system

Abstract

The system according to this embodiment aims to streamline scheduling and planning. [Solution] The system according to the embodiment comprises a collection unit, an adjustment unit, an automation unit, a reminder unit, a traffic information provision unit, and a reservation unit. The collection unit collects information. The adjustment unit compares the participants' schedules based on the information collected by the collection unit and proposes the optimal date and time. The automation unit automatically incorporates regular events and habits into the schedule based on the date and time proposed by the adjustment unit. The reminder unit sends reminders based on the schedule incorporated by the automation unit. The traffic information provision unit presents means of transportation and travel time based on the reminders sent by the reminder unit. The reservation unit makes reservations based on the information presented by the traffic information provision unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including 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 conventional technology, there are problems that schedule adjustment and planning are complicated, and it is difficult to adjust schedules and share information among multiple people.

[0005] The system according to the embodiment aims to improve the efficiency of schedule adjustment and planning.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an adjustment unit, an automation unit, a reminder unit, a traffic information provision unit, and a reservation unit. The collection unit collects information. The adjustment unit compares the participants' schedules based on the information collected by the collection unit and proposes the optimal date and time. The automation unit automatically incorporates regular events and habits into the schedule based on the date and time proposed by the adjustment unit. The reminder unit sends reminders based on the schedule incorporated by the automation unit. The traffic information provision unit presents means of transportation and travel time based on the reminders sent by the reminder unit. The reservation unit makes reservations based on the information presented by the traffic information provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline scheduling and planning. [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 multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The scheduling system according to an embodiment of the present invention is a system that uses an AI agent to streamline scheduling and planning. This scheduling system collects information, compares participants' schedules, and proposes the optimal date and time. Furthermore, it automatically incorporates regular events and habits into the schedule, sends reminders, provides transportation information, and makes reservations. For example, the scheduling system collects information on potential locations, restaurants, and events. In this process, it collects information from the internet and provides information tailored to the user's preferences. Next, the scheduling system compares participants' schedules and proposes the optimal date and time. For example, it checks the schedules of all participants and proposes a date and time when everyone can attend. Furthermore, the scheduling system automatically incorporates regular events and habits into the schedule. For example, it automatically adds weekly meetings and monthly reporting meetings to the schedule. The scheduling system uses a reminder function to notify users so they don't forget important appointments. For example, it sends a reminder the day before a meeting. The scheduling system also provides transportation information, suggesting means of transport and travel time. For example, it suggests the optimal means of transport and travel time based on train and bus timetables. Finally, the scheduling system makes reservations for restaurants, concerts, and events. For example, one can use an online reservation system to book restaurants or concerts. This allows scheduling systems to streamline scheduling and planning, reducing time loss and stress.

[0029] The scheduling system according to this embodiment comprises a collection unit, a scheduling unit, an automation unit, a reminder unit, a traffic information provision unit, and a reservation unit. The collection unit collects information. For example, the collection unit collects information from the internet and provides information tailored to the user's preferences. For example, the collection unit collects information such as candidate locations, restaurants, and events. The scheduling unit compares the schedules of participants based on the information collected by the collection unit and proposes the optimal date and time. For example, the scheduling unit checks the schedules of all participants and proposes a date and time when everyone can attend. The scheduling unit can also analyze the schedules of participants using AI and calculate the optimal date and time. The automation unit automatically incorporates regular events and habits into the schedule based on the date and time proposed by the scheduling unit. For example, the automation unit automatically adds weekly regular meetings and monthly reporting meetings to the schedule. The automation unit can also automatically update the schedule using AI. The reminder unit sends reminders based on the schedule incorporated by the automation unit. For example, the reminder unit sends a reminder the day before a meeting. The reminder unit can, for example, use AI to optimize the timing of sending reminders. The traffic information unit presents transportation options and travel times based on the reminders sent by the reminder unit. The traffic information unit presents the optimal transportation options and travel times based on train and bus timetables, for example. The traffic information unit can also provide real-time traffic information using AI, for example. The reservation unit makes reservations based on the information presented by the traffic information unit. The reservation unit makes reservations for restaurants and concerts, for example, using online reservation systems. The reservation unit can also optimize reservations using AI, for example. As a result, the scheduling system according to this embodiment can streamline scheduling and planning, reducing time loss and stress.

[0030] The data collection unit collects information. For example, it collects information from the internet and provides information tailored to the user's preferences. Specifically, the data collection unit uses web crawlers and APIs to collect data from various sources on the internet. For example, it obtains the latest information from restaurant review sites, event information sites, and official websites of tourist destinations. The data collection unit filters this information based on the user's preferences and past behavior history to provide the most relevant information to the user. For example, it can suggest similar locations and events based on the user's ratings of restaurants they have visited in the past or the types of events they have attended. Furthermore, the data collection unit can also select the most suitable locations and events by considering the user's current location and planned schedule. In this way, the data collection unit can efficiently collect and provide information that is useful to the user.

[0031] The scheduling unit compares participants' schedules based on information collected by the data collection unit and proposes the optimal date and time. Specifically, the scheduling unit obtains calendar information from all participants and analyzes each participant's availability. The scheduling unit can also use AI to analyze participants' schedules and calculate the optimal date and time. For example, the AI ​​considers each participant's past schedule patterns and priorities to propose the most suitable date and time. Furthermore, the scheduling unit can readjust the schedule in real time if there are changes to participants' schedules. For example, if a participant makes a sudden change of plans, the scheduling unit immediately reconfirms the schedules of other participants and proposes a new optimal date and time. The scheduling unit can also suggest the most suitable locations and events considering participants' preferences and past participation history. In this way, the scheduling unit can efficiently adjust and propose a schedule that satisfies all participants.

[0032] The automation unit automatically incorporates recurring events and habits into the schedule based on the dates and times proposed by the coordination unit. Specifically, the automation unit automatically adds weekly meetings, monthly reports, and other similar events to the schedule. The automation unit can also automatically update the schedule using AI. For example, the AI ​​analyzes past schedule data and learns patterns of recurring events to predict and automatically add future schedules. The automation unit can also suggest the optimal schedule by considering the user's preferences and past behavior history. For example, it can automatically add similar events to the schedule based on the types and frequency of events the user has previously attended. Furthermore, the automation unit can update the schedule in real time and notify the user if changes or cancellations occur. This allows the automation unit to streamline the user's schedule management and significantly reduce the effort required.

[0033] The reminder unit sends reminders based on schedules programmed by the automation unit. Specifically, the reminder unit sends a reminder the day before a meeting. The reminder unit can also use AI to optimize the timing of reminder sending. For example, the AI ​​analyzes the user's past behavior patterns and reminder response history to learn the optimal sending timing. This allows the reminder to be sent at the time when the user can act most effectively upon receiving it. Furthermore, the reminder unit can send reminders using multiple notification methods. For example, it can use a combination of smartphone notifications, email, SMS, and voice calls to ensure that reminders are delivered reliably. The reminder unit can also collect user feedback and continuously improve the content and timing of reminders. This allows the reminder unit to support users in remembering important appointments and improve the accuracy of schedule management.

[0034] The traffic information unit provides information on transportation options and travel times based on reminders sent by the reminder unit. Specifically, the traffic information unit provides optimal transportation options and travel times based on train and bus timetables. The traffic information unit can also provide real-time traffic information using AI. For example, the AI ​​analyzes current traffic conditions and weather information to suggest the optimal travel route. Furthermore, the traffic information unit can select the optimal transportation option considering the user's current location and destination. For example, if a user uses a train, it will suggest the optimal route from the nearest station to the destination and calculate the travel time. The traffic information unit can also provide real-time information on delays and operating status of transportation services and notify users quickly. In this way, the traffic information unit can support users in traveling efficiently and reduce the time and stress associated with travel.

[0035] The reservation department makes reservations based on information provided by the traffic information department. Specifically, the reservation department uses online reservation systems to make reservations for restaurants and concerts. The reservation department can also use AI to optimize reservations. For example, the AI ​​analyzes the user's preferences and past reservation history to suggest the best reservation destination and time slot. Furthermore, the reservation department can search across multiple reservation sites to find the best-suited reservation destination. For example, in the case of restaurant reservations, it checks availability from multiple reservation sites and suggests the earliest available time slot. In addition, the reservation department can respond in real time to changes or cancellations of reservations and notify the user. In this way, the reservation department can support users in making reservations smoothly and ensuring that their plans are carried out reliably.

[0036] The data collection unit can collect information from the internet and provide information tailored to the user's preferences. For example, the data collection unit can collect information from the internet and provide information tailored to the user's preferences. The data collection unit can also analyze information from the internet using AI and provide information tailored to the user's preferences. For example, the data collection unit can identify the user's preferences based on the user's past search history or survey results. This improves the accuracy of information collection by providing information tailored to the user's preferences. Some or all of the above-described processes in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can collect information from the internet, input it into a generating AI, and the generating AI can provide information tailored to the user's preferences.

[0037] The scheduling unit can check the schedules of all participants and propose a date and time when everyone can attend. For example, the scheduling unit can check the schedules of all participants and propose a date and time when everyone can attend. The scheduling unit can also analyze participants' schedules using AI and calculate the optimal date and time. The scheduling unit can collect the schedules of all participants through calendar sharing or schedule input, for example. This improves the accuracy of scheduling by allowing the scheduling unit to propose a date and time when everyone can attend. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can collect the schedules of all participants, input them into a generating AI, and the generating AI can propose the optimal date and time.

[0038] The automation unit can automatically add weekly regular meetings and monthly reporting sessions to the schedule. The automation unit can, for example, automatically add weekly regular meetings and monthly reporting sessions to the schedule. The automation unit can also automatically update the schedule using AI. The automation unit can, for example, add regular events and habits such as weekly meetings and monthly reporting sessions to the schedule. This makes planning more efficient by allowing the automation unit to automatically add regular events and habits to the schedule. Some or all of the above processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input information about regular events and habits into a generating AI, which can then add them to the schedule.

[0039] The reminder function can send a reminder the day before a meeting. For example, the reminder function can send a reminder the day before a meeting. The reminder function can also optimize the timing of sending reminders using AI, for example. The reminder function can send an email notification 24 hours in advance, for example. This makes it easier to manage appointments by notifying users so they don't forget important events. Some or all of the above processes in the reminder function may be performed using AI, for example, or without AI. For example, the reminder function can input information to send a reminder the day before a meeting into a generating AI, and the generating AI can send the reminder.

[0040] The traffic information provision unit can suggest the optimal mode of transportation and travel time based on train and bus timetables. For example, the traffic information provision unit can suggest the optimal mode of transportation and travel time based on train and bus timetables. The traffic information provision unit can also provide real-time traffic information using AI, for example. The traffic information provision unit can suggest modes of transportation such as trains, buses, and walking, along with their travel times. This improves travel efficiency by suggesting the optimal mode of transportation and travel time. Some or all of the above-described processes in the traffic information provision unit may be performed using AI, for example, or without AI. For example, the traffic information provision unit can input information based on train and bus timetables into a generating AI, which can then suggest the optimal mode of transportation and travel time.

[0041] The reservation department can make reservations for restaurants and concerts using online reservation systems. The reservation department can, for example, make reservations for restaurants and concerts using online reservation systems. The reservation department can also optimize reservations using AI, for example. The reservation department can make reservations using restaurant reservation sites and concert ticket reservation sites, for example. This reduces the effort required for reservations by the reservation department using online reservation systems. Some or all of the above processes in the reservation department may be performed using AI, for example, or not using AI. For example, the reservation department can input information obtained using online reservation systems into a generating AI, and the generating AI can make reservations.

[0042] The data collection unit can analyze the user's past search history and select the optimal information collection method. For example, the data collection unit can prioritize collecting relevant information based on keywords the user has searched for in the past. The data collection unit can also select reliable information sources based on websites the user has visited in the past. For example, the data collection unit can select information to collect at specific time periods based on the user's past search history. This improves the accuracy of information collection by allowing the data collection unit to select the optimal information collection method based on past search history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past search history into a generating AI, which can then select the optimal information collection method.

[0043] The data collection unit can filter information based on the user's current areas of interest during data collection. For example, the data collection unit can prioritize collecting information related to topics the user is currently interested in. The data collection unit can also filter relevant information based on the content of posts from social media accounts the user follows. The data collection unit can also collect relevant information based on topics in online communities the user participates in. In this way, the data collection unit can collect highly relevant information by filtering information based on the user's areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information about the user's current areas of interest into a generating AI, which can then filter the information.

[0044] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location during data collection. For example, the data collection unit can prioritize collecting event information in the area where the user is currently located. For example, if the user is traveling, the data collection unit can prioritize collecting tourist information for the travel destination. For example, if the user is considering moving, the data collection unit can prioritize collecting information for the area they are considering moving to. In this way, the data collection unit can collect highly relevant information by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize collecting highly relevant information.

[0045] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information related to articles the user has shared on social media. The data collection unit can also collect relevant information based on the content of posts from accounts the user follows. The data collection unit can also collect relevant information based on the topics of groups the user participates in. In this way, the data collection unit can collect highly relevant information by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into a generating AI, which can then collect relevant information.

[0046] The scheduling unit can adjust the level of detail in scheduling based on the importance of the participants. For example, if there are important participants, the scheduling unit will prioritize scheduling their schedules. For example, if there are participants of lower importance, the scheduling unit can postpone their schedules. The scheduling unit can also change the level of detail in scheduling according to importance. This allows the scheduling unit to perform more appropriate scheduling by adjusting the level of detail in scheduling based on the importance of the participants. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input information about the importance of participants into a generating AI, which can then adjust the level of detail in scheduling.

[0047] The adjustment unit can apply different adjustment algorithms depending on the attributes of the participants during the adjustment process. For example, if there are many participants, the adjustment unit can apply an algorithm that adjusts each group. If there are few participants, the adjustment unit can also apply an algorithm that adjusts each participant individually. The adjustment unit can also apply the most suitable adjustment algorithm depending on the attributes of the participants (age, occupation, etc.). This allows the adjustment unit to perform more appropriate schedule adjustments by applying adjustment algorithms according to the attributes of the participants. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input information about the participants' attributes into a generating AI, which can then apply an adjustment algorithm.

[0048] The scheduling unit can determine scheduling priorities based on participants' submission times during scheduling. For example, the scheduling unit may prioritize scheduling for participants who submit early. For example, the scheduling unit may also postpone scheduling for participants who submit late. For example, the scheduling unit may also determine scheduling priorities according to submission times. This allows the scheduling unit to perform scheduling more efficiently by determining scheduling priorities based on submission times. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or not using AI. For example, the scheduling unit may input information about participants' submission times into a generating AI, which can then determine scheduling priorities.

[0049] The scheduling unit can adjust the order of scheduling based on the relevance of the participants during scheduling. For example, if there is an important participant, the scheduling unit will prioritize scheduling that participant's schedule. For example, if there is a less relevant participant, the scheduling unit may postpone that participant's schedule. The scheduling unit can also determine the order of scheduling based on the relevance of the participants. This allows the scheduling unit to perform more appropriate scheduling by determining the order of scheduling based on the relevance of the participants. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input information about the relevance of the participants into a generating AI, which can then determine the order of scheduling.

[0050] The automation unit can analyze the user's past schedules to select the optimal automation method during automation. For example, the automation unit can select the optimal automation method based on the user's past schedules. The automation unit can also extract specific patterns from the user's past schedules to select an automation method. For example, the automation unit can analyze the user's past schedules to select an efficient automation method. This improves the accuracy of automation by allowing the automation unit to select the optimal automation method based on past schedules. Some or all of the above processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input the user's past schedules into a generating AI, which can then select the optimal automation method.

[0051] The automation unit can customize the means of automation based on the user's current living situation during automation. For example, if the user is busy, the automation unit can provide a simple automation method. For example, if the user is relaxed, the automation unit can also provide a more detailed automation method. The automation unit can also customize the automation method according to the user's living situation. This allows the automation unit to perform more appropriate automation by customizing the automation method based on the current living situation. Some or all of the above-described processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input information about the user's current living situation into a generating AI, which can then customize the automation method.

[0052] The automation unit can select the optimal automation method by considering the user's geographical location information during automation. For example, the automation unit can select the optimal automation method based on information about the user's current location. For example, if the user is traveling, the automation unit can also select the automation method based on information about the travel destination. For example, if the user is considering moving, the automation unit can also select the automation method based on information about the new destination. In this way, the automation unit can select the optimal automation method by considering geographical location information. Some or all of the above processing in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input the user's geographical location information into a generating AI, which can then select the optimal automation method.

[0053] The automation unit can analyze the user's social media activity and propose automation methods during the automation process. For example, the automation unit can automate tasks related to articles shared by the user on social media. The automation unit can also propose automation methods based on the content of posts from accounts the user follows. The automation unit can also automate tasks related to topics in groups the user participates in. This allows the automation unit to propose highly relevant automation methods by analyzing social media activity. Some or all of the above processing in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input the user's social media activity into a generating AI, which can then propose automation methods.

[0054] The reminder unit can select the optimal sending method by referring to the user's past reminder history when sending a reminder. For example, the reminder unit can select the optimal sending method based on the sending methods of reminders the user has received in the past. The reminder unit can also select the optimal sending time based on the time periods when the user has received reminders in the past. For example, the reminder unit can extract specific patterns from the user's past reminder history and select the optimal sending method. As a result, the accuracy of the reminder unit is improved by selecting the optimal sending method based on past reminder history. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's past reminder history into a generating AI, which can then select the optimal sending method.

[0055] The reminder unit can customize the content of reminders based on the user's current schedule when sending them. For example, if the user is busy, the reminder unit can send a concise reminder. For example, if the user is relaxed, the reminder unit can also send a detailed reminder. The reminder unit can also customize the content of reminders according to the user's schedule. This allows the reminder unit to send more appropriate reminders by customizing the content based on the current schedule. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's current schedule into a generating AI, which can then customize the content of the reminder.

[0056] The reminder unit can select the optimal sending method when sending a reminder, taking into account the user's geographical location information. For example, the reminder unit can select the optimal sending method based on information about the user's current location. For example, if the user is traveling, the reminder unit can send a reminder based on information about the travel destination. For example, if the user is considering moving, the reminder unit can send a reminder based on information about the new destination. In this way, the reminder unit can select the optimal reminder sending method by taking geographical location information into consideration. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's geographical location information into a generating AI, which can then select the optimal sending method.

[0057] The reminder unit can analyze the user's social media activity and suggest reminder content when sending a reminder. For example, the reminder unit can send reminders related to articles the user has shared on social media. The reminder unit can also suggest reminder content based on the content of posts from accounts the user follows. The reminder unit can also send reminders related to topics in groups the user participates in. In this way, the reminder unit can suggest highly relevant reminder content by analyzing social media activity. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's social media activity into a generating AI, which can then suggest reminder content.

[0058] The traffic information provision unit can select the optimal method of providing traffic information by referring to the user's past travel history. For example, the traffic information provision unit can select the optimal method of providing traffic information based on the mode of transportation the user has used in the past. For example, the traffic information provision unit can also select the optimal method of providing traffic information by extracting specific patterns from the user's past travel history. For example, the traffic information provision unit can analyze the user's past travel history and select an efficient method of providing traffic information. As a result, the accuracy of traffic information can be improved by the traffic information provision unit selecting the optimal method of providing traffic information based on past travel history. Some or all of the above processing in the traffic information provision unit may be performed using AI, for example, or without using AI. For example, the traffic information provision unit can input the user's past travel history into a generating AI, and the generating AI can select the optimal method of providing traffic information.

[0059] The traffic information provision unit can customize the content of traffic information based on the user's current movement status when providing traffic information. For example, the traffic information provision unit provides real-time traffic information when the user is in motion. For example, the traffic information provision unit can also provide the optimal mode of transportation when the user is approaching their destination. For example, the traffic information provision unit can customize the content of traffic information according to the user's movement status. As a result, the traffic information provision unit can provide more appropriate traffic information by customizing the content of traffic information based on the current movement status. Some or all of the above processing in the traffic information provision unit may be performed using AI, for example, or without AI. For example, the traffic information provision unit can input the user's current movement status into a generating AI, and the generating AI can customize the content of the traffic information.

[0060] The traffic information provision unit can select the optimal method of providing traffic information by considering the user's geographical location. For example, the traffic information provision unit may prioritize providing traffic information for the user's current location. For example, if the user is traveling, the traffic information provision unit may prioritize providing traffic information for the travel destination. For example, if the user is considering moving, the traffic information provision unit may prioritize providing traffic information for the new destination. In this way, the traffic information provision unit can select the optimal method of providing traffic information by considering geographical location. Some or all of the above processing in the traffic information provision unit may be performed using AI, for example, or without AI. For example, the traffic information provision unit can input the user's geographical location information into a generating AI, which can then select the optimal method of providing the information.

[0061] The traffic information provision unit can analyze the user's social media activity and suggest content for traffic information when providing it. For example, the traffic information provision unit can provide traffic information related to articles the user has shared on social media. For example, the traffic information provision unit can also suggest content for traffic information based on the content of posts from accounts the user follows. For example, the traffic information provision unit can provide traffic information related to topics in groups the user participates in. In this way, the traffic information provision unit can provide highly relevant traffic information by analyzing social media activity. Some or all of the above processing in the traffic information provision unit may be performed using AI, for example, or not using AI. For example, the traffic information provision unit can input the user's social media activity into a generating AI, and the generating AI can suggest content for traffic information.

[0062] The reservation unit can select the optimal reservation method by referring to the user's past reservation history when a reservation is made. For example, the reservation unit can select the optimal reservation method based on the reservation methods the user has used in the past. For example, the reservation unit can also select the optimal reservation method by extracting specific patterns from the user's past reservation history. For example, the reservation unit can analyze the user's past reservation history and select an efficient reservation method. As a result, the reservation unit improves the accuracy of reservations by selecting the optimal reservation method based on past reservation history. Some or all of the above processes in the reservation unit may be performed using AI, for example, or without AI. For example, the reservation unit can input the user's past reservation history into a generating AI, which can then select the optimal reservation method.

[0063] The reservation system can customize the reservation details based on the user's current situation at the time of booking. For example, if the user is busy, the reservation system can provide simple reservation details. If the user is relaxed, the reservation system can also provide detailed reservation details. The reservation system can customize the reservation details according to the user's situation. This allows the reservation system to make more appropriate reservations by customizing the reservation details based on the current situation. Some or all of the above processing in the reservation system may be performed using AI, for example, or not using AI. For example, the reservation system can input the user's current situation into a generating AI, which can then customize the reservation details.

[0064] The reservation unit can select the optimal reservation method when a reservation is made, taking into account the user's geographical location information. For example, the reservation unit can select the optimal reservation method based on information about the user's current location. For example, if the user is traveling, the reservation unit can also select a reservation method based on information about the travel destination. For example, if the user is considering moving, the reservation unit can also select a reservation method based on information about the new destination. In this way, the reservation unit can select the optimal reservation method by taking geographical location information into consideration. Some or all of the above processing in the reservation unit may be performed using AI, for example, or without AI. For example, the reservation unit can input the user's geographical location information into a generating AI, and the generating AI can select the optimal reservation method.

[0065] The reservation unit can analyze the user's social media activity and suggest reservation content when a reservation is made. For example, the reservation unit can suggest reservations related to articles the user has shared on social media. The reservation unit can also suggest reservations based on the content of posts from accounts the user follows. The reservation unit can also suggest reservations related to topics in groups the user participates in. In this way, the reservation unit can suggest highly relevant reservations by analyzing social media activity. Some or all of the above processing in the reservation unit may be performed using AI, for example, or not using AI. For example, the reservation unit can input the user's social media activity into a generating AI, which can then suggest reservation content.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The scheduling system can analyze a user's past schedule history and select the most suitable scheduling method. For example, it can select the best method based on the user's past schedules. It can also extract specific patterns from the user's past schedules and select a method based on those patterns. Furthermore, it can analyze the user's past schedules to select the most efficient method. This improves the accuracy of scheduling suggestions by selecting the most suitable method based on past schedule history.

[0068] The scheduling system can customize the scheduling suggestions based on the user's current lifestyle. For example, if the user is busy, it can provide simple suggestions. If the user is relaxed, it can provide more detailed suggestions. Furthermore, it can customize the suggestions according to the user's lifestyle. This allows for more appropriate scheduling suggestions by customizing the suggestions based on the user's current lifestyle.

[0069] The scheduling system can customize schedule suggestions based on the user's geographical location. For example, it can prioritize suggesting events in the user's current location. If the user is traveling, it can prioritize suggesting tourist information for their destination. Furthermore, if the user is considering moving, it can prioritize suggesting information for their potential new location. This allows for more relevant schedule suggestions by considering geographical location.

[0070] The scheduling system can analyze users' social media activity, collect relevant information, and incorporate it into schedule suggestions. For example, it can suggest event information related to posts users have shared on social media. It can also collect and suggest relevant information based on posts from accounts users follow. Furthermore, it can collect and suggest information related to topics in groups users participate in. This allows for highly relevant schedule suggestions by analyzing social media activity.

[0071] The scheduling system can analyze a user's past search history and select the most suitable information gathering method. For example, it can prioritize the collection of relevant information based on keywords the user has previously searched for. It can also select reliable information sources based on websites the user has previously visited. Furthermore, it can select information to collect at specific time periods based on the user's past search history. This improves the accuracy of information gathering by selecting the most suitable method based on past search history.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The collection unit collects information. The collection unit collects information from the internet, for example, and provides information tailored to the user's preferences. The collection unit collects information such as potential locations, restaurants, and events. Step 2: The scheduling unit compares participants' schedules based on the information collected by the data collection unit and proposes the optimal date and time. For example, the scheduling unit checks the schedules of all participants and proposes a date and time when everyone can attend. The scheduling unit can also use AI to analyze participants' schedules and calculate the optimal date and time. Step 3: The automation unit automatically schedules recurring events and routines based on the dates and times proposed by the coordination unit. For example, the automation unit automatically adds weekly meetings and monthly reports to the schedule. The automation unit can also automatically update the schedule using AI, for example. Step 4: The reminder unit sends reminders based on the schedule programmed by the automation unit. For example, the reminder unit sends a reminder the day before a meeting. The reminder unit can also optimize the timing of reminder sending using AI, for example. Step 5: The traffic information unit presents transportation options and travel times based on reminders sent by the reminder unit. For example, the traffic information unit may present the optimal transportation options and travel times based on train and bus timetables. The traffic information unit can also provide real-time traffic information using AI, for example. Step 6: The reservation department makes reservations based on the information provided by the traffic information department. The reservation department makes reservations for restaurants and concerts, for example, using online reservation systems. The reservation department can also optimize reservations using AI, for example.

[0074] (Example of form 2) The scheduling system according to an embodiment of the present invention is a system that uses an AI agent to streamline scheduling and planning. This scheduling system collects information, compares participants' schedules, and proposes the optimal date and time. Furthermore, it automatically incorporates regular events and habits into the schedule, sends reminders, provides transportation information, and makes reservations. For example, the scheduling system collects information on potential locations, restaurants, and events. In this process, it collects information from the internet and provides information tailored to the user's preferences. Next, the scheduling system compares participants' schedules and proposes the optimal date and time. For example, it checks the schedules of all participants and proposes a date and time when everyone can attend. Furthermore, the scheduling system automatically incorporates regular events and habits into the schedule. For example, it automatically adds weekly meetings and monthly reporting meetings to the schedule. The scheduling system uses a reminder function to notify users so they don't forget important appointments. For example, it sends a reminder the day before a meeting. The scheduling system also provides transportation information, suggesting means of transport and travel time. For example, it suggests the optimal means of transport and travel time based on train and bus timetables. Finally, the scheduling system makes reservations for restaurants, concerts, and events. For example, one can use an online reservation system to book restaurants or concerts. This allows scheduling systems to streamline scheduling and planning, reducing time loss and stress.

[0075] The scheduling system according to this embodiment comprises a collection unit, a scheduling unit, an automation unit, a reminder unit, a traffic information provision unit, and a reservation unit. The collection unit collects information. For example, the collection unit collects information from the internet and provides information tailored to the user's preferences. For example, the collection unit collects information such as candidate locations, restaurants, and events. The scheduling unit compares the schedules of participants based on the information collected by the collection unit and proposes the optimal date and time. For example, the scheduling unit checks the schedules of all participants and proposes a date and time when everyone can attend. The scheduling unit can also analyze the schedules of participants using AI and calculate the optimal date and time. The automation unit automatically incorporates regular events and habits into the schedule based on the date and time proposed by the scheduling unit. For example, the automation unit automatically adds weekly regular meetings and monthly reporting meetings to the schedule. The automation unit can also automatically update the schedule using AI. The reminder unit sends reminders based on the schedule incorporated by the automation unit. For example, the reminder unit sends a reminder the day before a meeting. The reminder unit can, for example, use AI to optimize the timing of sending reminders. The traffic information unit presents transportation options and travel times based on the reminders sent by the reminder unit. The traffic information unit presents the optimal transportation options and travel times based on train and bus timetables, for example. The traffic information unit can also provide real-time traffic information using AI, for example. The reservation unit makes reservations based on the information presented by the traffic information unit. The reservation unit makes reservations for restaurants and concerts, for example, using online reservation systems. The reservation unit can also optimize reservations using AI, for example. As a result, the scheduling system according to this embodiment can streamline scheduling and planning, reducing time loss and stress.

[0076] The data collection unit collects information. For example, it collects information from the internet and provides information tailored to the user's preferences. Specifically, the data collection unit uses web crawlers and APIs to collect data from various sources on the internet. For example, it obtains the latest information from restaurant review sites, event information sites, and official websites of tourist destinations. The data collection unit filters this information based on the user's preferences and past behavior history to provide the most relevant information to the user. For example, it can suggest similar locations and events based on the user's ratings of restaurants they have visited in the past or the types of events they have attended. Furthermore, the data collection unit can also select the most suitable locations and events by considering the user's current location and planned schedule. In this way, the data collection unit can efficiently collect and provide information that is useful to the user.

[0077] The scheduling unit compares participants' schedules based on information collected by the data collection unit and proposes the optimal date and time. Specifically, the scheduling unit obtains calendar information from all participants and analyzes each participant's availability. The scheduling unit can also use AI to analyze participants' schedules and calculate the optimal date and time. For example, the AI ​​considers each participant's past schedule patterns and priorities to propose the most suitable date and time. Furthermore, the scheduling unit can readjust the schedule in real time if there are changes to participants' schedules. For example, if a participant makes a sudden change of plans, the scheduling unit immediately reconfirms the schedules of other participants and proposes a new optimal date and time. The scheduling unit can also suggest the most suitable locations and events considering participants' preferences and past participation history. In this way, the scheduling unit can efficiently adjust and propose a schedule that satisfies all participants.

[0078] The automation unit automatically incorporates recurring events and habits into the schedule based on the dates and times proposed by the coordination unit. Specifically, the automation unit automatically adds weekly meetings, monthly reports, and other similar events to the schedule. The automation unit can also automatically update the schedule using AI. For example, the AI ​​analyzes past schedule data and learns patterns of recurring events to predict and automatically add future schedules. The automation unit can also suggest the optimal schedule by considering the user's preferences and past behavior history. For example, it can automatically add similar events to the schedule based on the types and frequency of events the user has previously attended. Furthermore, the automation unit can update the schedule in real time and notify the user if changes or cancellations occur. This allows the automation unit to streamline the user's schedule management and significantly reduce the effort required.

[0079] The reminder unit sends reminders based on schedules programmed by the automation unit. Specifically, the reminder unit sends a reminder the day before a meeting. The reminder unit can also use AI to optimize the timing of reminder sending. For example, the AI ​​analyzes the user's past behavior patterns and reminder response history to learn the optimal sending timing. This allows the reminder to be sent at the time when the user can act most effectively upon receiving it. Furthermore, the reminder unit can send reminders using multiple notification methods. For example, it can use a combination of smartphone notifications, email, SMS, and voice calls to ensure that reminders are delivered reliably. The reminder unit can also collect user feedback and continuously improve the content and timing of reminders. This allows the reminder unit to support users in remembering important appointments and improve the accuracy of schedule management.

[0080] The traffic information unit provides information on transportation options and travel times based on reminders sent by the reminder unit. Specifically, the traffic information unit provides optimal transportation options and travel times based on train and bus timetables. The traffic information unit can also provide real-time traffic information using AI. For example, the AI ​​analyzes current traffic conditions and weather information to suggest the optimal travel route. Furthermore, the traffic information unit can select the optimal transportation option considering the user's current location and destination. For example, if a user uses a train, it will suggest the optimal route from the nearest station to the destination and calculate the travel time. The traffic information unit can also provide real-time information on delays and operating status of transportation services and notify users quickly. In this way, the traffic information unit can support users in traveling efficiently and reduce the time and stress associated with travel.

[0081] The reservation department makes reservations based on information provided by the traffic information department. Specifically, the reservation department uses online reservation systems to make reservations for restaurants and concerts. The reservation department can also use AI to optimize reservations. For example, the AI ​​analyzes the user's preferences and past reservation history to suggest the best reservation destination and time slot. Furthermore, the reservation department can search across multiple reservation sites to find the best-suited reservation destination. For example, in the case of restaurant reservations, it checks availability from multiple reservation sites and suggests the earliest available time slot. In addition, the reservation department can respond in real time to changes or cancellations of reservations and notify the user. In this way, the reservation department can support users in making reservations smoothly and ensuring that their plans are carried out reliably.

[0082] The data collection unit can collect information from the internet and provide information tailored to the user's preferences. For example, the data collection unit can collect information from the internet and provide information tailored to the user's preferences. The data collection unit can also analyze information from the internet using AI and provide information tailored to the user's preferences. For example, the data collection unit can identify the user's preferences based on the user's past search history or survey results. This improves the accuracy of information collection by providing information tailored to the user's preferences. Some or all of the above-described processes in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can collect information from the internet, input it into a generating AI, and the generating AI can provide information tailored to the user's preferences.

[0083] The scheduling unit can check the schedules of all participants and propose a date and time when everyone can attend. For example, the scheduling unit can check the schedules of all participants and propose a date and time when everyone can attend. The scheduling unit can also analyze participants' schedules using AI and calculate the optimal date and time. The scheduling unit can collect the schedules of all participants through calendar sharing or schedule input, for example. This improves the accuracy of scheduling by allowing the scheduling unit to propose a date and time when everyone can attend. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can collect the schedules of all participants, input them into a generating AI, and the generating AI can propose the optimal date and time.

[0084] The automation unit can automatically add weekly regular meetings and monthly reporting sessions to the schedule. The automation unit can, for example, automatically add weekly regular meetings and monthly reporting sessions to the schedule. The automation unit can also automatically update the schedule using AI. The automation unit can, for example, add regular events and habits such as weekly meetings and monthly reporting sessions to the schedule. This makes planning more efficient by allowing the automation unit to automatically add regular events and habits to the schedule. Some or all of the above processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input information about regular events and habits into a generating AI, which can then add them to the schedule.

[0085] The reminder function can send a reminder the day before a meeting. For example, the reminder function can send a reminder the day before a meeting. The reminder function can also optimize the timing of sending reminders using AI, for example. The reminder function can send an email notification 24 hours in advance, for example. This makes it easier to manage appointments by notifying users so they don't forget important events. Some or all of the above processes in the reminder function may be performed using AI, for example, or without AI. For example, the reminder function can input information to send a reminder the day before a meeting into a generating AI, and the generating AI can send the reminder.

[0086] The traffic information provision unit can suggest the optimal mode of transportation and travel time based on train and bus timetables. For example, the traffic information provision unit can suggest the optimal mode of transportation and travel time based on train and bus timetables. The traffic information provision unit can also provide real-time traffic information using AI, for example. The traffic information provision unit can suggest modes of transportation such as trains, buses, and walking, along with their travel times. This improves travel efficiency by suggesting the optimal mode of transportation and travel time. Some or all of the above-described processes in the traffic information provision unit may be performed using AI, for example, or without AI. For example, the traffic information provision unit can input information based on train and bus timetables into a generating AI, which can then suggest the optimal mode of transportation and travel time.

[0087] The reservation department can make reservations for restaurants and concerts using online reservation systems. The reservation department can, for example, make reservations for restaurants and concerts using online reservation systems. The reservation department can also optimize reservations using AI, for example. The reservation department can make reservations using restaurant reservation sites and concert ticket reservation sites, for example. This reduces the effort required for reservations by the reservation department using online reservation systems. Some or all of the above processes in the reservation department may be performed using AI, for example, or not using AI. For example, the reservation department can input information obtained using online reservation systems into a generating AI, and the generating AI can make reservations.

[0088] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can collect information during times when the user is relaxed. If the user is busy, the data collection unit can also collect information during free time. If the user is relaxed, the data collection unit can even collect information in real time. This allows the data collection unit to collect more appropriate information by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI, which can then adjust the timing of information collection.

[0089] The data collection unit can analyze the user's past search history and select the optimal information collection method. For example, the data collection unit can prioritize collecting relevant information based on keywords the user has searched for in the past. The data collection unit can also select reliable information sources based on websites the user has visited in the past. For example, the data collection unit can select information to collect at specific time periods based on the user's past search history. This improves the accuracy of information collection by allowing the data collection unit to select the optimal information collection method based on past search history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past search history into a generating AI, which can then select the optimal information collection method.

[0090] The data collection unit can filter information based on the user's current areas of interest during data collection. For example, the data collection unit can prioritize collecting information related to topics the user is currently interested in. The data collection unit can also filter relevant information based on the content of posts from social media accounts the user follows. The data collection unit can also collect relevant information based on topics in online communities the user participates in. In this way, the data collection unit can collect highly relevant information by filtering information based on the user's areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information about the user's current areas of interest into a generating AI, which can then filter the information.

[0091] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting information that promotes relaxation. For example, if the user is excited, the data collection unit may prioritize collecting entertainment information. For example, if the user is tired, the data collection unit may prioritize collecting health-related information. This allows the data collection unit to collect more appropriate information by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI, which can then determine the priority of information.

[0092] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location during data collection. For example, the data collection unit can prioritize collecting event information in the area where the user is currently located. For example, if the user is traveling, the data collection unit can prioritize collecting tourist information for the travel destination. For example, if the user is considering moving, the data collection unit can prioritize collecting information for the area they are considering moving to. In this way, the data collection unit can collect highly relevant information by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize collecting highly relevant information.

[0093] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information related to articles the user has shared on social media. The data collection unit can also collect relevant information based on the content of posts from accounts the user follows. The data collection unit can also collect relevant information based on the topics of groups the user participates in. In this way, the data collection unit can collect highly relevant information by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into a generating AI, which can then collect relevant information.

[0094] The adjustment unit can estimate the user's emotions and adjust the presentation of the schedule adjustment based on the estimated emotions. For example, if the user is stressed, the adjustment unit may present a simple and visually easy-to-understand schedule. For example, if the user is relaxed, the adjustment unit may present a detailed schedule. For example, if the user is in a hurry, the adjustment unit may present a concise schedule. In this way, the adjustment unit can make more appropriate schedule adjustments by adjusting the presentation of the schedule adjustment according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input user emotion data into the generative AI, and the generative AI can adjust the presentation of the schedule adjustment.

[0095] The scheduling unit can adjust the level of detail in scheduling based on the importance of the participants. For example, if there are important participants, the scheduling unit will prioritize scheduling their schedules. For example, if there are participants of lower importance, the scheduling unit can postpone their schedules. The scheduling unit can also change the level of detail in scheduling according to importance. This allows the scheduling unit to perform more appropriate scheduling by adjusting the level of detail in scheduling based on the importance of the participants. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input information about the importance of participants into a generating AI, which can then adjust the level of detail in scheduling.

[0096] The adjustment unit can apply different adjustment algorithms depending on the attributes of the participants during the adjustment process. For example, if there are many participants, the adjustment unit can apply an algorithm that adjusts each group. If there are few participants, the adjustment unit can also apply an algorithm that adjusts each participant individually. The adjustment unit can also apply the most suitable adjustment algorithm depending on the attributes of the participants (age, occupation, etc.). This allows the adjustment unit to perform more appropriate schedule adjustments by applying adjustment algorithms according to the attributes of the participants. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input information about the participants' attributes into a generating AI, which can then apply an adjustment algorithm.

[0097] The adjustment unit can estimate the user's emotions and adjust the length of the adjustment based on the estimated emotions. For example, if the user is stressed, the adjustment unit will complete the adjustment in a short time. For example, if the user is relaxed, the adjustment unit can also perform a detailed adjustment. For example, if the user is in a hurry, the adjustment unit can perform a rapid adjustment. This allows the adjustment unit to perform a more appropriate schedule adjustment by adjusting the length of the adjustment according to the user's 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 adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input user emotion data into the generative AI, and the generative AI can adjust the length of the adjustment.

[0098] The scheduling unit can determine scheduling priorities based on participants' submission times during scheduling. For example, the scheduling unit may prioritize scheduling for participants who submit early. For example, the scheduling unit may also postpone scheduling for participants who submit late. For example, the scheduling unit may also determine scheduling priorities according to submission times. This allows the scheduling unit to perform scheduling more efficiently by determining scheduling priorities based on submission times. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or not using AI. For example, the scheduling unit may input information about participants' submission times into a generating AI, which can then determine scheduling priorities.

[0099] The scheduling unit can adjust the order of scheduling based on the relevance of the participants during scheduling. For example, if there is an important participant, the scheduling unit will prioritize scheduling that participant's schedule. For example, if there is a less relevant participant, the scheduling unit may postpone that participant's schedule. The scheduling unit can also determine the order of scheduling based on the relevance of the participants. This allows the scheduling unit to perform more appropriate scheduling by determining the order of scheduling based on the relevance of the participants. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input information about the relevance of the participants into a generating AI, which can then determine the order of scheduling.

[0100] The automation unit can estimate the user's emotions and adjust the automation method based on the estimated user emotions. For example, if the user is stressed, the automation unit may apply a simple automation method. For example, if the user is relaxed, the automation unit may apply a more detailed automation method. For example, if the user is in a hurry, the automation unit may apply a more rapid automation method. This allows the automation unit to provide more appropriate automation by adjusting the automation method according to the user's 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 automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input user emotion data into a generative AI, which can then adjust the automation method.

[0101] The automation unit can analyze the user's past schedules to select the optimal automation method during automation. For example, the automation unit can select the optimal automation method based on the user's past schedules. The automation unit can also extract specific patterns from the user's past schedules to select an automation method. For example, the automation unit can analyze the user's past schedules to select an efficient automation method. This improves the accuracy of automation by allowing the automation unit to select the optimal automation method based on past schedules. Some or all of the above processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input the user's past schedules into a generating AI, which can then select the optimal automation method.

[0102] The automation unit can customize the means of automation based on the user's current living situation during automation. For example, if the user is busy, the automation unit can provide a simple automation method. For example, if the user is relaxed, the automation unit can also provide a more detailed automation method. The automation unit can also customize the automation method according to the user's living situation. This allows the automation unit to perform more appropriate automation by customizing the automation method based on the current living situation. Some or all of the above-described processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input information about the user's current living situation into a generating AI, which can then customize the automation method.

[0103] The automation unit can estimate the user's emotions and determine automation priorities based on the estimated emotions. For example, if the user is stressed, the automation unit may prioritize automating important tasks. If the user is relaxed, the automation unit may also prioritize automating detailed tasks. If the user is in a hurry, the automation unit may also prioritize automating tasks that can be completed quickly. This allows the automation unit to perform more appropriate automation by determining automation priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input user emotion data into a generative AI, which can then determine automation priorities.

[0104] The automation unit can select the optimal automation method by considering the user's geographical location information during automation. For example, the automation unit can select the optimal automation method based on information about the user's current location. For example, if the user is traveling, the automation unit can also select the automation method based on information about the travel destination. For example, if the user is considering moving, the automation unit can also select the automation method based on information about the new destination. In this way, the automation unit can select the optimal automation method by considering geographical location information. Some or all of the above processing in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input the user's geographical location information into a generating AI, which can then select the optimal automation method.

[0105] The automation unit can analyze the user's social media activity and propose automation methods during the automation process. For example, the automation unit can automate tasks related to articles shared by the user on social media. The automation unit can also propose automation methods based on the content of posts from accounts the user follows. The automation unit can also automate tasks related to topics in groups the user participates in. This allows the automation unit to propose highly relevant automation methods by analyzing social media activity. Some or all of the above processing in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input the user's social media activity into a generating AI, which can then propose automation methods.

[0106] The reminder unit can estimate the user's emotions and adjust the method of sending reminders based on the estimated emotions. For example, if the user is stressed, the reminder unit may send a simple reminder. For example, if the user is relaxed, the reminder unit may send a detailed reminder. For example, if the user is in a hurry, the reminder unit may send a quick reminder. In this way, the reminder unit sends more appropriate reminders by adjusting the method of sending reminders according to the user's 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 reminder unit may be performed using AI or not using AI. For example, the reminder unit can input user emotion data into the generative AI, and the generative AI can adjust the method of sending reminders.

[0107] The reminder unit can select the optimal sending method by referring to the user's past reminder history when sending a reminder. For example, the reminder unit can select the optimal sending method based on the sending methods of reminders the user has received in the past. The reminder unit can also select the optimal sending time based on the time periods when the user has received reminders in the past. For example, the reminder unit can extract specific patterns from the user's past reminder history and select the optimal sending method. As a result, the accuracy of the reminder unit is improved by selecting the optimal sending method based on past reminder history. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's past reminder history into a generating AI, which can then select the optimal sending method.

[0108] The reminder unit can customize the content of reminders based on the user's current schedule when sending them. For example, if the user is busy, the reminder unit can send a concise reminder. For example, if the user is relaxed, the reminder unit can also send a detailed reminder. The reminder unit can also customize the content of reminders according to the user's schedule. This allows the reminder unit to send more appropriate reminders by customizing the content based on the current schedule. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's current schedule into a generating AI, which can then customize the content of the reminder.

[0109] The reminder unit can estimate the user's emotions and determine the priority of reminders based on the estimated emotions. For example, if the user is stressed, the reminder unit will prioritize sending important reminders. For example, if the user is relaxed, the reminder unit may also prioritize sending detailed reminders. For example, if the user is in a hurry, the reminder unit may also prioritize sending reminders that require immediate attention. In this way, the reminder unit sends more appropriate reminders by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reminder unit may be performed using AI or not using AI. For example, the reminder unit can input user emotion data into a generative AI, which can then determine the priority of reminders.

[0110] The reminder unit can select the optimal sending method when sending a reminder, taking into account the user's geographical location information. For example, the reminder unit can select the optimal sending method based on information about the user's current location. For example, if the user is traveling, the reminder unit can send a reminder based on information about the travel destination. For example, if the user is considering moving, the reminder unit can send a reminder based on information about the new destination. In this way, the reminder unit can select the optimal reminder sending method by taking geographical location information into consideration. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's geographical location information into a generating AI, which can then select the optimal sending method.

[0111] The reminder unit can analyze the user's social media activity and suggest reminder content when sending a reminder. For example, the reminder unit can send reminders related to articles the user has shared on social media. The reminder unit can also suggest reminder content based on the content of posts from accounts the user follows. The reminder unit can also send reminders related to topics in groups the user participates in. In this way, the reminder unit can suggest highly relevant reminder content by analyzing social media activity. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's social media activity into a generating AI, which can then suggest reminder content.

[0112] The traffic information provision unit can estimate the user's emotions and adjust the method of providing traffic information based on the estimated emotions. For example, if the user is feeling stressed, the traffic information provision unit can provide simple traffic information. For example, if the user is relaxed, the traffic information provision unit can also provide detailed traffic information. For example, if the user is in a hurry, the traffic information provision unit can also provide traffic information quickly. In this way, the traffic information provision unit can provide more appropriate traffic information by adjusting the method of providing traffic information according to the user's 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 traffic information provision unit may be performed using AI, for example, or without AI. For example, the traffic information provision unit can input user emotion data into a generative AI, and the generative AI can adjust the method of providing traffic information.

[0113] The traffic information provision unit can select the optimal method of providing traffic information by referring to the user's past travel history. For example, the traffic information provision unit can select the optimal method of providing traffic information based on the mode of transportation the user has used in the past. For example, the traffic information provision unit can also select the optimal method of providing traffic information by extracting specific patterns from the user's past travel history. For example, the traffic information provision unit can analyze the user's past travel history and select an efficient method of providing traffic information. As a result, the accuracy of traffic information can be improved by the traffic information provision unit selecting the optimal method of providing traffic information based on past travel history. Some or all of the above processing in the traffic information provision unit may be performed using AI, for example, or without using AI. For example, the traffic information provision unit can input the user's past travel history into a generating AI, and the generating AI can select the optimal method of providing traffic information.

[0114] The traffic information provision unit can customize the content of traffic information based on the user's current movement status when providing traffic information. For example, the traffic information provision unit provides real-time traffic information when the user is in motion. For example, the traffic information provision unit can also provide the optimal mode of transportation when the user is approaching their destination. For example, the traffic information provision unit can customize the content of traffic information according to the user's movement status. As a result, the traffic information provision unit can provide more appropriate traffic information by customizing the content of traffic information based on the current movement status. Some or all of the above processing in the traffic information provision unit may be performed using AI, for example, or without AI. For example, the traffic information provision unit can input the user's current movement status into a generating AI, and the generating AI can customize the content of the traffic information.

[0115] The traffic information provider can estimate the user's emotions and prioritize traffic information based on the estimated emotions. For example, if the user is feeling stressed, the traffic information provider will prioritize providing important traffic information. For example, if the user is relaxed, the traffic information provider may also prioritize providing detailed traffic information. For example, if the user is in a hurry, the traffic information provider may also prioritize providing traffic information that requires immediate attention. In this way, the traffic information provider can provide more appropriate traffic information by prioritizing traffic information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the traffic information provider may be performed using AI, for example, or not using AI. For example, the traffic information provider can input user emotion data into a generative AI, and the generative AI can determine the priority of traffic information.

[0116] The traffic information provision unit can select the optimal method of providing traffic information by considering the user's geographical location. For example, the traffic information provision unit may prioritize providing traffic information for the user's current location. For example, if the user is traveling, the traffic information provision unit may prioritize providing traffic information for the travel destination. For example, if the user is considering moving, the traffic information provision unit may prioritize providing traffic information for the new destination. In this way, the traffic information provision unit can select the optimal method of providing traffic information by considering geographical location. Some or all of the above processing in the traffic information provision unit may be performed using AI, for example, or without AI. For example, the traffic information provision unit can input the user's geographical location information into a generating AI, which can then select the optimal method of providing the information.

[0117] The traffic information provision unit can analyze the user's social media activity and suggest content for traffic information when providing it. For example, the traffic information provision unit can provide traffic information related to articles the user has shared on social media. For example, the traffic information provision unit can also suggest content for traffic information based on the content of posts from accounts the user follows. For example, the traffic information provision unit can provide traffic information related to topics in groups the user participates in. In this way, the traffic information provision unit can provide highly relevant traffic information by analyzing social media activity. Some or all of the above processing in the traffic information provision unit may be performed using AI, for example, or not using AI. For example, the traffic information provision unit can input the user's social media activity into a generating AI, and the generating AI can suggest content for traffic information.

[0118] The reservation unit can estimate the user's emotions and adjust the reservation method based on the estimated emotions. For example, if the user is stressed, the reservation unit can provide a simple reservation method. For example, if the user is relaxed, the reservation unit can provide a detailed reservation method. For example, if the user is in a hurry, the reservation unit can provide a quick reservation method. This allows the reservation unit to make more appropriate reservations by adjusting the reservation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reservation unit may be performed using AI, for example, or not using AI. For example, the reservation unit can input user emotion data into a generative AI, and the generative AI can adjust the reservation method.

[0119] The reservation unit can select the optimal reservation method by referring to the user's past reservation history when a reservation is made. For example, the reservation unit can select the optimal reservation method based on the reservation methods the user has used in the past. For example, the reservation unit can also select the optimal reservation method by extracting specific patterns from the user's past reservation history. For example, the reservation unit can analyze the user's past reservation history and select an efficient reservation method. As a result, the reservation unit improves the accuracy of reservations by selecting the optimal reservation method based on past reservation history. Some or all of the above processes in the reservation unit may be performed using AI, for example, or without AI. For example, the reservation unit can input the user's past reservation history into a generating AI, which can then select the optimal reservation method.

[0120] The reservation system can customize the reservation details based on the user's current situation at the time of booking. For example, if the user is busy, the reservation system can provide simple reservation details. If the user is relaxed, the reservation system can also provide detailed reservation details. The reservation system can customize the reservation details according to the user's situation. This allows the reservation system to make more appropriate reservations by customizing the reservation details based on the current situation. Some or all of the above processing in the reservation system may be performed using AI, for example, or not using AI. For example, the reservation system can input the user's current situation into a generating AI, which can then customize the reservation details.

[0121] The reservation system can estimate the user's emotions and prioritize reservations based on those emotions. For example, if the user is stressed, the system will prioritize important reservations. If the user is relaxed, the system may prioritize detailed reservations. If the user is in a hurry, the system may prioritize reservations that require immediate attention. This allows the system to prioritize reservations according to the user's emotions, enabling more appropriate reservations. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reservation system may be performed using AI or not. For example, the reservation system can input user emotion data into a generative AI, which can then determine the reservation priority.

[0122] The reservation unit can select the optimal reservation method when a reservation is made, taking into account the user's geographical location information. For example, the reservation unit can select the optimal reservation method based on information about the user's current location. For example, if the user is traveling, the reservation unit can also select a reservation method based on information about the travel destination. For example, if the user is considering moving, the reservation unit can also select a reservation method based on information about the new destination. In this way, the reservation unit can select the optimal reservation method by taking geographical location information into consideration. Some or all of the above processing in the reservation unit may be performed using AI, for example, or without AI. For example, the reservation unit can input the user's geographical location information into a generating AI, and the generating AI can select the optimal reservation method.

[0123] The reservation unit can analyze the user's social media activity and suggest reservation content when a reservation is made. For example, the reservation unit can suggest reservations related to articles the user has shared on social media. The reservation unit can also suggest reservations based on the content of posts from accounts the user follows. The reservation unit can also suggest reservations related to topics in groups the user participates in. In this way, the reservation unit can suggest highly relevant reservations by analyzing social media activity. Some or all of the above processing in the reservation unit may be performed using AI, for example, or not using AI. For example, the reservation unit can input the user's social media activity into a generating AI, which can then suggest reservation content.

[0124] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0125] The scheduling system can estimate the user's emotions and adjust the way it suggests schedules based on those emotions. For example, if the user is stressed, the system can suggest a simple and visually easy-to-understand schedule. If the user is relaxed, it can suggest a more detailed schedule. Furthermore, if the user is in a hurry, it can suggest a schedule that gets straight to the point. This enables schedule suggestions that are tailored to the user's emotions, resulting in more appropriate scheduling. Emotion estimation is performed using an emotion engine or generative AI, among other things. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI.

[0126] The scheduling system can analyze a user's past schedule history and select the most suitable scheduling method. For example, it can select the best method based on the user's past schedules. It can also extract specific patterns from the user's past schedules and select a method based on those patterns. Furthermore, it can analyze the user's past schedules to select the most efficient method. This improves the accuracy of scheduling suggestions by selecting the most suitable method based on past schedule history.

[0127] The scheduling system can customize the scheduling suggestions based on the user's current lifestyle. For example, if the user is busy, it can provide simple suggestions. If the user is relaxed, it can provide more detailed suggestions. Furthermore, it can customize the suggestions according to the user's lifestyle. This allows for more appropriate scheduling suggestions by customizing the suggestions based on the user's current lifestyle.

[0128] The scheduling system can estimate the user's emotions and prioritize schedules based on those emotions. For example, if the user is stressed, it can prioritize important appointments. If the user is relaxed, it can prioritize detailed appointments. Furthermore, if the user is in a hurry, it can prioritize appointments that require immediate attention. This allows for more appropriate schedule suggestions by prioritizing appointments according to the user's emotions. Emotion estimation is performed using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI.

[0129] The scheduling system can customize schedule suggestions based on the user's geographical location. For example, it can prioritize suggesting events in the user's current location. If the user is traveling, it can prioritize suggesting tourist information for their destination. Furthermore, if the user is considering moving, it can prioritize suggesting information for their potential new location. This allows for more relevant schedule suggestions by considering geographical location.

[0130] The scheduling system can estimate the user's emotions and adjust the way the schedule is presented based on those emotions. For example, if the user is stressed, a simple and visually easy-to-understand schedule can be presented. If the user is relaxed, a detailed schedule can be presented. Furthermore, if the user is in a hurry, a concise schedule can be presented. This enables schedule presentation tailored to the user's emotions, resulting in more appropriate scheduling. Emotion estimation is performed using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI.

[0131] The scheduling system can analyze users' social media activity, collect relevant information, and incorporate it into schedule suggestions. For example, it can suggest event information related to posts users have shared on social media. It can also collect and suggest relevant information based on posts from accounts users follow. Furthermore, it can collect and suggest information related to topics in groups users participate in. This allows for highly relevant schedule suggestions by analyzing social media activity.

[0132] The scheduling system can estimate the user's emotions and adjust the scheduling method based on those emotions. For example, if the user is stressed, the scheduling can be completed quickly. If the user is relaxed, more detailed scheduling can be performed. Furthermore, if the user is in a hurry, the scheduling can be done quickly. This enables scheduling that is tailored to the user's emotions, resulting in more appropriate scheduling. Emotion estimation is performed using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI.

[0133] The scheduling system can analyze a user's past search history and select the most suitable information gathering method. For example, it can prioritize the collection of relevant information based on keywords the user has previously searched for. It can also select reliable information sources based on websites the user has previously visited. Furthermore, it can select information to collect at specific time periods based on the user's past search history. This improves the accuracy of information gathering by selecting the most suitable method based on past search history.

[0134] The scheduling system can estimate the user's emotions and prioritize schedules based on those emotions. For example, if the user is stressed, it can prioritize important appointments. If the user is relaxed, it can prioritize detailed appointments. Furthermore, if the user is in a hurry, it can prioritize appointments that require immediate attention. This allows for more appropriate schedule suggestions by prioritizing appointments according to the user's emotions. Emotion estimation is performed using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: The collection unit collects information. The collection unit collects information from the internet, for example, and provides information tailored to the user's preferences. The collection unit collects information such as potential locations, restaurants, and events. Step 2: The scheduling unit compares participants' schedules based on the information collected by the data collection unit and proposes the optimal date and time. For example, the scheduling unit checks the schedules of all participants and proposes a date and time when everyone can attend. The scheduling unit can also use AI to analyze participants' schedules and calculate the optimal date and time. Step 3: The automation unit automatically schedules recurring events and routines based on the dates and times proposed by the coordination unit. For example, the automation unit automatically adds weekly meetings and monthly reports to the schedule. The automation unit can also automatically update the schedule using AI, for example. Step 4: The reminder unit sends reminders based on the schedule programmed by the automation unit. For example, the reminder unit sends a reminder the day before a meeting. The reminder unit can also optimize the timing of reminder sending using AI, for example. Step 5: The traffic information unit presents transportation options and travel times based on reminders sent by the reminder unit. For example, the traffic information unit may present the optimal transportation options and travel times based on train and bus timetables. The traffic information unit can also provide real-time traffic information using AI, for example. Step 6: The reservation department makes reservations based on the information provided by the traffic information department. The reservation department makes reservations for restaurants and concerts, for example, using online reservation systems. The reservation department can also optimize reservations using AI, for example.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the collection unit, adjustment unit, automation unit, reminder unit, traffic information provision unit, and reservation unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14, which collects information from the internet and provides information tailored to the user's preferences. The adjustment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which compares the participant's schedule based on the collected information and proposes the optimal date and time. The automation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which automatically incorporates regular events and habits into the schedule based on the proposed date and time. The reminder unit is implemented, for example, by the control unit 46A of the smart device 14, which sends reminders based on the schedule. The traffic information provision unit is implemented, for example, by the control unit 46A of the smart device 14, which presents means of transportation and travel time based on the reminders. The reservation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which makes reservations based on the presented information. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0141] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the collection unit, adjustment unit, automation unit, reminder unit, traffic information provision unit, and reservation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214, which collects information from the internet and provides information tailored to the user's preferences. The adjustment unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which compares the participant's schedule based on the collected information and proposes the optimal date and time. The automation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which automatically incorporates regular events and habits into the schedule based on the proposed date and time. The reminder unit is implemented, for example, by the control unit 46A of the smart glasses 214, which sends reminders based on the schedule. The traffic information provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, which presents means of transportation and travel time based on the reminders. The reservation function is implemented, for example, by the specific processing unit 290 of the data processing device 12, and makes reservations based on the presented information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0157] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements described above, including the collection unit, adjustment unit, automation unit, reminder unit, traffic information provision unit, and reservation unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314, which collects information from the internet and provides information tailored to the user's preferences. The adjustment unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which compares the participant's schedule based on the collected information and proposes the optimal date and time. The automation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which automatically incorporates regular events and habits into the schedule based on the proposed date and time. The reminder unit is implemented, for example, by the control unit 46A of the headset terminal 314, which sends reminders based on the schedule. The traffic information provision unit is implemented, for example, by the control unit 46A of the headset terminal 314, which presents means of transportation and travel time based on the reminders. The reservation function is implemented, for example, by the specific processing unit 290 of the data processing device 12, and makes reservations based on the presented information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0173] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.).

[0186] 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.

[0187] 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.

[0188] 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.

[0189] Each of the multiple elements described above, including the collection unit, adjustment unit, automation unit, reminder unit, traffic information provision unit, and reservation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414, which collects information from the internet and provides information tailored to the user's preferences. The adjustment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which compares the participant's schedule based on the collected information and proposes the optimal date and time. The automation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically incorporates regular events and habits into the schedule based on the proposed date and time. The reminder unit is implemented by, for example, the control unit 46A of the robot 414, which sends reminders based on the schedule. The traffic information provision unit is implemented by, for example, the control unit 46A of the robot 414, which presents means of transportation and travel time based on the reminders. The reservation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which makes reservations based on the presented information. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] (Note 1) The information collection unit, Based on the information collected by the aforementioned collection unit, the adjustment unit compares the participants' schedules and proposes the most suitable date and time. An automation unit that automatically incorporates regular events and habits into a schedule based on the date and time proposed by the adjustment unit, A reminder unit that sends reminders based on a schedule incorporated by the automation unit, A traffic information provision unit that provides transportation methods and travel time based on the reminder transmitted by the reminder unit, The system includes a reservation unit that makes reservations based on information provided by the aforementioned traffic information provision unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect information from the internet and provide information tailored to the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 3) The adjustment unit is, Check everyone's schedule and suggest a date and time that works for everyone. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned automation unit, Automatically add weekly meetings, monthly reports, and other scheduled events to the schedule. The system described in Appendix 1, characterized by the features described herein. (Note 5) The reminder unit is, Send a reminder the day before the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned traffic information provision department, Based on train and bus timetables, it suggests the most suitable mode of transportation and travel time. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reservation section is, Use online reservation systems to book restaurants and concerts. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past search history to select the most suitable information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting information, filtering is performed based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The adjustment unit is, The system estimates the user's emotions and adjusts the way scheduling is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The adjustment unit is, During the adjustment process, the level of detail will be adjusted based on the importance of each participant. The system described in Appendix 1, characterized by the features described herein. (Note 16) The adjustment unit is, During the adjustment process, different adjustment algorithms are applied depending on the participant's attributes. The system described in Appendix 1, characterized by the features described herein. (Note 17) The adjustment unit is, The system estimates the user's emotions and adjusts the length of the adjustment based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The adjustment unit is, During the coordination process, priority will be determined based on the submission timing of participants. The system described in Appendix 1, characterized by the features described herein. (Note 19) The adjustment unit is, During the adjustment process, the order of adjustments will be adjusted based on the relevance of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned automation unit, It estimates the user's emotions and adjusts the automation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned automation unit, During automation, the system analyzes the user's past schedule to select the optimal automation method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned automation unit, During automation, the automation methods are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned automation unit, It estimates user emotions and determines automation priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned automation unit, When automating processes, the system selects the optimal automation method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned automation unit, During automation, we analyze users' social media activity and suggest automation methods. The system described in Appendix 1, characterized by the features described herein. (Note 26) The reminder unit is, It estimates the user's emotions and adjusts how reminders are sent based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The reminder unit is, When sending a reminder, the system will refer to the user's past reminder history to select the most suitable sending method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The reminder unit is, When sending a reminder, customize the reminder content based on the user's current schedule. The system described in Appendix 1, characterized by the features described herein. (Note 29) The reminder unit is, It estimates the user's emotions and prioritizes reminders based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The reminder unit is, When sending reminders, the system selects the optimal sending method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The reminder unit is, When sending reminders, the system analyzes the user's social media activity to suggest appropriate reminder content. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned traffic information provision department, The system estimates the user's emotions and adjusts how traffic information is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned traffic information provision department, When providing traffic information, the system selects the optimal method of delivery by referring to the user's past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned traffic information provision department, When providing traffic information, customize the content of the information based on the user's current travel status. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned traffic information provision department, The system estimates the user's emotions and prioritizes traffic information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned traffic information provision department, When providing traffic information, the optimal method of delivery is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned traffic information provision department, When providing traffic information, we analyze users' social media activity to suggest content for the information. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned reservation section is, It estimates the user's emotions and adjusts the booking method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned reservation section is, When a reservation is made, the system will refer to the user's past reservation history to select the most suitable reservation method. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned reservation section is, When making a reservation, customize the reservation details based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned reservation section is, The system estimates the user's emotions and determines reservation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned reservation section is, When making a reservation, the system will select the most suitable reservation method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned reservation section is, When you make a reservation, we analyze your social media activity and suggest reservation options. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0209] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The information collection unit, Based on the information collected by the aforementioned collection unit, the adjustment unit compares the participants' schedules and proposes the most suitable date and time. An automation unit that automatically incorporates regular events and habits into a schedule based on the date and time proposed by the adjustment unit, A reminder unit that sends reminders based on a schedule incorporated by the automation unit, A traffic information provision unit that provides transportation methods and travel time based on the reminder transmitted by the reminder unit, The system includes a reservation unit that makes reservations based on information provided by the aforementioned traffic information provision unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect information from the internet and provide information tailored to the user's preferences. The system according to feature 1.

3. The adjustment unit is, Check everyone's schedule and suggest a date and time that works for everyone. The system according to feature 1.

4. The aforementioned automation unit, Automatically add weekly meetings, monthly reports, and other scheduled events to the schedule. The system according to feature 1.

5. The reminder unit is, Send a reminder the day before the meeting. The system according to feature 1.

6. The aforementioned traffic information provision department, Based on train and bus timetables, it suggests the most suitable mode of transportation and travel time. The system according to feature 1.

7. The aforementioned reservation section is, Use online reservation systems to book restaurants and concerts. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

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