System

Abstract

An object of a system according to an embodiment is to improve efficiency of information collection from a plurality of sites and schedule management.SOLUTION: A system includes an information collection unit, a listing unit, and a schedule check unit. An information collecting part collects information from X or an official site. The listing unit lists the information collected by the information collection unit. The schedule check unit checks the application information of the user based on the information listed by the listing 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 technology has the drawback of requiring time-consuming collection of information and schedule management from multiple sites, and there is a risk of duplicate applications.

[0005] The system according to the embodiment aims to improve the efficiency of information collection from multiple sites and schedule management. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, a listing unit, and a schedule check unit. The information collection unit collects information from X or an official website. The listing unit lists the information collected by the information collection unit. The schedule check unit checks the application information of the user based on the information listed by the listing unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of information collection and schedule management from multiple sites. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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) A schedule management system according to an embodiment of the present invention uses AI to manage the schedules of ticket lottery applications and advance sales held by fans on multiple sites. This system has the ability to automatically collect information from sources such as X (formerly Twitter (registered trademark)) and official websites, and create a list of such information. When a user inputs the ticket information they have applied for, the AI ​​checks the schedule to ensure there are no overlapping applications. This allows the schedule management system to efficiently manage the schedules of ticket lottery applications and advance sales held by fans on multiple sites.

[0029] A schedule management system according to an embodiment includes an information collection unit, a listing unit, and a schedule check unit. The information collection unit collects information from X (formerly Twitter (registered trademark)), official websites, and the like. For example, the information collection unit obtains ticket information from the official website using an API. The information collection unit can also collect information from X (formerly Twitter (registered trademark)) using scraping technology. The information collection unit can also obtain the latest ticket information using an RSS feed. The listing unit lists the information collected by the information collection unit. For example, the listing unit sorts the collected information by date and time. The listing unit can also filter the information by event type or artist name. The listing unit can also classify the information based on conditions specified by the user. The schedule check unit checks the user's application information based on the information listed by the listing unit. For example, the schedule check unit analyzes the application information entered by the user and checks for duplicates. The schedule check unit can also check the deadline for the application information and notify the user. The schedule check unit can also set priorities for the application information and check important applications first. As a result, the schedule management system according to the embodiment can centrally manage information from multiple sites and efficiently check user application information. For example, a user can centrally manage the schedules of lottery applications and advance sales for tickets held on multiple sites, preventing duplication or oversight. Furthermore, the user can efficiently manage their schedule without missing deadlines for application information.

[0030] The information collection unit can predict the popularity of an event or the competition rate for tickets and suggest the optimal timing for application to the user. For example, the generation AI analyzes past event data to predict the popularity of a particular event. For example, the generation AI calculates the competition rate for an event based on past ticket sales data and social media responses, and suggests the optimal timing for application to the user. The information collection unit also predicts the popularity of an event based on information collected in real time by the generation AI and notifies the user. For example, the generation AI analyzes social media trends and search volume on search engines to predict the competition rate for an event. The information collection unit also learns the user's past application history and suggests the optimal timing for application. For example, the generation AI predicts the optimal timing for application for a similar event based on data on events for which the user has previously won. This can increase the user's chances of winning by suggesting the optimal timing for application to the user.

[0031] The information collection unit can predict the success rate or satisfaction level of an event by comparing it with past event data. For example, the information collection unit has the generation AI analyze past event data and predict the success rate of a particular event. For example, it calculates the satisfaction level of an event based on feedback and reviews from past participants. The information collection unit also predicts the success rate by comparing it with similar past events based on the information collected by the generation AI. For example, it analyzes data from past events held by the same artist or venue and predicts the success rate. The information collection unit also has the generation AI learn the user's past participation history and predict the satisfaction level of an event. For example, it predicts the satisfaction level of similar events based on data from events the user has attended in the past. This can help the user select the best event by predicting the success rate and satisfaction level of an event.

[0032] The information collection unit can expand the sources of information it collects to include unofficial information from social media, blogs, and forums. For example, the generation AI collects information from unofficial sources such as social media, blogs, and forums. For example, it analyzes posts and comments on fan communities and lists event information. The information collection unit also collects more information by having the generation AI include unofficial sources of information in its collection. For example, it analyzes information from fan sites and personal blogs and lists event information. The information collection unit also collects posts from social media and forums in real time and lists event information. For example, it monitors specific hashtags and keywords and collects related information. In this way, more information can be collected by including unofficial sources of information in its collection.

[0033] The information collection unit can learn a user's past participation history or preferences and provide an individually customized event list. For example, the information collection unit uses a generation AI to learn a user's past participation history and provide an individually customized event list. For example, the information collection unit creates a list of similar events based on data on events the user has previously attended. Furthermore, the information collection unit uses the generation AI to learn a user's preferences and provide a customized event list. For example, if a user likes a particular artist or genre, the information collection unit creates a list of events based on that information. Furthermore, the information collection unit uses the generation AI to update the individually customized event list in real time based on the user's past participation history and preferences. For example, the list is updated each time new event information is added. This improves user satisfaction by providing an event list customized based on the user's past participation history and preferences.

[0034] The schedule check unit can analyze the user's application information and propose the optimal application strategy. In the schedule check unit, for example, the generation AI analyzes the user's application information and proposes the optimal application strategy. For example, spreading applications across multiple lotteries maximizes the probability of winning. In addition, the schedule check unit has the generation AI learn from past application data and propose the optimal application strategy. For example, applying at a specific time period or date increases the probability of winning. In addition, the schedule check unit has the generation AI propose the optimal application strategy based on the user's application history. For example, it proposes a similar strategy based on data on events that the user has won in the past. In this way, the probability of winning can be increased by proposing the optimal application strategy to the user.

[0035] The schedule check unit can predict the probability of winning by analyzing past winning history and trends based on the user's application information. In the schedule check unit, for example, the generation AI analyzes the user's application information and predicts the probability of winning based on past winning history and trends. For example, it calculates the probability of winning for a specific event or artist. In addition, the schedule check unit predicts the probability of winning by learning past application data. For example, it analyzes the tendency that the probability of winning increases by applying at a specific time period or date. In addition, the schedule check unit predicts the probability of winning based on the user's application history. For example, it predicts the probability of winning for a similar event based on data on events that were won in the past. In this way, by analyzing past winning history and trends, it is possible to predict the probability of winning and provide useful information to the user.

[0036] The schedule check unit can suggest cooperation applications with other users based on the user's application information. In the schedule check unit, for example, the generation AI analyzes the user's application information and suggests cooperation applications with other users. For example, applying together with a friend increases the chances of winning. In addition, the schedule check unit has the generation AI learn from past application data and analyze the effectiveness of cooperation applications. For example, it analyzes the tendency for the chances of winning to increase when multiple users apply together. In addition, the schedule check unit has the generation AI suggest cooperation applications based on the user's application history. For example, it suggests a similar strategy based on data on events in which past cooperation applications were successful. In this way, the chances of winning can be increased by suggesting cooperation applications with other users.

[0037] The schedule checking unit can automatically generate an optimal schedule to avoid overlapping events based on the user's application information. In the schedule checking unit, for example, the generation AI analyzes the user's application information and automatically generates an optimal schedule to avoid overlapping events. For example, the schedule is adjusted so that multiple events do not overlap. In addition, the schedule checking unit has the generation AI learn from past application data and automatically generate a schedule to avoid overlaps. For example, adjustments are made to prevent events from concentrating in specific time periods or dates. In addition, the schedule checking unit has the generation AI automatically generate a schedule to avoid overlaps based on the user's application history. For example, a schedule that avoids similar overlaps is proposed based on data on overlapping events in the past. In this way, the automatic generation of an optimal schedule to avoid overlapping events can make user schedule management more efficient.

[0038] The schedule check unit can analyze the user's schedule, predict the optimal notification timing, and send a reminder. In the schedule check unit, for example, the generation AI analyzes the user's schedule and predicts the optimal notification timing. For example, it identifies the time period when the user is most likely to receive a reminder and sends the notification at that time. In addition, the schedule check unit predicts the optimal notification timing by learning the effectiveness of past reminders. For example, it sends notifications avoiding time periods when the user has missed reminders in the past. In addition, the schedule check unit predicts the optimal notification timing by analyzing the user's behavioral patterns. For example, it sends reminders during the time period when the user is most active. This makes it possible to predict the optimal notification timing and send reminders, preventing the user from missing important dates.

[0039] The schedule check unit can analyze the effectiveness of past reminders based on the user's schedule and optimize the notification method. In the schedule check unit, for example, the generation AI analyzes the user's schedule and optimizes the notification method based on the effectiveness of past reminders. For example, it identifies the notification method that the user is most likely to respond to and sends the reminder using that method. In addition, the schedule check unit has the generation AI learn the effectiveness of past reminders and suggest the optimal notification method. For example, it analyzes the method that most effectively receives reminders for the user and sends the notification using that method. In addition, the schedule check unit has the generation AI analyze the user's behavioral patterns and suggest the optimal notification method. For example, it sends a reminder using the optimal notification method during the time period when the user is most active. In this way, by analyzing the effectiveness of past reminders and optimizing the notification method, it is possible to prevent the user from missing reminders.

[0040] The schedule check unit can provide a function for automatically coordinating schedules with other users based on the user's schedule. In the schedule check unit, for example, the generation AI analyzes the user's schedule and automatically coordinates schedules with other users. For example, it suggests an optimal schedule for attending an event with friends and family. In addition, the schedule check unit has the generation AI learn past schedule data and automatically coordinate schedules with other users. For example, it adjusts schedules so that multiple users can participate in the same event. In addition, the schedule check unit has the generation AI automatically coordinate schedules with other users based on the user's schedule history. For example, it adjusts schedules with users who have participated in events together in the past. In this way, automatic schedule coordination with other users can make planning joint events more efficient.

[0041] The schedule check unit can provide a function that notifies the user of event changes or cancellation information in real time based on the user's schedule. In the schedule check unit, for example, the generation AI analyzes the user's schedule and notifies the user of event change or cancellation information in real time. For example, it immediately notifies the user if the date, time, or location of an event changes. In addition, the schedule check unit has the generation AI learn from past event data and notify the user of change or cancellation information in real time. For example, it immediately notifies the user if an event changes or cancellation occurs. In addition, the schedule check unit has the generation AI notify the user of event change or cancellation information in real time based on the user's schedule history. For example, it immediately notifies the user of change or cancellation information for an event that the user was scheduled to attend in the past. This allows the user to keep up with the latest information by notifying the user of event change or cancellation information in real time.

[0042] The list creation unit can learn the user's preferences and automatically suggest the optimal schedule display format. In the list creation unit, for example, the generation AI learns the user's preferences and suggests the optimal schedule display format. For example, if the user prefers a calendar format, the schedule is displayed in that format. In addition, the list creation unit has the generation AI learn past schedule display data and suggest the optimal display format. For example, if the user prefers a list format, the schedule is displayed in that format. In addition, in the list creation unit, the generation AI analyzes the user's behavior patterns and suggests the optimal schedule display format. For example, if the user checks the schedule during a specific time period, the optimal display format for that time period is provided. This makes it possible to improve user convenience by suggesting the optimal schedule display format based on the user's preferences.

[0043] The listing unit can analyze the user's schedule and analyze the effectiveness of past display formats to optimize the display method. In the listing unit, for example, the generation AI analyzes the user's schedule and optimizes the display method based on the effectiveness of past display formats. For example, the list unit identifies a display format that allows the user to check the schedule most effectively and displays it in that format. In addition, the listing unit has the generation AI learn past schedule display data and propose an optimal display method. For example, the list unit analyzes a display format that allows the user to check the schedule most effectively and displays it in that format. In addition, the listing unit has the generation AI analyze the user's behavior patterns and propose an optimal display method. For example, if a user checks their schedule during a specific time period, the list unit provides the optimal display format for that time period. In this way, by analyzing the effectiveness of past display formats and optimizing the display method, it is possible to enable the user to check their schedule most effectively.

[0044] The listing unit can provide a display format that makes it easy to share a schedule with other users based on the user's schedule. For example, the listing unit uses a generation AI to analyze the user's schedule and provide a display format that makes it easy to share the schedule with other users. For example, the listing unit generates a link for sharing the schedule with friends and family. The listing unit also uses the generation AI to learn from past schedule sharing data and propose an optimal display format. For example, the listing unit analyzes the display format that allows the user to share the schedule most effectively and displays it in that format. The listing unit also uses the generation AI to analyze the user's behavior patterns and propose a display format that makes it easy to share the schedule with other users. For example, if a user wants to share their schedule during a specific time period, the listing unit provides a display format that is optimal for that time period. This provides a display format that makes it easy to share schedules with other users, making it possible to more efficiently plan joint events.

[0045] The listing unit can provide a display format according to the importance and priority of events based on the user's schedule. In the listing unit, for example, the generation AI analyzes the user's schedule and provides a display format according to the importance and priority of events. For example, important events are highlighted. In addition, the listing unit has the generation AI learn past schedule data and propose a display format according to the importance and priority of events. For example, the listing unit highlights the event that the user considers most important. In addition, the listing unit has the generation AI analyze the user's behavior patterns and propose a display format according to the importance and priority of events. For example, if the user considers a particular event to be important, that event is highlighted. In this way, by providing a display format according to the importance and priority of events, it is possible to prevent the user from missing important events.

[0046] The list creation unit can analyze the user's schedule and suggest the optimal sharing method. In the list creation unit, for example, the generation AI analyzes the user's schedule and suggests the optimal sharing method. For example, it generates a link for sharing the schedule with friends and family. In addition, the list creation unit has the generation AI learn from past schedule sharing data and suggest the optimal sharing method. For example, it analyzes the most effective way for the user to share their schedule and shares it in that way. In addition, in the list creation unit, the generation AI analyzes the user's behavior patterns and suggests the optimal sharing method. For example, if a user wants to share their schedule during a specific time period, it provides the optimal sharing method for that time period. In this way, by suggesting the optimal sharing method, it is possible to enable the user to share their schedule effectively.

[0047] The list creation unit can provide a function for automatically coordinating schedules with other users based on a shared schedule. In the list creation unit, for example, the generation AI analyzes the shared schedule and automatically coordinates schedules with other users. For example, the list creation unit suggests an optimal schedule for attending an event with friends and family. In addition, the list creation unit has the generation AI learn past schedule data and automatically coordinate schedules with other users. For example, the list creation unit adjusts schedules so that multiple users can participate in the same event. In addition, the list creation unit has the generation AI automatically coordinate schedules with other users based on the user's schedule history. For example, the list creation unit adjusts schedules with users who have participated in events together in the past. This makes it possible to efficiently plan joint events by automatically coordinating schedules with other users.

[0048] The list creation unit can automatically suggest plans for joint events with other users based on the user's schedule. In the list creation unit, for example, the generation AI analyzes the user's schedule and automatically suggests plans for joint events with other users. For example, it suggests an optimal schedule for attending an event with friends and family. In addition, the list creation unit has the generation AI learn past schedule data and automatically suggest plans for joint events. For example, it adjusts schedules so that multiple users can participate in the same event. In addition, the list creation unit has the generation AI automatically suggest plans for joint events based on the user's schedule history. For example, it adjusts schedules with users who have participated in events together in the past. In this way, event planning can be made more efficient by automatically suggesting plans for joint events with other users.

[0049] The listing unit can provide a function to notify event changes or cancellation information in real time based on the shared schedule. In the listing unit, for example, the generation AI analyzes the shared schedule and notifies event change or cancellation information in real time. For example, it immediately notifies if the date, time, or location of an event is changed. In addition, the listing unit has the generation AI learn past event data and notify change or cancellation information in real time. For example, it immediately notifies if an event is changed or canceled. In addition, the listing unit has the generation AI notify event change or cancellation information in real time based on the user's schedule history. For example, it immediately notifies if an event that the user was scheduled to attend in the past is changed or canceled. This allows the user to keep up with the latest information by notifying event change or cancellation information in real time.

[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] The schedule management system may further include a health management unit that monitors the user's health condition and adjusts the schedule. For example, the health management unit may analyze the user's sleep data and exercise data and adjust the schedule to avoid excessive stress. The health management unit may also suggest appropriate rest times based on the user's health condition. Furthermore, the health management unit may provide advice to help the user maintain a healthy lifestyle. This allows the user to manage their schedule with health in mind.

[0052] The schedule management system may further include a hobby suggestion unit that suggests events based on the user's hobbies and interests. For example, the hobby suggestion unit may analyze the user's past event participation history and social media posts to suggest events that the user may be interested in. The hobby suggestion unit may also suggest events that will help the user discover a new hobby. Furthermore, the hobby suggestion unit may also suggest events that can be enjoyed together based on the hobbies of the user's friends and family. This makes it easier for the user to find events that match their hobbies and interests.

[0053] The schedule management system may further include a prediction unit that analyzes a user's past event participation history and predicts future event participation trends. For example, the prediction unit predicts future event participation trends based on data on events the user has previously participated in. The prediction unit may also predict future event participation trends based on the user's interests and hobbies. Furthermore, the prediction unit may analyze the event participation history of the user's friends and family and predict trends in events that the user will participate in together. This makes it easier for users to understand future event participation trends.

[0054] The schedule management system may further include a satisfaction prediction unit that analyzes a user's past event participation history and predicts the level of satisfaction with an event. For example, the satisfaction prediction unit predicts the level of satisfaction with a future event based on data on events the user has previously participated in. The satisfaction prediction unit may also predict the level of satisfaction with an event based on the user's interests and hobbies. Furthermore, the satisfaction prediction unit may analyze the event participation history of the user's friends and family and predict the level of satisfaction with an event that the user will be participating in jointly. This makes it easier for users to understand the level of satisfaction with an event in advance.

[0055] The schedule management system may further include a success rate prediction unit that analyzes the user's past event participation history and predicts the success rate of an event. For example, the success rate prediction unit predicts the success rate of a future event based on data on events the user has previously participated in. The success rate prediction unit may also predict the success rate of an event based on the user's interests and hobbies. Furthermore, the success rate prediction unit may analyze the event participation history of the user's friends and family and predict the success rate of an event in which the user will participate jointly. This makes it easier for the user to know the success rate of an event in advance.

[0056] The schedule management system may further include a competition rate prediction unit that analyzes the user's past event participation history and predicts the competition rate of an event. For example, the competition rate prediction unit predicts the competition rate of a future event based on data on events the user has previously participated in. The competition rate prediction unit may also predict the competition rate of an event based on the user's interests and hobbies. Furthermore, the competition rate prediction unit may analyze the event participation history of the user's friends and family and predict the competition rate of an event that the user will be participating in jointly. This makes it easier for the user to understand the competition rate of an event in advance.

[0057] The schedule management system may further include a popularity prediction unit that analyzes a user's past event participation history and predicts the popularity of an event. For example, the popularity prediction unit predicts the popularity of a future event based on data on events the user has previously participated in. The popularity prediction unit may also predict the popularity of an event based on the user's interests and hobbies. Furthermore, the popularity prediction unit may analyze the event participation history of the user's friends and family and predict the popularity of an event that the user will be participating in jointly. This makes it easier for users to understand the popularity of an event in advance.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The information collection unit collects information from X (formerly Twitter (registered trademark)), official websites, etc. For example, the information collection unit uses an API to obtain ticket information from the official website. The information collection unit can also collect information from X (formerly Twitter (registered trademark)) using scraping technology. Furthermore, the information collection unit can also obtain the latest ticket information using an RSS feed. Step 2: The listing unit lists the information collected by the information collection unit. For example, the listing unit sorts the collected information by date and time. The listing unit can also filter by event type or artist name. Furthermore, the listing unit can classify information based on conditions specified by the user. Step 3: The schedule check unit checks the user's application information based on the information listed by the listing unit. For example, the schedule check unit analyzes the application information entered by the user to check for any duplication. The schedule check unit can also check the deadline for the application information and notify the user. Furthermore, the schedule check unit can set priorities for the application information and check important applications first.

[0060] (Example 2) A schedule management system according to an embodiment of the present invention uses AI to manage the schedules of ticket lottery applications and advance sales held by fans on multiple sites. This system has the ability to automatically collect information from sources such as X (formerly Twitter (registered trademark)) and official websites, and create a list of such information. When a user inputs the ticket information they have applied for, the AI ​​checks the schedule to ensure there are no overlapping applications. This allows the schedule management system to efficiently manage the schedules of ticket lottery applications and advance sales held by fans on multiple sites.

[0061] A schedule management system according to an embodiment includes an information collection unit, a listing unit, and a schedule check unit. The information collection unit collects information from X (formerly Twitter (registered trademark)), official websites, and the like. For example, the information collection unit obtains ticket information from the official website using an API. The information collection unit can also collect information from X (formerly Twitter (registered trademark)) using scraping technology. The information collection unit can also obtain the latest ticket information using an RSS feed. The listing unit lists the information collected by the information collection unit. For example, the listing unit sorts the collected information by date and time. The listing unit can also filter the information by event type or artist name. The listing unit can also classify the information based on conditions specified by the user. The schedule check unit checks the user's application information based on the information listed by the listing unit. For example, the schedule check unit analyzes the application information entered by the user and checks for duplicates. The schedule check unit can also check the deadline for the application information and notify the user. The schedule check unit can also set priorities for the application information and check important applications first. As a result, the schedule management system according to the embodiment can centrally manage information from multiple sites and efficiently check user application information. For example, a user can centrally manage the schedules of lottery applications and advance sales for tickets held on multiple sites, preventing duplication or oversight. Furthermore, the user can efficiently manage their schedule without missing deadlines for application information.

[0062] The information collection unit can predict the popularity of an event or the competition rate for tickets and suggest the optimal timing for application to the user. For example, the generation AI analyzes past event data to predict the popularity of a particular event. For example, the generation AI calculates the competition rate for an event based on past ticket sales data and social media responses, and suggests the optimal timing for application to the user. The information collection unit also predicts the popularity of an event based on information collected in real time by the generation AI and notifies the user. For example, the generation AI analyzes social media trends and search volume on search engines to predict the competition rate for an event. The information collection unit also learns the user's past application history and suggests the optimal timing for application. For example, the generation AI predicts the optimal timing for application for a similar event based on data on events for which the user has previously won. This can increase the user's chances of winning by suggesting the optimal timing for application to the user.

[0063] The information collection unit can predict the success rate or satisfaction level of an event by comparing it with past event data. For example, the information collection unit has the generation AI analyze past event data and predict the success rate of a particular event. For example, it calculates the satisfaction level of an event based on feedback and reviews from past participants. The information collection unit also predicts the success rate by comparing it with similar past events based on the information collected by the generation AI. For example, it analyzes data from past events held by the same artist or venue and predicts the success rate. The information collection unit also has the generation AI learn the user's past participation history and predict the satisfaction level of an event. For example, it predicts the satisfaction level of similar events based on data from events the user has attended in the past. This can help the user select the best event by predicting the success rate and satisfaction level of an event.

[0064] The information collection unit can use the emotion estimation function to analyze fans' emotions toward the collected information and prioritize listing events that elicit positive emotions. For example, the information collection unit uses the generation AI to analyze social media and blog posts and estimate fans' emotions. For example, it prioritizes listing events that elicit a large number of positive emotions. The information collection unit also uses the emotion estimation function to monitor fans' emotions toward the collected information in real time and list events that elicit positive emotions. For example, it analyzes social media reactions and comments. The information collection unit also uses the generation AI to learn the user's past emotional data and list events that elicit positive emotions. For example, it lists similar events based on events in which the user previously expressed positive emotions. This prioritizes listing events that elicit positive emotions, thereby improving user satisfaction.

[0065] The information collection unit can expand the sources of information it collects to include unofficial information from social media, blogs, and forums. For example, the generation AI collects information from unofficial sources such as social media, blogs, and forums. For example, it analyzes posts and comments on fan communities and lists event information. The information collection unit also collects more information by having the generation AI include unofficial sources of information in its collection. For example, it analyzes information from fan sites and personal blogs and lists event information. The information collection unit also collects posts from social media and forums in real time and lists event information. For example, it monitors specific hashtags and keywords and collects related information. In this way, more information can be collected by including unofficial sources of information in its collection.

[0066] The information collection unit can learn a user's past participation history or preferences and provide an individually customized event list. For example, the information collection unit uses a generation AI to learn a user's past participation history and provide an individually customized event list. For example, the information collection unit creates a list of similar events based on data on events the user has previously attended. Furthermore, the information collection unit uses the generation AI to learn a user's preferences and provide a customized event list. For example, if a user likes a particular artist or genre, the information collection unit creates a list of events based on that information. Furthermore, the information collection unit uses the generation AI to update the individually customized event list in real time based on the user's past participation history and preferences. For example, the list is updated each time new event information is added. This improves user satisfaction by providing an event list customized based on the user's past participation history and preferences.

[0067] The information collection unit can use the emotion estimation function to monitor the user's emotions regarding the collected information in real time and create a list according to the emotions. The information collection unit, for example, uses the emotion estimation function to monitor the user's emotions regarding the collected information in real time. For example, it prioritizes listing events in which the user expressed positive emotions. The information collection unit also creates a list according to emotions based on the user's emotion data using the generation AI. For example, it lists similar events based on events in which the user expressed positive emotions in the past. The information collection unit also uses the emotion estimation function to analyze the user's emotions regarding the collected information in real time and create a list according to the emotions. For example, it prioritizes listing events with a high user emotion score. In this way, by creating a list according to the user's emotions, user satisfaction can be improved.

[0068] The schedule check unit can analyze the user's application information and propose the optimal application strategy. In the schedule check unit, for example, the generation AI analyzes the user's application information and proposes the optimal application strategy. For example, spreading applications across multiple lotteries maximizes the probability of winning. In addition, the schedule check unit has the generation AI learn from past application data and propose the optimal application strategy. For example, applying at a specific time period or date increases the probability of winning. In addition, the schedule check unit has the generation AI propose the optimal application strategy based on the user's application history. For example, it proposes a similar strategy based on data on events that the user has won in the past. In this way, the probability of winning can be increased by proposing the optimal application strategy to the user.

[0069] The schedule check unit can predict the probability of winning by analyzing past winning history and trends based on the user's application information. In the schedule check unit, for example, the generation AI analyzes the user's application information and predicts the probability of winning based on past winning history and trends. For example, it calculates the probability of winning for a specific event or artist. In addition, the schedule check unit predicts the probability of winning by learning past application data. For example, it analyzes the tendency that the probability of winning increases by applying at a specific time period or date. In addition, the schedule check unit predicts the probability of winning based on the user's application history. For example, it predicts the probability of winning for a similar event based on data on events that were won in the past. In this way, by analyzing past winning history and trends, it is possible to predict the probability of winning and provide useful information to the user.

[0070] The schedule check unit can use the emotion estimation function to analyze the emotions of the user when entering application information and provide an interface for reducing stress. For example, the schedule check unit uses the emotion estimation function to analyze the emotions of the user when entering application information in real time. For example, if the user is feeling stressed, it provides an interface that helps the user relax. The schedule check unit also provides an interface for reducing stress based on the user's emotion data using the generation AI. For example, it designs an interface that allows the user to show positive emotions. The schedule check unit also uses the emotion estimation function to monitor the emotions of the user when entering application information and provides feedback for reducing stress. For example, it provides a message or music that helps the user relax. This makes the application process more comfortable by providing an interface that reduces the user's stress.

[0071] The schedule check unit can suggest cooperation applications with other users based on the user's application information. In the schedule check unit, for example, the generation AI analyzes the user's application information and suggests cooperation applications with other users. For example, applying together with a friend increases the chances of winning. In addition, the schedule check unit has the generation AI learn from past application data and analyze the effectiveness of cooperation applications. For example, it analyzes the tendency for the chances of winning to increase when multiple users apply together. In addition, the schedule check unit has the generation AI suggest cooperation applications based on the user's application history. For example, it suggests a similar strategy based on data on events in which past cooperation applications were successful. In this way, the chances of winning can be increased by suggesting cooperation applications with other users.

[0072] The schedule checking unit can automatically generate an optimal schedule to avoid overlapping events based on the user's application information. In the schedule checking unit, for example, the generation AI analyzes the user's application information and automatically generates an optimal schedule to avoid overlapping events. For example, the schedule is adjusted so that multiple events do not overlap. In addition, the schedule checking unit has the generation AI learn from past application data and automatically generate a schedule to avoid overlaps. For example, adjustments are made to prevent events from concentrating in specific time periods or dates. In addition, the schedule checking unit has the generation AI automatically generate a schedule to avoid overlaps based on the user's application history. For example, a schedule that avoids similar overlaps is proposed based on data on overlapping events in the past. In this way, the automatic generation of an optimal schedule to avoid overlapping events can make user schedule management more efficient.

[0073] The schedule check unit can use the emotion estimation function to monitor the emotions of the user when entering application information in real time and provide feedback that elicits positive emotions. For example, the schedule check unit uses the emotion estimation function to monitor the emotions of the user when entering application information in real time. For example, if the user shows positive emotions, it displays an encouraging message. The schedule check unit also provides feedback that elicits positive emotions based on the user's emotion data using the generation AI. For example, it provides messages or music that help the user to relax. The schedule check unit also uses the emotion estimation function to monitor the emotions of the user when entering application information and provides an interface that elicits positive emotions. For example, it uses designs and colors that help the user to relax. This makes the application process more comfortable by providing feedback that elicits positive emotions from the user.

[0074] The schedule check unit can analyze the user's schedule, predict the optimal notification timing, and send a reminder. In the schedule check unit, for example, the generation AI analyzes the user's schedule and predicts the optimal notification timing. For example, it identifies the time period when the user is most likely to receive a reminder and sends the notification at that time. In addition, the schedule check unit predicts the optimal notification timing by learning the effectiveness of past reminders. For example, it sends notifications avoiding time periods when the user has missed reminders in the past. In addition, the schedule check unit predicts the optimal notification timing by analyzing the user's behavioral patterns. For example, it sends reminders during the time period when the user is most active. This makes it possible to predict the optimal notification timing and send reminders, preventing the user from missing important dates.

[0075] The schedule check unit can analyze the effectiveness of past reminders based on the user's schedule and optimize the notification method. In the schedule check unit, for example, the generation AI analyzes the user's schedule and optimizes the notification method based on the effectiveness of past reminders. For example, it identifies the notification method that the user is most likely to respond to and sends the reminder using that method. In addition, the schedule check unit has the generation AI learn the effectiveness of past reminders and suggest the optimal notification method. For example, it analyzes the method that most effectively receives reminders for the user and sends the notification using that method. In addition, the schedule check unit has the generation AI analyze the user's behavioral patterns and suggest the optimal notification method. For example, it sends a reminder using the optimal notification method during the time period when the user is most active. In this way, by analyzing the effectiveness of past reminders and optimizing the notification method, it is possible to prevent the user from missing reminders.

[0076] The schedule check unit can use the emotion estimation function to analyze the user's emotions at the time of the reminder notification and provide notification content that elicits positive emotions. For example, the schedule check unit uses the emotion estimation function to analyze the user's emotions at the time of the reminder notification in real time. For example, if the user shows positive emotions, it displays an encouraging message. The schedule check unit also provides notification content that elicits positive emotions based on the user's emotion data using the generation AI. For example, it provides messages or music that help the user relax. The schedule check unit also uses the emotion estimation function to monitor the user's emotions at the time of the reminder notification and provides an interface that elicits positive emotions. For example, it uses designs and colors that help the user relax. This can enhance the effectiveness of the reminder by providing notification content that elicits positive emotions from the user.

[0077] The schedule check unit can provide a function for automatically coordinating schedules with other users based on the user's schedule. In the schedule check unit, for example, the generation AI analyzes the user's schedule and automatically coordinates schedules with other users. For example, it suggests an optimal schedule for attending an event with friends and family. In addition, the schedule check unit has the generation AI learn past schedule data and automatically coordinate schedules with other users. For example, it adjusts schedules so that multiple users can participate in the same event. In addition, the schedule check unit has the generation AI automatically coordinate schedules with other users based on the user's schedule history. For example, it adjusts schedules with users who have participated in events together in the past. In this way, automatic schedule coordination with other users can make planning joint events more efficient.

[0078] The schedule check unit can provide a function that notifies the user of event changes or cancellation information in real time based on the user's schedule. In the schedule check unit, for example, the generation AI analyzes the user's schedule and notifies the user of event change or cancellation information in real time. For example, it immediately notifies the user if the date, time, or location of an event changes. In addition, the schedule check unit has the generation AI learn from past event data and notify the user of change or cancellation information in real time. For example, it immediately notifies the user if an event changes or cancellation occurs. In addition, the schedule check unit has the generation AI notify the user of event change or cancellation information in real time based on the user's schedule history. For example, it immediately notifies the user of change or cancellation information for an event that the user was scheduled to attend in the past. This allows the user to keep up with the latest information by notifying the user of event change or cancellation information in real time.

[0079] The schedule check unit can use the emotion estimation function to monitor the user's emotions in real time when a reminder is issued, and provide notification content that corresponds to the emotions. For example, the schedule check unit uses the emotion estimation function to monitor the user's emotions in real time when a reminder is issued. For example, if the user shows positive emotions, an encouraging message is displayed. The schedule check unit also provides notification content that corresponds to the emotions based on the user's emotion data generated by the generation AI. For example, it provides messages or music that help the user to relax. The schedule check unit also uses the emotion estimation function to monitor the user's emotions when a reminder is issued, and provides an interface that corresponds to the emotions. For example, it uses designs and colors that help the user to relax. This can increase the effectiveness of reminders by providing notification content that corresponds to the user's emotions.

[0080] The list creation unit can learn the user's preferences and automatically suggest the optimal schedule display format. In the list creation unit, for example, the generation AI learns the user's preferences and suggests the optimal schedule display format. For example, if the user prefers a calendar format, the schedule is displayed in that format. In addition, the list creation unit has the generation AI learn past schedule display data and suggest the optimal display format. For example, if the user prefers a list format, the schedule is displayed in that format. In addition, in the list creation unit, the generation AI analyzes the user's behavior patterns and suggests the optimal schedule display format. For example, if the user checks the schedule during a specific time period, the optimal display format for that time period is provided. This makes it possible to improve user convenience by suggesting the optimal schedule display format based on the user's preferences.

[0081] The listing unit can analyze the user's schedule and analyze the effectiveness of past display formats to optimize the display method. In the listing unit, for example, the generation AI analyzes the user's schedule and optimizes the display method based on the effectiveness of past display formats. For example, the list unit identifies a display format that allows the user to check the schedule most effectively and displays it in that format. In addition, the listing unit has the generation AI learn past schedule display data and propose an optimal display method. For example, the list unit analyzes a display format that allows the user to check the schedule most effectively and displays it in that format. In addition, the listing unit has the generation AI analyze the user's behavior patterns and propose an optimal display method. For example, if a user checks their schedule during a specific time period, the list unit provides the optimal display format for that time period. In this way, by analyzing the effectiveness of past display formats and optimizing the display method, it is possible to enable the user to check their schedule most effectively.

[0082] The list creation unit can use the emotion estimation function to analyze the user's emotions when the schedule is displayed and provide a display format that elicits positive emotions. The list creation unit, for example, uses the emotion estimation function to analyze the user's emotions when the schedule is displayed in real time. For example, if the user shows positive emotions, it provides a display format that elicits relaxation. The list creation unit also provides a display format that elicits positive emotions based on the user's emotion data using a generation AI. For example, it uses designs and colors that allow the user to relax. The list creation unit also uses the emotion estimation function to monitor the user's emotions when the schedule is displayed and provides an interface for eliciting positive emotions. For example, it provides messages or music that allow the user to relax. In this way, by providing a display format that elicits positive emotions from the user, it is possible to improve satisfaction with schedule management.

[0083] The listing unit can provide a display format that makes it easy to share a schedule with other users based on the user's schedule. For example, the listing unit uses a generation AI to analyze the user's schedule and provide a display format that makes it easy to share the schedule with other users. For example, the listing unit generates a link for sharing the schedule with friends and family. The listing unit also uses the generation AI to learn from past schedule sharing data and propose an optimal display format. For example, the listing unit analyzes the display format that allows the user to share the schedule most effectively and displays it in that format. The listing unit also uses the generation AI to analyze the user's behavior patterns and propose a display format that makes it easy to share the schedule with other users. For example, if a user wants to share their schedule during a specific time period, the listing unit provides a display format that is optimal for that time period. This provides a display format that makes it easy to share schedules with other users, making it possible to more efficiently plan joint events.

[0084] The listing unit can provide a display format according to the importance and priority of events based on the user's schedule. In the listing unit, for example, the generation AI analyzes the user's schedule and provides a display format according to the importance and priority of events. For example, important events are highlighted. In addition, the listing unit has the generation AI learn past schedule data and propose a display format according to the importance and priority of events. For example, the listing unit highlights the event that the user considers most important. In addition, the listing unit has the generation AI analyze the user's behavior patterns and propose a display format according to the importance and priority of events. For example, if the user considers a particular event to be important, that event is highlighted. In this way, by providing a display format according to the importance and priority of events, it is possible to prevent the user from missing important events.

[0085] The list creation unit can use the emotion estimation function to monitor the user's emotions in real time when the schedule is displayed, and provide a display format that corresponds to the emotions. The list creation unit, for example, uses the emotion estimation function to monitor the user's emotions in real time when the schedule is displayed. For example, if the user shows positive emotions, it provides a display format that is relaxing. The list creation unit also provides a display format that corresponds to the emotions based on the user's emotion data using the generation AI. For example, it uses designs and colors that allow the user to relax. The list creation unit also uses the emotion estimation function to monitor the user's emotions when the schedule is displayed, and provides an interface that corresponds to the emotions. For example, it provides messages or music that allow the user to relax. In this way, by providing a display format that corresponds to the user's emotions, it is possible to improve satisfaction with schedule management.

[0086] The list creation unit can analyze the user's schedule and suggest the optimal sharing method. In the list creation unit, for example, the generation AI analyzes the user's schedule and suggests the optimal sharing method. For example, it generates a link for sharing the schedule with friends and family. In addition, the list creation unit has the generation AI learn from past schedule sharing data and suggest the optimal sharing method. For example, it analyzes the most effective way for the user to share their schedule and shares it in that way. In addition, in the list creation unit, the generation AI analyzes the user's behavior patterns and suggests the optimal sharing method. For example, if a user wants to share their schedule during a specific time period, it provides the optimal sharing method for that time period. In this way, by suggesting the optimal sharing method, it is possible to enable the user to share their schedule effectively.

[0087] The list creation unit can provide a function for automatically coordinating schedules with other users based on a shared schedule. In the list creation unit, for example, the generation AI analyzes the shared schedule and automatically coordinates schedules with other users. For example, the list creation unit suggests an optimal schedule for attending an event with friends and family. In addition, the list creation unit has the generation AI learn past schedule data and automatically coordinate schedules with other users. For example, the list creation unit adjusts schedules so that multiple users can participate in the same event. In addition, the list creation unit has the generation AI automatically coordinate schedules with other users based on the user's schedule history. For example, the list creation unit adjusts schedules with users who have participated in events together in the past. This makes it possible to efficiently plan joint events by automatically coordinating schedules with other users.

[0088] The list creation unit can use the emotion estimation function to analyze the user's emotions when sharing a schedule and provide a sharing method that elicits positive emotions. The list creation unit, for example, uses the emotion estimation function to analyze the user's emotions when sharing a schedule in real time. For example, if the user shows positive emotions, it displays an encouraging message. The list creation unit also provides a sharing method in which the generation AI elicits positive emotions based on the user's emotion data. For example, it provides messages or music that help the user relax. The list creation unit also uses the emotion estimation function to monitor the user's emotions when sharing a schedule and provides an interface for eliciting positive emotions. For example, it uses designs and colors that help the user relax. This provides a sharing method that elicits positive emotions from the user, thereby improving satisfaction with schedule sharing.

[0089] The list creation unit can automatically suggest plans for joint events with other users based on the user's schedule. In the list creation unit, for example, the generation AI analyzes the user's schedule and automatically suggests plans for joint events with other users. For example, it suggests an optimal schedule for attending an event with friends and family. In addition, the list creation unit has the generation AI learn past schedule data and automatically suggest plans for joint events. For example, it adjusts schedules so that multiple users can participate in the same event. In addition, the list creation unit has the generation AI automatically suggest plans for joint events based on the user's schedule history. For example, it adjusts schedules with users who have participated in events together in the past. In this way, event planning can be made more efficient by automatically suggesting plans for joint events with other users.

[0090] The listing unit can provide a function to notify event changes or cancellation information in real time based on the shared schedule. In the listing unit, for example, the generation AI analyzes the shared schedule and notifies event change or cancellation information in real time. For example, it immediately notifies if the date, time, or location of an event is changed. In addition, the listing unit has the generation AI learn past event data and notify change or cancellation information in real time. For example, it immediately notifies if an event is changed or canceled. In addition, the listing unit has the generation AI notify event change or cancellation information in real time based on the user's schedule history. For example, it immediately notifies if an event that the user was scheduled to attend in the past is changed or canceled. This allows the user to keep up with the latest information by notifying event change or cancellation information in real time.

[0091] The list creation unit can use the emotion estimation function to monitor the user's emotions in real time when sharing a schedule, and provide a sharing method that suits the emotions. The list creation unit, for example, uses the emotion estimation function to monitor the user's emotions in real time when sharing a schedule. For example, if the user shows positive emotions, an encouraging message is displayed. The list creation unit also provides a sharing method that suits the emotions based on the user's emotion data using the generation AI. For example, it provides messages or music that help the user relax. The list creation unit also uses the emotion estimation function to monitor the user's emotions when sharing a schedule, and provides an interface that suits the emotions. For example, it uses designs and colors that help the user relax. This makes it possible to improve satisfaction with schedule sharing by providing a sharing method that suits the user's emotions.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The schedule management system may further include a health management unit that monitors the user's health condition and adjusts the schedule. For example, the health management unit may analyze the user's sleep data and exercise data and adjust the schedule to avoid excessive stress. The health management unit may also suggest appropriate rest times based on the user's health condition. Furthermore, the health management unit may provide advice to help the user maintain a healthy lifestyle. This allows the user to manage their schedule with health in mind.

[0094] The schedule management system may further include a hobby suggestion unit that suggests events based on the user's hobbies and interests. For example, the hobby suggestion unit may analyze the user's past event participation history and social media posts to suggest events that the user may be interested in. The hobby suggestion unit may also suggest events that will help the user discover a new hobby. Furthermore, the hobby suggestion unit may also suggest events that can be enjoyed together based on the hobbies of the user's friends and family. This makes it easier for the user to find events that match their hobbies and interests.

[0095] The schedule management system may further include a reminder unit that estimates the user's emotions and customizes the content of reminders based on the estimated emotions. For example, the reminder unit may send a relaxing message when the user is feeling stressed. Alternatively, the reminder unit may send an encouraging message when the user is showing positive emotions. Furthermore, the reminder unit may adjust the timing of sending reminders according to the user's emotions. This allows the user to receive reminders that correspond to their emotions.

[0096] The schedule management system may further include a priority setting unit that estimates the user's emotions and sets the priority of events based on the estimated emotions. For example, the priority setting unit prioritizes listing events for which the user is feeling positive. The priority setting unit may also postpone events for which the user is feeling stressed. Furthermore, the priority setting unit may adjust the event schedule according to the user's emotions. This allows the user to manage their schedule according to their emotions.

[0097] The schedule management system may further include a suggestion unit that estimates the user's emotions and suggests events based on the estimated emotions. For example, the suggestion unit may suggest fun events when the user is feeling positive. The suggestion unit may also suggest relaxing events when the user is feeling stressed. Furthermore, the suggestion unit may customize the content of the suggested events according to the user's emotions. This allows the user to receive event suggestions that match their emotions.

[0098] The schedule management system may further include a prediction unit that analyzes a user's past event participation history and predicts future event participation trends. For example, the prediction unit predicts future event participation trends based on data on events the user has previously participated in. The prediction unit may also predict future event participation trends based on the user's interests and hobbies. Furthermore, the prediction unit may analyze the event participation history of the user's friends and family and predict trends in events that the user will participate in together. This makes it easier for users to understand future event participation trends.

[0099] The schedule management system may further include a satisfaction prediction unit that analyzes a user's past event participation history and predicts the level of satisfaction with an event. For example, the satisfaction prediction unit predicts the level of satisfaction with a future event based on data on events the user has previously participated in. The satisfaction prediction unit may also predict the level of satisfaction with an event based on the user's interests and hobbies. Furthermore, the satisfaction prediction unit may analyze the event participation history of the user's friends and family and predict the level of satisfaction with an event that the user will be participating in jointly. This makes it easier for users to understand the level of satisfaction with an event in advance.

[0100] The schedule management system may further include a success rate prediction unit that analyzes the user's past event participation history and predicts the success rate of an event. For example, the success rate prediction unit predicts the success rate of a future event based on data on events the user has previously participated in. The success rate prediction unit may also predict the success rate of an event based on the user's interests and hobbies. Furthermore, the success rate prediction unit may analyze the event participation history of the user's friends and family and predict the success rate of an event in which the user will participate jointly. This makes it easier for the user to know the success rate of an event in advance.

[0101] The schedule management system may further include a competition rate prediction unit that analyzes the user's past event participation history and predicts the competition rate of an event. For example, the competition rate prediction unit predicts the competition rate of a future event based on data on events the user has previously participated in. The competition rate prediction unit may also predict the competition rate of an event based on the user's interests and hobbies. Furthermore, the competition rate prediction unit may analyze the event participation history of the user's friends and family and predict the competition rate of an event that the user will be participating in jointly. This makes it easier for the user to understand the competition rate of an event in advance.

[0102] The schedule management system may further include a popularity prediction unit that analyzes a user's past event participation history and predicts the popularity of an event. For example, the popularity prediction unit predicts the popularity of a future event based on data on events the user has previously participated in. The popularity prediction unit may also predict the popularity of an event based on the user's interests and hobbies. Furthermore, the popularity prediction unit may analyze the event participation history of the user's friends and family and predict the popularity of an event that the user will be participating in jointly. This makes it easier for users to understand the popularity of an event in advance.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The information collection unit collects information from X (formerly Twitter (registered trademark)), official websites, etc. For example, the information collection unit uses an API to obtain ticket information from the official website. The information collection unit can also collect information from X (formerly Twitter (registered trademark)) using scraping technology. Furthermore, the information collection unit can also obtain the latest ticket information using an RSS feed. Step 2: The listing unit lists the information collected by the information collection unit. For example, the listing unit sorts the collected information by date and time. The listing unit can also filter by event type or artist name. Furthermore, the listing unit can classify information based on conditions specified by the user. Step 3: The schedule check unit checks the user's application information based on the information listed by the listing unit. For example, the schedule check unit analyzes the application information entered by the user to check for any duplication. The schedule check unit can also check the deadline for the application information and notify the user. Furthermore, the schedule check unit can set priorities for the application information and check important applications first.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] 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.

[0120] 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.

[0121] 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 AI 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.

[0122] 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.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0131] 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.

[0132] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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 AI 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.

[0137] 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.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, 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. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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 AI 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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, in order to avoid confusion and to 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.

[0171] 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]

[0172] 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. An information gathering department that collects information from X or official websites; a listing unit that lists the information collected by the information collecting unit; a schedule check unit that checks the application information of the user based on the information listed by the listing unit. A system characterized by:

2. The information collecting unit Predict the popularity of the event or the competition rate for tickets, and suggest the optimal timing for application to the user.

2. The system of claim 1.

3. The information collecting unit Predict event success or satisfaction by comparing with past event data 2. The system of claim 1.

4. The information collecting unit Analyze fans' feelings about the collected information and prioritize events that elicit positive emotions 2. The system of claim 1.

5. The information collecting unit Expand the sources of information collected to include unofficial information from the aforementioned social media platforms, blogs, and forums.

2. The system of claim 1.

Images