is stressed, the behavior recognition unit can recognize actions and give instructions in a calm voice. If the user is relaxed, the behavior recognition unit can also recognize actions and give instructions in a cheerful voice. For example, if the user is in a hurry, the behavior recognition unit can provide quick and concise behavior recognition and instructions. By adjusting the behavior recognition method according to the user's emotions, optimal behavior recognition for the user becomes possible. Emotion estimation is achieved using an emotion estimation function, 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 processing in the behavior recognition unit may be performed using AI, for example, or without AI. For example, the behavior recognition unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the behavior recognition method.

[0100] The behavior recognition unit can analyze the user's past behavior history and select the optimal recognition method during behavior recognition. For example, the behavior recognition unit selects the optimal recognition method based on actions the user has frequently performed in the past. The behavior recognition unit can also propose an efficient recognition method from the user's past behavior history. For example, the behavior recognition unit analyzes the user's past behavior history and selects the optimal recognition method. In this way, the optimal behavior recognition method can be selected by analyzing the user's past behavior history. Some or all of the above processing in the behavior recognition unit may be performed using AI, for example, or without using AI. For example, the behavior recognition unit can input the user's behavior history data into a generating AI and have the generating AI perform the selection of the optimal recognition method.

[0101] The behavior recognition unit can customize the means of behavior recognition based on the user's current living situation during behavior recognition. For example, the behavior recognition unit can select an appropriate behavior recognition means based on the user's current living situation. The behavior recognition unit can also adjust the timing of behavior recognition to match the user's daily rhythm. For example, the behavior recognition unit can customize the method of behavior recognition based on the user's living environment. By customizing the means of behavior recognition based on the user's current living situation, optimal behavior recognition for the user becomes possible. Some or all of the above processing in the behavior recognition unit may be performed using AI, for example, or without using AI. For example, the behavior recognition unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of the behavior recognition means.

[0102] The behavior recognition unit can estimate the user's emotions and determine the priority of behavior recognition based on the estimated user emotions. For example, if the user is stressed, the behavior recognition unit will postpone less important behaviors and prioritize relaxing behaviors. If the user is relaxed, the behavior recognition unit can also prioritize recognizing high-importance behaviors. For example, if the user is in a hurry, the behavior recognition unit will prioritize the most important behaviors and postpone other behaviors. This allows for optimal behavior recognition for the user by determining the priority of behavior recognition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the behavior recognition unit may be performed using AI, for example, or without AI. For example, the behavior recognition unit can input user emotion data into the generative AI and have the generative AI perform the determination of behavior recognition priorities.

[0103] The behavior recognition unit can select the optimal recognition method when recognizing an action, taking into account the user's geographical location information. For example, if the user is in a specific location, the behavior recognition unit will prioritize recognizing actions related to that location. The behavior recognition unit can also prioritize recognizing actions performed in locations close to the user's current location. For example, the behavior recognition unit will propose an efficient behavior recognition method based on the user's travel route. This enables efficient behavior recognition by selecting the optimal recognition method while considering the user's geographical location information. Some or all of the above processing in the behavior recognition unit may be performed using AI, for example, or without AI. For example, the behavior recognition unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal recognition method.

[0104] The behavior recognition unit can analyze the user's social media activity and propose methods for behavior recognition during behavior recognition. For example, the behavior recognition unit proposes methods for behavior recognition based on the user's social media activity information. The behavior recognition unit can also recognize relevant behaviors based on the user's interests on social media. For example, the behavior recognition unit proposes methods for behavior recognition based on the user's activities with friends on social media. In this way, by analyzing the user's social media activity, the optimal method for behavior recognition can be proposed. Some or all of the above processing in the behavior recognition unit may be performed using AI, for example, or without AI. For example, the behavior recognition unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of methods for behavior recognition.

[0105] The emotion analysis unit can estimate the user's emotions and adjust the emotion analysis method based on the estimated user emotions. For example, if the user is stressed, the emotion analysis unit can perform the emotion analysis in a calm voice. If the user is relaxed, the emotion analysis unit can also perform the emotion analysis in a cheerful voice. For example, if the user is in a hurry, the emotion analysis unit can perform a quick and concise emotion analysis. By adjusting the emotion analysis method according to the user's emotions, it becomes possible to perform the optimal emotion analysis for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input the user's emotion data into the generative AI and have the generative AI perform the adjustment of the emotion analysis method.

[0106] The emotion analysis unit can analyze the user's past emotional history to select the optimal analysis method during emotion analysis. For example, the emotion analysis unit can select the optimal analysis method based on emotions the user has frequently felt in the past. The emotion analysis unit can also propose an efficient analysis method based on the user's past emotional history. For example, the emotion analysis unit can analyze the user's past emotional history and select the optimal analysis method. In this way, the optimal emotion analysis method can be selected by analyzing the user's past emotional history. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without using AI. For example, the emotion analysis unit can input the user's emotional history data into a generating AI and have the generating AI perform the selection of the optimal analysis method.

[0107] The emotion analysis unit can customize the means of emotion analysis based on the user's current living situation. For example, the emotion analysis unit can select an appropriate emotion analysis method based on the user's current living situation. The emotion analysis unit can also adjust the timing of emotion analysis to match the user's daily rhythm. For example, the emotion analysis unit can customize the method of emotion analysis based on the user's living environment. By customizing the means of emotion analysis based on the user's current living situation, it becomes possible to perform optimal emotion analysis for the user. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without using AI. For example, the emotion analysis unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of the emotion analysis means.

[0108] The emotion analysis unit can estimate the user's emotions and determine the priority of emotion analysis based on the estimated user emotions. For example, if the user is stressed, the emotion analysis unit will prioritize relaxing emotions and postpone less important emotions. If the user is relaxed, the emotion analysis unit can also prioritize analyzing high-importance emotions. For example, if the user is in a hurry, the emotion analysis unit will prioritize the most important emotions and postpone other emotions. This allows for optimal emotion analysis for the user by determining the priority of emotion analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of emotion analysis.

[0109] The emotion analysis unit can select the optimal analysis method by considering the user's geographical location information during emotion analysis. For example, if the user is in a specific location, the emotion analysis unit will prioritize analyzing emotions associated with that location. The emotion analysis unit can also prioritize analyzing emotions felt in locations close to the user's current location. For example, the emotion analysis unit can propose an efficient emotion analysis method based on the user's travel route. This enables efficient emotion analysis by selecting the optimal analysis method while considering the user's geographical location information. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal analysis method.

[0110] The sentiment analysis unit can analyze the user's social media activity and propose methods for sentiment analysis during sentiment analysis. For example, the sentiment analysis unit proposes methods for sentiment analysis based on the user's social media activity information. The sentiment analysis unit can also analyze relevant emotions based on the user's interests on social media. For example, the sentiment analysis unit proposes methods for sentiment analysis based on the user's activities with friends on social media. In this way, by analyzing the user's social media activity, the optimal method for sentiment analysis can be proposed. Some or all of the above processing in the sentiment analysis unit may be performed using AI, for example, or without AI. For example, the sentiment analysis unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of methods for sentiment analysis.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The AI ​​agent system can monitor the user's health status and adjust their schedule based on that status. For example, if a user reports feeling unwell, the scheduling system will increase rest time and postpone less important tasks. It can also analyze the user's health data and provide reminders for regular health checks and exercise. Furthermore, it can offer meal suggestions and hydration reminders based on the user's health status. This enables optimal schedule management tailored to the user's health condition.

[0113] The AI ​​agent system can suggest schedules based on the user's hobbies and interests. For example, if the user is interested in music, the schedule management unit will suggest music-related events and lessons. If the user enjoys reading, reading time can be incorporated into the schedule. Furthermore, it can remind the user of new activities and events related to their hobbies. This provides a fulfilling schedule tailored to the user's interests.

[0114] The AI ​​agent system can monitor the user's sleep patterns and suggest an optimal sleep schedule. For example, if the user is staying up late, the schedule management unit will send reminders to encourage earlier bedtimes and wake-up times. It can also analyze the user's sleep data and provide advice to improve sleep quality. Furthermore, it can adjust the daytime activity schedule based on the user's sleep patterns. This supports the user's healthy sleep habits.

[0115] The AI ​​agent system can monitor the user's stress level and suggest activities to reduce stress. For example, if the user is experiencing high stress, the scheduling system will increase relaxation time and suggest stress-reducing activities. It can also adjust the way reminders are presented according to the user's stress level. Furthermore, it can provide reminders for stress-reducing breathing exercises and meditation. This supports the user's stress management.

[0116] The AI ​​agent system can be equipped with features to facilitate communication with friends and family in order to support the user's social activities. For example, if the user is feeling lonely, the scheduling function can remind them of video calls or messages with friends and family. It can also analyze the user's social activity history and suggest regular communication. Furthermore, it can suggest social activities at the appropriate time based on the user's emotions. In this way, the system supports the user's social activities.

[0117] The AI ​​agent system can suggest study schedules to support the user's learning activities. For example, if a user is studying for an exam, the schedule management unit will suggest an efficient study schedule. It can also monitor the user's learning progress and provide reminders as needed. Furthermore, it can suggest the optimal learning method based on the user's learning style. This supports the user's learning activities.

[0118] The AI ​​agent system can suggest exercise schedules to support users' exercise habits. For example, if a user feels they are not getting enough exercise, the schedule management unit will suggest appropriate exercise times. It can also analyze the user's exercise history and provide an optimal exercise plan. Furthermore, it can provide reminders to boost motivation based on the user's emotions. In this way, the system supports users' exercise habits.

[0119] The AI ​​agent system can suggest meal schedules to support users in managing their diet. For example, if a user wants to eat healthily, the scheduling unit will suggest a balanced meal plan. It can also analyze the user's eating history and suggest improvements to their nutritional balance. Furthermore, it can provide meal reminders based on the user's emotions. This supports users in developing healthy eating habits.

[0120] The AI ​​agent system can suggest travel schedules to support users' travel planning. For example, if a user is planning a trip, the schedule management unit can suggest an efficient travel schedule. It can also analyze the user's travel history and provide the optimal travel plan. Furthermore, it can provide travel reminders based on the user's emotions. In this way, the system supports the user's travel planning.

[0121] The AI ​​agent system can suggest project schedules to support users' project management. For example, if a user has multiple projects, the scheduling unit will suggest an efficient project schedule. It can also monitor the user's project progress and provide task reminders as needed. Furthermore, it can adjust project management methods based on the user's emotions. This supports the user's project management.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The schedule management unit manages the user's daily schedule. The schedule management unit displays the schedule in a calendar format and allows the user to prioritize tasks. It can also analyze the user's past schedule history and select the optimal schedule management method. Step 2: The voice reminder unit provides voice reminders based on the schedule managed by the schedule management unit. The voice reminder unit provides voice reminders at appropriate times and can also estimate the user's emotions and adjust the way the reminder is expressed. Step 3: The task confirmation unit confirms the completion of the task reminded by the voice reminder unit. The task confirmation unit understands the user's voice response and enables two-way communication. It can also estimate the user's emotions and adjust the task confirmation method accordingly. Step 4: The behavior recognition unit recognizes the user's actions based on the progress of the task confirmed by the task confirmation unit. The behavior recognition unit can use the camera to recognize the user's actions and prompt the user to check necessary items. It can also estimate the user's emotions and adjust the behavior recognition method accordingly. Step 5: The emotion analysis unit analyzes the user's emotions based on the actions recognized by the action recognition unit. The emotion analysis unit analyzes emotions from the user's voice and facial expressions and estimates the emotions using voice analysis technology and facial expression recognition technology. Step 6: The response unit takes appropriate action based on the emotions analyzed by the emotion analysis unit. The response unit provides feedback according to the user's emotions, offering encouraging messages and advice.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the schedule management unit, voice reminder unit, task confirmation unit, behavior recognition unit, emotion analysis unit, response unit, and belongings checklist generation unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the schedule management unit is implemented by the control unit 46A of the smart device 14 and manages the user's daily schedule. The voice reminder unit is implemented by the specific processing unit 290 of the data processing device 12 and provides voice reminders at appropriate times. The task confirmation unit is implemented by the control unit 46A of the smart device 14 and understands voice responses from the user and confirms the completion of tasks. The behavior recognition unit recognizes the user's behavior using the camera 42 of the smart device 14. The emotion analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes emotions from the user's voice and facial expressions. The response unit is implemented by the control unit 46A of the smart device 14 and provides appropriate responses based on the analyzed emotions. The packing checklist generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates a packing checklist based on the user's daily activities. 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.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the schedule management unit, voice reminder unit, task confirmation unit, behavior recognition unit, emotion analysis unit, response unit, and belongings checklist generation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the schedule management unit is implemented by the control unit 46A of the smart glasses 214 and manages the user's daily schedule. The voice reminder unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides voice reminders at appropriate times. The task confirmation unit is implemented by the control unit 46A of the smart glasses 214 and understands voice responses from the user and confirms the completion of tasks. The behavior recognition unit recognizes the user's behavior using the camera 42 of the smart glasses 214. The emotion analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes emotions from the user's voice and facial expressions. The response unit is implemented by the control unit 46A of the smart glasses 214 and provides appropriate responses based on the analyzed emotions. The packing checklist generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates a packing checklist based on the user's daily activities. 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.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[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 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.

[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 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.

[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 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.

[0159] Each of the multiple elements described above, including the schedule management unit, voice reminder unit, task confirmation unit, behavior recognition unit, emotion analysis unit, response unit, and belongings checklist generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the schedule management unit is implemented by the control unit 46A of the headset terminal 314 and manages the user's daily schedule. The voice reminder unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides voice reminders at appropriate times. The task confirmation unit is implemented by the control unit 46A of the headset terminal 314 and understands voice responses from the user and confirms the completion of tasks. The behavior recognition unit recognizes the user's behavior using the camera 42 of the headset terminal 314. The emotion analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes emotions from the user's voice and facial expressions. The response unit is implemented by the control unit 46A of the headset terminal 314 and provides appropriate responses based on the analyzed emotions. The packing checklist generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates a packing checklist based on the user's daily activities. 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] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the schedule management unit, voice reminder unit, task confirmation unit, behavior recognition unit, emotion analysis unit, response unit, and belongings checklist generation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the schedule management unit is implemented by the control unit 46A of the robot 414 and manages the user's daily schedule. The voice reminder unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides voice reminders at appropriate times. The task confirmation unit is implemented by, for example, the control unit 46A of the robot 414 and understands voice responses from the user and confirms the completion of the task. The behavior recognition unit recognizes the user's behavior using, for example, the camera 42 of the robot 414. The emotion analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes emotions from the user's voice and facial expressions. The response unit is implemented by, for example, the control unit 46A of the robot 414 and provides appropriate responses based on the analyzed emotions. The packing checklist generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates a packing checklist based on the user's daily activities. 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) The schedule management department manages the user's daily schedule, A voice reminder unit provides voice reminders based on the schedule managed by the aforementioned schedule management unit, A task confirmation unit confirms the completion of a task reminded by the aforementioned voice reminder unit, An action recognition unit recognizes user actions based on the progress of the task confirmed by the task confirmation unit, An emotion analysis unit analyzes the user's emotions based on the actions recognized by the aforementioned action recognition unit, The system includes a response unit that performs an appropriate response based on the emotions analyzed by the emotion analysis unit. A system characterized by the following features. (Note 2) The aforementioned schedule management unit, Visually display the user's daily schedule. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned voice reminder unit is Send voice reminders at the appropriate time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The task confirmation unit, Understanding user voice responses and enabling two-way communication. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned behavior recognition unit, Recognizing user behavior using a camera The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned emotion analysis unit, Analyzes user emotions from their voice and facial expressions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The corresponding part is, Take appropriate action as needed. The system described in Appendix 1, characterized by the features described herein. (Note 8) Equipped with a packing list generator. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned schedule management unit, It estimates the user's emotions and adjusts how the schedule is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned schedule management unit, Analyze the user's past schedule history and select the optimal schedule management method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned schedule management unit, When managing your schedule, customize it based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned schedule management unit, It estimates the user's emotions and determines schedule priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned schedule management unit, When managing schedules, the system prioritizes displaying highly relevant schedules by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned schedule management unit, When managing schedules, the system analyzes the user's social media activity and suggests relevant schedules. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned voice reminder unit is It estimates the user's emotions and adjusts the way reminders are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned voice reminder unit is When sending reminders, adjust the level of detail based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned voice reminder unit is When sending reminders, apply different reminder algorithms depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned voice reminder unit is It estimates the user's emotions and adjusts the length of the reminder based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned voice reminder unit is When sending reminders, prioritize them based on the task submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned voice reminder unit is When sending reminders, adjust the order of reminders based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 21) The task confirmation unit, The system estimates the user's emotions and adjusts the task confirmation method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The task confirmation unit, When reviewing a task, the system analyzes the user's past task history to select the most suitable review method. The system described in Appendix 1, characterized by the features described herein. (Note 23) The task confirmation unit, The system estimates the user's emotions and prioritizes task confirmations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The task confirmation unit, When reviewing tasks, the optimal review method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned behavior recognition unit, It estimates the user's emotions and adjusts the behavior recognition method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned behavior recognition unit, During behavior recognition, the system analyzes the user's past behavior history to select the optimal recognition method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned behavior recognition unit, During behavior recognition, the means of behavior recognition are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned behavior recognition unit, It estimates the user's emotions and determines the priority of behavioral recognition based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned behavior recognition unit, When recognizing user behavior, the system selects the optimal recognition method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned behavior recognition unit, When recognizing user behavior, we analyze their social media activity and propose methods for behavior recognition. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned emotion analysis unit, The system estimates the user's emotions and adjusts the emotion analysis method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned emotion analysis unit, During sentiment analysis, the system analyzes the user's past emotional history to select the optimal analysis method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned emotion analysis unit, During sentiment analysis, the method of sentiment analysis is customized based on the user's current life situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned emotion analysis unit, It estimates the user's emotions and determines the priority of emotion analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned emotion analysis unit, When performing sentiment analysis, the optimal analysis method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned emotion analysis unit, During sentiment analysis, we analyze users' social media activity and propose methods for sentiment analysis. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The schedule management department manages the user's daily schedule, A voice reminder unit provides voice reminders based on the schedule managed by the aforementioned schedule management unit, A task confirmation unit confirms the completion of a task reminded by the aforementioned voice reminder unit, An action recognition unit recognizes user actions based on the progress of the task confirmed by the task confirmation unit, An emotion analysis unit analyzes the user's emotions based on the actions recognized by the aforementioned action recognition unit, The system includes a response unit that performs an appropriate response based on the emotions analyzed by the emotion analysis unit. A system characterized by the following features.

2. The aforementioned schedule management unit, Visually display the user's daily schedule. The system according to feature 1.

3. The aforementioned voice reminder unit is Send voice reminders at the appropriate time. The system according to feature 1.

4. The task confirmation unit, Understanding user voice responses and enabling two-way communication. The system according to feature 1.

5. The aforementioned behavior recognition unit, Recognizing user behavior using a camera The system according to feature 1.

6. The aforementioned emotion analysis unit, Analyzes user emotions from their voice and facial expressions. The system according to feature 1.

7. The corresponding part is, Take appropriate action as needed. The system according to feature 1.

8. Equipped with a packing list generator. The system according to feature 1.

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