System

Abstract

An object of a system according to an embodiment is to efficiently manage schedules of all family members and automatically generate a new schedule.SOLUTION: A system includes a schedule data collection part, an information collection part, and a schedule generation part. The schedule data collection unit collects past schedule data. The information collection unit collects information on the age, residential area, and hobbies of each family member. The schedule generation unit generates a new schedule based on the information collected by the schedule data collection unit and the information collection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem of making it difficult to efficiently manage the schedules of all family members and automatically generate new schedules.

[0005] The system according to the embodiment aims to efficiently manage the schedules of all family members and automatically generate new schedules. [Means for solving the problem]

[0006] The system according to the embodiment includes a schedule data collection unit, an information collection unit, and a schedule generation unit. The schedule data collection unit collects past schedule data. The information collection unit collects information on the age, residential area, and hobbies of each family member. The schedule generation unit generates a new schedule based on the information collected by the schedule data collection unit and the information collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage the schedules of all family members and automatically generate new schedules. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An automatic schedule generation app according to an embodiment of the present invention is an app in which a generation AI automatically generates a new schedule every year based on the schedules previously registered by each family member, taking into consideration information such as age, residential area, hobbies, etc. As a result, the automatic schedule generation app can automatically generate a new schedule based on the past schedule data of each family member and information on age, residential area, and hobbies, and provide a schedule that can be customized and shared.

[0029] An automatic schedule generation app according to an embodiment includes a schedule data collection unit, an information collection unit, and a schedule generation unit. The schedule data collection unit collects past schedule data. For example, it collects data from a calendar app. The schedule data collection unit can also scan handwritten notes and convert them into digital data. The schedule data collection unit collects information such as past events, appointments, and daily routines. The information collection unit collects information on the age, residential area, and hobbies of each family member. For example, age is collected as a specific number, and residential area is collected at the city, ward, town, or village level. The information collection unit can also collect specific activity names related to hobbies. The information collection unit also considers appropriate activities and events according to age, schedules based on residential area characteristics, and events related to hobbies. The schedule generation unit generates a new schedule based on the information collected by the schedule data collection unit and the information collection unit. For example, the generation AI receives a prompt such as, "Create a new schedule based on past schedule data, taking into account age, residential area, and hobbies," and generates a schedule based on the prompt. The generation AI can also automatically generate a new schedule suitable for each family member based on past schedule data and information on age, residential area, and hobbies. Furthermore, the generation AI can also adjust the priority of dates and time allocation, taking into account the schedule generation criteria and method. As a result, the automatic schedule generation app according to the embodiment can automatically generate a new schedule based on the past schedule data and information on age, residential area, and hobbies of each family member. For example, all family members can efficiently manage their schedules and create schedules that meet their individual needs.

[0030] The schedule data collection unit additionally collects detailed metadata for each event when collecting schedule data, and the generation AI can analyze it. For example, the schedule data collection unit additionally collects location information for each event when collecting schedule data. For example, it obtains geographic information for the location where the event was held, and the generation AI analyzes that information. The schedule data collection unit also collects additional participant information for each event. For example, it can obtain a list of people who attended the event, and the generation AI can analyze that information. The schedule data collection unit also collects weather information for each event. For example, it can obtain weather information for the day the event was held, and the generation AI can analyze that information. In this way, by collecting detailed metadata and having the generation AI analyze it, a more accurate schedule can be generated.

[0031] The schedule data collection unit can predict a user's behavioral patterns based on the results of analyzing the schedule data and reflect them in future schedules. The schedule data collection unit, for example, analyzes past schedule data and identifies the user's behavioral patterns. For example, it extracts events that occur repeatedly on specific days of the week or during specific time periods. The schedule data collection unit also predicts the user's behavioral patterns based on the analysis results. For example, it can reflect these in future schedules based on past behavioral patterns. The schedule data collection unit can also optimize future schedules based on the user's behavioral patterns. For example, it can place events in time periods when the user can be most efficient. In this way, by predicting the user's behavioral patterns and reflecting them in future schedules, a more appropriate schedule can be generated.

[0032] The schedule data collection unit can also incorporate events recorded by the user verbally or with photos as data using voice input or image recognition. The schedule data collection unit, for example, uses a voice input function to incorporate events recorded by the user verbally as schedule data. For example, it uses voice recognition technology to convert the user's utterances into text data. The schedule data collection unit can also incorporate events recorded by the user with photos as schedule data using image recognition technology. For example, it can analyze information about events captured in photos and reflect the information in the schedule data. The schedule data collection unit can also scan handwritten notes by the user and convert them into digital data. For example, it can read handwritten notes with a scanner and convert them into text data using OCR technology. This allows for the generation of more detailed schedules by incorporating events recorded by the user verbally or with photos as data using voice input or image recognition.

[0033] The schedule data collection unit enables cross-referencing of schedule data between different family members and can analyze the behavioral patterns of the entire family. For example, the schedule data collection unit cross-references the schedule data of different family members and analyzes the behavioral patterns of the entire family. For example, it identifies events that all family members will participate in and reflects them in the next schedule. The schedule data collection unit also considers how to link the schedule data of family members and performs cross-referencing. For example, it can centrally manage the schedule data of family members and enable mutual reference. The schedule data collection unit can also optimize future schedules based on the behavioral patterns of the entire family. For example, it can schedule events to be scheduled during times when all family members can be active efficiently. This enables cross-referencing of schedule data between different family members and analyzes the behavioral patterns of the entire family, thereby generating a more appropriate schedule.

[0034] The information collection unit can analyze the characteristics of the residential area in detail and reflect the local event calendar and weather forecast in the schedule. The information collection unit, for example, analyzes the characteristics of the residential area in detail and reflects the local event calendar in the schedule. For example, it collects information about local festivals and events and incorporates them into the next schedule. The information collection unit also reflects the weather forecast for the residential area in the schedule. For example, it can adjust the date of an outdoor event based on the weather forecast. The information collection unit can also optimize the schedule based on the characteristics of the residential area. For example, it can incorporate activities and events that are appropriate for the area into the schedule. In this way, a more appropriate schedule can be generated by analyzing the characteristics of the residential area in detail and reflecting the local event calendar and weather forecast in the schedule.

[0035] The information collecting unit can predict activities and events that the user is likely to become interested in based on hobby information and incorporate them into the schedule. The information collecting unit, for example, predicts activities and events that the user is likely to become interested in based on hobby information. For example, it analyzes past events related to a hobby and suggests a new hobby. The information collecting unit also uses a generation AI to predict activities and events that the user is likely to become interested in. For example, it can suggest new activities and events using a recommendation algorithm based on hobby information. The information collecting unit can also incorporate activities and events that the user is likely to become interested in into the schedule. For example, it can add an event related to a new hobby to the schedule. In this way, a more fulfilling schedule can be generated by predicting activities and events that the user is likely to become interested in based on hobby information and incorporating them into the schedule.

[0036] The information collecting unit can suggest events from different cultural spheres or regions based on information on age, residential area, and hobbies, thereby providing the user with a new experience. The information collecting unit can suggest events from different cultural spheres or regions based on information on age, residential area, and hobbies, for example. For example, overseas festivals and cultural events can be incorporated into the schedule. The information collecting unit can also suggest events from different regions. For example, events held in other regions can be added to the schedule. The information collecting unit can also provide the user with a new experience based on events from different cultural spheres or regions. For example, intercultural exchange events can be incorporated into the schedule. In this way, events from different cultural spheres or regions can be suggested based on information on age, residential area, and hobbies, thereby providing the user with a new experience.

[0037] The information collection unit can analyze the age composition and hobby trends of the entire family and suggest common events that the whole family can enjoy. The information collection unit, for example, analyzes the age composition of the entire family and suggests events that everyone can enjoy. For example, it can incorporate family events that all generations can participate in into the schedule. The information collection unit can also analyze the hobby trends of the entire family and suggest common events. For example, it can add activities that interest all family members to the schedule. The information collection unit can also optimize common events based on the age composition and hobby trends of the entire family. For example, it can prioritize events that everyone can enjoy and incorporate them into the schedule. In this way, it is possible to analyze the age composition and hobby trends of the entire family and suggest common events that the whole family can enjoy.

[0038] The schedule generation unit can propose a healthy schedule by taking into account the user's health data when generating a schedule. For example, the schedule generation unit can propose a healthy schedule by taking into account the user's sleep patterns when generating a schedule. For example, it can generate a schedule that ensures sufficient sleep time. The schedule generation unit can also propose a healthy schedule by taking into account the user's amount of exercise. For example, it can incorporate regular exercise time into the schedule. The schedule generation unit can also optimize the schedule based on the user's health data. For example, it can generate a schedule that allows the user to live the healthiest life based on the health data. In this way, by taking the user's health data into account and proposing a healthy schedule, it is possible to contribute to maintaining the user's health.

[0039] The schedule generation unit can collect user feedback on the generated schedule and reflect it in the generation of the next schedule. The schedule generation unit, for example, collects user feedback on the generated schedule. For example, it collects schedule satisfaction and areas for improvement in the form of a questionnaire. The schedule generation unit also reflects the user feedback in the generation of the next schedule. For example, it can incorporate user opinions to optimize the next schedule. The schedule generation unit can also adjust the schedule generation criteria based on the feedback data. For example, it can prioritize events that generate high user satisfaction in the schedule. In this way, by collecting user feedback and reflecting it in the generation of the next schedule, it is possible to provide a schedule that provides greater satisfaction.

[0040] The schedule generation unit can integrate the schedules of different family members and generate an optimal schedule for the entire family. The schedule generation unit, for example, integrates the schedules of different family members and generates an optimal schedule for the entire family. For example, it prioritizes incorporating events that everyone can participate in. The schedule generation unit also optimizes the schedule for the entire family. For example, it can adjust each member's schedule so that everyone can be active efficiently. The schedule generation unit can also suggest common events based on the schedule for the entire family. For example, it can incorporate events that the whole family can enjoy into the schedule. In this way, by integrating the schedules of different family members and generating an optimal schedule for the entire family, all family members can efficiently manage their schedules.

[0041] The schedule generation unit can link the generated schedule with other applications. For example, the schedule generation unit links the generated schedule with a calendar app. For example, the schedule can be automatically added to Google Calendar or Apple Calendar. The schedule generation unit also links the generated schedule with a reminder app. For example, the schedule can be synchronized with the reminder app and notifications can be set. The schedule generation unit also links the generated schedule with other applications, allowing the user to use multiple applications efficiently. For example, the schedule can be centrally managed and data can be synchronized with other applications. In this way, linking the generated schedule with other applications allows the user to use multiple applications efficiently.

[0042] The schedule generation unit can refer to the user's past customization history during customization and propose optimal customization options. The schedule generation unit, for example, refers to the user's past customization history and proposes optimal customization options. For example, customizations that were well received in the past can be reflected in the next schedule. The schedule generation unit can also propose customization options that will most satisfy the user based on the customization history. For example, it can analyze past customization data and reflect the user's preferences. The schedule generation unit can also adjust schedule generation criteria based on the customization history. For example, it can preferentially propose customization options that will highly satisfy the user. In this way, by referring to the user's past customization history and proposing optimal customization options, it is possible to provide a schedule that will provide greater satisfaction.

[0043] The schedule generation unit can reflect the user's health data in the customization function and make suggestions for maintaining a healthy schedule. The schedule generation unit, for example, can reflect the user's health data in the customization function and make suggestions for maintaining a healthy schedule. For example, the schedule generation unit can make suggestions for ensuring sufficient sleep and exercise time. The schedule generation unit can also suggest a schedule that allows the user to live the healthiest life based on the health data. For example, the schedule generation unit can generate a schedule based on the health data in accordance with the user's health condition. The schedule generation unit can also adjust the schedule generation criteria based on the health data. For example, the schedule generation unit can provide customization options in accordance with the user's health condition. In this way, the user's health data can be reflected and suggestions for maintaining a healthy schedule can be made, thereby contributing to maintaining the user's health.

[0044] The schedule generation unit can share the customization function with other family members and provide a function for collaboratively customizing a schedule. The schedule generation unit, for example, can share the customization function with other family members and provide a function for collaboratively customizing a schedule. For example, events that all family members will participate in can be decided together. The schedule generation unit can also perform collaborative customization taking into account how family members exchange opinions. For example, family members can adjust the schedule while exchanging opinions in real time. The schedule generation unit can also perform collaborative customization to provide a schedule that satisfies all family members. For example, a schedule that reflects all family members' opinions can be generated. As a result, by sharing the customization function with other family members and collaboratively customizing the schedule, a schedule that satisfies all family members can be provided.

[0045] The schedule generation unit can link the customized schedule with other applications. For example, the schedule generation unit links the customized schedule with a fitness app. For example, the schedule generation unit automatically adds an exercise schedule to the fitness app. The schedule generation unit also links the customized schedule with a nutrition management app. For example, the schedule generation unit can synchronize a meal schedule with the nutrition management app. The schedule generation unit also links the customized schedule with other applications, allowing the user to use multiple applications efficiently. For example, the schedule can be managed centrally and data can be synchronized with other applications. In this way, linking the customized schedule with other applications allows the user to use multiple applications efficiently.

[0046] The schedule generation unit can reflect the location information of family members in the sharing function, enabling real-time schedule adjustments. The schedule generation unit, for example, can reflect the location information of family members in the sharing function, enabling real-time schedule adjustments. For example, the schedule generation unit can suggest an optimal meeting place based on the current locations of family members. The schedule generation unit can also generate a schedule that allows all family members to efficiently engage in activities based on the location information. For example, the schedule can be adjusted taking into account places and time periods that are convenient for all family members to gather. The schedule generation unit can also dynamically adjust the schedule based on real-time location information. For example, the schedule can be changed in response to changes in traffic conditions or weather. In this way, by reflecting the location information of family members and enabling real-time schedule adjustments, all family members can efficiently manage their schedules.

[0047] The schedule generation unit can collect feedback on the shared schedule and reflect it in the generation of the next schedule. The schedule generation unit, for example, collects feedback from family members on the shared schedule. For example, it collects satisfaction with the schedule and areas for improvement in the form of a questionnaire. The schedule generation unit also reflects the feedback data in the generation of the next schedule. For example, it can incorporate the opinions of family members to optimize the next schedule. The schedule generation unit can also adjust the schedule generation criteria based on the feedback data. For example, it can prioritize the inclusion of events that satisfy all family members in the schedule. In this way, by collecting feedback on the shared schedule and reflecting it in the generation of the next schedule, it is possible to provide a schedule that satisfies all family members.

[0048] The schedule generation unit can extend the sharing function to groups other than other family members. For example, the schedule generation unit can extend the sharing function to groups other than other family members, enabling schedule sharing with groups of friends or teams at work. For example, events for a group of friends can be shared. The schedule generation unit can also perform efficient schedule management by considering how to share schedules with groups other than other family members. For example, a project schedule can be shared with a team at work. The schedule generation unit can also suggest common events based on schedule sharing with groups other than other family members. For example, an event that all members of a group of friends can participate in can be incorporated into the schedule. In this way, by extending the sharing function to groups other than other family members, schedules can be efficiently managed even for groups of friends or teams at work.

[0049] The schedule generation unit can link the shared schedule with other applications. For example, the schedule generation unit links the shared schedule with social media. For example, the schedule can be automatically posted to Facebook or Twitter. The schedule generation unit can also link the shared schedule with a messaging app. For example, the schedule can be shared to LINE or WhatsApp. The schedule generation unit can also link the shared schedule with other applications, allowing the user to use multiple applications efficiently. For example, the schedule can be managed centrally and data can be synchronized with other applications. In this way, the shared schedule can be linked with other applications, allowing the user to use multiple applications efficiently.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] An automatic schedule generation app can also collect a user's health data and generate a schedule based on their health condition. For example, it can analyze the user's sleep patterns and suggest a schedule that ensures sufficient sleep. It can also take into account the amount of exercise and incorporate regular exercise times into the schedule. It can also optimize the timing and content of meals to provide a schedule that supports healthy lifestyle habits. This allows it to generate a schedule that contributes to maintaining the user's health.

[0052] The schedule data collection unit can also collect data from the user's social media accounts and reflect it in the schedule. For example, events the user plans to attend and appointments with friends can be automatically added to the schedule. It can also analyze the user's posts on social media and suggest events that interest them. Furthermore, by incorporating common events and activities based on posts from friends and family, it is possible to provide a more fulfilling schedule. This makes it possible to generate a schedule that takes the user's social media activity into consideration.

[0053] The schedule data collection unit can collect data from the user's fitness device or health app and reflect it in the schedule. For example, it can incorporate appropriate exercise times into the schedule based on the user's exercise data and heart rate. It can also analyze sleep data and suggest schedules that ensure sufficient rest time. Furthermore, it can optimize meal times and contents based on meal record data and provide a schedule that supports healthy lifestyle habits. This makes it possible to generate a schedule that takes the user's health data into consideration.

[0054] The schedule data collection unit can collect data related to the user's hobbies and interests and suggest new hobbies and activities. For example, it can analyze past events related to hobbies and suggest new activities that the user may be interested in. It can also use generative AI to incorporate new events into the schedule using a recommendation algorithm based on the user's hobbies. Furthermore, by adding new hobbies and activities that interest the user to the schedule, it is possible to provide a more fulfilling schedule. This makes it possible to generate a schedule that takes the user's hobbies and interests into consideration.

[0055] The schedule data collection unit can analyze the user's past schedule data and identify seasonal behavioral patterns. For example, it can extract events and activities that occur repeatedly in specific seasons and reflect them in the next schedule. It can also adjust the dates of outdoor events taking into account seasonal weather information. Furthermore, by incorporating activities and events that correspond to the characteristics of each season into the schedule, it is possible to provide a more appropriate schedule. This makes it possible to generate a schedule that takes into account seasonal behavioral patterns.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The schedule data collection unit collects past schedule data. For example, it can collect data from calendar apps and scan handwritten notes to convert them into digital data. It also collects information on past events, appointments, and daily routines. Step 2: The information collection unit collects information on the age, residential area, and hobbies of each family member. For example, age is collected as a specific number, and residential area is collected at the city / ward / town / village level. Specific activity names related to hobbies are also collected, and appropriate activities and events according to age, schedules based on the characteristics of the residential area, and events related to hobbies are considered. Step 3: The schedule generation unit generates a new schedule based on the information collected by the schedule data collection unit and the information collection unit. For example, the generation AI receives a prompt such as, "Please create a new schedule based on past schedule data, taking into consideration age, residential area, and hobbies," and generates a schedule based on that prompt. The generation AI can also automatically generate a new schedule suitable for each family member based on past schedule data and information on age, residential area, and hobbies. Furthermore, the generation AI can adjust the priority of dates and time allocation, taking into account the schedule generation criteria and method.

[0058] (Example 2) An automatic schedule generation app according to an embodiment of the present invention is an app in which a generation AI automatically generates a new schedule every year based on the schedules previously registered by each family member, taking into consideration information such as age, residential area, hobbies, etc. As a result, the automatic schedule generation app can automatically generate a new schedule based on the past schedule data of each family member and information on age, residential area, and hobbies, and provide a schedule that can be customized and shared.

[0059] An automatic schedule generation app according to an embodiment includes a schedule data collection unit, an information collection unit, and a schedule generation unit. The schedule data collection unit collects past schedule data. For example, it collects data from a calendar app. The schedule data collection unit can also scan handwritten notes and convert them into digital data. The schedule data collection unit collects information such as past events, appointments, and daily routines. The information collection unit collects information on the age, residential area, and hobbies of each family member. For example, age is collected as a specific number, and residential area is collected at the city, ward, town, or village level. The information collection unit can also collect specific activity names related to hobbies. The information collection unit also considers appropriate activities and events according to age, schedules based on residential area characteristics, and events related to hobbies. The schedule generation unit generates a new schedule based on the information collected by the schedule data collection unit and the information collection unit. For example, the generation AI receives a prompt such as, "Create a new schedule based on past schedule data, taking into account age, residential area, and hobbies," and generates a schedule based on the prompt. The generation AI can also automatically generate a new schedule suitable for each family member based on past schedule data and information on age, residential area, and hobbies. Furthermore, the generation AI can also adjust the priority of dates and time allocation, taking into account the schedule generation criteria and method. As a result, the automatic schedule generation app according to the embodiment can automatically generate a new schedule based on the past schedule data and information on age, residential area, and hobbies of each family member. For example, all family members can efficiently manage their schedules and create schedules that meet their individual needs.

[0060] The schedule data collection unit can perform emotion analysis on past schedule data using a generation AI and evaluate the importance of the schedule based on the intensity and type of emotion. The schedule data collection unit, for example, performs emotion analysis on each event included in past schedule data using a generation AI. For example, it analyzes the content of the event and the emotions of the participants and quantifies the intensity and type of emotion. The schedule data collection unit also evaluates the importance of the schedule based on the intensity and type of emotion. For example, it can evaluate events with high emotional intensity as having high importance. The schedule data collection unit can also evaluate the importance of the schedule based on the type of emotion. For example, it can evaluate events with strong positive emotions as having high importance. This allows the importance of the schedule to be evaluated using emotion analysis and reflected in the next schedule generation.

[0061] The schedule data collection unit additionally collects detailed metadata for each event when collecting schedule data, and the generation AI can analyze it. For example, the schedule data collection unit additionally collects location information for each event when collecting schedule data. For example, it obtains geographic information for the location where the event was held, and the generation AI analyzes that information. The schedule data collection unit also collects additional participant information for each event. For example, it can obtain a list of people who attended the event, and the generation AI can analyze that information. The schedule data collection unit also collects weather information for each event. For example, it can obtain weather information for the day the event was held, and the generation AI can analyze that information. In this way, by collecting detailed metadata and having the generation AI analyze it, a more accurate schedule can be generated.

[0062] The schedule data collection unit can predict a user's behavioral patterns based on the results of analyzing the schedule data and reflect them in future schedules. The schedule data collection unit, for example, analyzes past schedule data and identifies the user's behavioral patterns. For example, it extracts events that occur repeatedly on specific days of the week or during specific time periods. The schedule data collection unit also predicts the user's behavioral patterns based on the analysis results. For example, it can reflect these in future schedules based on past behavioral patterns. The schedule data collection unit can also optimize future schedules based on the user's behavioral patterns. For example, it can place events in time periods when the user can be most efficient. In this way, by predicting the user's behavioral patterns and reflecting them in future schedules, a more appropriate schedule can be generated.

[0063] The schedule data collection unit can also incorporate events recorded by the user verbally or with photos as data using voice input or image recognition. The schedule data collection unit, for example, uses a voice input function to incorporate events recorded by the user verbally as schedule data. For example, it uses voice recognition technology to convert the user's utterances into text data. The schedule data collection unit can also incorporate events recorded by the user with photos as schedule data using image recognition technology. For example, it can analyze information about events captured in photos and reflect the information in the schedule data. The schedule data collection unit can also scan handwritten notes by the user and convert them into digital data. For example, it can read handwritten notes with a scanner and convert them into text data using OCR technology. This allows for the generation of more detailed schedules by incorporating events recorded by the user verbally or with photos as data using voice input or image recognition.

[0064] The schedule data collection unit enables cross-referencing of schedule data between different family members and can analyze the behavioral patterns of the entire family. For example, the schedule data collection unit cross-references the schedule data of different family members and analyzes the behavioral patterns of the entire family. For example, it identifies events that all family members will participate in and reflects them in the next schedule. The schedule data collection unit also considers how to link the schedule data of family members and performs cross-referencing. For example, it can centrally manage the schedule data of family members and enable mutual reference. The schedule data collection unit can also optimize future schedules based on the behavioral patterns of the entire family. For example, it can schedule events to be scheduled during times when all family members can be active efficiently. This enables cross-referencing of schedule data between different family members and analyzes the behavioral patterns of the entire family, thereby generating a more appropriate schedule.

[0065] The schedule data collection unit can use the emotion estimation function to collect the user's emotional reactions to past schedule data and propose a schedule that elicits positive emotions. The schedule data collection unit, for example, uses the emotion estimation function to collect the user's emotional reactions to past schedule data. For example, the schedule data collection unit analyzes the user's facial expressions and voice and calculates an emotion score. The schedule data collection unit also uses the emotion estimation function to propose a schedule that elicits positive emotions from the user. For example, events that evoke strong positive emotions can be prioritized in the schedule. The schedule data collection unit can also use the emotion estimation function to optimize the schedule based on the user's emotional reactions. For example, the activities that the user enjoys most can be reflected in the schedule. In this way, the emotion estimation function can be used to propose a schedule that elicits positive emotions from the user.

[0066] The information collection unit can use the generation AI to perform sentiment analysis on information such as age, residential area, and hobby information to identify the activity the user enjoys most. The information collection unit can, for example, perform sentiment analysis on information such as age, residential area, and hobby information to identify the activity the user enjoys most. For example, it can analyze the sentiment scores of events related to the hobby. The information collection unit can also use the generation AI to identify the activity the user enjoys most. For example, it can analyze emotional responses to past events and activities to identify the activity the user enjoys most. The information collection unit can also reflect the activity the user enjoys most in the schedule based on the results of the sentiment analysis. For example, it can prioritize activities with strong positive emotions in the schedule. In this way, the activity the user enjoys most can be identified and reflected in the schedule using sentiment analysis.

[0067] The information collection unit can analyze the characteristics of the residential area in detail and reflect the local event calendar and weather forecast in the schedule. The information collection unit, for example, analyzes the characteristics of the residential area in detail and reflects the local event calendar in the schedule. For example, it collects information about local festivals and events and incorporates them into the next schedule. The information collection unit also reflects the weather forecast for the residential area in the schedule. For example, it can adjust the date of an outdoor event based on the weather forecast. The information collection unit can also optimize the schedule based on the characteristics of the residential area. For example, it can incorporate activities and events that are appropriate for the area into the schedule. In this way, a more appropriate schedule can be generated by analyzing the characteristics of the residential area in detail and reflecting the local event calendar and weather forecast in the schedule.

[0068] The information collecting unit can predict activities and events that the user is likely to become interested in based on hobby information and incorporate them into the schedule. The information collecting unit, for example, predicts activities and events that the user is likely to become interested in based on hobby information. For example, it analyzes past events related to a hobby and suggests a new hobby. The information collecting unit also uses a generation AI to predict activities and events that the user is likely to become interested in. For example, it can suggest new activities and events using a recommendation algorithm based on hobby information. The information collecting unit can also incorporate activities and events that the user is likely to become interested in into the schedule. For example, it can add an event related to a new hobby to the schedule. In this way, a more fulfilling schedule can be generated by predicting activities and events that the user is likely to become interested in based on hobby information and incorporating them into the schedule.

[0069] The information collecting unit can suggest events from different cultural spheres or regions based on information on age, residential area, and hobbies, thereby providing the user with a new experience. The information collecting unit can suggest events from different cultural spheres or regions based on information on age, residential area, and hobbies, for example. For example, overseas festivals and cultural events can be incorporated into the schedule. The information collecting unit can also suggest events from different regions. For example, events held in other regions can be added to the schedule. The information collecting unit can also provide the user with a new experience based on events from different cultural spheres or regions. For example, intercultural exchange events can be incorporated into the schedule. In this way, events from different cultural spheres or regions can be suggested based on information on age, residential area, and hobbies, thereby providing the user with a new experience.

[0070] The information collection unit can analyze the age composition and hobby trends of the entire family and suggest common events that the whole family can enjoy. The information collection unit, for example, analyzes the age composition of the entire family and suggests events that everyone can enjoy. For example, it can incorporate family events that all generations can participate in into the schedule. The information collection unit can also analyze the hobby trends of the entire family and suggest common events. For example, it can add activities that interest all family members to the schedule. The information collection unit can also optimize common events based on the age composition and hobby trends of the entire family. For example, it can prioritize events that everyone can enjoy and incorporate them into the schedule. In this way, it is possible to analyze the age composition and hobby trends of the entire family and suggest common events that the whole family can enjoy.

[0071] The information collection unit can use the emotion estimation function to collect the user's emotional reactions to information related to age, residential area, and hobbies, and make suggestions that elicit positive emotions. The information collection unit, for example, uses the emotion estimation function to collect the user's emotional reactions to information related to age, residential area, and hobbies. For example, the information collection unit analyzes the user's facial expressions and voice and calculates an emotion score. The information collection unit also uses the emotion estimation function to make suggestions that elicit positive emotions from the user. For example, events that evoke strong positive emotions can be prioritized in the schedule. The information collection unit can also use the emotion estimation function to optimize the schedule based on the user's emotional reactions. For example, the activities that the user enjoys most can be reflected in the schedule. As a result, the emotion estimation function can be used to make suggestions that elicit positive emotions from the user, thereby generating a more satisfying schedule.

[0072] The schedule generation unit can use the generation AI to generate a schedule that takes the user's emotions into consideration based on past schedule data and information on age, residential area, and hobbies. The schedule generation unit, for example, uses the generation AI to generate a schedule that takes the user's emotions into consideration based on past schedule data and information on age, residential area, and hobbies. For example, events that evoke strong positive emotions can be prioritized. The schedule generation unit also uses the generation AI to optimize the schedule that takes the user's emotions into consideration. For example, the schedule can reflect the activities that the user enjoys most based on emotional data. The schedule generation unit can also use the generation AI to dynamically adjust the schedule that takes the user's emotions into consideration. For example, the schedule can be changed based on emotional reactions in real time. In this way, by generating a schedule that takes the user's emotions into consideration, a more satisfying schedule can be provided.

[0073] The schedule generation unit can propose a healthy schedule by taking into account the user's health data when generating a schedule. For example, the schedule generation unit can propose a healthy schedule by taking into account the user's sleep patterns when generating a schedule. For example, it can generate a schedule that ensures sufficient sleep time. The schedule generation unit can also propose a healthy schedule by taking into account the user's amount of exercise. For example, it can incorporate regular exercise time into the schedule. The schedule generation unit can also optimize the schedule based on the user's health data. For example, it can generate a schedule that allows the user to live the healthiest life based on the health data. In this way, by taking the user's health data into account and proposing a healthy schedule, it is possible to contribute to maintaining the user's health.

[0074] The schedule generation unit can collect user feedback on the generated schedule and reflect it in the generation of the next schedule. The schedule generation unit, for example, collects user feedback on the generated schedule. For example, it collects schedule satisfaction and areas for improvement in the form of a questionnaire. The schedule generation unit also reflects the user feedback in the generation of the next schedule. For example, it can incorporate user opinions to optimize the next schedule. The schedule generation unit can also adjust the schedule generation criteria based on the feedback data. For example, it can prioritize events that generate high user satisfaction in the schedule. In this way, by collecting user feedback and reflecting it in the generation of the next schedule, it is possible to provide a schedule that provides greater satisfaction.

[0075] The schedule generation unit can integrate the schedules of different family members and generate an optimal schedule for the entire family. The schedule generation unit, for example, integrates the schedules of different family members and generates an optimal schedule for the entire family. For example, it prioritizes incorporating events that everyone can participate in. The schedule generation unit also optimizes the schedule for the entire family. For example, it can adjust each member's schedule so that everyone can be active efficiently. The schedule generation unit can also suggest common events based on the schedule for the entire family. For example, it can incorporate events that the whole family can enjoy into the schedule. In this way, by integrating the schedules of different family members and generating an optimal schedule for the entire family, all family members can efficiently manage their schedules.

[0076] The schedule generation unit can link the generated schedule with other applications. For example, the schedule generation unit links the generated schedule with a calendar app. For example, the schedule can be automatically added to Google Calendar or Apple Calendar. The schedule generation unit also links the generated schedule with a reminder app. For example, the schedule can be synchronized with the reminder app and notifications can be set. The schedule generation unit also links the generated schedule with other applications, allowing the user to use multiple applications efficiently. For example, the schedule can be centrally managed and data can be synchronized with other applications. In this way, linking the generated schedule with other applications allows the user to use multiple applications efficiently.

[0077] The schedule generation unit can use the emotion estimation function to monitor the user's emotional reactions to the generated schedule in real time and dynamically adjust the schedule. The schedule generation unit, for example, uses the emotion estimation function to monitor the user's emotional reactions to the generated schedule in real time. For example, the schedule generation unit analyzes the user's facial expressions and voice and calculates an emotion score. The schedule generation unit also uses the emotion estimation function to dynamically adjust the schedule based on the user's emotional reactions. For example, the schedule can be changed based on the emotional reactions in real time. The schedule generation unit can also use the emotion estimation function to optimize the schedule based on the user's emotional reactions. For example, events that evoke strong positive emotions can be preferentially incorporated into the schedule. In this way, by monitoring the user's emotional reactions in real time and dynamically adjusting the schedule, a more satisfying schedule can be provided.

[0078] The schedule generation unit can use the generation AI to perform sentiment analysis when the user customizes the schedule and make suggestions that elicit positive emotions. The schedule generation unit, for example, uses the generation AI to perform sentiment analysis when the user customizes the schedule. For example, it analyzes the user's input and makes suggestions that elicit positive emotions. The schedule generation unit also suggests activities that the user enjoys most based on the results of the sentiment analysis. For example, it can suggest events that the user prefers based on past customization history. The schedule generation unit can also use the generation AI to provide customization options that take the user's emotions into consideration. For example, it can prioritize suggestions for events that evoke strong positive emotions. As a result, by performing sentiment analysis when the user customizes the schedule and making suggestions that elicit positive emotions, it is possible to provide a more satisfying schedule.

[0079] The schedule generation unit can refer to the user's past customization history during customization and propose optimal customization options. The schedule generation unit, for example, refers to the user's past customization history and proposes optimal customization options. For example, customizations that were well received in the past can be reflected in the next schedule. The schedule generation unit can also propose customization options that will most satisfy the user based on the customization history. For example, it can analyze past customization data and reflect the user's preferences. The schedule generation unit can also adjust schedule generation criteria based on the customization history. For example, it can preferentially propose customization options that will highly satisfy the user. In this way, by referring to the user's past customization history and proposing optimal customization options, it is possible to provide a schedule that will provide greater satisfaction.

[0080] The schedule generation unit can reflect the user's health data in the customization function and make suggestions for maintaining a healthy schedule. The schedule generation unit, for example, can reflect the user's health data in the customization function and make suggestions for maintaining a healthy schedule. For example, the schedule generation unit can make suggestions for ensuring sufficient sleep and exercise time. The schedule generation unit can also suggest a schedule that allows the user to live the healthiest life based on the health data. For example, the schedule generation unit can generate a schedule based on the health data in accordance with the user's health condition. The schedule generation unit can also adjust the schedule generation criteria based on the health data. For example, the schedule generation unit can provide customization options in accordance with the user's health condition. In this way, the user's health data can be reflected and suggestions for maintaining a healthy schedule can be made, thereby contributing to maintaining the user's health.

[0081] The schedule generation unit can share the customization function with other family members and provide a function for collaboratively customizing a schedule. The schedule generation unit, for example, can share the customization function with other family members and provide a function for collaboratively customizing a schedule. For example, events that all family members will participate in can be decided together. The schedule generation unit can also perform collaborative customization taking into account how family members exchange opinions. For example, family members can adjust the schedule while exchanging opinions in real time. The schedule generation unit can also perform collaborative customization to provide a schedule that satisfies all family members. For example, a schedule that reflects all family members' opinions can be generated. As a result, by sharing the customization function with other family members and collaboratively customizing the schedule, a schedule that satisfies all family members can be provided.

[0082] The schedule generation unit can link the customized schedule with other applications. For example, the schedule generation unit links the customized schedule with a fitness app. For example, the schedule generation unit automatically adds an exercise schedule to the fitness app. The schedule generation unit also links the customized schedule with a nutrition management app. For example, the schedule generation unit can synchronize a meal schedule with the nutrition management app. The schedule generation unit also links the customized schedule with other applications, allowing the user to use multiple applications efficiently. For example, the schedule can be managed centrally and data can be synchronized with other applications. In this way, linking the customized schedule with other applications allows the user to use multiple applications efficiently.

[0083] The schedule generation unit can use the emotion estimation function to collect the user's emotional reactions to the customized schedule and reflect them in the next customization. The schedule generation unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the customized schedule. For example, the schedule generation unit analyzes the user's facial expressions and voice and calculates an emotion score. The schedule generation unit also uses the emotion estimation function to reflect the user's emotional reactions in the next customization. For example, based on the emotional reaction data, it can suggest customization options that will most satisfy the user. The schedule generation unit can also use the emotion estimation function to optimize the customization options based on the user's emotional reactions. For example, it can preferentially suggest events that evoke strong positive emotions. In this way, by collecting the user's emotional reactions to the customized schedule and reflecting them in the next customization, it is possible to provide a schedule that is more satisfying.

[0084] The schedule generation unit can use the generation AI to analyze the emotional reactions of family members to the shared schedule and make suggestions that will elicit positive emotions. The schedule generation unit can, for example, use the generation AI to analyze the emotional reactions of family members to the shared schedule. For example, it can analyze the facial expressions and voices of family members and calculate an emotion score. The schedule generation unit can also use the generation AI to make suggestions that will elicit positive emotions based on the emotional reactions of family members. For example, it can prioritize events that evoke strong positive emotions in the schedule. The schedule generation unit can also use the generation AI to optimize the schedule based on the emotional reactions of family members. For example, it can suggest events that everyone can enjoy. In this way, by analyzing the emotional reactions of family members to the shared schedule and making suggestions that will elicit positive emotions, it is possible to provide a schedule that satisfies the whole family.

[0085] The schedule generation unit can reflect the location information of family members in the sharing function, enabling real-time schedule adjustments. The schedule generation unit, for example, can reflect the location information of family members in the sharing function, enabling real-time schedule adjustments. For example, the schedule generation unit can suggest an optimal meeting place based on the current locations of family members. The schedule generation unit can also generate a schedule that allows all family members to efficiently engage in activities based on the location information. For example, the schedule can be adjusted taking into account places and time periods that are convenient for all family members to gather. The schedule generation unit can also dynamically adjust the schedule based on real-time location information. For example, the schedule can be changed in response to changes in traffic conditions or weather. In this way, by reflecting the location information of family members and enabling real-time schedule adjustments, all family members can efficiently manage their schedules.

[0086] The schedule generation unit can collect feedback on the shared schedule and reflect it in the generation of the next schedule. The schedule generation unit, for example, collects feedback from family members on the shared schedule. For example, it collects satisfaction with the schedule and areas for improvement in the form of a questionnaire. The schedule generation unit also reflects the feedback data in the generation of the next schedule. For example, it can incorporate the opinions of family members to optimize the next schedule. The schedule generation unit can also adjust the schedule generation criteria based on the feedback data. For example, it can prioritize the inclusion of events that satisfy all family members in the schedule. In this way, by collecting feedback on the shared schedule and reflecting it in the generation of the next schedule, it is possible to provide a schedule that satisfies all family members.

[0087] The schedule generation unit can extend the sharing function to groups other than other family members. For example, the schedule generation unit can extend the sharing function to groups other than other family members, enabling schedule sharing with groups of friends or teams at work. For example, events for a group of friends can be shared. The schedule generation unit can also perform efficient schedule management by considering how to share schedules with groups other than other family members. For example, a project schedule can be shared with a team at work. The schedule generation unit can also suggest common events based on schedule sharing with groups other than other family members. For example, an event that all members of a group of friends can participate in can be incorporated into the schedule. In this way, by extending the sharing function to groups other than other family members, schedules can be efficiently managed even for groups of friends or teams at work.

[0088] The schedule generation unit can link the shared schedule with other applications. For example, the schedule generation unit links the shared schedule with social media. For example, the schedule can be automatically posted to Facebook or Twitter. The schedule generation unit can also link the shared schedule with a messaging app. For example, the schedule can be shared to LINE or WhatsApp. The schedule generation unit can also link the shared schedule with other applications, allowing the user to use multiple applications efficiently. For example, the schedule can be managed centrally and data can be synchronized with other applications. In this way, the shared schedule can be linked with other applications, allowing the user to use multiple applications efficiently.

[0089] The schedule generation unit can use the emotion estimation function to monitor the emotional reactions of family members to the shared schedule in real time and dynamically adjust the schedule. The schedule generation unit, for example, uses the emotion estimation function to monitor the emotional reactions of family members to the shared schedule in real time. For example, it analyzes the facial expressions and voices of family members and calculates an emotion score. The schedule generation unit also uses the emotion estimation function to dynamically adjust the schedule based on the emotional reactions of family members. For example, it can change the schedule based on the emotional reactions in real time. The schedule generation unit can also use the emotion estimation function to optimize the schedule based on the emotional reactions of family members. For example, it can suggest events that everyone can enjoy. In this way, by monitoring the emotional reactions of family members to the shared schedule in real time and dynamically adjusting the schedule, it is possible to provide a schedule that satisfies the whole family.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] An automatic schedule generation app can also collect a user's health data and generate a schedule based on their health condition. For example, it can analyze the user's sleep patterns and suggest a schedule that ensures sufficient sleep. It can also take into account the amount of exercise and incorporate regular exercise times into the schedule. It can also optimize the timing and content of meals to provide a schedule that supports healthy lifestyle habits. This allows it to generate a schedule that contributes to maintaining the user's health.

[0092] The schedule data collection unit can use the emotion estimation function to perform emotion analysis on the user's past schedule data and identify events that elicit positive emotions. For example, it can extract events and activities that the user particularly enjoyed and prioritize them in the next schedule. It can also improve user satisfaction by avoiding events that evoke strong negative emotions. Furthermore, based on the results of the emotion analysis, it can identify the time periods and places where the user feels most relaxed and reflect these in the schedule. This makes it possible to provide a schedule that takes the user's emotions into consideration.

[0093] The schedule data collection unit can also collect data from the user's social media accounts and reflect it in the schedule. For example, events the user plans to attend and appointments with friends can be automatically added to the schedule. It can also analyze the user's posts on social media and suggest events that interest them. Furthermore, by incorporating common events and activities based on posts from friends and family, it is possible to provide a more fulfilling schedule. This makes it possible to generate a schedule that takes the user's social media activity into consideration.

[0094] The schedule data collection unit can analyze the user's past schedule data and use the emotion estimation function to evaluate the user's stress level. For example, it can determine whether a particular event or activity was stressful for the user and reflect this in the next schedule. It can also help reduce the user's stress by prioritizing relaxing activities during periods when the stress level is high. It can also identify time periods and places where stress levels are low and reflect these in the schedule. This makes it possible to provide a schedule that takes user stress management into consideration.

[0095] The schedule data collection unit can utilize emotion estimation functions to collect the user's emotional responses in real time using the user's voice input and image recognition. For example, it can analyze the user's verbally recorded emotions regarding events and prioritize events associated with strong positive emotions in the schedule. It can also analyze event information captured in photographs to determine whether the user is enjoying the event. It can also scan handwritten notes and convert them into digital data, optimizing the schedule based on the user's emotional responses. This makes it possible to provide a schedule that reflects the user's emotions in real time.

[0096] The schedule data collection unit can collect data from the user's fitness device or health app and reflect it in the schedule. For example, it can incorporate appropriate exercise times into the schedule based on the user's exercise data and heart rate. It can also analyze sleep data and suggest schedules that ensure sufficient rest time. Furthermore, it can optimize meal times and contents based on meal record data and provide a schedule that supports healthy lifestyle habits. This makes it possible to generate a schedule that takes the user's health data into consideration.

[0097] The schedule data collection unit can use the emotion estimation function to collect the user's emotional responses to past schedule data and propose a schedule that elicits positive emotions. For example, it can analyze the user's facial expressions and voice to calculate an emotion score. It can also prioritize events that evoke strong positive emotions in the schedule. It can also generate an optimal schedule based on the user's emotional responses. This makes it possible to use the emotion estimation function to propose a schedule that elicits positive emotions in the user.

[0098] The schedule data collection unit can collect data related to the user's hobbies and interests and suggest new hobbies and activities. For example, it can analyze past events related to hobbies and suggest new activities that the user may be interested in. It can also use generative AI to incorporate new events into the schedule using a recommendation algorithm based on the user's hobbies. Furthermore, by adding new hobbies and activities that interest the user to the schedule, it is possible to provide a more fulfilling schedule. This makes it possible to generate a schedule that takes the user's hobbies and interests into consideration.

[0099] The schedule data collection unit can analyze the user's past schedule data and use the emotion estimation function to evaluate the user's happiness level. For example, it can determine whether a particular event or activity made the user happy and reflect this in the next schedule. It can also improve the user's happiness level by prioritizing positive activities during periods when happiness levels are high. It can also identify time periods and places that make the user happiest and reflect these in the schedule. This makes it possible to provide a schedule that takes the user's happiness level into consideration.

[0100] The schedule data collection unit can analyze the user's past schedule data and identify seasonal behavioral patterns. For example, it can extract events and activities that occur repeatedly in specific seasons and reflect them in the next schedule. It can also adjust the dates of outdoor events taking into account seasonal weather information. Furthermore, by incorporating activities and events that correspond to the characteristics of each season into the schedule, it is possible to provide a more appropriate schedule. This makes it possible to generate a schedule that takes into account seasonal behavioral patterns.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The schedule data collection unit collects past schedule data. For example, it can collect data from calendar apps and scan handwritten notes to convert them into digital data. It also collects information on past events, appointments, and daily routines. Step 2: The information collection unit collects information on the age, residential area, and hobbies of each family member. For example, age is collected as a specific number, and residential area is collected at the city / ward / town / village level. Specific activity names related to hobbies are also collected, and appropriate activities and events according to age, schedules based on the characteristics of the residential area, and events related to hobbies are considered. Step 3: The schedule generation unit generates a new schedule based on the information collected by the schedule data collection unit and the information collection unit. For example, the generation AI receives a prompt such as, "Please create a new schedule based on past schedule data, taking into consideration age, residential area, and hobbies," and generates a schedule based on that prompt. The generation AI can also automatically generate a new schedule suitable for each family member based on past schedule data and information on age, residential area, and hobbies. Furthermore, the generation AI can adjust the priority of dates and time allocation, taking into account the schedule generation criteria and method.

[0103] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a 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.

[0138] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] 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.

[0162] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a schedule data collection unit that collects past schedule data; An information gathering department that collects information on the age, area of ​​residence, and hobbies of each family member; a schedule generating unit that generates a new schedule based on the information collected by the schedule data collecting unit and the information collecting unit. A system characterized by:

2. The schedule data collection unit The generative AI performs sentiment analysis on past schedule data and evaluates the importance of the schedule based on the intensity and type of emotion.

2. The system of claim 1.

3. The schedule data collection unit Using voice input and image recognition, events recorded verbally or with photos by the user can also be captured as data.

2. The system of claim 1.

4. The information collecting unit The generative AI performs sentiment analysis on information such as age, residential area, and hobbies to identify the activities the user enjoys most.

2. The system of claim 1.

5. The schedule generation unit Using the generation AI, a schedule is generated that takes into account the user's emotions based on the past schedule data and the information on the age, the residential area, and the hobbies.

2. The system of claim 1.

6. The schedule generation unit Monitoring the user's emotional response to the generated schedule in real time and dynamically adjusting the schedule.

2. The system of claim 1.

7. The schedule generation unit The generative AI is used to analyze family members' emotional reactions to the shared schedule and make suggestions that elicit positive emotions.

2. The system of claim 1.

8. The schedule generation unit Monitoring family members' emotional responses to the shared schedule in real time and dynamically adjusting the schedule.

2. The system of claim 1.

Images