system

Abstract

The system according to this embodiment aims to simplify the process of canceling or rescheduling lessons. [Solution] The system according to the embodiment comprises a reception unit, a confirmation unit, a selection unit, and a procedure unit. The reception unit receives information on cancellations and rescheduling of lessons. The confirmation unit checks available time slots based on the information received by the reception unit. The selection unit selects a rescheduled date and time based on the available time slots confirmed by the confirmation unit. The procedure unit performs the rescheduling procedure based on the rescheduled date and time selected by the selection 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 performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, there is a problem that the cancellation of learning tasks and the procedure of risks are complicated, which is a great burden for parents.

[0005] The system according to the embodiment aims to simplify the cancellation of learning tasks and the procedure of risks.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a confirmation unit, a selection unit, and a procedure unit. The reception unit receives information about cancellations and rescheduling of lessons. The confirmation unit checks available time slots based on the information received by the reception unit. The selection unit selects a rescheduled date and time based on the available time slots confirmed by the confirmation unit. The procedure unit performs the rescheduling procedure based on the rescheduled date and time selected by the selection unit. [Effects of the Invention]

[0007] The system according to this embodiment allows for easy cancellation and rescheduling of lessons. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applicable 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The hobby scheduling support system according to an embodiment of the present invention is a system that reduces the burden of "hobby scheduling", which is becoming a major burden for parents with children in modern life, by utilizing an AI agent. When cancellation or rescheduling of a hobby is necessary, the hobby scheduling support system substitutes or supports a series of operations such as the AI agent's confirmation of available time slots, selection of rescheduling date and time, and rescheduling procedures. Depending on the acceptance method (such as a website, a messaging app, email, etc.) for each hobby, the AI agent performs appropriate procedures. For example, when a parent needs to cancel or reschedule a hobby, they inform the AI agent to that effect. For example, when a child gets sick, the parent inputs "I want to cancel the hobby of XX" to the AI agent. This information is input into the AI agent. Next, the AI agent analyzes the input information and checks the available time slots for the corresponding hobby. For a hobby with a website, the AI agent generates an HTTP request necessary for procedures such as querying available time slots and changing reservations according to the configuration of the corresponding website. For example, the AI agent accesses the website to check the available time slots. Also, for a hobby that requires procedures via messages such as a messaging app or email, the AI agent generates a message draft according to the situation. For example, the AI agent sends a message in the messaging app saying "I want to cancel the hobby of XX". Furthermore, after the AI agent checks the available time slots, it selects the rescheduling date and time. The AI agent proposes the optimal rescheduling date and time considering the parent's schedule and the schedules of the child's other hobbies. For example, the AI agent proposes "I propose rescheduling the hobby of XX to Tuesday next week". Finally, the AI agent performs the rescheduling procedure. The AI agent completes the rescheduling procedure through procedures such as a website, a messaging app, or email. For example, the AI agent performs the rescheduling procedure on the website and notifies the parent that the rescheduling is complete. With this mechanism, parents can leave the procedures for canceling or rescheduling hobbies to the AI agent, reducing their burden.Particularly for parents with multiple children, there is no need to rearrange the schedules of each child like a puzzle, significantly reducing the burden. As a result, the hobby scheduling support system can reduce the burden on parents and achieve efficient scheduling.

[0029] The hobby scheduling support system according to the embodiment includes a reception unit, a confirmation unit, a selection unit, and a procedure unit. The reception unit receives information on hobby cancellations and rescheduling. For example, when a parent needs to cancel a hobby or reschedule, the parent can convey this to the reception unit. The reception unit can receive information, for example, when a parent inputs "I want to cancel the hobby of XX". The confirmation unit checks for available time slots based on the information received by the reception unit. For example, for hobbies with a website, the confirmation unit generates HTTP requests necessary for procedures such as checking for available time slots and changing reservations according to the configuration of the corresponding website. The confirmation unit can, for example, access the website to check for available time slots. Also, for hobbies that require procedures via messages such as a message app or email, the confirmation unit generates a message draft according to the situation. For example, the confirmation unit can send a message "I want to cancel the hobby of XX" via the message app. The selection unit selects a rescheduling date and time based on the available time slots confirmed by the confirmation unit. For example, the selection unit proposes an optimal rescheduling date and time considering the parent's schedule and the schedules of other hobbies of the child. The selection unit can, for example, propose "I propose rescheduling the hobby of XX to Tuesday next week". The procedure unit performs the rescheduling procedure based on the rescheduling date and time selected by the selection unit. For example, the procedure unit completes the rescheduling procedure through procedures on the website, message app, email, etc. The procedure unit can, for example, perform the rescheduling procedure on the website and notify the parent that the rescheduling has been completed. Thus, the hobby scheduling support system according to the embodiment can efficiently perform procedures for hobby cancellations and rescheduling.

[0030] The reception desk receives information regarding cancellations and rescheduling of extracurricular activities. For example, if a parent needs to cancel or reschedule an extracurricular activity, they can inform the reception desk. The reception desk can receive this information by, for example, a parent typing, "I want to cancel the extracurricular activity for [activity name]." Specifically, the reception desk provides an interface where parents can access a dedicated application or website using their smartphone or computer and enter their cancellation or rescheduling request. Parents can log in to their account, select the details of the relevant activity, and enter the reason for cancellation or rescheduling and their preferred date and time. The reception desk stores this information in a database and manages it as information necessary for subsequent processing. The reception desk also displays a confirmation screen of the entered information so that parents can reconfirm what they have entered. Furthermore, the reception desk has a function to display error messages if there are any errors in the entered information or if necessary information is missing, prompting the parent to make corrections. In this way, the reception desk enables parents to easily and accurately request cancellations and rescheduling, supporting a smooth process.

[0031] The verification unit checks available time slots based on the information received by the reception unit. For example, for lessons with a website, the verification unit generates the necessary HTTP requests for procedures such as checking available time slots and changing reservations, according to the structure of the website. The verification unit can, for example, access the website and check the available time slots. The verification unit also generates appropriate message suggestions for lessons that require procedures via messaging apps or email. For example, the verification unit can send a message via a messaging app saying, "I would like to cancel the lesson for XX." Specifically, the verification unit automatically logs into the lesson's website, generates and sends an HTTP request to check for available time slots. This retrieves the available time slot information returned from the website and stores it in the database. Furthermore, if procedures using a messaging app or email are required, the verification unit automatically generates an appropriate message based on the information entered by the parent. For example, it generates a message such as, "I would like to cancel the lesson for XX, please tell me the available time slots," and sends it to the specified contact. The verification unit automates these procedures, eliminating the need for parents to perform them manually and allowing them to quickly and accurately check available time slots. Furthermore, the verification unit notifies the parents of the acquired available time slot information and provides the information necessary for the next step, which is selecting a rescheduled date and time.

[0032] The selection unit selects a rescheduled date and time based on the available time slots confirmed by the verification unit. For example, the selection unit proposes the optimal rescheduled date and time, taking into account the parents' schedules and the children's other extracurricular activity schedules. The selection unit can, for example, propose, "I suggest rescheduling the XX extracurricular activity to next Tuesday." Specifically, the selection unit retrieves the schedule information entered by the parents and the children's other extracurricular activity schedules from the database and compares it with the available time slots obtained by the verification unit. Based on this information, the selection unit calculates the optimal rescheduled date and time and proposes it to the parents. For example, the selection unit considers the parents' work schedules and the children's school timetables and selects the most convenient date and time. The selection unit also has a function to present multiple candidate dates and times, allowing parents to choose. This allows parents to choose the most suitable rescheduled date and time according to their own convenience. Furthermore, the selection unit also provides a function to set a reminder for the selected date and time. For example, by sending a notification the day before the rescheduled date and time, parents can reconfirm the rescheduled date and time, ensuring they don't forget to attend their lessons. This allows the selection department to support parents in managing their schedules and facilitate the rescheduling process.

[0033] The Procedures Department performs the rescheduling procedure based on the rescheduled date and time selected by the Selection Department. For example, the Procedures Department completes the rescheduling procedure through procedures such as websites, messaging apps, and email. For example, the Procedures Department can perform the rescheduling procedure on a website and notify the parents that the rescheduling is complete. Specifically, the Procedures Department accesses the website for the extracurricular activity based on the rescheduled date and time selected by the Selection Department and automatically performs the rescheduling procedure. The Procedures Department inputs the necessary information and generates and sends an HTTP request to confirm the rescheduling. As a result, it receives a rescheduling confirmation message from the website and confirms that the rescheduling procedure is complete. In addition, if procedures using a messaging app or email are necessary, the Procedures Department automatically generates a message notifying the parents that the rescheduling procedure is complete and sends it to them. For example, it sends a message such as, "The rescheduling of [child's name]'s extracurricular activity is complete. The new date and time is next Tuesday." By automating these procedures, the Procedures Department eliminates the need for parents to perform the procedure manually and enables the rescheduling procedure to be completed quickly and accurately. Furthermore, the procedures department can save the history of rescheduling procedures in a database, which can be used for future reference and troubleshooting. This allows the procedures department to provide parents with peace of mind and facilitate the rescheduling process.

[0034] The verification unit can generate HTTP requests necessary for procedures such as checking available time slots and changing reservations, depending on the configuration of the website. For example, the verification unit generates appropriate HTTP requests based on the website's page layout and URL structure. For example, the verification unit can generate a GET request to check available time slots. The verification unit can also generate a POST request to perform reservation change procedures. This improves the efficiency of procedures by automating procedures according to the website's configuration. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input website configuration information into a generation AI and have the generation AI execute the generation of appropriate HTTP requests.

[0035] The confirmation unit can generate message drafts appropriate to the situation when procedures via messaging apps or email are required. For example, the confirmation unit generates appropriate message drafts based on the reason for cancellation or rescheduling. For example, the confirmation unit can generate a message draft such as, "I would like to cancel the XX lesson." It can also generate a message draft such as, "I would like to reschedule the XX lesson to next Tuesday." This improves the efficiency of the procedure by automating the message-based procedures. Some or all of the above processing in the confirmation unit may be performed using AI, for example, or without AI. For example, the confirmation unit can input the reason for cancellation or rescheduling into a generation AI and have the generation AI generate appropriate message drafts.

[0036] The selection unit can propose the optimal rescheduling date and time by considering the parents' schedules and the children's other extracurricular activity schedules. For example, the selection unit can propose a rescheduling date and time by considering the parents' work schedules and family schedules. For example, the selection unit can propose, "I suggest rescheduling the XX extracurricular activity to next Tuesday." The selection unit can also propose a rescheduling date and time by considering the children's other extracurricular activity schedules. For example, the selection unit can propose, "I suggest rescheduling the XX extracurricular activity so that it does not conflict with other extracurricular activities." In this way, the optimal rescheduling date and time can be proposed by considering the schedules of the parents and children. Some or all of the above processing in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can input the parents' schedules and the children's other extracurricular activity schedules into a generating AI and have the generating AI propose the optimal rescheduling date and time.

[0037] The procedure unit can complete the rescheduling process through procedures such as websites, messaging apps, and email. For example, the procedure unit can perform the rescheduling process on a website and notify the parent that the rescheduling is complete. For example, the procedure unit can perform the rescheduling process on a messaging app and notify the parent that the rescheduling is complete. The procedure unit can also perform the rescheduling process via email and notify the parent that the rescheduling is complete. This improves the efficiency of the process by allowing the rescheduling process to be completed through procedures such as websites, messaging apps, and email. Some or all of the above-described processes in the procedure unit may be performed using AI, for example, or not using AI. For example, the procedure unit can input the rescheduling information into a generating AI and have the generating AI execute the completion of the rescheduling process.

[0038] The reception desk can analyze parents' past cancellation and rescheduling history and select the most suitable registration method. For example, the reception desk can automatically display as candidates lessons that parents have frequently canceled in the past. For example, the reception desk can prioritize suggesting registration methods that parents have used in the past (phone, email, etc.). The reception desk can also predict and suggest registration methods to be used at specific time slots based on the parents' past cancellation history. In this way, the most suitable registration method can be selected by analyzing past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the parents' past cancellation and rescheduling history into a generating AI and have the generating AI select the most suitable registration method.

[0039] The reception desk can filter cancellation and rescheduling requests based on the parents' current living situation and areas of interest. For example, if a parent is currently busy, the reception desk can prioritize suggesting simpler procedures. If a parent has a specific area of ​​interest, the reception desk can prioritize cancellations and rescheduling of lessons related to that area. The reception desk can also suggest the most suitable reception method according to the parent's living situation. In this way, by filtering according to the parent's living situation and areas of interest, the reception desk can provide the most suitable reception method. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input data on the parent's current living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0040] The reception desk can prioritize receiving highly relevant information when accepting cancellations or rescheduling requests, taking into account the parent's geographical location. For example, the reception desk can prioritize cancellations or rescheduling of lessons that are close to the parent's current location. For example, if the parent is in a specific region, the reception desk can prioritize providing information about lessons related to that region. The reception desk can also suggest the most suitable reception method based on the parent's geographical location. This allows for the priority of receiving highly relevant information by considering the parent's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the parent's geographical location information into a generating AI and have the generating AI perform the reception of highly relevant information.

[0041] The reception desk can analyze parents' social media activity and receive relevant information when receiving cancellation or rescheduling requests. For example, the reception desk can prioritize cancellations and rescheduling of extracurricular activities mentioned by parents on social media. For example, the reception desk can prioritize providing information on extracurricular activities of high interest based on parents' social media activity. The reception desk can also suggest the most suitable reception method based on parents' social media activity. In this way, by analyzing parents' social media activity, relevant information can be received preferentially. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input data on parents' social media activity into a generating AI and have the generating AI perform the reception of relevant information.

[0042] The verification unit can optimize its verification algorithm by referring to past verification data when checking for available time slots. For example, the verification unit can propose the optimal available time slot based on past verification data. For example, the verification unit can propose an available time slot that avoids congestion based on past verification data. The verification unit can also analyze past verification data and propose the most efficient available time slot. In this way, the verification algorithm can be optimized by referring to past verification data. Some or all of the above processing in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input past verification data into a generating AI and have the generating AI perform the optimization of the verification algorithm.

[0043] The verification unit can apply different verification methods to each category of lesson when checking available time slots. For example, the verification unit can display real-time available time slots for sports lessons, for example, it can display available time slots for music lessons, and for learning lessons, it can display online available time slots. By applying different verification methods to each category of lesson, the accuracy of the verification is improved. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input lesson category information into a generating AI and have the generating AI execute the application of verification methods.

[0044] The verification unit can consider the geographical distribution of extracurricular activities when checking available time slots. For example, the verification unit can prioritize displaying available time slots for activities close to the parent's current location. For example, if the parent is in a specific area, the verification unit can display available time slots related to that area. The verification unit can also suggest the optimal available time slot based on the parent's geographical location information. This allows for the confirmation of the optimal available time slot by considering the geographical distribution of extracurricular activities. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the parent's geographical location information into a generating AI and have the generating AI perform the confirmation of the optimal available time slot.

[0045] The verification unit can improve the accuracy of its verification of available time slots by referring to relevant literature on the activity. For example, the verification unit can suggest the optimal available time slot based on the relevant literature. For example, the verification unit can suggest an available time slot that avoids congestion from the relevant literature. The verification unit can also analyze the relevant literature and suggest the most efficient available time slot. In this way, the accuracy of the verification is improved by referring to the relevant literature. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input data from the relevant literature into a generating AI and have the generating AI perform the task of improving the accuracy of the verification.

[0046] The selection unit can select the optimal selection method when choosing a rescheduled date and time by referring to the parent's past schedule history. For example, the selection unit can propose the optimal rescheduled date and time based on the parent's past rescheduled dates and times. For example, the selection unit can propose a rescheduled date and time that avoids congestion based on the parent's past schedule history. The selection unit can also analyze the parent's past schedule history and propose the most efficient rescheduled date and time. In this way, the optimal rescheduled date and time can be proposed by referring to past schedule history. Some or all of the above processing in the selection unit may be performed using AI, for example, or without using AI. For example, the selection unit can input the parent's past schedule history into a generating AI and have the generating AI perform the selection of the optimal rescheduled date and time.

[0047] The selection unit can customize the selection method based on the parent's current living situation when selecting a rescheduled date and time. For example, if the parent is currently busy, the selection unit will prioritize suggesting a simple rescheduled date and time. For example, if the parent has a particular area of ​​interest, the selection unit can prioritize suggesting a rescheduled date and time related to that area. The selection unit can also suggest the most suitable rescheduled date and time according to the parent's living situation. This reduces the burden on the parent by suggesting a rescheduled date and time according to the parent's living situation. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input data on the parent's current living situation into a generating AI and have the generating AI perform the customization of the selection method.

[0048] The selection unit can select the optimal selection method when choosing a rescheduled date and time, taking into account the parent's geographical location information. For example, the selection unit may prioritize suggesting rescheduled dates and times that are close to the parent's current location. For example, if the parent is in a specific region, the selection unit may suggest rescheduled dates and times related to that region. The selection unit can also suggest the optimal rescheduled date and time based on the parent's geographical location information. In this way, the optimal rescheduled date and time can be suggested by taking the parent's geographical location information into consideration. Some or all of the above processing in the selection unit may be performed using AI, for example, or without using AI. For example, the selection unit can input the parent's geographical location information into a generating AI and have the generating AI perform the selection of the optimal rescheduled date and time.

[0049] The selection unit can analyze the parent's social media activity and propose selection methods when selecting a rescheduled date and time. For example, the selection unit can prioritize suggesting rescheduled dates and times mentioned by the parent on social media. For example, the selection unit can suggest rescheduled dates and times of high interest based on the parent's social media activity. Furthermore, the selection unit can propose the optimal rescheduled date and time based on the parent's social media activity. In this way, the optimal rescheduled date and time can be proposed by analyzing the parent's social media activity. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input data on the parent's social media activity into a generating AI and have the generating AI execute the proposal of selection methods.

[0050] The procedure unit can select the optimal procedure method by referring to the parent's past procedure history during the rescheduling procedure. For example, the procedure unit can propose the optimal procedure based on the rescheduling procedure method previously used by the parent. For example, the procedure unit can propose a procedure that avoids congestion based on the parent's past procedure history. The procedure unit can also analyze the parent's past procedure history and propose the most efficient procedure. In this way, the optimal procedure method can be selected by referring to the past procedure history. Some or all of the above processing in the procedure unit may be performed using AI, for example, or without AI. For example, the procedure unit can input the parent's past procedure history into a generating AI and have the generating AI select the optimal procedure method.

[0051] The procedure unit can customize the procedure based on the parent's current living situation during the rescheduling process. For example, if the parent is currently busy, the procedure unit will prioritize suggesting a simpler procedure. If the parent has a particular area of ​​interest, the procedure unit can prioritize suggesting procedures related to that area. The procedure unit can also suggest the most suitable procedure according to the parent's living situation. This reduces the burden on the parent by customizing the procedure according to their living situation. Some or all of the above processing in the procedure unit may be performed using AI, for example, or not. For example, the procedure unit can input data on the parent's current living situation into a generating AI and have the generating AI perform the customization of the procedure.

[0052] The procedure unit can select the optimal procedure method when rescheduling, taking into account the parent's geographical location information. For example, the procedure unit may prioritize rescheduling procedures that are close to the parent's current location. For example, if the parent is in a specific region, the procedure unit may suggest procedures related to that region. The procedure unit can also suggest the optimal procedure based on the parent's geographical location information. This allows the optimal procedure method to be selected by considering the parent's geographical location information. Some or all of the above processing in the procedure unit may be performed using AI, for example, or without AI. For example, the procedure unit may input the parent's geographical location information into a generating AI and have the generating AI select the optimal procedure method.

[0053] The procedure unit can analyze the parents' social media activity during the rescheduling process and propose procedural measures. For example, the procedure unit can prioritize rescheduling procedures mentioned by the parents on social media. For example, the procedure unit can propose procedures of high interest based on the parents' social media activity. Furthermore, the procedure unit can propose the most suitable procedure based on the parents' social media activity. In this way, by analyzing the parents' social media activity, the optimal procedural measures can be proposed. Some or all of the above processing in the procedure unit may be performed using AI, for example, or not using AI. For example, the procedure unit can input data on the parents' social media activity into a generating AI and have the generating AI execute the procedural measures proposal.

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

[0055] The reception desk can analyze parents' past cancellation and rescheduling history and select the most suitable reception method. For example, it can automatically display as candidates the lessons that parents have frequently canceled in the past. It can also prioritize suggesting reception methods that parents have used in the past (phone, email, etc.). Furthermore, it can predict and suggest reception methods to be used at specific time slots based on the parents' past cancellation history. In this way, the most suitable reception method can be selected by analyzing past history. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the parents' past cancellation and rescheduling history into a generating AI and have the generating AI select the most suitable reception method.

[0056] The verification unit can optimize its verification algorithm by referring to past verification data when checking available time slots. For example, it can suggest the optimal available time slot based on past verification data. It can also suggest available time slots that avoid congestion based on past verification data. Furthermore, it can analyze past verification data and suggest the most efficient available time slot. In this way, the verification algorithm can be optimized by referring to past verification data. Some or all of the above processing in the verification unit may be performed using AI or not. For example, the verification unit can input past verification data into a generating AI and have the generating AI perform the optimization of the verification algorithm.

[0057] The selection unit can select the optimal selection method when choosing a rescheduled date and time by referring to the parent's past schedule history. For example, it can suggest the optimal rescheduled date and time based on the parent's past rescheduled dates and times. It can also suggest a rescheduled date and time that avoids congestion based on the parent's past schedule history. Furthermore, it can analyze the parent's past schedule history and suggest the most efficient rescheduled date and time. In this way, the optimal rescheduled date and time can be suggested by referring to past schedule history. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the parent's past schedule history into a generating AI and have the generating AI perform the selection of the optimal rescheduled date and time.

[0058] The procedure unit can select the optimal procedure method by referring to the parent's past procedure history during the rescheduling procedure. For example, it can propose the optimal procedure based on the rescheduling procedure method the parent has used in the past. It can also propose a procedure that avoids congestion based on the parent's past procedure history. Furthermore, it can analyze the parent's past procedure history and propose the most efficient procedure. In this way, the optimal procedure method can be selected by referring to the past procedure history. Some or all of the above processing in the procedure unit may be performed using AI or not. For example, the procedure unit can input the parent's past procedure history into a generating AI and have the generating AI select the optimal procedure method.

[0059] The reception desk can filter cancellation and rescheduling requests based on the parents' current living situation and areas of interest. For example, if a parent is currently busy, it can prioritize suggesting simpler procedures. If a parent has specific areas of interest, it can prioritize cancellations and rescheduling of lessons related to those areas. Furthermore, it can suggest the most suitable application method based on the parent's living situation. This allows the reception desk to provide the most appropriate application method by filtering according to the parent's living situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the parent's current living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0060] The verification unit can apply different verification methods to each category of lesson when checking available time slots. For example, it can display real-time available time slots for sports lessons, and available time slots for music lessons. Furthermore, it can display online available time slots for academic lessons. By applying different verification methods to each category of lesson, the accuracy of the verification can be improved. Some or all of the above processing in the verification unit may be performed using AI or not. For example, the verification unit can input lesson category information into a generating AI and have the generating AI execute the application of verification methods.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk receives information about cancellations and rescheduling of extracurricular activities. For example, if a parent needs to cancel or reschedule an extracurricular activity, they can inform the reception desk. The reception desk can receive this information, for example, if a parent enters "I want to cancel the extracurricular activity for [child's name]." Step 2: The confirmation unit checks for available time slots based on the information received by the reception unit. For example, for lessons with a website, the confirmation unit generates the necessary HTTP requests for procedures such as checking available time slots or changing reservations, according to the structure of the website. The confirmation unit can, for example, access the website and check for available time slots. The confirmation unit also generates appropriate message suggestions for lessons that require procedures via messaging apps or email. For example, the confirmation unit can send a message via a messaging app saying, "I would like to cancel my lesson at [lesson name]." Step 3: The selection unit selects a rescheduled date and time based on the available time slots confirmed by the verification unit. For example, the selection unit will propose the optimal rescheduled date and time, taking into account the parents' schedules and the child's other extracurricular activities. The selection unit may propose, for example, "I suggest rescheduling the XX extracurricular activity to next Tuesday." Step 4: The Procedures Department carries out the rescheduling procedure based on the rescheduled date and time selected by the Selection Department. For example, the Procedures Department completes the rescheduling procedure through a website, messaging app, email, etc. For example, the Procedures Department can carry out the rescheduling procedure on a website and notify the parents that the rescheduling is complete.

[0063] (Example of form 2) The hobby scheduling support system according to an embodiment of the present invention is a system that aims to reduce the burden of "hobby scheduling", which has become a major burden for parents with children in modern life, by utilizing an AI agent. When cancellation or rescheduling of a hobby is required, the AI agent substitutes or supports a series of operations such as checking free time slots, selecting a rescheduling date and time, and performing rescheduling procedures. Depending on the acceptance method (such as a website, a messaging app, email, etc.) for each hobby, the AI agent performs appropriate procedures. For example, when a parent needs to cancel or reschedule a hobby, they inform the AI agent to that effect. For example, when a child gets sick, the parent inputs "want to cancel the hobby of XX" to the AI agent. This information is input into the AI agent. Next, the AI agent analyzes the input information and checks the free time slots for the corresponding hobby. For hobbies with a website, the AI agent generates HTTP requests necessary for procedures such as checking free time slots and changing reservations according to the configuration of the corresponding website. For example, the AI agent accesses the website to check the free time slots. Also, for hobbies that require procedures via messages such as a messaging app or email, the AI agent generates a message draft according to the situation. For example, the AI agent sends a message in the messaging app saying "want to cancel the hobby of XX". Furthermore, after the AI agent checks the free time slots, it selects a rescheduling date and time. The AI agent proposes an optimal rescheduling date and time considering the parent's schedule and the schedule of the child's other hobbies. For example, the AI agent proposes "I propose rescheduling the hobby of XX to Tuesday next week". Finally, the AI agent performs the rescheduling procedure. The AI agent completes the rescheduling procedure through procedures such as a website, a messaging app, or email. For example, the AI agent performs the rescheduling procedure on the website and notifies the parent that the rescheduling has been completed. With this mechanism, parents can entrust the procedures for canceling or rescheduling hobbies to the AI agent, reducing their burden.Particularly, for parents with multiple children, there is no need to rearrange the schedules of each child like a puzzle, and the burden is significantly reduced. As a result, the hobby scheduling support system can reduce the burden on parents and achieve efficient scheduling.

[0064] The hobby scheduling support system according to the embodiment includes a reception unit, a confirmation unit, a selection unit, and a procedure unit. The reception unit receives information on hobby cancellations and rescheduling risks. For example, when a parent needs to cancel a hobby or there is a rescheduling risk, the parent can convey this to the reception unit. The reception unit can receive information, for example, when the parent inputs "I want to cancel the hobby of XX". The confirmation unit checks the available time slots based on the information received by the reception unit. For example, for hobbies with a website, the confirmation unit generates the necessary HTTP requests for procedures such as querying available time slots and changing reservations according to the configuration of the corresponding website. The confirmation unit can, for example, access the website to check the available time slots. Also, for hobbies that require procedures via messages such as a message app or email, the confirmation unit generates a message draft according to the situation. For example, the confirmation unit can send a message "I want to cancel the hobby of XX" via the message app. The selection unit selects the rescheduling target date and time based on the available time slots confirmed by the confirmation unit. For example, the selection unit proposes the optimal rescheduling target date and time considering the parent's schedule and the schedules of other hobbies of the child. The selection unit can, for example, propose "I propose to reschedule the hobby of XX to Tuesday next week". The procedure unit performs the rescheduling procedure based on the rescheduling target date and time selected by the selection unit. For example, the procedure unit completes the rescheduling procedure through procedures such as websites, message apps, and emails. The procedure unit can, for example, perform the rescheduling procedure on the website and notify the parent that the rescheduling has been completed. Thus, the hobby scheduling support system according to the embodiment can efficiently perform the procedures for canceling and rescheduling hobbies.

[0065] The reception desk receives information regarding cancellations and rescheduling of extracurricular activities. For example, if a parent needs to cancel or reschedule an extracurricular activity, they can inform the reception desk. The reception desk can receive this information by, for example, a parent typing, "I want to cancel the extracurricular activity for [activity name]." Specifically, the reception desk provides an interface where parents can access a dedicated application or website using their smartphone or computer and enter their cancellation or rescheduling request. Parents can log in to their account, select the details of the relevant activity, and enter the reason for cancellation or rescheduling and their preferred date and time. The reception desk stores this information in a database and manages it as information necessary for subsequent processing. The reception desk also displays a confirmation screen of the entered information so that parents can reconfirm what they have entered. Furthermore, the reception desk has a function to display error messages if there are any errors in the entered information or if necessary information is missing, prompting the parent to make corrections. In this way, the reception desk enables parents to easily and accurately request cancellations and rescheduling, supporting a smooth process.

[0066] The verification unit checks available time slots based on the information received by the reception unit. For example, for lessons with a website, the verification unit generates the necessary HTTP requests for procedures such as checking available time slots and changing reservations, according to the structure of the website. The verification unit can, for example, access the website and check the available time slots. The verification unit also generates appropriate message suggestions for lessons that require procedures via messaging apps or email. For example, the verification unit can send a message via a messaging app saying, "I would like to cancel the lesson for XX." Specifically, the verification unit automatically logs into the lesson's website, generates and sends an HTTP request to check for available time slots. This retrieves the available time slot information returned from the website and stores it in the database. Furthermore, if procedures using a messaging app or email are required, the verification unit automatically generates an appropriate message based on the information entered by the parent. For example, it generates a message such as, "I would like to cancel the lesson for XX, please tell me the available time slots," and sends it to the specified contact. The verification unit automates these procedures, eliminating the need for parents to perform them manually and allowing them to quickly and accurately check available time slots. Furthermore, the verification unit notifies the parents of the acquired available time slot information and provides the information necessary for the next step, which is selecting a rescheduled date and time.

[0067] The selection unit selects a rescheduled date and time based on the available time slots confirmed by the verification unit. For example, the selection unit proposes the optimal rescheduled date and time, taking into account the parents' schedules and the children's other extracurricular activity schedules. The selection unit can, for example, propose, "I suggest rescheduling the XX extracurricular activity to next Tuesday." Specifically, the selection unit retrieves the schedule information entered by the parents and the children's other extracurricular activity schedules from the database and compares it with the available time slots obtained by the verification unit. Based on this information, the selection unit calculates the optimal rescheduled date and time and proposes it to the parents. For example, the selection unit considers the parents' work schedules and the children's school timetables and selects the most convenient date and time. The selection unit also has a function to present multiple candidate dates and times, allowing parents to choose. This allows parents to choose the most suitable rescheduled date and time according to their own convenience. Furthermore, the selection unit also provides a function to set a reminder for the selected date and time. For example, by sending a notification the day before the rescheduled date and time, parents can reconfirm the rescheduled date and time, ensuring they don't forget to attend their lessons. This allows the selection department to support parents in managing their schedules and facilitate the rescheduling process.

[0068] The Procedures Department performs the rescheduling procedure based on the rescheduled date and time selected by the Selection Department. For example, the Procedures Department completes the rescheduling procedure through procedures such as websites, messaging apps, and email. For example, the Procedures Department can perform the rescheduling procedure on a website and notify the parents that the rescheduling is complete. Specifically, the Procedures Department accesses the website for the extracurricular activity based on the rescheduled date and time selected by the Selection Department and automatically performs the rescheduling procedure. The Procedures Department inputs the necessary information and generates and sends an HTTP request to confirm the rescheduling. As a result, it receives a rescheduling confirmation message from the website and confirms that the rescheduling procedure is complete. In addition, if procedures using a messaging app or email are necessary, the Procedures Department automatically generates a message notifying the parents that the rescheduling procedure is complete and sends it to them. For example, it sends a message such as, "The rescheduling of [child's name]'s extracurricular activity is complete. The new date and time is next Tuesday." By automating these procedures, the Procedures Department eliminates the need for parents to perform the procedure manually and enables the rescheduling procedure to be completed quickly and accurately. Furthermore, the procedures department can save the history of rescheduling procedures in a database, which can be used for future reference and troubleshooting. This allows the procedures department to provide parents with peace of mind and facilitate the rescheduling process.

[0069] The verification unit can generate HTTP requests necessary for procedures such as checking available time slots and changing reservations, depending on the configuration of the website. For example, the verification unit generates appropriate HTTP requests based on the website's page layout and URL structure. For example, the verification unit can generate a GET request to check available time slots. The verification unit can also generate a POST request to perform reservation change procedures. This improves the efficiency of procedures by automating procedures according to the website's configuration. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input website configuration information into a generation AI and have the generation AI execute the generation of appropriate HTTP requests.

[0070] The confirmation unit can generate message drafts appropriate to the situation when procedures via messaging apps or email are required. For example, the confirmation unit generates appropriate message drafts based on the reason for cancellation or rescheduling. For example, the confirmation unit can generate a message draft such as, "I would like to cancel the XX lesson." It can also generate a message draft such as, "I would like to reschedule the XX lesson to next Tuesday." This improves the efficiency of the procedure by automating the message-based procedures. Some or all of the above processing in the confirmation unit may be performed using AI, for example, or without AI. For example, the confirmation unit can input the reason for cancellation or rescheduling into a generation AI and have the generation AI generate appropriate message drafts.

[0071] The selection unit can propose the optimal rescheduling date and time by considering the parents' schedules and the children's other extracurricular activity schedules. For example, the selection unit can propose a rescheduling date and time by considering the parents' work schedules and family schedules. For example, the selection unit can propose, "I suggest rescheduling the XX extracurricular activity to next Tuesday." The selection unit can also propose a rescheduling date and time by considering the children's other extracurricular activity schedules. For example, the selection unit can propose, "I suggest rescheduling the XX extracurricular activity so that it does not conflict with other extracurricular activities." In this way, the optimal rescheduling date and time can be proposed by considering the schedules of the parents and children. Some or all of the above processing in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can input the parents' schedules and the children's other extracurricular activity schedules into a generating AI and have the generating AI propose the optimal rescheduling date and time.

[0072] The procedure unit can complete the rescheduling process through procedures such as websites, messaging apps, and email. For example, the procedure unit can perform the rescheduling process on a website and notify the parent that the rescheduling is complete. For example, the procedure unit can perform the rescheduling process on a messaging app and notify the parent that the rescheduling is complete. The procedure unit can also perform the rescheduling process via email and notify the parent that the rescheduling is complete. This improves the efficiency of the process by allowing the rescheduling process to be completed through procedures such as websites, messaging apps, and email. Some or all of the above-described processes in the procedure unit may be performed using AI, for example, or not using AI. For example, the procedure unit can input the rescheduling information into a generating AI and have the generating AI execute the completion of the rescheduling process.

[0073] The reception desk estimates the parent's emotions and adjusts the cancellation and rescheduling process based on the estimated emotions. The reception desk can estimate the parent's emotions and adjust the cancellation and rescheduling process based on the estimated emotions. For example, if the parent is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if the parent is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. Furthermore, if the parent is in a hurry, the reception desk can prioritize voice input and quickly process cancellations and rescheduling requests. This reduces the burden on the parent by adjusting the reception process according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input parental emotional data into a generating AI and have the AI ​​perform emotional estimation.

[0074] The reception desk can analyze parents' past cancellation and rescheduling history and select the most suitable registration method. For example, the reception desk can automatically display as candidates lessons that parents have frequently canceled in the past. For example, the reception desk can prioritize suggesting registration methods that parents have used in the past (phone, email, etc.). The reception desk can also predict and suggest registration methods to be used at specific time slots based on the parents' past cancellation history. In this way, the most suitable registration method can be selected by analyzing past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the parents' past cancellation and rescheduling history into a generating AI and have the generating AI select the most suitable registration method.

[0075] The reception desk can filter cancellation and rescheduling requests based on the parents' current living situation and areas of interest. For example, if a parent is currently busy, the reception desk can prioritize suggesting simpler procedures. If a parent has a specific area of ​​interest, the reception desk can prioritize cancellations and rescheduling of lessons related to that area. The reception desk can also suggest the most suitable reception method according to the parent's living situation. In this way, by filtering according to the parent's living situation and areas of interest, the reception desk can provide the most suitable reception method. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input data on the parent's current living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0076] The reception desk can estimate the parent's emotions and determine the priority of cancellations and rescheduling based on the estimated emotions. For example, if the parent is stressed, the reception desk will prioritize important cancellations and rescheduling. If the parent is relaxed, for example, the reception desk can provide more detailed information and adjust priorities. Also, if the parent is in a hurry, the reception desk can prioritize cancellations and rescheduling that require immediate attention. This ensures that important cancellations and rescheduling are prioritized by determining priorities according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input parent emotion data into a generative AI and have the generative AI perform emotion estimation.

[0077] The reception desk can prioritize receiving highly relevant information when accepting cancellations or rescheduling requests, taking into account the parent's geographical location. For example, the reception desk can prioritize cancellations or rescheduling of lessons that are close to the parent's current location. For example, if the parent is in a specific region, the reception desk can prioritize providing information about lessons related to that region. The reception desk can also suggest the most suitable reception method based on the parent's geographical location. This allows for the priority of receiving highly relevant information by considering the parent's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the parent's geographical location information into a generating AI and have the generating AI perform the reception of highly relevant information.

[0078] The reception desk can analyze parents' social media activity and receive relevant information when receiving cancellation or rescheduling requests. For example, the reception desk can prioritize cancellations and rescheduling of extracurricular activities mentioned by parents on social media. For example, the reception desk can prioritize providing information on extracurricular activities of high interest based on parents' social media activity. The reception desk can also suggest the most suitable reception method based on parents' social media activity. In this way, by analyzing parents' social media activity, relevant information can be received preferentially. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input data on parents' social media activity into a generating AI and have the generating AI perform the reception of relevant information.

[0079] The confirmation unit can estimate the parent's emotions and adjust the method of checking available time slots based on the estimated emotions. For example, if the parent is stressed, the confirmation unit can display available time slots in a simple interface. For example, if the parent is relaxed, the confirmation unit can provide detailed information about available time slots. The confirmation unit can also provide a way for the parent to quickly check available time slots if they are in a hurry. This reduces the burden on the parent by adjusting the confirmation method according to their emotions. 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 confirmation unit may be performed using AI, for example, or not using AI. For example, the confirmation unit can input the parent's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The verification unit can optimize its verification algorithm by referring to past verification data when checking for available time slots. For example, the verification unit can propose the optimal available time slot based on past verification data. For example, the verification unit can propose an available time slot that avoids congestion based on past verification data. The verification unit can also analyze past verification data and propose the most efficient available time slot. In this way, the verification algorithm can be optimized by referring to past verification data. Some or all of the above processing in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input past verification data into a generating AI and have the generating AI perform the optimization of the verification algorithm.

[0081] The verification unit can apply different verification methods to each category of lesson when checking available time slots. For example, the verification unit can display real-time available time slots for sports lessons, for example, it can display available time slots for music lessons, and for learning lessons, it can display online available time slots. By applying different verification methods to each category of lesson, the accuracy of the verification is improved. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input lesson category information into a generating AI and have the generating AI execute the application of verification methods.

[0082] The confirmation unit can estimate the parent's emotions and adjust the display method of the free time slots based on the estimated emotions. For example, if the parent is nervous, the confirmation unit can provide a simple and highly visible display method. For example, if the parent is relaxed, the confirmation unit can provide a display method that includes detailed information. Furthermore, if the parent is in a hurry, the confirmation unit can provide a concise display method. This reduces the burden on the parent by adjusting the display method according to their emotions. 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 confirmation unit may be performed using AI, for example, or without AI. For example, the confirmation unit can input the parent's emotion data into the generative AI and have the generative AI perform emotion estimation.

[0083] The verification unit can consider the geographical distribution of extracurricular activities when checking available time slots. For example, the verification unit can prioritize displaying available time slots for activities close to the parent's current location. For example, if the parent is in a specific area, the verification unit can display available time slots related to that area. The verification unit can also suggest the optimal available time slot based on the parent's geographical location information. This allows for the confirmation of the optimal available time slot by considering the geographical distribution of extracurricular activities. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the parent's geographical location information into a generating AI and have the generating AI perform the confirmation of the optimal available time slot.

[0084] The verification unit can improve the accuracy of its verification of available time slots by referring to relevant literature on the activity. For example, the verification unit can suggest the optimal available time slot based on the relevant literature. For example, the verification unit can suggest an available time slot that avoids congestion from the relevant literature. The verification unit can also analyze the relevant literature and suggest the most efficient available time slot. In this way, the accuracy of the verification is improved by referring to the relevant literature. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input data from the relevant literature into a generating AI and have the generating AI perform the task of improving the accuracy of the verification.

[0085] The selection unit can estimate the parent's emotions and adjust the method for selecting a rescheduled date and time based on the estimated emotions. For example, if the parent is stressed, the selection unit can suggest a rescheduled date and time using a simple interface. If the parent is relaxed, for example, the selection unit can provide detailed information about the rescheduled date and time. Furthermore, if the parent is in a hurry, the selection unit can provide a method for quickly selecting a rescheduled date and time. This reduces the burden on the parent by adjusting the selection method according to their emotions. 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 selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can input the parent's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0086] The selection unit can select the optimal selection method when choosing a rescheduled date and time by referring to the parent's past schedule history. For example, the selection unit can propose the optimal rescheduled date and time based on the parent's past rescheduled dates and times. For example, the selection unit can propose a rescheduled date and time that avoids congestion based on the parent's past schedule history. The selection unit can also analyze the parent's past schedule history and propose the most efficient rescheduled date and time. In this way, the optimal rescheduled date and time can be proposed by referring to past schedule history. Some or all of the above processing in the selection unit may be performed using AI, for example, or without using AI. For example, the selection unit can input the parent's past schedule history into a generating AI and have the generating AI perform the selection of the optimal rescheduled date and time.

[0087] The selection unit can customize the selection method based on the parent's current living situation when selecting a rescheduled date and time. For example, if the parent is currently busy, the selection unit will prioritize suggesting a simple rescheduled date and time. For example, if the parent has a particular area of ​​interest, the selection unit can prioritize suggesting a rescheduled date and time related to that area. The selection unit can also suggest the most suitable rescheduled date and time according to the parent's living situation. This reduces the burden on the parent by suggesting a rescheduled date and time according to the parent's living situation. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input data on the parent's current living situation into a generating AI and have the generating AI perform the customization of the selection method.

[0088] The selection unit can estimate the parent's emotions and determine the priority of rescheduling dates and times based on the estimated emotions. For example, if the parent is stressed, the selection unit will prioritize suggesting important rescheduling dates and times. For example, if the parent is relaxed, the selection unit can provide more detailed information and adjust the priorities. Also, if the parent is in a hurry, the selection unit can prioritize suggesting rescheduling dates and times that require immediate attention. In this way, by determining the priority of rescheduling dates and times according to the parent's emotions, important rescheduling dates and times can be suggested preferentially. 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 selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can input parent emotion data into a generative AI and have the generative AI perform emotion estimation.

[0089] The selection unit can select the optimal selection method when choosing a rescheduled date and time, taking into account the parent's geographical location information. For example, the selection unit may prioritize suggesting rescheduled dates and times that are close to the parent's current location. For example, if the parent is in a specific region, the selection unit may suggest rescheduled dates and times related to that region. The selection unit can also suggest the optimal rescheduled date and time based on the parent's geographical location information. In this way, the optimal rescheduled date and time can be suggested by taking the parent's geographical location information into consideration. Some or all of the above processing in the selection unit may be performed using AI, for example, or without using AI. For example, the selection unit can input the parent's geographical location information into a generating AI and have the generating AI perform the selection of the optimal rescheduled date and time.

[0090] The selection unit can analyze the parent's social media activity and propose selection methods when selecting a rescheduled date and time. For example, the selection unit can prioritize suggesting rescheduled dates and times mentioned by the parent on social media. For example, the selection unit can suggest rescheduled dates and times of high interest based on the parent's social media activity. Furthermore, the selection unit can propose the optimal rescheduled date and time based on the parent's social media activity. In this way, the optimal rescheduled date and time can be proposed by analyzing the parent's social media activity. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input data on the parent's social media activity into a generating AI and have the generating AI execute the proposal of selection methods.

[0091] The procedure unit can estimate the parent's emotions and adjust the rescheduling procedure based on the estimated emotions. For example, if the parent is stressed, the procedure unit can perform the rescheduling procedure with a simple interface. If the parent is relaxed, the procedure unit can provide detailed rescheduling procedure information. The procedure unit can also provide a way to perform the rescheduling procedure quickly if the parent is in a hurry. This reduces the burden on the parent by adjusting the procedure according to their emotions. 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 procedure unit may be performed using AI, for example, or not using AI. For example, the procedure unit can input parent emotion data into a generative AI and have the generative AI perform emotion estimation.

[0092] The procedure unit can select the optimal procedure method by referring to the parent's past procedure history during the rescheduling procedure. For example, the procedure unit can propose the optimal procedure based on the rescheduling procedure method previously used by the parent. For example, the procedure unit can propose a procedure that avoids congestion based on the parent's past procedure history. The procedure unit can also analyze the parent's past procedure history and propose the most efficient procedure. In this way, the optimal procedure method can be selected by referring to the past procedure history. Some or all of the above processing in the procedure unit may be performed using AI, for example, or without AI. For example, the procedure unit can input the parent's past procedure history into a generating AI and have the generating AI select the optimal procedure method.

[0093] The procedure unit can customize the procedure based on the parent's current living situation during the rescheduling process. For example, if the parent is currently busy, the procedure unit will prioritize suggesting a simpler procedure. If the parent has a particular area of ​​interest, the procedure unit can prioritize suggesting procedures related to that area. The procedure unit can also suggest the most suitable procedure according to the parent's living situation. This reduces the burden on the parent by customizing the procedure according to their living situation. Some or all of the above processing in the procedure unit may be performed using AI, for example, or not. For example, the procedure unit can input data on the parent's current living situation into a generating AI and have the generating AI perform the customization of the procedure.

[0094] The procedure unit can estimate the parent's emotions and determine the priority of rescheduling procedures based on the estimated parent's emotions. For example, if the parent is stressed, the procedure unit will prioritize important rescheduling procedures. If the parent is relaxed, the procedure unit can provide more detailed information and adjust priorities. If the parent is in a hurry, the procedure unit can also prioritize rescheduling procedures that require immediate attention. This allows important rescheduling procedures to be prioritized by determining the priority of procedures according to the parent'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 processing described above in the procedure unit may be performed using AI or not using AI. For example, the procedure unit can input parent emotion data into a generative AI and have the generative AI perform emotion estimation.

[0095] The procedure unit can select the optimal procedure method when rescheduling, taking into account the parent's geographical location information. For example, the procedure unit may prioritize rescheduling procedures that are close to the parent's current location. For example, if the parent is in a specific region, the procedure unit may suggest procedures related to that region. The procedure unit can also suggest the optimal procedure based on the parent's geographical location information. This allows the optimal procedure method to be selected by considering the parent's geographical location information. Some or all of the above processing in the procedure unit may be performed using AI, for example, or without AI. For example, the procedure unit may input the parent's geographical location information into a generating AI and have the generating AI select the optimal procedure method.

[0096] The procedure unit can analyze the parents' social media activity during the rescheduling process and propose procedural measures. For example, the procedure unit can prioritize rescheduling procedures mentioned by the parents on social media. For example, the procedure unit can propose procedures of high interest based on the parents' social media activity. Furthermore, the procedure unit can propose the most suitable procedure based on the parents' social media activity. In this way, by analyzing the parents' social media activity, the optimal procedural measures can be proposed. Some or all of the above processing in the procedure unit may be performed using AI, for example, or not using AI. For example, the procedure unit can input data on the parents' social media activity into a generating AI and have the generating AI execute the procedural measures proposal.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The reception desk can estimate the parent's emotions and adjust the cancellation or rescheduling process based on the estimated emotions. For example, if the parent is stressed, a simple interface can be provided, minimizing the input steps. If the parent is relaxed, detailed input options can be provided, and a customizable input method can be suggested. Furthermore, if the parent is in a hurry, voice input can be prioritized, allowing for quick cancellation or rescheduling. This reduces the burden on the parent by adjusting the reception process according to their emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI may be, but is not limited to, text generation AI or multimodal generation AI. Some or all of the processing described above in the reception desk may be performed using AI or not. For example, the reception desk can input parent emotion data into a generative AI and have the generative AI perform emotion estimation.

[0099] The confirmation unit can estimate the parent's emotions and adjust the method of checking available time slots based on the estimated emotions. For example, if the parent is stressed, it can display available time slots with a simple interface. If the parent is relaxed, it can provide detailed information about available time slots. Furthermore, if the parent is in a hurry, it can provide a way to quickly check available time slots. In this way, the burden on the parent can be reduced by adjusting the confirmation method according to their emotions. Emotion estimation is achieved using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the confirmation unit may be performed using AI or not using AI. For example, the confirmation unit can input the parent's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0100] The selection unit can estimate the parent's emotions and adjust the method for selecting a rescheduled date and time based on the estimated emotions. For example, if the parent is stressed, it can suggest a rescheduled date and time with a simple interface. If the parent is relaxed, it can provide detailed information about the rescheduled date and time. Furthermore, if the parent is in a hurry, it can provide a method for quickly selecting a rescheduled date and time. In this way, the burden on the parent can be reduced by adjusting the selection method according to their emotions. Emotion estimation is achieved using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the parent's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0101] The procedure unit can estimate the parent's emotions and adjust the rescheduling procedure based on the estimated emotions. For example, if the parent is stressed, the rescheduling procedure can be performed using a simple interface. If the parent is relaxed, detailed rescheduling procedure information can be provided. Furthermore, if the parent is in a hurry, a method for quickly rescheduling can be provided. In this way, the burden on the parent can be reduced by adjusting the procedure according to their emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the procedure unit may be performed using AI or not using AI. For example, the procedure unit can input parent emotion data into a generative AI and have the generative AI perform emotion estimation.

[0102] The reception desk can analyze parents' past cancellation and rescheduling history and select the most suitable reception method. For example, it can automatically display as candidates the lessons that parents have frequently canceled in the past. It can also prioritize suggesting reception methods that parents have used in the past (phone, email, etc.). Furthermore, it can predict and suggest reception methods to be used at specific time slots based on the parents' past cancellation history. In this way, the most suitable reception method can be selected by analyzing past history. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the parents' past cancellation and rescheduling history into a generating AI and have the generating AI select the most suitable reception method.

[0103] The verification unit can optimize its verification algorithm by referring to past verification data when checking available time slots. For example, it can suggest the optimal available time slot based on past verification data. It can also suggest available time slots that avoid congestion based on past verification data. Furthermore, it can analyze past verification data and suggest the most efficient available time slot. In this way, the verification algorithm can be optimized by referring to past verification data. Some or all of the above processing in the verification unit may be performed using AI or not. For example, the verification unit can input past verification data into a generating AI and have the generating AI perform the optimization of the verification algorithm.

[0104] The selection unit can select the optimal selection method when choosing a rescheduled date and time by referring to the parent's past schedule history. For example, it can suggest the optimal rescheduled date and time based on the parent's past rescheduled dates and times. It can also suggest a rescheduled date and time that avoids congestion based on the parent's past schedule history. Furthermore, it can analyze the parent's past schedule history and suggest the most efficient rescheduled date and time. In this way, the optimal rescheduled date and time can be suggested by referring to past schedule history. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the parent's past schedule history into a generating AI and have the generating AI perform the selection of the optimal rescheduled date and time.

[0105] The procedure unit can select the optimal procedure method by referring to the parent's past procedure history during the rescheduling procedure. For example, it can propose the optimal procedure based on the rescheduling procedure method the parent has used in the past. It can also propose a procedure that avoids congestion based on the parent's past procedure history. Furthermore, it can analyze the parent's past procedure history and propose the most efficient procedure. In this way, the optimal procedure method can be selected by referring to the past procedure history. Some or all of the above processing in the procedure unit may be performed using AI or not. For example, the procedure unit can input the parent's past procedure history into a generating AI and have the generating AI select the optimal procedure method.

[0106] The reception desk can filter cancellation and rescheduling requests based on the parents' current living situation and areas of interest. For example, if a parent is currently busy, it can prioritize suggesting simpler procedures. If a parent has specific areas of interest, it can prioritize cancellations and rescheduling of lessons related to those areas. Furthermore, it can suggest the most suitable application method based on the parent's living situation. This allows the reception desk to provide the most appropriate application method by filtering according to the parent's living situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the parent's current living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0107] The verification unit can apply different verification methods to each category of lesson when checking available time slots. For example, it can display real-time available time slots for sports lessons, and available time slots for music lessons. Furthermore, it can display online available time slots for academic lessons. By applying different verification methods to each category of lesson, the accuracy of the verification can be improved. Some or all of the above processing in the verification unit may be performed using AI or not. For example, the verification unit can input lesson category information into a generating AI and have the generating AI execute the application of verification methods.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The reception desk receives information about cancellations and rescheduling of extracurricular activities. For example, if a parent needs to cancel or reschedule an extracurricular activity, they can inform the reception desk. The reception desk can receive this information, for example, if a parent enters "I want to cancel the extracurricular activity for [child's name]." Step 2: The confirmation unit checks for available time slots based on the information received by the reception unit. For example, for lessons with a website, the confirmation unit generates the necessary HTTP requests for procedures such as checking available time slots or changing reservations, according to the structure of the website. The confirmation unit can, for example, access the website and check for available time slots. The confirmation unit also generates appropriate message suggestions for lessons that require procedures via messaging apps or email. For example, the confirmation unit can send a message via a messaging app saying, "I would like to cancel my lesson at [lesson name]." Step 3: The selection unit selects a rescheduled date and time based on the available time slots confirmed by the verification unit. For example, the selection unit will propose the optimal rescheduled date and time, taking into account the parents' schedules and the child's other extracurricular activities. The selection unit may propose, for example, "I suggest rescheduling the XX extracurricular activity to next Tuesday." Step 4: The Procedures Department carries out the rescheduling procedure based on the rescheduled date and time selected by the Selection Department. For example, the Procedures Department completes the rescheduling procedure through a website, messaging app, email, etc. For example, the Procedures Department can carry out the rescheduling procedure on a website and notify the parents that the rescheduling is complete.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the reception unit, confirmation unit, selection unit, and procedure unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, allowing parents to input cancellations or rescheduling of lessons. The confirmation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, to check available time slots. The selection unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, to propose a rescheduled date and time. The procedure unit is implemented, for example, by the control unit 46A of the smart device 14, to complete the rescheduling procedure. The reception unit can estimate the parent's emotions and adjust the reception method based on the estimated parent's emotions. Emotion estimation is implemented, for example, by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the reception unit, confirmation unit, selection unit, and procedure unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, allowing parents to input cancellations or rescheduling of lessons. The confirmation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, to check for available time slots. The selection unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, to propose a rescheduled date and time. The procedure unit is implemented, for example, by the control unit 46A of the smart glasses 214, to complete the rescheduling procedure. The reception unit can estimate the parent's emotions and adjust the reception method based on the estimated parent's emotions. Emotion estimation is implemented, for example, by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the reception unit, confirmation unit, selection unit, and procedure unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, allowing parents to input cancellations or rescheduling of lessons. The confirmation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, to check available time slots. The selection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, to propose a rescheduled date and time. The procedure unit is implemented by, for example, the control unit 46A of the headset terminal 314, to complete the rescheduling procedure. The reception unit can estimate the parent's emotions and adjust the reception method based on the estimated parent's emotions. Emotion estimation is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the reception unit, confirmation unit, selection unit, and procedure unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, allowing parents to input cancellations or rescheduling of lessons. The confirmation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, to check for available time slots. The selection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, to propose a rescheduled date and time. The procedure unit is implemented by, for example, the control unit 46A of the robot 414, to complete the rescheduling procedure. The reception unit can estimate the parent's emotions and adjust the reception method based on the estimated parent's emotions. Emotion estimation is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) A reception desk that accepts information regarding cancellations and rescheduling of extracurricular activities, A confirmation unit that checks available time slots based on the information received by the reception unit, A selection unit selects a rescheduled date and time based on the available time slots confirmed by the aforementioned confirmation unit, A procedure unit that carries out the rescheduling procedure based on the rescheduling date and time selected by the aforementioned selection unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned verification unit is Depending on the website's structure, it generates the HTTP requests necessary for procedures such as checking available time slots and changing reservations. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned verification unit is If a procedure requires messaging, generate a message draft appropriate to the situation. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned selection unit is We will suggest the best rescheduled date and time, taking into account the parents' schedules and the child's other extracurricular activities. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned procedural department, Complete the rescheduling process through the website or email. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is We estimate the parents' emotions and adjust the cancellation and rescheduling procedures based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We analyze the parents' past cancellation and rescheduling history to select the most suitable registration method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When accepting cancellations or rescheduling requests, filtering will be performed based on the parents' current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the parents' emotions and determines the priority of cancellations and rescheduling based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When accepting cancellations or rescheduling requests, the system prioritizes requests for information that is highly relevant, taking into account the parents' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When accepting cancellations or rescheduling requests, the system analyzes the parents' social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned verification unit is The system estimates the parents' emotions and adjusts the method of checking available time slots based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned verification unit is When checking available time slots, the confirmation algorithm is optimized by referring to past confirmation data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned verification unit is When checking available time slots, different checking methods are applied depending on the category of the lesson. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned verification unit is The system estimates the parent's emotions and adjusts how the available time slots are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned verification unit is When checking available time slots, the geographical distribution of the lessons will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned verification unit is When checking available time slots, refer to relevant literature related to the lessons to improve accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned selection unit is We estimate the parents' emotions and adjust the method for selecting a rescheduled date and time based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned selection unit is When selecting a new date and time for rescheduling, refer to the parent's past schedule history to determine the most suitable selection method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned selection unit is When selecting a rescheduled date and time, customize the selection method based on the parents' current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned selection unit is The system estimates the parents' emotions and prioritizes rescheduled dates and times based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned selection unit is When selecting a new date and time for rescheduling, the optimal selection method will be chosen considering the parents' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned selection unit is When selecting a new date and time for rescheduling, we will analyze the parents' social media activity and propose a selection method. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned procedural department, We estimate the parents' feelings and adjust the rescheduling procedure based on those estimated feelings. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned procedural department, During the rescheduling process, the most suitable procedure will be selected by referring to the parents' past procedure history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned procedural department, During the rescheduling process, the procedure will be customized based on the parents' current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned procedural department, We estimate the parents' emotions and determine the priority of the rescheduling process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned procedural department, When rescheduling, the most appropriate procedure will be selected considering the parents' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned procedural department, During the rescheduling process, we analyze the parents' social media activity and propose a course of action. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts information regarding cancellations and rescheduling of extracurricular activities, A confirmation unit that checks available time slots based on the information received by the reception unit, A selection unit selects a rescheduled date and time based on the available time slots confirmed by the aforementioned confirmation unit, A procedure unit that carries out the rescheduling procedure based on the rescheduling date and time selected by the aforementioned selection unit, Equipped with A system characterized by the following features.

2. The aforementioned verification unit is Depending on the website's structure, it generates the HTTP requests necessary for procedures such as checking available time slots and changing reservations. The system according to feature 1.

3. The aforementioned verification unit is If a procedure requires messaging, generate a message draft appropriate to the situation. The system according to feature 1.

4. The aforementioned selection unit is We will suggest the best rescheduled date and time, taking into account the parents' schedules and the child's other extracurricular activities. The system according to feature 1.

5. The aforementioned procedural department, Complete the rescheduling process through the website or email. The system according to feature 1.

6. The aforementioned reception unit is We estimate the parents' emotions and adjust the cancellation and rescheduling procedures based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is We analyze the parents' past cancellation and rescheduling history to select the most suitable registration method. The system according to feature 1.

8. The aforementioned reception unit is When accepting cancellations or rescheduling requests, filtering will be performed based on the parents' current living situation and areas of interest. The system according to feature 1.

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