system

Abstract

The system according to this embodiment aims to efficiently manage and coordinate the schedules of all family members. [Solution] The system according to the embodiment comprises a management unit, a centralized management unit, a coordination unit, and a proposal unit. The management unit manages the schedules of each member. The centralized management unit centrally manages the schedules managed by the management unit. The coordination unit finds time slots when all family members are free based on the schedules centrally managed by the centralized management unit. The proposal unit proposes plans such as dining out or traveling based on the time slots found by the coordination 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to efficiently manage and adjust the schedules of all family members.

[0005] The system according to the embodiment aims to efficiently manage and adjust the schedules of all family members.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a management unit, a centralized management unit, a coordination unit, and a proposal unit. The management unit manages the schedules of each member. The centralized management unit centrally manages the schedules managed by the management unit. The coordination unit finds time slots when all family members are free based on the schedules centrally managed by the centralized management unit. The proposal unit proposes plans such as dining out or traveling based on the time slots found by the coordination unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage and coordinate the schedules of all family members. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable 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 receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The schedule management system according to an embodiment of the present invention is a schedule application that can be used by all family members. This schedule management system has the function of having an AI agent for each member manage and adjust schedules. It centrally manages family schedules, and the AI ​​agent can answer questions about family schedules and suggest activities such as dining out or traveling when all family members are free. First, each member's AI agent manages their individual schedule. For example, the AI ​​agent grasps children's school events and parents' work schedules and reflects them in the schedule. This allows for centralized management of all family members' schedules, making it easy to see who is free at what time. Next, the AI ​​agent adjusts the schedules of all family members. For example, it finds time slots when all family members are free and suggests plans such as dining out or traveling at those times. This allows for efficient planning of time for all family members to spend together. Furthermore, the AI ​​agent can answer questions about family schedules. For example, in response to a question such as, "What time is everyone free this weekend?", the AI ​​agent will answer immediately. This eliminates the need to check family schedules and allows for smooth planning. Because this schedule application can be used by all family members, it facilitates smoother family communication. Furthermore, by having an AI agent manage and adjust schedules, families can efficiently plan time to spend together. This strengthens family bonds and allows them to spend more fulfilling time together. Thus, the schedule management system can efficiently manage, adjust, and suggest schedules for the entire family.

[0029] The schedule management system according to this embodiment comprises a management unit, a centralized management unit, a coordination unit, and a proposal unit. The management unit manages the schedules of each member. The management unit, for example, grasps the schedules of children's school events and parents' work schedules and reflects them in the schedule. The management unit can also manage schedules using AI. The centralized management unit centrally manages the schedules managed by the management unit. The centralized management unit, for example, integrates the schedules of each member and centrally manages the schedules of the entire family. The centralized management unit can also centrally manage schedules using AI. The coordination unit finds time slots when all family members are free based on the schedules centrally managed by the centralized management unit. The coordination unit, for example, finds time slots when all family members are free and proposes plans such as dining out or traveling during those times. The coordination unit can also adjust schedules using AI. The proposal unit proposes plans such as dining out or traveling based on the time slots found by the coordination unit. The proposal unit, for example, proposes plans such as dining out or traveling during time slots when all family members are free. The proposal unit can also propose plans using AI. This allows the schedule management system to efficiently manage, adjust, and suggest schedules for the entire family.

[0030] The management department manages each member's schedule. For example, the management department keeps track of children's school events and parents' work schedules and incorporates them into the schedule. Specifically, the management department collects schedules from each member's calendar app or planner and manages them centrally in a digital format. This allows everyone in the family to see their schedule at a glance. The management department can also manage schedules using AI. The AI ​​learns from each member's past schedule data and recognizes patterns to predict future events and automatically set reminders. For example, the AI ​​learns that a child's school event takes place on the second Saturday of every month and sends a reminder the day before. Also, if a parent's work schedule changes frequently, the AI ​​understands this trend and immediately updates the schedule when changes occur. Furthermore, the management department has a function to set priorities for each member's schedule and highlight important events. This allows everyone in the family to manage their schedules efficiently without missing important events.

[0031] The Centralized Management Department centrally manages schedules managed by the Management Department. For example, the Centralized Management Department integrates each member's schedule and centrally manages the schedules of the entire family. Specifically, the Centralized Management Department stores each member's schedule data on the cloud and updates it in real time. This allows all family members to check the latest schedule anytime, anywhere. The Centralized Management Department can also centrally manage schedules using AI. The AI ​​analyzes each member's schedule data and automatically detects and corrects duplicates and inconsistencies. The AI ​​also records the change history of each member's schedule and improves the reliability of the schedule by referring to past changes. Furthermore, the Centralized Management Department has a function to protect privacy by setting access permissions for each member's schedule. For example, parents can check their children's schedules, but children cannot check their parents' work schedules. In this way, the Centralized Management Department can efficiently centrally manage the schedules of the entire family, maintain schedule consistency while protecting privacy.

[0032] The scheduling unit finds time slots when all family members are free, based on a schedule centrally managed by the centralized management unit. For example, the scheduling unit finds time slots when all family members are free and proposes plans such as dining out or traveling during those times. Specifically, the scheduling unit analyzes each member's schedule data to identify common free time. It can also adjust schedules using AI. The AI ​​analyzes each member's schedule data and calculates the optimal free time. The AI ​​can also learn from each member's past schedule data and predict times when all family members are likely to participate. For example, the AI ​​learns from past data that all family members are relatively free on Sunday afternoons and proposes dining out or traveling plans for that time slot. Furthermore, the scheduling unit also has the function to adjust schedules considering each member's priorities and preferences. For example, if a parent needs to attend an important meeting, the scheduling unit will adjust the family's schedule to avoid that time slot. This allows the scheduling unit to efficiently adjust the schedules of all family members and find common free time.

[0033] The suggestion department proposes plans for activities such as dining out and travel based on time slots identified by the coordination department. For example, the suggestion department proposes plans for dining out or travel during times when all family members are free. Specifically, the suggestion department proposes the optimal plan by considering each member's preferences and past history. It can also propose plans using AI. The AI ​​learns each member's past selection history and ratings to generate the optimal plan. For example, the AI ​​prioritizes suggesting restaurants and tourist destinations that all family members have given high ratings to in the past. The AI ​​can also propose the optimal plan in real time by considering the current weather and traffic conditions. For example, it will suggest indoor activities in case of rain and suggest nearby spots if traffic congestion is expected. Furthermore, the suggestion department has a function to collect feedback on proposed plans and reflect it in future suggestions. This allows the suggestion department to propose the optimal plan that suits the preferences and circumstances of all family members, making family time more fulfilling.

[0034] The shared section allows each member's AI agent to share schedules. For example, the shared section can share each member's schedule, allowing for centralized management of the entire family's plans. The shared section can also use AI to share schedules. This allows for centralized management of the entire family's plans by sharing each member's schedule. The AI ​​agent, for example, shares each member's schedule, allowing for centralized management of the entire family's plans. The AI ​​agent can also use AI technology to share schedules. The AI ​​agent, for example, shares each member's schedule, allowing for centralized management of the entire family's plans. The AI ​​agent can also use AI technology to share schedules. This allows for centralized management of the entire family's plans by sharing each member's schedule.

[0035] The answering unit will answer questions about family plans. For example, the answering unit will instantly answer questions about family plans. The answering unit can also use AI to answer questions about family plans. This allows for instant answers to questions about family plans. For example, the answering unit will instantly answer questions about family plans. The answering unit can also use AI technology to answer questions about family plans. For example, the answering unit will instantly answer questions about family plans. The answering unit can also use AI technology to answer questions about family plans. This allows for instant answers to questions about family plans.

[0036] The algorithm unit executes a schedule adjustment algorithm. The algorithm unit, for example, executes a schedule adjustment algorithm to improve the accuracy of the adjustment. The algorithm unit can also execute the schedule adjustment algorithm using AI. This improves the accuracy of the adjustment by executing the schedule adjustment algorithm. The algorithm unit, for example, executes a schedule adjustment algorithm to improve the accuracy of the adjustment. The algorithm unit can also execute the schedule adjustment algorithm using AI technology. The algorithm unit, for example, executes a schedule adjustment algorithm to improve the accuracy of the adjustment. The algorithm unit can also execute the schedule adjustment algorithm using AI technology. This improves the accuracy of the adjustment by executing the schedule adjustment algorithm.

[0037] The management department can analyze each member's past schedule history and select the optimal schedule management method. For example, the management department can analyze each member's past schedule history and propose the most efficient schedule management method. The management department can also analyze each member's past schedule history and propose the least stressful schedule management method. The management department can also analyze each member's past schedule history and propose the most comprehensive schedule management method. In this way, the optimal schedule management method can be selected by analyzing past schedule history. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input each member's past schedule history data into a generating AI and have the generating AI select the optimal schedule management method.

[0038] The management department can adjust schedules while considering the health status and fatigue levels of its members. For example, the management department can monitor the health status of its members and adjust schedules accordingly. The management department can also monitor the fatigue levels of its members and adjust schedules accordingly. The management department can also propose manageable schedules while considering the health status and fatigue levels of its members. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input member health status and fatigue data into a generating AI and have the generating AI perform schedule adjustments.

[0039] The management department can propose an optimal schedule by considering the geographical location information of the members when managing schedules. For example, the management department can propose an optimal schedule by considering the current location of the members. The management department can also propose an optimal schedule by considering the travel route of the members. The management department can also propose an optimal schedule by considering the geographical location information of the members. In this way, an optimal schedule can be proposed by considering the geographical location information of the members. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input the geographical location information data of the members into a generating AI and have the generating AI execute the process of proposing an optimal schedule.

[0040] The management department can analyze members' social media activity and add relevant events to the schedule when managing it. For example, the management department can analyze members' social media activity and add relevant events to the schedule. The management department can also analyze members' social media activity and add events of interest to the schedule. The management department can also analyze members' social media activity and add events with friends to the schedule. In this way, relevant events can be added to the schedule by analyzing social media activity. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input members' social media activity data into a generating AI and have the generating AI add relevant events.

[0041] The centralized management unit can determine the display priority based on the importance of each member's schedule during centralized management. For example, the centralized management unit can determine the display priority based on the importance of each member's schedule. The centralized management unit can also determine the display priority based on the importance of each member's schedule. The centralized management unit can also determine the display priority based on the importance of each member's schedule. This allows important schedules to be displayed preferentially by determining the display priority based on the importance of the schedule. Some or all of the above processing in the centralized management unit may be performed using AI or not. For example, the centralized management unit can input the importance data of each member's schedule into a generating AI and have the generating AI perform the determination of the display priority.

[0042] The centralized management unit can apply different display algorithms to each schedule category during centralized management. For example, the centralized management unit can apply different display algorithms to each schedule category. The centralized management unit can also apply different display algorithms to each schedule category. By applying different display algorithms to each schedule category, a more appropriate display becomes possible. Some or all of the above processing in the centralized management unit may be performed using AI or not. For example, the centralized management unit can input schedule category data into a generating AI and have the generating AI execute the application of the display algorithm.

[0043] The centralized management unit can determine the display priority based on the schedule submission date during centralized management. For example, the centralized management unit can determine the display priority based on the schedule submission date. The centralized management unit can also determine the display priority based on the schedule submission date. By determining the display priority based on the schedule submission date, it becomes possible to display schedules according to their submission date. Some or all of the above processing in the centralized management unit may be performed using AI or not. For example, the centralized management unit can input schedule submission date data into a generating AI and have the generating AI perform the determination of the display priority.

[0044] The centralized management unit can adjust the display order based on the relevance of schedules during centralized management. For example, the centralized management unit can adjust the display order based on the relevance of schedules. The centralized management unit can also adjust the display order based on the relevance of schedules. By adjusting the display order based on the relevance of schedules, schedules with high relevance can be displayed preferentially. Some or all of the above processing in the centralized management unit may be performed using AI or not. For example, the centralized management unit can input schedule relevance data into a generating AI and have the generating AI perform the adjustment of the display order.

[0045] The adjustment unit can improve the accuracy of the adjustment by considering the interrelationships of the schedules during the adjustment process. The adjustment unit can, for example, improve the accuracy of the adjustment by considering the interrelationships of the schedules. The adjustment unit can also improve the accuracy of the adjustment by considering the interrelationships of the schedules. The adjustment unit can also improve the accuracy of the adjustment by considering the interrelationships of the schedules. As a result, the accuracy of the adjustment is improved by considering the interrelationships of the schedules. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input schedule interrelationship data into a generating AI and have the generating AI perform the adjustment accuracy improvement.

[0046] The adjustment unit can perform adjustments while considering the attribute information of the schedule submitter. For example, the adjustment unit can perform adjustments while considering the attribute information of the schedule submitter. The adjustment unit can also perform adjustments while considering the attribute information of the schedule submitter. The adjustment unit can also perform adjustments while considering the attribute information of the schedule submitter. This makes it possible to perform more appropriate adjustments by considering the attribute information of the schedule submitter. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the attribute information data of the schedule submitter into a generating AI and have the generating AI perform the adjustments.

[0047] The adjustment unit can perform adjustments while considering the geographical distribution of the schedule. For example, the adjustment unit can perform adjustments while considering the geographical distribution of the schedule. The adjustment unit can also perform adjustments while considering the geographical distribution of the schedule. This makes it possible to perform more appropriate adjustments by considering the geographical distribution of the schedule. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input geographical distribution data of the schedule into a generating AI and have the generating AI perform the adjustments.

[0048] The adjustment unit can improve the accuracy of the adjustment by referring to relevant literature on the schedule during the adjustment process. The adjustment unit can, for example, improve the accuracy of the adjustment by referring to relevant literature on the schedule. The adjustment unit can also improve the accuracy of the adjustment by referring to relevant literature on the schedule. The adjustment unit can also improve the accuracy of the adjustment by referring to relevant literature on the schedule. As a result, the accuracy of the adjustment is improved by referring to relevant literature on the schedule. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input relevant literature data on the schedule into a generating AI and have the generating AI perform the improvement of the adjustment accuracy.

[0049] The proposal unit can adjust the level of detail of a proposal based on its importance. For example, if the proposal is highly important, the proposal unit will provide a detailed proposal. If the proposal is of moderate importance, the proposal unit can provide a proposal with an appropriate level of detail. If the proposal is of low importance, the proposal unit can provide a concise proposal. By adjusting the level of detail of a proposal based on its importance, more appropriate proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI, or not. For example, the proposal unit can input the importance data of the proposal into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposal.

[0050] The proposal unit can apply different proposal algorithms depending on the category of the proposal. For example, if the proposal is for dining out, the proposal unit will apply a restaurant proposal algorithm. If the proposal is for travel, the proposal unit can also apply a travel plan proposal algorithm. If the proposal is for an event, the proposal unit can also apply an event proposal algorithm. By applying different proposal algorithms depending on the category of the proposal, more appropriate proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input category data of the proposal into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0051] The proposal department can determine the priority of proposals based on the submission timing of each proposal. For example, if a proposal is submitted early, the department will prioritize it. If a proposal is submitted at a moderate time, the department may give it a moderate priority. If a proposal is submitted late, the department may postpone it. By determining the priority of proposals based on the submission timing, it becomes possible to make more appropriate proposals. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input proposal submission timing data into a generating AI and have the generating AI determine the priority of the proposals.

[0052] The proposal unit can adjust the order of proposals based on their relevance. For example, if the proposals are highly relevant, the unit will prioritize them. If the proposals are moderately relevant, the unit can also propose them in an appropriate order. If the proposals are less relevant, the unit can postpone proposing them. By adjusting the order of proposals based on their relevance, more appropriate proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input relevance data of the proposals into a generating AI and have the generating AI adjust the order of proposals.

[0053] The sharing unit can determine the priority of sharing based on the importance of each member's schedule when sharing. The sharing unit can, for example, determine the priority of sharing based on the importance of each member's schedule. The sharing unit can also determine the priority of sharing based on the importance of each member's schedule. The sharing unit can also determine the priority of sharing based on the importance of each member's schedule. This allows important schedules to be shared preferentially by determining the priority of sharing based on the importance of each member's schedule. Some or all of the above processing in the sharing unit may be performed using AI or not. For example, the sharing unit can input the importance data of each member's schedule into a generating AI and have the generating AI perform the determination of the sharing priority.

[0054] The sharing unit can determine the priority of sharing based on the schedule submission date when sharing. The sharing unit can, for example, determine the priority of sharing based on the schedule submission date. The sharing unit can also determine the priority of sharing based on the schedule submission date. The sharing unit can also determine the priority of sharing based on the schedule submission date. This makes it possible to share according to the submission date by determining the priority of sharing based on the schedule submission date. Some or all of the above processing in the sharing unit may be performed using AI or not. For example, the sharing unit can input schedule submission date data into a generating AI and have the generating AI perform the determination of the sharing priority.

[0055] The answering unit can adjust the level of detail in its response based on the importance of the question. For example, if the question is highly important, the answering unit will provide a detailed response. If the question is of moderate importance, the answering unit may provide a response with an appropriate level of detail. If the question is of low importance, the answering unit may provide a concise response. By adjusting the level of detail in the response based on the importance of the question, a more appropriate response can be provided. Some or all of the above processing in the answering unit may be performed using AI, or not. For example, the answering unit can input question importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the response.

[0056] The response unit can determine the priority of responses based on when the questions were submitted. For example, if the question was submitted early, the response unit will prioritize the response. If the question was submitted at a moderate time, the response unit can also provide a response with an appropriate priority. If the question was submitted late, the response unit can postpone the response. This allows for responses tailored to the submission timing by determining the priority of responses based on the submission timing. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input the question submission timing data into a generating AI and have the generating AI determine the priority of responses.

[0057] The algorithm unit can optimize the algorithm by referring to past data during algorithm execution. For example, the algorithm unit can optimize the algorithm by referring to past data. The algorithm unit can also optimize the algorithm by referring to past data. This makes it possible to optimize the algorithm by referring to past data. Some or all of the above processing in the algorithm unit may be performed using AI or not. For example, the algorithm unit can input past data into a generating AI and have the generating AI perform the algorithm optimization.

[0058] The algorithm unit can weight the algorithms based on the schedule submission timing when executing the algorithms. For example, the algorithm unit weights the algorithms based on the schedule submission timing. The algorithm unit can also weight the algorithms based on the schedule submission timing. By weighting the algorithms based on the schedule submission timing, it becomes possible to execute more appropriate algorithms. Some or all of the above processing in the algorithm unit may be performed using AI or not. For example, the algorithm unit can input schedule submission timing data into a generating AI and have the generating AI perform the algorithm weighting.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The management team can provide a reminder function based on each member's schedule. For example, reminders can be sent before important meetings or school events to help members remember their appointments. Furthermore, users can choose how reminders are sent, such as email, SMS, or in-app notifications, according to their preferences. The content of the reminders can also include details of the appointment and necessary preparations. This allows users to smoothly manage their daily lives without forgetting important appointments.

[0061] The shared section can generate a common calendar based on each member's schedule. For example, by consolidating the schedules of the entire family into a single calendar, it's possible to see at a glance who is free at what time. Furthermore, the common calendar can use color coding and icons to visually display the type and importance of appointments. In addition, the common calendar can be accessed from a web browser or smartphone app, allowing it to be checked anytime, anywhere. This makes managing the schedules of the entire family more efficient.

[0062] The answering function can respond to questions about family plans through a voice assistant. For example, if a user asks "What are our plans for this weekend?", the voice assistant will respond immediately. The voice assistant can also provide additional information in response to the user's question. For example, in response to "What are our plans for this weekend?", it can provide a more detailed answer such as "We're planning a family picnic on Saturday and going to the movies on Sunday." Furthermore, the voice assistant can adjust the tone and style of its responses according to the user's preferences. This makes it easy for users to check their family's plans.

[0063] The algorithm unit can consider the priority of each member when executing the schedule adjustment algorithm. For example, if there is an important meeting or exam, the schedule of that member will be prioritized. Also, for events involving the whole family, it can adjust everyone's schedule to find the optimal time. Furthermore, the algorithm unit can analyze past schedule data and learn the best adjustment methods. This improves the accuracy of schedule adjustments and allows it to propose a schedule that satisfies the whole family.

[0064] The management department can analyze each member's past schedule history and select the optimal schedule management method. For example, they can analyze each member's past schedule history and propose the most efficient schedule management method. The management department can also analyze each member's past schedule history and propose the least stressful schedule management method. The management department can also analyze each member's past schedule history and propose the most comprehensive schedule management method. In this way, the optimal schedule management method can be selected by analyzing past schedule history.

[0065] The management department can adjust schedules while considering the health and fatigue levels of team members. For example, the management department can monitor the health status of team members and adjust schedules accordingly. The management department can also monitor the fatigue levels of team members and adjust schedules accordingly. The management department can also propose manageable schedules that take into account the health and fatigue levels of team members. This allows for the proposal of manageable schedules that consider the health and fatigue levels of team members.

[0066] The management department can propose the optimal schedule by considering the geographical location information of the members when managing schedules. For example, the management department can propose the optimal schedule by considering the current location of the members. The management department can also propose the optimal schedule by considering the travel route of the members. The management department can also propose the optimal schedule by considering the geographical location information of the members. In this way, the optimal schedule can be proposed by considering the geographical location information of the members.

[0067] The management department can analyze members' social media activity and add relevant events to the schedule when managing it. For example, the management department can analyze members' social media activity and add relevant events to the schedule. The management department can also analyze members' social media activity and add events of interest to the schedule. The management department can also analyze members' social media activity and add events with friends to the schedule. This allows for the addition of relevant events to the schedule by analyzing social media activity.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The management department manages each member's schedule. For example, they keep track of children's school events and parents' work schedules and reflect them in the schedule. The management department can also use AI to manage schedules. Step 2: The centralized management department centrally manages the schedules managed by the management department. For example, it integrates each member's schedule and centrally manages the schedules of the entire family. The centralized management department can also use AI to centrally manage schedules. Step 3: The scheduling unit finds time slots when all family members are free, based on the schedule centrally managed by the centralized management unit. For example, it finds time slots when all family members are free and suggests plans such as dining out or traveling during those times. The scheduling unit can also use AI to adjust the schedule. Step 4: The proposal department suggests plans such as dining out or traveling based on the time slots identified by the coordination department. For example, it might suggest plans for dining out or traveling during times when the whole family is free. The proposal department can also use AI to suggest plans.

[0070] (Example of form 2) The schedule management system according to an embodiment of the present invention is a schedule application that can be used by all family members. This schedule management system has the function of having an AI agent for each member manage and adjust schedules. It centrally manages family schedules, and the AI ​​agent can answer questions about family schedules and suggest activities such as dining out or traveling when all family members are free. First, each member's AI agent manages their individual schedule. For example, the AI ​​agent grasps children's school events and parents' work schedules and reflects them in the schedule. This allows for centralized management of all family members' schedules, making it easy to see who is free at what time. Next, the AI ​​agent adjusts the schedules of all family members. For example, it finds time slots when all family members are free and suggests plans such as dining out or traveling at those times. This allows for efficient planning of time for all family members to spend together. Furthermore, the AI ​​agent can answer questions about family schedules. For example, in response to a question such as, "What time is everyone free this weekend?", the AI ​​agent will answer immediately. This eliminates the need to check family schedules and allows for smooth planning. Because this schedule application can be used by all family members, it facilitates smoother family communication. Furthermore, by having an AI agent manage and adjust schedules, families can efficiently plan time to spend together. This strengthens family bonds and allows them to spend more fulfilling time together. Thus, the schedule management system can efficiently manage, adjust, and suggest schedules for the entire family.

[0071] The schedule management system according to this embodiment comprises a management unit, a centralized management unit, a coordination unit, and a proposal unit. The management unit manages the schedules of each member. The management unit, for example, grasps the schedules of children's school events and parents' work schedules and reflects them in the schedule. The management unit can also manage schedules using AI. The centralized management unit centrally manages the schedules managed by the management unit. The centralized management unit, for example, integrates the schedules of each member and centrally manages the schedules of the entire family. The centralized management unit can also centrally manage schedules using AI. The coordination unit finds time slots when all family members are free based on the schedules centrally managed by the centralized management unit. The coordination unit, for example, finds time slots when all family members are free and proposes plans such as dining out or traveling during those times. The coordination unit can also adjust schedules using AI. The proposal unit proposes plans such as dining out or traveling based on the time slots found by the coordination unit. The proposal unit, for example, proposes plans such as dining out or traveling during time slots when all family members are free. The proposal unit can also propose plans using AI. This allows the schedule management system to efficiently manage, adjust, and suggest schedules for the entire family.

[0072] The management department manages each member's schedule. For example, the management department keeps track of children's school events and parents' work schedules and incorporates them into the schedule. Specifically, the management department collects schedules from each member's calendar app or planner and manages them centrally in a digital format. This allows everyone in the family to see their schedule at a glance. The management department can also manage schedules using AI. The AI ​​learns from each member's past schedule data and recognizes patterns to predict future events and automatically set reminders. For example, the AI ​​learns that a child's school event takes place on the second Saturday of every month and sends a reminder the day before. Also, if a parent's work schedule changes frequently, the AI ​​understands this trend and immediately updates the schedule when changes occur. Furthermore, the management department has a function to set priorities for each member's schedule and highlight important events. This allows everyone in the family to manage their schedules efficiently without missing important events.

[0073] The Centralized Management Department centrally manages schedules managed by the Management Department. For example, the Centralized Management Department integrates each member's schedule and centrally manages the schedules of the entire family. Specifically, the Centralized Management Department stores each member's schedule data on the cloud and updates it in real time. This allows all family members to check the latest schedule anytime, anywhere. The Centralized Management Department can also centrally manage schedules using AI. The AI ​​analyzes each member's schedule data and automatically detects and corrects duplicates and inconsistencies. The AI ​​also records the change history of each member's schedule and improves the reliability of the schedule by referring to past changes. Furthermore, the Centralized Management Department has a function to protect privacy by setting access permissions for each member's schedule. For example, parents can check their children's schedules, but children cannot check their parents' work schedules. In this way, the Centralized Management Department can efficiently centrally manage the schedules of the entire family, maintain schedule consistency while protecting privacy.

[0074] The scheduling unit finds time slots when all family members are free, based on a schedule centrally managed by the centralized management unit. For example, the scheduling unit finds time slots when all family members are free and proposes plans such as dining out or traveling during those times. Specifically, the scheduling unit analyzes each member's schedule data to identify common free time. It can also adjust schedules using AI. The AI ​​analyzes each member's schedule data and calculates the optimal free time. The AI ​​can also learn from each member's past schedule data and predict times when all family members are likely to participate. For example, the AI ​​learns from past data that all family members are relatively free on Sunday afternoons and proposes dining out or traveling plans for that time slot. Furthermore, the scheduling unit also has the function to adjust schedules considering each member's priorities and preferences. For example, if a parent needs to attend an important meeting, the scheduling unit will adjust the family's schedule to avoid that time slot. This allows the scheduling unit to efficiently adjust the schedules of all family members and find common free time.

[0075] The suggestion department proposes plans for activities such as dining out and travel based on time slots identified by the coordination department. For example, the suggestion department proposes plans for dining out or travel during times when all family members are free. Specifically, the suggestion department proposes the optimal plan by considering each member's preferences and past history. It can also propose plans using AI. The AI ​​learns each member's past selection history and ratings to generate the optimal plan. For example, the AI ​​prioritizes suggesting restaurants and tourist destinations that all family members have given high ratings to in the past. The AI ​​can also propose the optimal plan in real time by considering the current weather and traffic conditions. For example, it will suggest indoor activities in case of rain and suggest nearby spots if traffic congestion is expected. Furthermore, the suggestion department has a function to collect feedback on proposed plans and reflect it in future suggestions. This allows the suggestion department to propose the optimal plan that suits the preferences and circumstances of all family members, making family time more fulfilling.

[0076] The shared section allows each member's AI agent to share schedules. For example, the shared section can share each member's schedule, allowing for centralized management of the entire family's plans. The shared section can also use AI to share schedules. This allows for centralized management of the entire family's plans by sharing each member's schedule. The AI ​​agent, for example, shares each member's schedule, allowing for centralized management of the entire family's plans. The AI ​​agent can also use AI technology to share schedules. The AI ​​agent, for example, shares each member's schedule, allowing for centralized management of the entire family's plans. The AI ​​agent can also use AI technology to share schedules. This allows for centralized management of the entire family's plans by sharing each member's schedule.

[0077] The answering unit will answer questions about family plans. For example, the answering unit will instantly answer questions about family plans. The answering unit can also use AI to answer questions about family plans. This allows for instant answers to questions about family plans. For example, the answering unit will instantly answer questions about family plans. The answering unit can also use AI technology to answer questions about family plans. For example, the answering unit will instantly answer questions about family plans. The answering unit can also use AI technology to answer questions about family plans. This allows for instant answers to questions about family plans.

[0078] The algorithm unit executes a schedule adjustment algorithm. The algorithm unit, for example, executes a schedule adjustment algorithm to improve the accuracy of the adjustment. The algorithm unit can also execute the schedule adjustment algorithm using AI. This improves the accuracy of the adjustment by executing the schedule adjustment algorithm. The algorithm unit, for example, executes a schedule adjustment algorithm to improve the accuracy of the adjustment. The algorithm unit can also execute the schedule adjustment algorithm using AI technology. The algorithm unit, for example, executes a schedule adjustment algorithm to improve the accuracy of the adjustment. The algorithm unit can also execute the schedule adjustment algorithm using AI technology. This improves the accuracy of the adjustment by executing the schedule adjustment algorithm.

[0079] The management unit can estimate the emotions of family members and adjust schedule priorities based on those estimated emotions. For example, if a family member is stressed, the management unit can prioritize scheduling relaxation time. If a family member is tired, the management unit can also prioritize scheduling rest time. If a family member is having fun, the management unit can also prioritize scheduling enjoyable events. This allows for more appropriate schedule management by adjusting schedule priorities according to the emotions of family members. 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 management unit may be performed using AI or not. For example, the management unit can input family member emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The management department can analyze each member's past schedule history and select the optimal schedule management method. For example, the management department can analyze each member's past schedule history and propose the most efficient schedule management method. The management department can also analyze each member's past schedule history and propose the least stressful schedule management method. The management department can also analyze each member's past schedule history and propose the most comprehensive schedule management method. In this way, the optimal schedule management method can be selected by analyzing past schedule history. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input each member's past schedule history data into a generating AI and have the generating AI select the optimal schedule management method.

[0081] The management department can adjust schedules while considering the health status and fatigue levels of its members. For example, the management department can monitor the health status of its members and adjust schedules accordingly. The management department can also monitor the fatigue levels of its members and adjust schedules accordingly. The management department can also propose manageable schedules while considering the health status and fatigue levels of its members. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input member health status and fatigue data into a generating AI and have the generating AI perform schedule adjustments.

[0082] The management unit can estimate the emotions of family members and adjust the way schedules are notified based on the estimated emotions. For example, if a family member is feeling stressed, the management unit can suggest a relaxing notification method. If a family member is tired, the management unit can also suggest a notification method that encourages rest. If a family member is having fun, the management unit can also suggest a fun notification method. By adjusting the way schedules are notified according to the emotions of family members, more appropriate notifications can be made. Emotion estimation is achieved using an emotion estimation function, such as 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 management unit may be performed using AI or not. For example, the management unit can input family member emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The management department can propose an optimal schedule by considering the geographical location information of the members when managing schedules. For example, the management department can propose an optimal schedule by considering the current location of the members. The management department can also propose an optimal schedule by considering the travel route of the members. The management department can also propose an optimal schedule by considering the geographical location information of the members. In this way, an optimal schedule can be proposed by considering the geographical location information of the members. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input the geographical location information data of the members into a generating AI and have the generating AI execute the process of proposing an optimal schedule.

[0084] The management department can analyze members' social media activity and add relevant events to the schedule when managing it. For example, the management department can analyze members' social media activity and add relevant events to the schedule. The management department can also analyze members' social media activity and add events of interest to the schedule. The management department can also analyze members' social media activity and add events with friends to the schedule. In this way, relevant events can be added to the schedule by analyzing social media activity. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input members' social media activity data into a generating AI and have the generating AI add relevant events.

[0085] The centralized management unit can estimate the emotions of family members and adjust the display method of the centralized management based on the estimated emotions. For example, if a family member is feeling stressed, the centralized management unit can suggest a display method that promotes relaxation. If a family member is tired, the centralized management unit can also suggest a display method that encourages rest. If a family member is having fun, the centralized management unit can also suggest a fun display method. By adjusting the display method according to the emotions of family members, more appropriate displays become possible. 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 centralized management unit may be performed using AI or not. For example, the centralized management unit can input family member emotion data into a generative AI and have the generative AI perform emotion estimation.

[0086] The centralized management unit can determine the display priority based on the importance of each member's schedule during centralized management. For example, the centralized management unit can determine the display priority based on the importance of each member's schedule. The centralized management unit can also determine the display priority based on the importance of each member's schedule. The centralized management unit can also determine the display priority based on the importance of each member's schedule. This allows important schedules to be displayed preferentially by determining the display priority based on the importance of the schedule. Some or all of the above processing in the centralized management unit may be performed using AI or not. For example, the centralized management unit can input the importance data of each member's schedule into a generating AI and have the generating AI perform the determination of the display priority.

[0087] The centralized management unit can apply different display algorithms to each schedule category during centralized management. For example, the centralized management unit can apply different display algorithms to each schedule category. The centralized management unit can also apply different display algorithms to each schedule category. By applying different display algorithms to each schedule category, a more appropriate display becomes possible. Some or all of the above processing in the centralized management unit may be performed using AI or not. For example, the centralized management unit can input schedule category data into a generating AI and have the generating AI execute the application of the display algorithm.

[0088] The centralized management unit can estimate the emotions of family members and adjust the notification methods based on the estimated emotions. For example, if a family member is feeling stressed, the centralized management unit can suggest a relaxing notification method. If a family member is tired, the centralized management unit can also suggest a notification method that encourages rest. If a family member is having fun, the centralized management unit can also suggest a fun notification method. By adjusting the notification method according to the emotions of family members, more appropriate notifications become possible. 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 centralized management unit may be performed using AI or not. For example, the centralized management unit can input family member emotion data into a generative AI and have the generative AI perform emotion estimation.

[0089] The centralized management unit can determine the display priority based on the schedule submission date during centralized management. For example, the centralized management unit can determine the display priority based on the schedule submission date. The centralized management unit can also determine the display priority based on the schedule submission date. By determining the display priority based on the schedule submission date, it becomes possible to display schedules according to their submission date. Some or all of the above processing in the centralized management unit may be performed using AI or not. For example, the centralized management unit can input schedule submission date data into a generating AI and have the generating AI perform the determination of the display priority.

[0090] The centralized management unit can adjust the display order based on the relevance of schedules during centralized management. For example, the centralized management unit can adjust the display order based on the relevance of schedules. The centralized management unit can also adjust the display order based on the relevance of schedules. By adjusting the display order based on the relevance of schedules, schedules with high relevance can be displayed preferentially. Some or all of the above processing in the centralized management unit may be performed using AI or not. For example, the centralized management unit can input schedule relevance data into a generating AI and have the generating AI perform the adjustment of the display order.

[0091] The adjustment unit can estimate the emotions of family members and adjust the adjustment criteria based on the estimated emotions. For example, if a family member is feeling stressed, the adjustment unit can suggest adjustment criteria that promote relaxation. If a family member is tired, the adjustment unit can also suggest adjustment criteria that encourage rest. If a family member is having fun, the adjustment unit can also suggest adjustment criteria that promote enjoyment. By adjusting the adjustment criteria according to the emotions of family members, more appropriate adjustments can be made. 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 adjustment unit may be performed using AI or not. For example, the adjustment unit can input family member emotion data into a generative AI and have the generative AI perform emotion estimation.

[0092] The adjustment unit can improve the accuracy of the adjustment by considering the interrelationships of the schedules during the adjustment process. The adjustment unit can, for example, improve the accuracy of the adjustment by considering the interrelationships of the schedules. The adjustment unit can also improve the accuracy of the adjustment by considering the interrelationships of the schedules. The adjustment unit can also improve the accuracy of the adjustment by considering the interrelationships of the schedules. As a result, the accuracy of the adjustment is improved by considering the interrelationships of the schedules. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input schedule interrelationship data into a generating AI and have the generating AI perform the adjustment accuracy improvement.

[0093] The adjustment unit can perform adjustments while considering the attribute information of the schedule submitter. For example, the adjustment unit can perform adjustments while considering the attribute information of the schedule submitter. The adjustment unit can also perform adjustments while considering the attribute information of the schedule submitter. The adjustment unit can also perform adjustments while considering the attribute information of the schedule submitter. This makes it possible to perform more appropriate adjustments by considering the attribute information of the schedule submitter. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the attribute information data of the schedule submitter into a generating AI and have the generating AI perform the adjustments.

[0094] The adjustment unit can perform adjustments while considering the geographical distribution of the schedule. For example, the adjustment unit can perform adjustments while considering the geographical distribution of the schedule. The adjustment unit can also perform adjustments while considering the geographical distribution of the schedule. This makes it possible to perform more appropriate adjustments by considering the geographical distribution of the schedule. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input geographical distribution data of the schedule into a generating AI and have the generating AI perform the adjustments.

[0095] The adjustment unit can improve the accuracy of the adjustment by referring to relevant literature on the schedule during the adjustment process. The adjustment unit can, for example, improve the accuracy of the adjustment by referring to relevant literature on the schedule. The adjustment unit can also improve the accuracy of the adjustment by referring to relevant literature on the schedule. The adjustment unit can also improve the accuracy of the adjustment by referring to relevant literature on the schedule. As a result, the accuracy of the adjustment is improved by referring to relevant literature on the schedule. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input relevant literature data on the schedule into a generating AI and have the generating AI perform the improvement of the adjustment accuracy.

[0096] The suggestion unit can estimate the emotions of family members and adjust the way suggestions are expressed based on the estimated emotions. For example, if a family member is feeling stressed, the suggestion unit can suggest relaxing suggestions. If a family member is tired, the suggestion unit can also suggest restful suggestions. If a family member is having fun, the suggestion unit can also suggest cheerful suggestions. By adjusting the way suggestions are expressed according to the emotions of family members, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input family member emotion data into a generative AI and have the generative AI perform emotion estimation.

[0097] The proposal unit can adjust the level of detail of a proposal based on its importance. For example, if the proposal is highly important, the proposal unit will provide a detailed proposal. If the proposal is of moderate importance, the proposal unit can provide a proposal with an appropriate level of detail. If the proposal is of low importance, the proposal unit can provide a concise proposal. By adjusting the level of detail of a proposal based on its importance, more appropriate proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI, or not. For example, the proposal unit can input the importance data of the proposal into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposal.

[0098] The proposal unit can apply different proposal algorithms depending on the category of the proposal. For example, if the proposal is for dining out, the proposal unit will apply a restaurant proposal algorithm. If the proposal is for travel, the proposal unit can also apply a travel plan proposal algorithm. If the proposal is for an event, the proposal unit can also apply an event proposal algorithm. By applying different proposal algorithms depending on the category of the proposal, more appropriate proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input category data of the proposal into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0099] The suggestion unit can estimate the emotions of family members and adjust the length of the suggestion based on the estimated emotions. For example, if a family member is stressed, the suggestion unit can make a short, concise suggestion. If a family member is relaxed, the suggestion unit can also make a detailed suggestion. If a family member is having fun, the suggestion unit can also make a cheerful suggestion. By adjusting the length of the suggestion according to the emotions of the family members, more appropriate suggestions can be made. 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input family member emotion data into a generative AI and have the generative AI perform emotion estimation.

[0100] The proposal department can determine the priority of proposals based on the submission timing of each proposal. For example, if a proposal is submitted early, the department will prioritize it. If a proposal is submitted at a moderate time, the department may give it a moderate priority. If a proposal is submitted late, the department may postpone it. By determining the priority of proposals based on the submission timing, it becomes possible to make more appropriate proposals. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input proposal submission timing data into a generating AI and have the generating AI determine the priority of the proposals.

[0101] The proposal unit can adjust the order of proposals based on their relevance. For example, if the proposals are highly relevant, the unit will prioritize them. If the proposals are moderately relevant, the unit can also propose them in an appropriate order. If the proposals are less relevant, the unit can postpone proposing them. By adjusting the order of proposals based on their relevance, more appropriate proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input relevance data of the proposals into a generating AI and have the generating AI adjust the order of proposals.

[0102] The sharing unit can estimate the emotions of family members and adjust the sharing method based on the estimated emotions. For example, if a family member is feeling stressed, the sharing unit can suggest a relaxing sharing method. If a family member is tired, the sharing unit can also suggest a sharing method that encourages rest. If a family member is having fun, the sharing unit can also suggest a fun sharing method. This allows for more appropriate sharing by adjusting the sharing method according to the emotions of family members. 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 sharing unit may be performed using AI or not. For example, the sharing unit can input family member emotion data into a generative AI and have the generative AI perform emotion estimation.

[0103] The sharing unit can determine the priority of sharing based on the importance of each member's schedule when sharing. The sharing unit can, for example, determine the priority of sharing based on the importance of each member's schedule. The sharing unit can also determine the priority of sharing based on the importance of each member's schedule. The sharing unit can also determine the priority of sharing based on the importance of each member's schedule. This allows important schedules to be shared preferentially by determining the priority of sharing based on the importance of each member's schedule. Some or all of the above processing in the sharing unit may be performed using AI or not. For example, the sharing unit can input the importance data of each member's schedule into a generating AI and have the generating AI perform the determination of the sharing priority.

[0104] The sharing unit can estimate the emotions of family members and adjust the notification method based on the estimated emotions. For example, if a family member is feeling stressed, the sharing unit can suggest a relaxing notification method. If a family member is tired, the sharing unit can also suggest a notification method that encourages rest. If a family member is having fun, the sharing unit can also suggest a fun notification method. By adjusting the notification method according to the emotions of family members, more appropriate notifications become possible. Emotion estimation is achieved using an emotion estimation function, such as 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 sharing unit may be performed using AI or not. For example, the sharing unit can input family member emotion data into a generative AI and have the generative AI perform emotion estimation.

[0105] The sharing unit can determine the priority of sharing based on the schedule submission date when sharing. The sharing unit can, for example, determine the priority of sharing based on the schedule submission date. The sharing unit can also determine the priority of sharing based on the schedule submission date. The sharing unit can also determine the priority of sharing based on the schedule submission date. This makes it possible to share according to the submission date by determining the priority of sharing based on the schedule submission date. Some or all of the above processing in the sharing unit may be performed using AI or not. For example, the sharing unit can input schedule submission date data into a generating AI and have the generating AI perform the determination of the sharing priority.

[0106] The response unit can estimate the emotions of family members and adjust the expression of the response based on the estimated emotions. For example, if a family member is feeling stressed, the response unit can suggest a relaxing response. If a family member is tired, the response unit can also suggest a response that encourages rest. If a family member is having fun, the response unit can also suggest a cheerful response. By adjusting the expression of the response according to the emotions of the family members, more appropriate responses become possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input family member emotion data into a generative AI and have the generative AI perform emotion estimation.

[0107] The answering unit can adjust the level of detail in its response based on the importance of the question. For example, if the question is highly important, the answering unit will provide a detailed response. If the question is of moderate importance, the answering unit may provide a response with an appropriate level of detail. If the question is of low importance, the answering unit may provide a concise response. By adjusting the level of detail in the response based on the importance of the question, a more appropriate response can be provided. Some or all of the above processing in the answering unit may be performed using AI, or not. For example, the answering unit can input question importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the response.

[0108] The response unit can estimate the emotions of family members and adjust the length of its response based on the estimated emotions. For example, if a family member is stressed, the response unit will provide a short, concise response. If a family member is relaxed, the response unit can provide a detailed response. If a family member is having fun, the response unit can provide a cheerful response. By adjusting the length of the response according to the emotions of the family members, more appropriate responses can be provided. 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 response unit may be performed using AI or not. For example, the response unit can input family member emotion data into a generative AI and have the generative AI perform emotion estimation.

[0109] The response unit can determine the priority of responses based on when the questions were submitted. For example, if the question was submitted early, the response unit will prioritize the response. If the question was submitted at a moderate time, the response unit can also provide a response with an appropriate priority. If the question was submitted late, the response unit can postpone the response. This allows for responses tailored to the submission timing by determining the priority of responses based on the submission timing. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input the question submission timing data into a generating AI and have the generating AI determine the priority of responses.

[0110] The algorithm unit can estimate the emotions of family members and select an algorithm based on the estimated emotions. For example, if a family member is feeling stressed, the algorithm unit can select a relaxing algorithm. If a family member is tired, the algorithm unit can also select an algorithm that encourages rest. If a family member is having fun, the algorithm unit can also select a fun algorithm. In this way, by selecting an algorithm according to the emotions of family members, a more appropriate algorithm can be selected. 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 algorithm unit may be performed using AI or not. For example, the algorithm unit can input family member emotion data into a generative AI and have the generative AI perform emotion estimation.

[0111] The algorithm unit can optimize the algorithm by referring to past data during algorithm execution. For example, the algorithm unit can optimize the algorithm by referring to past data. The algorithm unit can also optimize the algorithm by referring to past data. This makes it possible to optimize the algorithm by referring to past data. Some or all of the above processing in the algorithm unit may be performed using AI or not. For example, the algorithm unit can input past data into a generating AI and have the generating AI perform the algorithm optimization.

[0112] The algorithm unit can estimate the emotions of family members and adjust the frequency of algorithm execution based on the estimated emotions. For example, if a family member is feeling stressed, the algorithm unit will execute the algorithm at a frequency that promotes relaxation. If a family member is tired, the algorithm unit can also execute the algorithm at a frequency that encourages rest. If a family member is having fun, the algorithm unit can also execute the algorithm at a frequency that promotes enjoyment. By adjusting the frequency of algorithm execution according to the emotions of family members, the algorithm is executed at a more appropriate frequency. 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 algorithm unit may be performed using AI or not. For example, the algorithm unit can input family member emotion data into a generative AI and have the generative AI perform emotion estimation.

[0113] The algorithm unit can weight the algorithms based on the schedule submission timing when executing the algorithms. For example, the algorithm unit weights the algorithms based on the schedule submission timing. The algorithm unit can also weight the algorithms based on the schedule submission timing. By weighting the algorithms based on the schedule submission timing, it becomes possible to execute more appropriate algorithms. Some or all of the above processing in the algorithm unit may be performed using AI or not. For example, the algorithm unit can input schedule submission timing data into a generating AI and have the generating AI perform the algorithm weighting.

[0114] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0115] The management team can provide a reminder function based on each member's schedule. For example, reminders can be sent before important meetings or school events to help members remember their appointments. Furthermore, users can choose how reminders are sent, such as email, SMS, or in-app notifications, according to their preferences. The content of the reminders can also include details of the appointment and necessary preparations. This allows users to smoothly manage their daily lives without forgetting important appointments.

[0116] The shared section can generate a common calendar based on each member's schedule. For example, by consolidating the schedules of the entire family into a single calendar, it's possible to see at a glance who is free at what time. Furthermore, the common calendar can use color coding and icons to visually display the type and importance of appointments. In addition, the common calendar can be accessed from a web browser or smartphone app, allowing it to be checked anytime, anywhere. This makes managing the schedules of the entire family more efficient.

[0117] The answering function can respond to questions about family plans through a voice assistant. For example, if a user asks "What are our plans for this weekend?", the voice assistant will respond immediately. The voice assistant can also provide additional information in response to the user's question. For example, in response to "What are our plans for this weekend?", it can provide a more detailed answer such as "We're planning a family picnic on Saturday and going to the movies on Sunday." Furthermore, the voice assistant can adjust the tone and style of its responses according to the user's preferences. This makes it easy for users to check their family's plans.

[0118] The algorithm unit can consider the priority of each member when executing the schedule adjustment algorithm. For example, if there is an important meeting or exam, the schedule of that member will be prioritized. Also, for events involving the whole family, it can adjust everyone's schedule to find the optimal time. Furthermore, the algorithm unit can analyze past schedule data and learn the best adjustment methods. This improves the accuracy of schedule adjustments and allows it to propose a schedule that satisfies the whole family.

[0119] The management system can estimate the emotions of family members and adjust the timing of schedule notifications based on those estimates. For example, if a family member is feeling stressed, it can send a notification during a time when they can relax. If a family member is tired, the management system can also send notifications before or after rest time. If a family member is having fun, the management system can send a notification before a fun event. This allows for more appropriate schedule management by adjusting notification timing according to the emotions of family members.

[0120] The management department can analyze each member's past schedule history and select the optimal schedule management method. For example, they can analyze each member's past schedule history and propose the most efficient schedule management method. The management department can also analyze each member's past schedule history and propose the least stressful schedule management method. The management department can also analyze each member's past schedule history and propose the most comprehensive schedule management method. In this way, the optimal schedule management method can be selected by analyzing past schedule history.

[0121] The management department can adjust schedules while considering the health and fatigue levels of team members. For example, the management department can monitor the health status of team members and adjust schedules accordingly. The management department can also monitor the fatigue levels of team members and adjust schedules accordingly. The management department can also propose manageable schedules that take into account the health and fatigue levels of team members. This allows for the proposal of manageable schedules that consider the health and fatigue levels of team members.

[0122] The management department can estimate the emotions of family members and adjust the way schedules are notified based on those estimates. For example, if a family member is feeling stressed, the management department can suggest a relaxing notification method. If a family member is tired, the management department can also suggest a notification method that encourages rest. If a family member is having fun, the management department can also suggest a fun notification method. By adjusting the way schedules are notified according to the emotions of family members, more appropriate notifications can be provided.

[0123] The management department can propose the optimal schedule by considering the geographical location information of the members when managing schedules. For example, the management department can propose the optimal schedule by considering the current location of the members. The management department can also propose the optimal schedule by considering the travel route of the members. The management department can also propose the optimal schedule by considering the geographical location information of the members. In this way, the optimal schedule can be proposed by considering the geographical location information of the members.

[0124] The management department can analyze members' social media activity and add relevant events to the schedule when managing it. For example, the management department can analyze members' social media activity and add relevant events to the schedule. The management department can also analyze members' social media activity and add events of interest to the schedule. The management department can also analyze members' social media activity and add events with friends to the schedule. This allows for the addition of relevant events to the schedule by analyzing social media activity.

[0125] The following briefly describes the processing flow for example form 2.

[0126] Step 1: The management department manages each member's schedule. For example, they keep track of children's school events and parents' work schedules and reflect them in the schedule. The management department can also use AI to manage schedules. Step 2: The centralized management department centrally manages the schedules managed by the management department. For example, it integrates each member's schedule and centrally manages the schedules of the entire family. The centralized management department can also use AI to centrally manage schedules. Step 3: The scheduling unit finds time slots when all family members are free, based on the schedule centrally managed by the centralized management unit. For example, it finds time slots when all family members are free and suggests plans such as dining out or traveling during those times. The scheduling unit can also use AI to adjust the schedule. Step 4: The proposal department suggests plans such as dining out or traveling based on the time slots identified by the coordination department. For example, it might suggest plans for dining out or traveling during times when the whole family is free. The proposal department can also use AI to suggest plans.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the management unit, centralized management unit, adjustment unit, proposal unit, sharing unit, response unit, and algorithm unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the management unit is implemented by the control unit 46A of the smart device 14 and manages the schedules of each member. The centralized management unit is implemented by the specific processing unit 290 of the data processing unit 12 and centrally manages the schedules of all family members. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and finds the free time slots for all family members. The proposal unit is implemented by the control unit 46A of the smart device 14 and proposes plans such as dining out or traveling. The sharing unit is implemented by the control unit 46A of the smart device 14 and shares the schedules of each member. The response unit is implemented by the specific processing unit 290 of the data processing unit 12 and answers questions about family plans. The algorithm unit is implemented by the specific processing unit 290 of the data processing unit 12 and executes a schedule adjustment algorithm. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0131] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the management unit, centralized management unit, coordination unit, proposal unit, sharing unit, response unit, and algorithm unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the management unit is implemented by the control unit 46A of the smart glasses 214 and manages the schedule of each member. The centralized management unit is implemented by the specific processing unit 290 of the data processing unit 12 and centrally manages the schedules of all family members. The coordination unit is implemented by the specific processing unit 290 of the data processing unit 12 and finds the time slots when all family members are free. The proposal unit is implemented by the control unit 46A of the smart glasses 214 and proposes plans such as dining out or traveling. The sharing unit is implemented by the control unit 46A of the smart glasses 214 and shares the schedules of each member. The response unit is implemented by the specific processing unit 290 of the data processing unit 12 and answers questions about family plans. The algorithm unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which executes the schedule adjustment algorithm. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0147] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In 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.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 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.

[0162] Each of the multiple elements described above, including the management unit, centralized management unit, coordination unit, proposal unit, sharing unit, response unit, and algorithm unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the management unit is implemented by the control unit 46A of the headset terminal 314 and manages the schedule of each member. The centralized management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and centrally manages the schedules of all family members. The coordination unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and finds time slots when all family members are free. The proposal unit is implemented by, for example, the control unit 46A of the headset terminal 314 and proposes plans such as dining out or traveling. The sharing unit is implemented by, for example, the control unit 46A of the headset terminal 314 and shares the schedules of each member. The response unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and answers questions about family plans. The algorithm unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which executes the schedule adjustment algorithm. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0163] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] Each of the multiple elements described above, including the management unit, centralized management unit, adjustment unit, proposal unit, sharing unit, answer unit, and algorithm unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the management unit is implemented by the control unit 46A of the robot 414 and manages the schedule of each member. The centralized management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and centrally manages the schedules of all family members. The adjustment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and finds the time slots when all family members are free. The proposal unit is implemented by, for example, the control unit 46A of the robot 414 and proposes plans such as dining out or traveling. The sharing unit is implemented by, for example, the control unit 46A of the robot 414 and shares the schedules of each member. The answer unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and answers questions about family plans. The algorithm unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and executes a schedule adjustment algorithm. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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."

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] (Note 1) The management department manages each member's schedule, A centralized management unit that centrally manages the schedules managed by the aforementioned management unit, Based on the schedule centrally managed by the aforementioned centralized management unit, the adjustment unit finds a time slot when all family members are free. The system includes a proposal unit that suggests plans such as dining out or traveling based on the time slots found by the adjustment unit. A system characterized by the following features. (Note 2) Each member's AI agent has a shared section where they can share schedules. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a section for answering questions about family plans. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes an algorithm section that executes a schedule adjustment algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned management department, It estimates the emotions of family members and adjusts schedule priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned management department, Analyze each member's past schedule history and select the optimal schedule management method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned management department, When managing schedules, we adjust them while taking into account the health status and fatigue levels of the members. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned management department, It estimates the emotions of family members and adjusts how schedule notifications are sent based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned management department, When managing schedules, we propose the optimal schedule by taking into account the geographical location of the members. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned management department, When managing schedules, analyze members' social media activity and add relevant events to the schedule. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned unified management unit, It estimates the emotions of family members and adjusts the display method for centralized management based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned unified management unit, When managing information centrally, display priority is determined based on the importance of each member's schedule. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned unified management unit, When managing schedules centrally, different display algorithms are applied to each schedule category. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned unified management unit, It estimates the emotions of family members and adjusts the notification method for centralized management based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned unified management unit, When managing everything centrally, prioritize the display based on the submission date of the schedule. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned unified management unit, When managing everything centrally, adjust the display order based on the relevance of the schedule. The system described in Appendix 1, characterized by the features described herein. (Note 17) The adjustment unit is, The system estimates the emotions of family members and adjusts the criteria for adjustment based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The adjustment unit is, During the adjustment process, we improve the accuracy of the adjustments by considering the interrelationships between schedules. The system described in Appendix 1, characterized by the features described herein. (Note 19) The adjustment unit is, During the adjustment process, the attribute information of the person submitting the schedule will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 20) The adjustment unit is, During the adjustment process, the geographical distribution of the schedule will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The adjustment unit is, During the adjustment process, refer to relevant literature on the schedule to improve the accuracy of the adjustments. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, Estimate the emotions of family members and adjust the way the proposal is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the proposed content. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When submitting a proposal, a different proposal algorithm is applied depending on the category of the proposal. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, Estimate the emotions of family members and adjust the length of the proposal based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When submitting a proposal, the priority of the proposals will be determined based on the timing of their submission. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When submitting proposals, adjust the order of the proposals based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned shared portion is, Estimate the emotions of family members and adjust sharing methods based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned shared portion is, When sharing, prioritize sharing based on the importance of each member's schedule. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned shared portion is, It estimates the emotions of family members and adjusts how shared notifications are made based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned shared portion is, When sharing, prioritize sharing based on the submission timing of the schedule. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned response section is, The system estimates the emotions of family members and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned response section is, When responding, adjust the level of detail in your answer based on the importance of the question. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned response section is, The system estimates the emotions of family members and adjusts the length of responses based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned response section is, When responding, prioritize your answers based on when you submitted your questions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned algorithm unit is The system estimates the emotions of family members and selects an algorithm based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 37) The aforementioned algorithm unit is When executing the algorithm, the algorithm is optimized by referring to past data. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned algorithm unit is The system estimates the emotions of family members and adjusts the frequency of algorithm execution based on the estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned algorithm unit is When executing the algorithm, weights are assigned to the algorithm based on the submission timing of the schedule. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0199] 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 management department manages each member's schedule, A centralized management unit that centrally manages the schedules managed by the aforementioned management unit, Based on the schedule centrally managed by the aforementioned centralized management unit, the adjustment unit finds a time slot when all family members are free. The system includes a proposal unit that suggests plans such as dining out or traveling based on the time slots found by the adjustment unit. A system characterized by the following features.

2. Each member's AI agent has a shared section where they can share schedules. The system according to feature 1.

3. It includes a section for answering questions about family plans. The system according to feature 1.

4. It includes an algorithm section that executes a schedule adjustment algorithm. The system according to feature 1.

5. The aforementioned management department, It estimates the emotions of family members and adjusts schedule priorities based on those estimated emotions. The system according to feature 1.

6. The aforementioned management department, Analyze each member's past schedule history and select the optimal schedule management method. The system according to feature 1.

7. The aforementioned management department, When managing schedules, we adjust them while taking into account the health status and fatigue levels of the members. The system according to feature 1.

8. The aforementioned management department, It estimates the emotions of family members and adjusts how schedule notifications are sent based on those estimated emotions. The system according to feature 1.

9. The aforementioned management department, When managing schedules, we propose the optimal schedule by taking into account the geographical location of the members. The system according to feature 1.

10. The aforementioned management department, When managing schedules, analyze members' social media activity and add relevant events to the schedule. The system according to feature 1.

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