system

Abstract

The system according to this embodiment aims to track the release dates and remaining quantities of various tickets and to efficiently consider the schedule of events that users wish to attend. [Solution] The system according to the embodiment comprises a collection unit, a review unit, a grasping unit, and a provision unit. The collection unit collects the release dates and remaining quantities of various tickets from information sources such as websites and social media. The review unit reviews the schedule of events that users wish to attend based on the information collected by the collection unit. The grasping unit grasps resale and ticket seekers based on the schedule reviewed by the review unit. The provision unit provides the information grasped by the grasping unit to the user.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to grasp the release dates and remaining quantities of various tickets and efficiently consider the schedules of events that users wish to participate in.

[0005] The system according to the embodiment aims to grasp the release dates and remaining quantities of various tickets and efficiently consider the schedules of events that users wish to participate in.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a review unit, a tracking unit, and a provision unit. The collection unit collects release dates and remaining quantities of various tickets from information sources such as websites and social media. The review unit reviews the schedule of events that users wish to attend based on the information collected by the collection unit. The tracking unit identifies resale and ticket seekers based on the schedule reviewed by the review unit. The provision unit provides the information identified by the tracking unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can track the release dates and remaining quantities of various tickets, and efficiently consider the schedule of events that users wish to attend. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database ......

[0019] It should be noted that the content in the original text from line 18 to the end seems to be incomplete. I have translated it as much as possible based on the existing content. If you can provide the complete text, I will be able to provide a more accurate translation.The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that uses a generating AI to grasp the release dates of various tickets and to consider events to attend on the same day and to manage the calendar. This system obtains the release dates and remaining number of tickets for various events from information sources such as websites and social media. Next, based on the acquired information, it considers the schedule of events that the user wishes to attend and reflects it in the calendar. Furthermore, if participation in events on the same day is decided, it also identifies resale opportunities and ticket seekers. For example, the generating AI collects information on events such as music, sports, and theater, and grasps the release dates and remaining number of tickets. The generating AI obtains the ticket release date for a music concert and notifies the user. Next, based on the acquired information, it considers the schedule of events that the user wishes to attend. The generating AI compares this with the user's calendar information and adjusts so that multiple events do not overlap on the same day. For example, if the user wishes to attend both a music concert and a sports event, the generating AI adjusts the schedule and proposes the optimal participation plan. Furthermore, if participation in events on the same day is decided, the generating AI also identifies resale opportunities and ticket seekers. For example, if a user already has plans to attend another event, the generating AI provides resale information and identifies potential ticket buyers, preventing the user from wasting tickets. This allows users to efficiently manage their event attendance schedules without missing ticket release dates. Furthermore, by identifying resale opportunities and potential ticket buyers, ticket waste can be prevented. The system allows users to efficiently manage their event attendance schedules without missing ticket release dates. Furthermore, by identifying resale opportunities and potential ticket buyers, ticket waste can be prevented.

[0029] The system according to this embodiment comprises a collection unit, a review unit, a comprehension unit, and a provision unit. The collection unit collects release dates and remaining quantities of various tickets from information sources such as websites and social media. The collection unit collects information on events such as music, sports, and theater performances. The collection unit can obtain release dates and remaining quantities of tickets for various events using a generation AI. For example, the collection unit obtains the release date of tickets for a music concert and notifies the user. The collection unit can also obtain the remaining quantity of tickets for a sports event and notify the user. The collection unit can also obtain the release date of tickets for a theater performance and notify the user. The review unit considers the schedule of events the user wishes to attend based on the information collected by the collection unit. The review unit adjusts the schedule by comparing it with the user's calendar information using a generation AI. For example, if the user wishes to attend both a music concert and a sports event, the review unit adjusts the schedule and proposes the optimal participation plan. If the user wishes to attend both a theater performance and a sports event, the review unit can also adjust the schedule and propose the optimal participation plan. The planning unit can adjust schedules and propose the optimal participation plan if the user wishes to attend both a music concert and a theatrical performance. The information gathering unit identifies resale and ticket seekers based on the schedule considered by the planning unit. The information gathering unit uses generative AI to provide resale information and identify ticket seekers. For example, if the information gathering unit has plans to attend another event, it provides resale information and identifies ticket seekers. The information gathering unit can also provide resale information and identify ticket seekers if the user is reselling tickets for a music concert. The information gathering unit can also provide resale information and identify ticket seekers if the user is reselling tickets for a sporting event. The information provision unit provides the information gathered by the information gathering unit to the user. The information provision unit uses generative AI to notify the user of the information. For example, the information provision unit notifies the user of resale information. The information provision unit can also notify the user of information about ticket seekers. The information provision unit can also notify the user of the event schedule.This allows the system to efficiently manage event attendance schedules without users missing ticket release dates. Furthermore, it prevents ticket waste by tracking resales and ticket seekers.

[0030] The data collection department gathers information on ticket release dates and remaining ticket quantities from sources such as websites and social media. Specifically, the department utilizes web scraping techniques and APIs to collect information on events such as music, sports, and theater performances. For example, it obtains the latest ticket release information from official music concert websites and social media accounts and stores it in a database. The data collection department can also use generative AI to obtain ticket release dates and remaining ticket quantities for various events. The generative AI employs natural language processing techniques to extract necessary information from web pages and social media posts and organize it as structured data. For example, the data collection department can obtain the ticket release date for a music concert and notify users. The data collection department can also obtain the remaining ticket quantity for a sports event and notify users. The data collection department can also obtain the ticket release date for a theater performance and notify users. This allows the data collection department to quickly and accurately collect and provide the latest information on events of interest to users. Furthermore, the data collection department can centrally manage the collected data and share information in cooperation with other departments. For example, the data collection department can store the collected ticket information in a cloud database, making it accessible to the review and information gathering departments. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific events and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The review department considers the schedules of events that users wish to attend based on the information collected by the data collection department. Specifically, the review department uses a generative AI to adjust the schedule by comparing it with the user's calendar information. The generative AI analyzes the user's calendar information, compares the event dates and times with the user's availability, and proposes the optimal schedule. For example, if a user wishes to attend both a music concert and a sporting event, the review department adjusts the schedule and proposes the optimal participation plan. The review department can also adjust the schedule and propose the optimal participation plan if the user wishes to attend both a theatrical performance and a sporting event. This allows the review department to optimize the schedule so that users can participate in events efficiently. Furthermore, the review department can make more personalized suggestions by considering the user's past participation history and preferences. For example, it can prioritize suggesting similar events based on the types and dates of events the user has attended in the past. The review department can also collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the review department to flexibly adjust schedules to meet user needs and improve user satisfaction.

[0032] The tracking unit identifies resale opportunities and ticket seekers based on the schedule considered by the review unit. Specifically, the tracking unit uses generative AI to provide resale information and identify ticket seekers. The generative AI analyzes the user's schedule and the popularity of events to identify tickets with a high probability of resale. For example, if a user already has plans to attend another event, the tracking unit provides resale information and identifies ticket seekers. The tracking unit can also provide resale information and identify ticket seekers if the user is reselling tickets for a music concert. The tracking unit can also provide resale information and identify ticket seekers if the user is reselling tickets for a sporting event. This allows the tracking unit to help users efficiently resell tickets and prevent ticket waste. Furthermore, the tracking unit can update resale information in real time, providing users with the latest information. For example, it can adjust resale prices and conditions according to fluctuations in resale demand and supply. The tracking unit can also collect user feedback and use it to improve the resale process. This allows the gripping unit to provide flexible resale support tailored to user needs, thereby improving user satisfaction.

[0033] The information provision unit provides users with information gathered by the information gathering unit. Specifically, the information provision unit uses a generation AI to notify users of information. The generation AI analyzes the user's preferences and past behavior history to notify them of information at the optimal time. For example, the information provision unit can notify users of resale information. The information provision unit can also notify users of information about people who want tickets. The information provision unit can also notify users of event schedules. In this way, the information provision unit helps users receive the information they need quickly and accurately. Furthermore, the information provision unit can reliably transmit information using multiple notification methods. For example, in addition to smartphone notifications, important information can be reliably delivered by using a combination of email, SMS, and voice calls. The information provision unit can also collect user feedback and continuously improve the accuracy of notification content and timing. In this way, the information provision unit can provide flexible information tailored to user needs and improve user satisfaction.

[0034] The data collection unit can collect information about events such as music, sports, and theater. For example, the data collection unit can collect information about music concerts. The data collection unit can also collect information about sports events. The data collection unit can also collect information about theater performances. This allows the data collection unit to collect diverse event information and provide information that meets the user's needs. Some or all of the processing described above in the data collection unit may be performed using generative AI, or it may be performed without generative AI. For example, the data collection unit can collect information about music concerts using generative AI. The data collection unit can collect information about sports events using generative AI. The data collection unit can collect information about theater performances using generative AI.

[0035] The review unit can adjust the schedule by comparing it with the user's calendar information. For example, the review unit can obtain the user's calendar information and adjust the schedule. The review unit can also propose a schedule that avoids overlapping events based on the user's calendar information. The review unit can also propose an optimal schedule based on the user's calendar information. This allows the review unit to propose a schedule that avoids overlapping events by considering the user's calendar information. Some or all of the above processing in the review unit may be performed using generative AI, or not. For example, the review unit can use generative AI to obtain the user's calendar information and adjust the schedule. The review unit can use generative AI to propose a schedule that avoids overlapping events based on the user's calendar information. The review unit can use generative AI to propose an optimal schedule based on the user's calendar information.

[0036] The tracking unit can provide resale information and identify ticket seekers. For example, the tracking unit can acquire resale information and provide it to users. The tracking unit can also acquire information on ticket seekers and provide it to users. The tracking unit can also identify ticket seekers based on resale information. In this way, the tracking unit can prevent ticket waste by providing resale information and identifying ticket seekers. Some or all of the above processing in the tracking unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the tracking unit can use a generation AI to acquire resale information and provide it to users. The tracking unit can use a generation AI to acquire information on ticket seekers and provide it to users. The tracking unit can use a generation AI to identify ticket seekers based on resale information.

[0037] The service provider can notify users of information. For example, the service provider can notify users of resale information. The service provider can also notify users of information about ticket applicants. The service provider can also notify users of event schedules. In this way, by notifying users of information, the service provider can ensure that users receive the information they need in a timely manner. Some or all of the above-described processes in the service provider may be performed using or without a generative AI. For example, the service provider can use a generative AI to notify users of resale information. The service provider can use a generative AI to notify users of information about ticket applicants. The service provider can use a generative AI to notify users of event schedules.

[0038] The data collection unit can analyze a user's past event participation history during data collection and prioritize the collection of highly relevant event information. For example, the data collection unit can prioritize the collection of event information by the same artist based on a user's past music concert attendance history. The data collection unit can also prioritize the collection of match information for the same team based on a user's past sports event attendance history. The data collection unit can also prioritize the collection of performance information for the same theater company based on a user's past theater attendance history. In this way, the data collection unit can prioritize the collection of highly relevant event information by analyzing a user's past event participation history. Some or all of the above processing in the data collection unit may be performed using generative AI, or it may be performed without generative AI. For example, the data collection unit can use generative AI to analyze a user's past event participation history and prioritize the collection of highly relevant event information. The data collection unit can use generative AI to prioritize the collection of event information by the same artist based on a user's past music concert attendance history. The data collection unit can use generative AI to prioritize the collection of match information for the same team based on a user's past sports event attendance history. The data collection unit uses a generation AI to prioritize the collection of performance information from the same theater company based on the user's past theater-going history.

[0039] The data collection unit can adjust the level of detail of the information it collects, taking into account the popularity and rating of the events. For example, the data collection unit can collect detailed information for highly popular events and provide it to the user. The data collection unit can also collect detailed information for highly rated events and provide it to the user. For events with low popularity or ratings, the data collection unit can collect only basic information and provide it to the user. This allows the data collection unit to collect detailed information that is important to the user by considering the popularity and rating of the events. Some or all of the above processing in the data collection unit may be performed using or without generative AI. For example, the data collection unit can use generative AI to collect detailed information for highly popular events and provide it to the user. The data collection unit can use generative AI to collect detailed information for highly rated events and provide it to the user. The data collection unit can use generative AI to collect only basic information for events with low popularity or ratings and provide it to the user.

[0040] The data collection unit can prioritize collecting nearby event information by considering the user's geographical location during collection. For example, the data collection unit can prioritize collecting event information within a 5km radius of the user's current location. The data collection unit can also prioritize collecting nearby event information based on the user's home address. The data collection unit can also prioritize collecting event information near the user's workplace based on the user's workplace address. In this way, the data collection unit can prioritize collecting nearby event information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using or without a generation AI. For example, the data collection unit can prioritize collecting event information within a 5km radius of the user's current location using a generation AI. The data collection unit can prioritize collecting nearby event information based on the user's home address using a generation AI. The data collection unit can prioritize collecting event information near the user's workplace based on the user's workplace address using a generation AI.

[0041] The data collection unit can collect relevant event information by analyzing the user's social media activity during collection. For example, the data collection unit can collect relevant event information based on event information that the user has "liked" on social media. The data collection unit can also collect event information from artists and organizations that the user follows on social media. The data collection unit can also collect relevant event information based on event information that the user has shared on social media. In this way, the data collection unit can collect relevant event information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using generative AI, or it may be performed without generative AI. For example, the data collection unit can use generative AI to collect relevant event information based on event information that the user has "liked" on social media. The data collection unit can use generative AI to collect event information from artists and organizations that the user follows on social media. The data collection unit can use generative AI to collect relevant event information based on event information that the user has shared on social media.

[0042] The review unit can propose the optimal schedule by referring to the user's past schedule history during the review process. For example, the review unit can propose the optimal schedule based on the schedules of events the user has previously attended. The review unit can also propose a schedule that avoids congestion based on the user's past schedule history. The review unit can also analyze the user's past schedule history and propose the most efficient schedule. This allows the review unit to propose the optimal schedule by referring to the user's past schedule history. Some or all of the above processing in the review unit may be performed using generative AI, or not. For example, the review unit can use generative AI to refer to the user's past schedule history and propose the optimal schedule. The review unit can use generative AI to propose the optimal schedule based on the schedules of events the user has previously attended. The review unit can use generative AI to propose a schedule that avoids congestion based on the user's past schedule history. The review unit can use generative AI to analyze the user's past schedule history and propose the most efficient schedule.

[0043] The review unit can adjust the level of detail in the schedule based on the importance and priority of the events during the review process. For example, the review unit can propose a detailed schedule for high-importance events. The review unit can also propose a detailed schedule for high-priority events. For events of low importance or priority, the review unit can propose only a basic schedule. In this way, the review unit can propose a detailed schedule for events that are important to the user by adjusting the level of detail in the schedule based on the importance and priority of the events. Some or all of the above processing in the review unit may be performed using generative AI, or not. For example, the review unit can use generative AI to propose a detailed schedule for high-importance events. The review unit can use generative AI to propose a detailed schedule for high-priority events. The review unit can use generative AI to propose only a basic schedule for events of low importance or priority.

[0044] The review unit can select the optimal schedule display method by considering the user's device information during the review process. For example, if the user is using a smartphone, the review unit can provide a display method that matches the screen size. If the user is using a tablet, the review unit can also provide a display method optimized for a larger screen. If the user is using a smartwatch, the review unit can also provide a concise and highly visible display method. In this way, the review unit can provide the optimal schedule display method by considering the user's device information. Some or all of the above processing in the review unit may be performed using a generation AI, or not. For example, the review unit can use a generation AI to select the optimal schedule display method by considering the user's device information. If the user is using a smartphone, the review unit can use a generation AI to provide a display method that matches the screen size. If the user is using a tablet, the review unit can use a generation AI to provide a display method optimized for a larger screen. If the user is using a smartwatch, the review unit can use a generation AI to provide a concise and highly visible display method.

[0045] The review unit can analyze a user's social media activity during the review process and propose relevant event schedules. For example, the review unit can propose the schedules of events that the user has "liked" on social media. The review unit can also propose the schedules of events by artists and organizations that the user follows on social media. The review unit can also propose the schedules of events that the user has shared on social media. In this way, the review unit can propose relevant event schedules by analyzing the user's social media activity. Some or all of the above processing in the review unit may be performed using generative AI or not. For example, the review unit can use generative AI to analyze a user's social media activity and propose relevant event schedules. The review unit can use generative AI to propose the schedules of events that the user has "liked" on social media. The review unit can use generative AI to propose the schedules of events by artists and organizations that the user follows on social media. The review unit can use generative AI to propose the schedules of events that the user has shared on social media.

[0046] The information gathering unit can provide optimal resale information by referring to the user's past resale history when gathering information. For example, the information gathering unit can provide optimal resale information based on the history of tickets the user has previously resold. The information gathering unit can also provide high-demand resale information from the user's past resale history. The information gathering unit can also analyze the user's past resale history and provide the most efficient resale information. In this way, the information gathering unit can provide optimal resale information by referring to the user's past resale history. Some or all of the above processing in the information gathering unit may be performed using or without a generation AI. For example, the information gathering unit can use a generation AI to refer to the user's past resale history and provide optimal resale information. The information gathering unit can use a generation AI to provide optimal resale information based on the history of tickets the user has previously resold. The information gathering unit can use a generation AI to provide high-demand resale information from the user's past resale history. The information gathering unit can use a generation AI to analyze the user's past resale history and provide the most efficient resale information.

[0047] The information gathering unit can adjust the level of detail of the information based on the supply and demand situation of resales when gathering information. For example, the information gathering unit can provide detailed information for resales with high demand. The information gathering unit can also provide detailed information for resales with high supply. The information gathering unit can also provide only basic information for resales with low demand or supply. In this way, the information gathering unit can provide users with detailed information that is important to them by adjusting the level of detail of the information based on the supply and demand situation of resales. Some or all of the above processing in the information gathering unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the information gathering unit can use a generation AI to provide detailed information for resales with high demand. The information gathering unit can use a generation AI to provide detailed information for resales with high supply. The information gathering unit can use a generation AI to provide only basic information for resales with low demand or supply.

[0048] The information gathering unit can prioritize providing nearby resale information by considering the user's geographical location information when gathering information. For example, the information gathering unit can prioritize providing resale information within a 5km radius of the user's current location. The information gathering unit can also prioritize providing nearby resale information based on the user's home address. The information gathering unit can also prioritize providing resale information near the user's workplace based on the user's workplace address. In this way, the information gathering unit can prioritize providing nearby resale information by considering the user's geographical location information. Some or all of the above processing in the information gathering unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the information gathering unit can use a generation AI to prioritize providing resale information within a 5km radius of the user's current location. The information gathering unit can use a generation AI to prioritize providing nearby resale information based on the user's home address. The information gathering unit can use a generation AI to prioritize providing resale information near the user's workplace based on the user's workplace address.

[0049] The tracking unit can analyze the user's social media activity and provide relevant resale information. For example, the tracking unit can provide relevant resale information based on resale information that the user has "liked" on social media. The tracking unit can also provide resale information for artists and organizations that the user follows on social media. The tracking unit can also provide relevant resale information based on resale information that the user has shared on social media. In this way, the tracking unit can provide relevant resale information by analyzing the user's social media activity. Some or all of the above processing in the tracking unit may be performed using generative AI, or without generative AI. For example, the tracking unit can use generative AI to analyze the user's social media activity and provide relevant resale information. The tracking unit can use generative AI to provide relevant resale information based on resale information that the user has "liked" on social media. The tracking unit can use generative AI to provide resale information for artists and organizations that the user follows on social media. The tracking unit can use generative AI to provide relevant resale information based on resale information that the user has shared on social media.

[0050] The service provider can select the optimal notification method by referring to the user's past notification history when providing notifications. For example, the service provider can prioritize providing notification methods that the user has previously preferred (email, push notifications, etc.). The service provider can also select the most effective notification method from the user's past notification history. The service provider can also analyze the user's past notification history and suggest the optimal notification timing. This allows the service provider to select the optimal notification method by referring to the user's past notification history. Some or all of the above processing in the service provider may be performed using or without generative AI. For example, the service provider can use generative AI to select the optimal notification method by referring to the user's past notification history. The service provider can use generative AI to prioritize providing notification methods that the user has previously preferred (email, push notifications, etc.). The service provider can use generative AI to select the most effective notification method from the user's past notification history. The service provider can use generative AI to analyze the user's past notification history and suggest the optimal notification timing.

[0051] The information provider can adjust the level of detail of notifications based on the importance and priority of the information at the time of delivery. For example, the provider can provide detailed notifications for information of high importance. The provider can also provide detailed notifications for information of high priority. The provider can also provide only basic notifications for information of low importance or priority. In this way, the provider can provide detailed notifications for information that is important to the user by adjusting the level of detail of notifications based on the importance and priority of the information. Some or all of the above processing in the information provider may be performed using or without a generative AI. For example, the provider can use a generative AI to provide detailed notifications for information of high importance. The provider can use a generative AI to provide detailed notifications for information of high priority. The provider can use a generative AI to provide only basic notifications for information of low importance or priority.

[0052] The service provider can select the optimal notification method by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider can provide a push notification. If the user is using a tablet, the service provider can also provide an email notification. If the user is using a smartwatch, the service provider can also provide a vibration notification. This allows the service provider to select the optimal notification method by considering the user's device information. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider can use a generative AI to select the optimal notification method by considering the user's device information. The service provider can use a generative AI to provide a push notification if the user is using a smartphone. The service provider can use a generative AI to provide an email notification if the user is using a tablet. The service provider can use a generative AI to provide a vibration notification if the user is using a smartwatch.

[0053] The service provider can analyze the user's social media activity and notify them of relevant information at the time of delivery. For example, the service provider can notify users of events that the user has "liked" on social media. The service provider can also notify users of events by artists or organizations that the user follows on social media. The service provider can also notify users of events that the user has shared on social media. In this way, the service provider can notify users of relevant information by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using generative AI or not. For example, the service provider can use generative AI to analyze the user's social media activity and notify them of relevant information. The service provider can use generative AI to notify users of events that the user has "liked" on social media. The service provider can use generative AI to notify users of events by artists or organizations that the user follows on social media. The service provider can use generative AI to notify users of events that the user has shared on social media.

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

[0055] The data collection unit can analyze a user's past event participation history and prioritize collecting highly relevant event information. For example, based on a user's past music concert attendance history, it can prioritize collecting event information by the same artist. The data collection unit can also prioritize collecting match information for the same team based on a user's past sporting event attendance history. The data collection unit can also prioritize collecting performance information by the same theater company based on a user's past theater attendance history. In this way, the data collection unit can prioritize collecting highly relevant event information by analyzing a user's past event participation history.

[0056] The data collection unit can adjust the level of detail of the information it collects, taking into account the popularity and rating of the event. For example, it can collect and provide detailed information to users for highly popular events. It can also collect and provide detailed information to users for highly rated events. For events with low popularity or ratings, it can collect and provide only basic information to users. This allows the data collection unit to collect detailed information that is important to users by considering the popularity and rating of the event.

[0057] The planning unit can propose the optimal schedule by referring to the user's past schedule history during the planning process. For example, it can propose the optimal schedule based on the schedules of events the user has previously attended. The planning unit can also propose a schedule that avoids congestion based on the user's past schedule history. The planning unit can also analyze the user's past schedule history and propose the most efficient schedule. In this way, the planning unit can propose the optimal schedule by referring to the user's past schedule history.

[0058] The data acquisition unit can adjust the level of detail of the information based on the supply and demand situation of resales. For example, it can provide detailed information for resales with high demand. It can also provide detailed information for resales with high supply. It can also provide only basic information for resales with low demand or supply. In this way, the data acquisition unit can provide users with detailed information that is important to them by adjusting the level of detail of the information based on the supply and demand situation of resales.

[0059] The service provider can select the most suitable notification method by referring to the user's past notification history at the time of delivery. For example, it can prioritize providing notification methods that the user has previously preferred (email, push notifications, etc.). The service provider can also select the most effective notification method from the user's past notification history. The service provider can also analyze the user's past notification history and suggest the optimal notification timing. In this way, the service provider can select the most suitable notification method by referring to the user's past notification history.

[0060] The service provider can adjust the level of detail in notifications based on the importance and priority of the information at the time of delivery. For example, detailed notifications can be provided for highly important information. The service provider can also provide detailed notifications for high-priority information. For information of low importance or priority, the service provider can provide only basic notifications. This allows the service provider to provide detailed notifications about information that is important to the user by adjusting the level of detail in notifications based on the importance and priority of the information.

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

[0062] Step 1: The data collection unit collects information on ticket release dates and remaining tickets from sources such as websites and social media. The data collection unit collects information on events such as music, sports, and theater performances. The data collection unit can use a generation AI to obtain ticket release dates and remaining tickets for various events. For example, the data collection unit can obtain the ticket release date for a music concert and notify the user. The data collection unit can also obtain the remaining ticket number for a sports event and notify the user. The data collection unit can also obtain the ticket release date for a theater performance and notify the user. Step 2: The review team considers the schedules of events the user wishes to attend based on the information collected by the data collection team. The review team uses a generation AI to adjust the schedules in comparison with the user's calendar information. For example, if the user wishes to attend both a music concert and a sporting event, the review team adjusts the schedules and proposes the optimal participation plan. The review team can also adjust the schedules and propose the optimal participation plan if the user wishes to attend both a theatrical performance and a sporting event. The review team can also adjust the schedules and propose the optimal participation plan if the user wishes to attend both a music concert and a theatrical performance. Step 3: The tracking unit identifies resale and ticket seekers based on the schedule considered by the review unit. The tracking unit uses a generation AI to provide resale information and identify ticket seekers. For example, if a user already has plans to attend another event, the tracking unit provides resale information and identifies ticket seekers. The tracking unit can also provide resale information and identify ticket seekers if a user is reselling tickets for a music concert. The tracking unit can also provide resale information and identify ticket seekers if a user is reselling tickets for a sporting event. Step 4: The provision unit provides the user with the information gathered by the information gathering unit. The provision unit uses a generation AI to notify the user of the information. For example, the provision unit notifies the user of resale information. The provision unit can also notify the user of information about ticket seekers. The provision unit can also notify the user of the event schedule.

[0063] (Example of form 2) The system according to an embodiment of the present invention is a system that uses a generating AI to grasp the release dates of various tickets and to consider events to attend on the same day and to manage the calendar. This system obtains the release dates and remaining number of tickets for various events from information sources such as websites and social media. Next, based on the acquired information, it considers the schedule of events that the user wishes to attend and reflects it in the calendar. Furthermore, if participation in events on the same day is decided, it also identifies resale opportunities and ticket seekers. For example, the generating AI collects information on events such as music, sports, and theater, and grasps the release dates and remaining number of tickets. The generating AI obtains the ticket release date for a music concert and notifies the user. Next, based on the acquired information, it considers the schedule of events that the user wishes to attend. The generating AI compares this with the user's calendar information and adjusts so that multiple events do not overlap on the same day. For example, if the user wishes to attend both a music concert and a sports event, the generating AI adjusts the schedule and proposes the optimal participation plan. Furthermore, if participation in events on the same day is decided, the generating AI also identifies resale opportunities and ticket seekers. For example, if a user already has plans to attend another event, the generating AI provides resale information and identifies potential ticket buyers, preventing the user from wasting tickets. This allows users to efficiently manage their event attendance schedules without missing ticket release dates. Furthermore, by identifying resale opportunities and potential ticket buyers, ticket waste can be prevented. The system allows users to efficiently manage their event attendance schedules without missing ticket release dates. Furthermore, by identifying resale opportunities and potential ticket buyers, ticket waste can be prevented.

[0064] The system according to this embodiment comprises a collection unit, a review unit, a comprehension unit, and a provision unit. The collection unit collects release dates and remaining quantities of various tickets from information sources such as websites and social media. The collection unit collects information on events such as music, sports, and theater performances. The collection unit can obtain release dates and remaining quantities of tickets for various events using a generation AI. For example, the collection unit obtains the release date of tickets for a music concert and notifies the user. The collection unit can also obtain the remaining quantity of tickets for a sports event and notify the user. The collection unit can also obtain the release date of tickets for a theater performance and notify the user. The review unit considers the schedule of events the user wishes to attend based on the information collected by the collection unit. The review unit adjusts the schedule by comparing it with the user's calendar information using a generation AI. For example, if the user wishes to attend both a music concert and a sports event, the review unit adjusts the schedule and proposes the optimal participation plan. If the user wishes to attend both a theater performance and a sports event, the review unit can also adjust the schedule and propose the optimal participation plan. The planning unit can adjust schedules and propose the optimal participation plan if the user wishes to attend both a music concert and a theatrical performance. The information gathering unit identifies resale and ticket seekers based on the schedule considered by the planning unit. The information gathering unit uses generative AI to provide resale information and identify ticket seekers. For example, if the information gathering unit has plans to attend another event, it provides resale information and identifies ticket seekers. The information gathering unit can also provide resale information and identify ticket seekers if the user is reselling tickets for a music concert. The information gathering unit can also provide resale information and identify ticket seekers if the user is reselling tickets for a sporting event. The information provision unit provides the information gathered by the information gathering unit to the user. The information provision unit uses generative AI to notify the user of the information. For example, the information provision unit notifies the user of resale information. The information provision unit can also notify the user of information about ticket seekers. The information provision unit can also notify the user of the event schedule.This allows the system to efficiently manage event attendance schedules without users missing ticket release dates. Furthermore, it prevents ticket waste by tracking resales and ticket seekers.

[0065] The data collection department gathers information on ticket release dates and remaining ticket quantities from sources such as websites and social media. Specifically, the department utilizes web scraping techniques and APIs to collect information on events such as music, sports, and theater performances. For example, it obtains the latest ticket release information from official music concert websites and social media accounts and stores it in a database. The data collection department can also use generative AI to obtain ticket release dates and remaining ticket quantities for various events. The generative AI employs natural language processing techniques to extract necessary information from web pages and social media posts and organize it as structured data. For example, the data collection department can obtain the ticket release date for a music concert and notify users. The data collection department can also obtain the remaining ticket quantity for a sports event and notify users. The data collection department can also obtain the ticket release date for a theater performance and notify users. This allows the data collection department to quickly and accurately collect and provide the latest information on events of interest to users. Furthermore, the data collection department can centrally manage the collected data and share information in cooperation with other departments. For example, the data collection department can store the collected ticket information in a cloud database, making it accessible to the review and information gathering departments. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific events and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0066] The review department considers the schedules of events that users wish to attend based on the information collected by the data collection department. Specifically, the review department uses a generative AI to adjust the schedule by comparing it with the user's calendar information. The generative AI analyzes the user's calendar information, compares the event dates and times with the user's availability, and proposes the optimal schedule. For example, if a user wishes to attend both a music concert and a sporting event, the review department adjusts the schedule and proposes the optimal participation plan. The review department can also adjust the schedule and propose the optimal participation plan if the user wishes to attend both a theatrical performance and a sporting event. This allows the review department to optimize the schedule so that users can participate in events efficiently. Furthermore, the review department can make more personalized suggestions by considering the user's past participation history and preferences. For example, it can prioritize suggesting similar events based on the types and dates of events the user has attended in the past. The review department can also collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the review department to flexibly adjust schedules to meet user needs and improve user satisfaction.

[0067] The tracking unit identifies resale opportunities and ticket seekers based on the schedule considered by the review unit. Specifically, the tracking unit uses generative AI to provide resale information and identify ticket seekers. The generative AI analyzes the user's schedule and the popularity of events to identify tickets with a high probability of resale. For example, if a user already has plans to attend another event, the tracking unit provides resale information and identifies ticket seekers. The tracking unit can also provide resale information and identify ticket seekers if the user is reselling tickets for a music concert. The tracking unit can also provide resale information and identify ticket seekers if the user is reselling tickets for a sporting event. This allows the tracking unit to help users efficiently resell tickets and prevent ticket waste. Furthermore, the tracking unit can update resale information in real time, providing users with the latest information. For example, it can adjust resale prices and conditions according to fluctuations in resale demand and supply. The tracking unit can also collect user feedback and use it to improve the resale process. This allows the gripping unit to provide flexible resale support tailored to user needs, thereby improving user satisfaction.

[0068] The information provision unit provides users with information gathered by the information gathering unit. Specifically, the information provision unit uses a generation AI to notify users of information. The generation AI analyzes the user's preferences and past behavior history to notify them of information at the optimal time. For example, the information provision unit can notify users of resale information. The information provision unit can also notify users of information about people who want tickets. The information provision unit can also notify users of event schedules. In this way, the information provision unit helps users receive the information they need quickly and accurately. Furthermore, the information provision unit can reliably transmit information using multiple notification methods. For example, in addition to smartphone notifications, important information can be reliably delivered by using a combination of email, SMS, and voice calls. The information provision unit can also collect user feedback and continuously improve the accuracy of notification content and timing. In this way, the information provision unit can provide flexible information tailored to user needs and improve user satisfaction.

[0069] The data collection unit can collect information about events such as music, sports, and theater. For example, the data collection unit can collect information about music concerts. The data collection unit can also collect information about sports events. The data collection unit can also collect information about theater performances. This allows the data collection unit to collect diverse event information and provide information that meets the user's needs. Some or all of the processing described above in the data collection unit may be performed using generative AI, or it may be performed without generative AI. For example, the data collection unit can collect information about music concerts using generative AI. The data collection unit can collect information about sports events using generative AI. The data collection unit can collect information about theater performances using generative AI.

[0070] The review unit can adjust the schedule by comparing it with the user's calendar information. For example, the review unit can obtain the user's calendar information and adjust the schedule. The review unit can also propose a schedule that avoids overlapping events based on the user's calendar information. The review unit can also propose an optimal schedule based on the user's calendar information. This allows the review unit to propose a schedule that avoids overlapping events by considering the user's calendar information. Some or all of the above processing in the review unit may be performed using generative AI, or not. For example, the review unit can use generative AI to obtain the user's calendar information and adjust the schedule. The review unit can use generative AI to propose a schedule that avoids overlapping events based on the user's calendar information. The review unit can use generative AI to propose an optimal schedule based on the user's calendar information.

[0071] The tracking unit can provide resale information and identify ticket seekers. For example, the tracking unit can acquire resale information and provide it to users. The tracking unit can also acquire information on ticket seekers and provide it to users. The tracking unit can also identify ticket seekers based on resale information. In this way, the tracking unit can prevent ticket waste by providing resale information and identifying ticket seekers. Some or all of the above processing in the tracking unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the tracking unit can use a generation AI to acquire resale information and provide it to users. The tracking unit can use a generation AI to acquire information on ticket seekers and provide it to users. The tracking unit can use a generation AI to identify ticket seekers based on resale information.

[0072] The service provider can notify users of information. For example, the service provider can notify users of resale information. The service provider can also notify users of information about ticket applicants. The service provider can also notify users of event schedules. In this way, by notifying users of information, the service provider can ensure that users receive the information they need in a timely manner. Some or all of the above-described processes in the service provider may be performed using or without a generative AI. For example, the service provider can use a generative AI to notify users of resale information. The service provider can use a generative AI to notify users of information about ticket applicants. The service provider can use a generative AI to notify users of event schedules.

[0073] The data collection unit can estimate the user's emotions and determine the priority of event information to collect based on the estimated emotions. For example, if the user is excited, the data collection unit can prioritize collecting popular event information using a generative AI. If the user is relaxed, the data collection unit can also prioritize collecting calming event information using a generative AI. If the user is stressed, the data collection unit can also prioritize collecting relaxing event information using a generative AI. This allows the data collection unit to provide the user with the most optimal information by prioritizing event information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using a generative AI or not. For example, the data collection unit can use a generative AI to estimate the user's emotions and determine the priority of event information to collect based on the estimated emotions. The data collection unit can use a generative AI to prioritize collecting popular event information if the user is excited. The data collection unit can use generative AI to prioritize collecting information about calm events when the user is relaxed. The data collection unit can also use generative AI to prioritize collecting information about relaxing events when the user is stressed.

[0074] The data collection unit can analyze a user's past event participation history during data collection and prioritize the collection of highly relevant event information. For example, the data collection unit can prioritize the collection of event information by the same artist based on a user's past music concert attendance history. The data collection unit can also prioritize the collection of match information for the same team based on a user's past sports event attendance history. The data collection unit can also prioritize the collection of performance information for the same theater company based on a user's past theater attendance history. In this way, the data collection unit can prioritize the collection of highly relevant event information by analyzing a user's past event participation history. Some or all of the above processing in the data collection unit may be performed using generative AI, or it may be performed without generative AI. For example, the data collection unit can use generative AI to analyze a user's past event participation history and prioritize the collection of highly relevant event information. The data collection unit can use generative AI to prioritize the collection of event information by the same artist based on a user's past music concert attendance history. The data collection unit can use generative AI to prioritize the collection of match information for the same team based on a user's past sports event attendance history. The data collection unit uses a generation AI to prioritize the collection of performance information from the same theater company based on the user's past theater-going history.

[0075] The data collection unit can adjust the level of detail of the information it collects, taking into account the popularity and rating of the events. For example, the data collection unit can collect detailed information for highly popular events and provide it to the user. The data collection unit can also collect detailed information for highly rated events and provide it to the user. For events with low popularity or ratings, the data collection unit can collect only basic information and provide it to the user. This allows the data collection unit to collect detailed information that is important to the user by considering the popularity and rating of the events. Some or all of the above processing in the data collection unit may be performed using or without generative AI. For example, the data collection unit can use generative AI to collect detailed information for highly popular events and provide it to the user. The data collection unit can use generative AI to collect detailed information for highly rated events and provide it to the user. The data collection unit can use generative AI to collect only basic information for events with low popularity or ratings and provide it to the user.

[0076] The data collection unit can estimate the user's emotions and adjust the categories of event information to be collected based on the estimated emotions. For example, if the user is excited, the generating AI will prioritize collecting entertainment-related event information. If the user is relaxed, the generating AI can also prioritize collecting relaxation-related event information. If the user is stressed, the generating AI can also prioritize collecting stress-relieving event information. This allows the data collection unit to provide optimal information to the user by adjusting the categories of event information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using a generating AI or not. For example, the data collection unit can use a generating AI to estimate the user's emotions and adjust the categories of event information to be collected based on the estimated emotions. The data collection unit can use a generating AI to prioritize collecting entertainment-related event information if the user is excited. The data collection unit can use generative AI to prioritize the collection of relaxation-related event information when the user is relaxed. The data collection unit can also use generative AI to prioritize the collection of stress-relieving event information when the user is stressed.

[0077] The data collection unit can prioritize collecting nearby event information by considering the user's geographical location during collection. For example, the data collection unit can prioritize collecting event information within a 5km radius of the user's current location. The data collection unit can also prioritize collecting nearby event information based on the user's home address. The data collection unit can also prioritize collecting event information near the user's workplace based on the user's workplace address. In this way, the data collection unit can prioritize collecting nearby event information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using or without a generation AI. For example, the data collection unit can prioritize collecting event information within a 5km radius of the user's current location using a generation AI. The data collection unit can prioritize collecting nearby event information based on the user's home address using a generation AI. The data collection unit can prioritize collecting event information near the user's workplace based on the user's workplace address using a generation AI.

[0078] The data collection unit can collect relevant event information by analyzing the user's social media activity during collection. For example, the data collection unit can collect relevant event information based on event information that the user has "liked" on social media. The data collection unit can also collect event information from artists and organizations that the user follows on social media. The data collection unit can also collect relevant event information based on event information that the user has shared on social media. In this way, the data collection unit can collect relevant event information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using generative AI, or it may be performed without generative AI. For example, the data collection unit can use generative AI to collect relevant event information based on event information that the user has "liked" on social media. The data collection unit can use generative AI to collect event information from artists and organizations that the user follows on social media. The data collection unit can use generative AI to collect relevant event information based on event information that the user has shared on social media.

[0079] The review unit can estimate the user's emotions and adjust the schedule suggestion method based on the estimated emotions. For example, if the user is relaxed, the review unit can suggest a relaxed schedule. If the user is in a hurry, the review unit can also suggest an efficient schedule. If the user is excited, the review unit can also suggest an active schedule. In this way, by adjusting the schedule suggestion method according to the user's emotions, the review unit can provide the optimal schedule suggestion for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the review unit may be performed using generative AI or not. For example, the review unit can use generative AI to estimate the user's emotions and adjust the schedule suggestion method based on the estimated emotions. The review unit can use generative AI to suggest a relaxed schedule if the user is relaxed. The review unit can use generative AI to suggest an efficient schedule if the user is in a hurry. The review unit can use generative AI to suggest an active schedule when the user is excited.

[0080] The review unit can propose the optimal schedule by referring to the user's past schedule history during the review process. For example, the review unit can propose the optimal schedule based on the schedules of events the user has previously attended. The review unit can also propose a schedule that avoids congestion based on the user's past schedule history. The review unit can also analyze the user's past schedule history and propose the most efficient schedule. This allows the review unit to propose the optimal schedule by referring to the user's past schedule history. Some or all of the above processing in the review unit may be performed using generative AI, or not. For example, the review unit can use generative AI to refer to the user's past schedule history and propose the optimal schedule. The review unit can use generative AI to propose the optimal schedule based on the schedules of events the user has previously attended. The review unit can use generative AI to propose a schedule that avoids congestion based on the user's past schedule history. The review unit can use generative AI to analyze the user's past schedule history and propose the most efficient schedule.

[0081] The review unit can adjust the level of detail in the schedule based on the importance and priority of the events during the review process. For example, the review unit can propose a detailed schedule for high-importance events. The review unit can also propose a detailed schedule for high-priority events. For events of low importance or priority, the review unit can propose only a basic schedule. In this way, the review unit can propose a detailed schedule for events that are important to the user by adjusting the level of detail in the schedule based on the importance and priority of the events. Some or all of the above processing in the review unit may be performed using generative AI, or not. For example, the review unit can use generative AI to propose a detailed schedule for high-importance events. The review unit can use generative AI to propose a detailed schedule for high-priority events. The review unit can use generative AI to propose only a basic schedule for events of low importance or priority.

[0082] The analysis unit can estimate the user's emotions and adjust the schedule display method based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a concise display method. In this way, the analysis unit can provide the optimal display method for the user by adjusting the schedule display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using generative AI or not. For example, the analysis unit can use generative AI to estimate the user's emotions and adjust the schedule display method based on the estimated emotions. The analysis unit can use generative AI to provide a simple and highly visible display method if the user is stressed. The design unit can use generative AI to provide a display method that includes detailed information when the user is relaxed. The design unit can also use generative AI to provide a display method that focuses on the essentials when the user is in a hurry.

[0083] The review unit can select the optimal schedule display method by considering the user's device information during the review process. For example, if the user is using a smartphone, the review unit can provide a display method that matches the screen size. If the user is using a tablet, the review unit can also provide a display method optimized for a larger screen. If the user is using a smartwatch, the review unit can also provide a concise and highly visible display method. In this way, the review unit can provide the optimal schedule display method by considering the user's device information. Some or all of the above processing in the review unit may be performed using a generation AI, or not. For example, the review unit can use a generation AI to select the optimal schedule display method by considering the user's device information. If the user is using a smartphone, the review unit can use a generation AI to provide a display method that matches the screen size. If the user is using a tablet, the review unit can use a generation AI to provide a display method optimized for a larger screen. If the user is using a smartwatch, the review unit can use a generation AI to provide a concise and highly visible display method.

[0084] The review unit can analyze a user's social media activity during the review process and propose relevant event schedules. For example, the review unit can propose the schedules of events that the user has "liked" on social media. The review unit can also propose the schedules of events by artists and organizations that the user follows on social media. The review unit can also propose the schedules of events that the user has shared on social media. In this way, the review unit can propose relevant event schedules by analyzing the user's social media activity. Some or all of the above processing in the review unit may be performed using generative AI or not. For example, the review unit can use generative AI to analyze a user's social media activity and propose relevant event schedules. The review unit can use generative AI to propose the schedules of events that the user has "liked" on social media. The review unit can use generative AI to propose the schedules of events by artists and organizations that the user follows on social media. The review unit can use generative AI to propose the schedules of events that the user has shared on social media.

[0085] The sensing unit can estimate the user's emotions and adjust the display method of resale information based on the estimated user emotions. For example, if the user is nervous, the sensing unit can provide a simple and highly visible display method. If the user is relaxed, the sensing unit can also provide a display method that includes detailed information. If the user is in a hurry, the sensing unit can also provide a display method that gets straight to the point. In this way, the sensing unit can provide the optimal display method for the user by adjusting the display method of resale information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sensing unit may be performed using generative AI or not. For example, the sensing unit can use generative AI to estimate the user's emotions and adjust the display method of resale information based on the estimated user emotions. The sensing unit can use generative AI to provide a simple and highly visible display method if the user is nervous. The information gathering unit can use generational AI to provide a display method that includes detailed information when the user is relaxed. The information gathering unit can also use generational AI to provide a display method that focuses on the essentials when the user is in a hurry.

[0086] The information gathering unit can provide optimal resale information by referring to the user's past resale history when gathering information. For example, the information gathering unit can provide optimal resale information based on the history of tickets the user has previously resold. The information gathering unit can also provide high-demand resale information from the user's past resale history. The information gathering unit can also analyze the user's past resale history and provide the most efficient resale information. In this way, the information gathering unit can provide optimal resale information by referring to the user's past resale history. Some or all of the above processing in the information gathering unit may be performed using or without a generation AI. For example, the information gathering unit can use a generation AI to refer to the user's past resale history and provide optimal resale information. The information gathering unit can use a generation AI to provide optimal resale information based on the history of tickets the user has previously resold. The information gathering unit can use a generation AI to provide high-demand resale information from the user's past resale history. The information gathering unit can use a generation AI to analyze the user's past resale history and provide the most efficient resale information.

[0087] The information gathering unit can adjust the level of detail of the information based on the supply and demand situation of resales when gathering information. For example, the information gathering unit can provide detailed information for resales with high demand. The information gathering unit can also provide detailed information for resales with high supply. The information gathering unit can also provide only basic information for resales with low demand or supply. In this way, the information gathering unit can provide users with detailed information that is important to them by adjusting the level of detail of the information based on the supply and demand situation of resales. Some or all of the above processing in the information gathering unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the information gathering unit can use a generation AI to provide detailed information for resales with high demand. The information gathering unit can use a generation AI to provide detailed information for resales with high supply. The information gathering unit can use a generation AI to provide only basic information for resales with low demand or supply.

[0088] The sensing unit can estimate the user's emotions and prioritize resale information based on the estimated emotions. For example, if the user is excited, the sensing unit's generating AI will prioritize providing popular resale information. If the user is relaxed, the sensing unit's generating AI can also prioritize providing calming resale information. If the user is stressed, the sensing unit's generating AI can also prioritize providing relaxing resale information. In this way, the sensing unit can provide optimal information to the user by prioritizing resale information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sensing unit may be performed using a generating AI or not. For example, the sensing unit can use a generating AI to estimate the user's emotions and prioritize resale information based on the estimated emotions. The sensing unit can use a generating AI to prioritize providing popular resale information if the user is excited. The information gathering unit can use generational AI to prioritize providing calm resale information when the user is relaxed. The information gathering unit can also use generational AI to prioritize providing relaxing resale information when the user is stressed.

[0089] The information gathering unit can prioritize providing nearby resale information by considering the user's geographical location information when gathering information. For example, the information gathering unit can prioritize providing resale information within a 5km radius of the user's current location. The information gathering unit can also prioritize providing nearby resale information based on the user's home address. The information gathering unit can also prioritize providing resale information near the user's workplace based on the user's workplace address. In this way, the information gathering unit can prioritize providing nearby resale information by considering the user's geographical location information. Some or all of the above processing in the information gathering unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the information gathering unit can use a generation AI to prioritize providing resale information within a 5km radius of the user's current location. The information gathering unit can use a generation AI to prioritize providing nearby resale information based on the user's home address. The information gathering unit can use a generation AI to prioritize providing resale information near the user's workplace based on the user's workplace address.

[0090] The tracking unit can analyze the user's social media activity and provide relevant resale information. For example, the tracking unit can provide relevant resale information based on resale information that the user has "liked" on social media. The tracking unit can also provide resale information for artists and organizations that the user follows on social media. The tracking unit can also provide relevant resale information based on resale information that the user has shared on social media. In this way, the tracking unit can provide relevant resale information by analyzing the user's social media activity. Some or all of the above processing in the tracking unit may be performed using generative AI, or without generative AI. For example, the tracking unit can use generative AI to analyze the user's social media activity and provide relevant resale information. The tracking unit can use generative AI to provide relevant resale information based on resale information that the user has "liked" on social media. The tracking unit can use generative AI to provide resale information for artists and organizations that the user follows on social media. The tracking unit can use generative AI to provide relevant resale information based on resale information that the user has shared on social media.

[0091] The service provider can estimate the user's emotions and adjust the method of information notification based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible notification method. If the user is relaxed, the service provider can also provide a notification method that includes detailed information. If the user is in a hurry, the service provider can also provide a notification method that gets straight to the point. In this way, the service provider can provide the optimal notification method for the user by adjusting the method of information notification according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using generative AI or not. For example, the service provider can use generative AI to estimate the user's emotions and adjust the method of information notification based on the estimated emotions. The service provider can use generative AI to provide a simple and highly visible notification method if the user is nervous. The service provider can use generative AI to provide notifications that include detailed information when the user is relaxed. The service provider can also use generative AI to provide notifications that are concise and to the point when the user is in a hurry.

[0092] The service provider can select the optimal notification method by referring to the user's past notification history when providing notifications. For example, the service provider can prioritize providing notification methods that the user has previously preferred (email, push notifications, etc.). The service provider can also select the most effective notification method from the user's past notification history. The service provider can also analyze the user's past notification history and suggest the optimal notification timing. This allows the service provider to select the optimal notification method by referring to the user's past notification history. Some or all of the above processing in the service provider may be performed using or without generative AI. For example, the service provider can use generative AI to select the optimal notification method by referring to the user's past notification history. The service provider can use generative AI to prioritize providing notification methods that the user has previously preferred (email, push notifications, etc.). The service provider can use generative AI to select the most effective notification method from the user's past notification history. The service provider can use generative AI to analyze the user's past notification history and suggest the optimal notification timing.

[0093] The information provider can adjust the level of detail of notifications based on the importance and priority of the information at the time of delivery. For example, the provider can provide detailed notifications for information of high importance. The provider can also provide detailed notifications for information of high priority. The provider can also provide only basic notifications for information of low importance or priority. In this way, the provider can provide detailed notifications for information that is important to the user by adjusting the level of detail of notifications based on the importance and priority of the information. Some or all of the above processing in the information provider may be performed using or without a generative AI. For example, the provider can use a generative AI to provide detailed notifications for information of high importance. The provider can use a generative AI to provide detailed notifications for information of high priority. The provider can use a generative AI to provide only basic notifications for information of low importance or priority.

[0094] The service provider can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is relaxed, the service provider can send a notification immediately. If the user is busy, the service provider can also postpone the notification. If the user is in a hurry, the service provider can also prioritize important notifications. In this way, the service provider can deliver notifications at the optimal time for the user by adjusting the timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using generative AI or not. For example, the service provider can use generative AI to estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. The service provider can use generative AI to send a notification immediately if the user is relaxed. The service provider can use generative AI to postpone the notification if the user is busy. The service provider can use generation AI to prioritize important notifications when the user is in a hurry.

[0095] The service provider can select the optimal notification method by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider can provide a push notification. If the user is using a tablet, the service provider can also provide an email notification. If the user is using a smartwatch, the service provider can also provide a vibration notification. This allows the service provider to select the optimal notification method by considering the user's device information. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider can use a generative AI to select the optimal notification method by considering the user's device information. The service provider can use a generative AI to provide a push notification if the user is using a smartphone. The service provider can use a generative AI to provide an email notification if the user is using a tablet. The service provider can use a generative AI to provide a vibration notification if the user is using a smartwatch.

[0096] The service provider can analyze the user's social media activity and notify them of relevant information at the time of delivery. For example, the service provider can notify users of events that the user has "liked" on social media. The service provider can also notify users of events by artists or organizations that the user follows on social media. The service provider can also notify users of events that the user has shared on social media. In this way, the service provider can notify users of relevant information by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using generative AI or not. For example, the service provider can use generative AI to analyze the user's social media activity and notify them of relevant information. The service provider can use generative AI to notify users of events that the user has "liked" on social media. The service provider can use generative AI to notify users of events by artists or organizations that the user follows on social media. The service provider can use generative AI to notify users of events that the user has shared on social media.

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

[0098] The data collection unit can analyze a user's past event participation history and prioritize collecting highly relevant event information. For example, based on a user's past music concert attendance history, it can prioritize collecting event information by the same artist. The data collection unit can also prioritize collecting match information for the same team based on a user's past sporting event attendance history. The data collection unit can also prioritize collecting performance information by the same theater company based on a user's past theater attendance history. In this way, the data collection unit can prioritize collecting highly relevant event information by analyzing a user's past event participation history.

[0099] The system can estimate the user's emotions and adjust its schedule suggestions based on those emotions. For example, if the user is relaxed, it will suggest a relaxed schedule. If the user is in a hurry, it can suggest an efficient schedule. If the user is excited, it can suggest an active schedule. By adjusting its schedule suggestions according to the user's emotions, the system can provide the most optimal schedule for the user.

[0100] The sensing unit can estimate the user's emotions and adjust the display method of resale information based on the estimated emotions. For example, if the user is nervous, it can provide a simple and highly visible display method. If the user is relaxed, it can also provide a display method that includes detailed information. If the user is in a hurry, it can also provide a display method that gets straight to the point. In this way, the sensing unit can provide the optimal display method for the user by adjusting the display method of resale information according to the user's emotions.

[0101] The service provider can estimate the user's emotions and adjust the way information is delivered based on those emotions. For example, if the user is stressed, it can provide a simple and highly visible notification. If the user is relaxed, it can provide a notification that includes more detailed information. If the user is in a hurry, it can provide a notification that gets straight to the point. In this way, the service provider can provide the optimal notification method for the user by adjusting the way information is delivered according to the user's emotions.

[0102] The data collection unit can adjust the level of detail of the information it collects, taking into account the popularity and rating of the event. For example, it can collect and provide detailed information to users for highly popular events. It can also collect and provide detailed information to users for highly rated events. For events with low popularity or ratings, it can collect and provide only basic information to users. This allows the data collection unit to collect detailed information that is important to users by considering the popularity and rating of the event.

[0103] The planning unit can propose the optimal schedule by referring to the user's past schedule history during the planning process. For example, it can propose the optimal schedule based on the schedules of events the user has previously attended. The planning unit can also propose a schedule that avoids congestion based on the user's past schedule history. The planning unit can also analyze the user's past schedule history and propose the most efficient schedule. In this way, the planning unit can propose the optimal schedule by referring to the user's past schedule history.

[0104] The data acquisition unit can adjust the level of detail of the information based on the supply and demand situation of resales. For example, it can provide detailed information for resales with high demand. It can also provide detailed information for resales with high supply. It can also provide only basic information for resales with low demand or supply. In this way, the data acquisition unit can provide users with detailed information that is important to them by adjusting the level of detail of the information based on the supply and demand situation of resales.

[0105] The service provider can select the most suitable notification method by referring to the user's past notification history at the time of delivery. For example, it can prioritize providing notification methods that the user has previously preferred (email, push notifications, etc.). The service provider can also select the most effective notification method from the user's past notification history. The service provider can also analyze the user's past notification history and suggest the optimal notification timing. In this way, the service provider can select the most suitable notification method by referring to the user's past notification history.

[0106] The data collection unit can estimate the user's emotions and adjust the categories of event information it collects based on those emotions. For example, if the user is excited, the generating AI will prioritize collecting entertainment-related event information. If the user is relaxed, the generating AI can also prioritize collecting relaxation-related event information. If the user is stressed, the generating AI can also prioritize collecting stress-relieving event information. This allows the data collection unit to provide the user with the most relevant information by adjusting the categories of event information according to their emotions.

[0107] The service provider can adjust the level of detail in notifications based on the importance and priority of the information at the time of delivery. For example, detailed notifications can be provided for highly important information. The service provider can also provide detailed notifications for high-priority information. For information of low importance or priority, the service provider can provide only basic notifications. This allows the service provider to provide detailed notifications about information that is important to the user by adjusting the level of detail in notifications based on the importance and priority of the information.

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

[0109] Step 1: The data collection unit collects information on ticket release dates and remaining tickets from sources such as websites and social media. The data collection unit collects information on events such as music, sports, and theater performances. The data collection unit can use a generation AI to obtain ticket release dates and remaining tickets for various events. For example, the data collection unit can obtain the ticket release date for a music concert and notify the user. The data collection unit can also obtain the remaining ticket number for a sports event and notify the user. The data collection unit can also obtain the ticket release date for a theater performance and notify the user. Step 2: The review team considers the schedules of events the user wishes to attend based on the information collected by the data collection team. The review team uses a generation AI to adjust the schedules in comparison with the user's calendar information. For example, if the user wishes to attend both a music concert and a sporting event, the review team adjusts the schedules and proposes the optimal participation plan. The review team can also adjust the schedules and propose the optimal participation plan if the user wishes to attend both a theatrical performance and a sporting event. The review team can also adjust the schedules and propose the optimal participation plan if the user wishes to attend both a music concert and a theatrical performance. Step 3: The tracking unit identifies resale and ticket seekers based on the schedule considered by the review unit. The tracking unit uses a generation AI to provide resale information and identify ticket seekers. For example, if a user already has plans to attend another event, the tracking unit provides resale information and identifies ticket seekers. The tracking unit can also provide resale information and identify ticket seekers if a user is reselling tickets for a music concert. The tracking unit can also provide resale information and identify ticket seekers if a user is reselling tickets for a sporting event. Step 4: The provision unit provides the user with the information gathered by the information gathering unit. The provision unit uses a generation AI to notify the user of the information. For example, the provision unit notifies the user of resale information. The provision unit can also notify the user of information about ticket seekers. The provision unit can also notify the user of the event schedule.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements described above, including the collection unit, review unit, understanding unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects the release dates and remaining quantities of various tickets from information sources such as websites and social media. The review unit is implemented by the specific processing unit 290 of the data processing device 12 and considers the schedule of events that users wish to attend based on the information collected by the collection unit. The understanding unit is implemented by the specific processing unit 290 of the data processing device 12 and identifies resale and ticket seekers based on the schedule considered by the review unit. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the information identified by the understanding unit to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the collection unit, examination unit, understanding unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects the release dates and remaining quantities of various tickets from information sources such as websites and social media. The examination unit is implemented by the identification processing unit 290 of the data processing unit 12 and examines the schedule of events that users wish to attend based on the information collected by the collection unit. The understanding unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies resale and ticket seekers based on the schedule examined by the examination unit. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the information identified by the understanding unit to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the collection unit, review unit, understanding unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects the release dates and remaining quantities of various tickets from information sources such as HP and SNS. The review unit is implemented by the specific processing unit 290 of the data processing unit 12 and considers the schedule of events that users wish to attend based on the information collected by the collection unit. The understanding unit is implemented by the specific processing unit 290 of the data processing unit 12 and considers resales and ticket seekers based on the schedule considered by the review unit. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the information considered by the understanding unit to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0147] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the collection unit, examination unit, understanding unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects the release dates and remaining quantities of various tickets from information sources such as HP and SNS. The examination unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and examines the schedule of events that users wish to attend based on the information collected by the collection unit. The understanding unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and identifies resale and ticket seekers based on the schedule examined by the examination unit. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides the information identified by the understanding unit to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0163] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0173] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) The collection department gathers information on the release dates and remaining quantities of various tickets from sources such as websites and social media, Based on the information collected by the aforementioned collection unit, the review unit considers the schedule of events that users wish to participate in, Based on the schedule considered by the aforementioned review unit, there is a tracking unit that identifies resale and ticket seekers, The system comprises a providing unit that provides the information grasped by the grasping unit to the user. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect information about events such as music, sports, and theater. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned examination unit is, Adjust the schedule by cross-referencing it with the user's calendar information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The gripping part is, We provide resale information and identify ticket seekers. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Notify the user of the information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates the user's emotions and determines the priority of event information to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During data collection, the system analyzes the user's past event participation history and prioritizes collecting highly relevant event information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting information, the level of detail collected is adjusted based on the popularity and rating of the event. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and adjusts the categories of event information collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the system prioritizes collecting nearby event information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the user's social media activity is analyzed, and relevant event information is gathered. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned examination unit is, It estimates the user's emotions and adjusts the scheduling suggestions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned examination unit is, During the planning stage, we refer to the user's past schedule history to suggest the optimal schedule. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned examination unit is, During the planning stage, adjust the level of detail in the schedule based on the importance and priority of the event. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned examination unit is, It estimates the user's emotions and adjusts how the schedule is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned examination unit is, During the planning stage, the optimal schedule display method is selected, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned examination unit is, During the planning stage, we analyze users' social media activity and propose relevant event schedules. The system described in Appendix 1, characterized by the features described herein. (Note 18) The gripping part is, The system estimates user sentiment and adjusts how resale information is displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The gripping part is, When assessing a user's resale history, the system provides optimal resale information by referencing the user's past resale history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The gripping part is, When gathering information, adjust the level of detail based on the supply and demand situation for resales. The system described in Appendix 1, characterized by the features described herein. (Note 21) The gripping part is, The system estimates user sentiment and prioritizes resale information based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The gripping part is, When identifying a user, the system prioritizes providing nearby resale information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The gripping part is, When identifying users, we analyze their social media activity and provide relevant resale information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the service, the system will refer to the user's past notification history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing information, the level of detail in the notification will be adjusted based on the importance and priority of the information. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, the optimal notification method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the system analyzes the user's social media activity and notifies them of relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. The collection department gathers information on the release dates and remaining quantities of various tickets from sources such as websites and social media, Based on the information collected by the aforementioned collection unit, the review unit considers the schedule of events that users wish to participate in, Based on the schedule considered by the aforementioned review unit, there is a tracking unit that identifies resale and ticket seekers, The system comprises a providing unit that provides the information grasped by the grasping unit to the user. A system characterized by the following features.

2. The aforementioned collection unit is Collect information about events such as music, sports, and theater. The system according to feature 1.

3. The aforementioned examination unit is, Adjust the schedule by cross-referencing it with the user's calendar information. The system according to feature 1.

4. The gripping part is, We provide resale information and identify ticket seekers. The system according to feature 1.

5. The aforementioned supply unit is, Notify the user of the information. The system according to feature 1.

6. The aforementioned collection unit is It estimates the user's emotions and determines the priority of event information to collect based on the estimated user emotions. The system according to feature 1.

7. The aforementioned collection unit is During data collection, the system analyzes the user's past event participation history and prioritizes collecting highly relevant event information. The system according to feature 1.

8. The aforementioned collection unit is When collecting information, the level of detail collected is adjusted based on the popularity and rating of the event. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and adjusts the categories of event information collected based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is During data collection, the system prioritizes collecting nearby event information, taking into account the user's geographical location. The system according to feature 1.

Images