system

Abstract

The system according to the embodiment aims to centrally manage schedule information from multiple ticket sales sites and notify users of the information. [Solution] The system according to the embodiment comprises a reception unit, a collection unit, a notification unit, a calendar unit, and a lottery result management unit. The reception unit receives ticket information from users. The collection unit collects schedule information from each ticket sales site based on the information received by the reception unit. The notification unit notifies users based on the schedule information collected by the collection unit. The calendar unit provides a calendar function for schedule management. The lottery result management unit manages the lottery results.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to centrally manage schedule information from multiple ticket sales sites and notify users of the information.

[0005] The system according to the embodiment aims to centrally manage schedule information from multiple ticket sales sites and notify users of the information. [Means for solving the problem]

[0006] The system according to the embodiment comprises a reception unit, a collection unit, a notification unit, a calendar unit, and a lottery result management unit. The reception unit receives ticket information from users. The collection unit collects schedule information from each ticket sales site based on the information received by the reception unit. The notification unit notifies users based on the schedule information collected by the collection unit. The calendar unit provides a calendar function for schedule management. The lottery result management unit manages the lottery results. [Effects of the Invention]

[0007] The system according to the embodiment can centrally manage schedule information from multiple ticket sales sites and notify users of the information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A ticket acquisition support system according to an embodiment of the present invention allows users to input information about tickets they wish to acquire, automatically collect schedule information from various ticket sales sites, and, in the case of lottery sales, manage the schedule of lottery results. This system allows users to centrally manage their ticket acquisition schedules and efficiently acquire tickets. For example, users input information about the tickets they wish to acquire, such as the artist name, event name, date, and venue. This information is then entered into the system. The system then automatically collects advance sales and general sales schedules from fan clubs, credit card companies, and ticket sales companies (ePlus, Ticket Pia, Lawson Ticket, etc.). The system collects schedule information from various ticket sales sites and provides it to users. For example, the system collects information about when fan club advance sales begin, when credit card company advance sales begin, when ticket sales companies advance sales begin, and when general sales begin. Furthermore, in the case of lottery sales, the system also manages the schedule when the results of the lottery sales are announced. The system notifies users of the date and time when the lottery sales results will be announced and updates the schedule based on the results. For example, when the results of a lottery sale are announced, the user is notified, and if they win, their schedule is updated to allow them to proceed with the ticket purchase procedure. This system allows users to centrally manage their ticket acquisition schedules and acquire tickets efficiently. For example, it can save users the trouble of checking multiple ticket sales sites, allowing them to smoothly acquire tickets without missing the results of the lottery sale. In this way, the ticket acquisition support system allows users to centrally manage their ticket acquisition schedules and acquire tickets efficiently.

[0029] A ticket acquisition support system according to an embodiment includes a reception unit, a collection unit, a notification unit, a calendar unit, and a ticket lottery management unit. The reception unit receives ticket information from a user. The ticket information from the user includes, but is not limited to, the artist name, event name, event date, and venue. The reception unit receives, for example, the artist name and event name entered by the user. The reception unit can also receive information on the event date and venue. The collection unit collects schedule information from each ticket sales site based on the information received by the reception unit. The collection unit collects schedule information using, for example, web scraping technology. The collection unit can also use an API to collect schedule information. For example, the collection unit acquires schedule information using the API of a specific ticket sales site. The notification unit notifies the user based on the schedule information collected by the collection unit. The notification unit notifies the user using, for example, email. The notification unit can also notify the user using push notification. For example, the notification unit sends schedule information to the user's email address. The calendar unit provides a calendar function for schedule management. The calendar unit, for example, displays the user's schedule in calendar format. The calendar unit can display the schedule in formats such as monthly, weekly, and daily. For example, the calendar unit displays the user's schedule in monthly format, allowing the user to visually check the schedule for each event. The lottery result management unit manages the lottery results. For example, the lottery result management unit automatically obtains the lottery result and notifies the user. The lottery result management unit can also update the schedule based on the lottery result. For example, the lottery result management unit notifies the user when the lottery result is announced, and updates the schedule so that the user can proceed with ticket purchase procedures if they are successful. In this way, the ticket acquisition support system according to the embodiment allows users to centrally manage their ticket acquisition schedules and efficiently acquire tickets.

[0030] The collection unit can collect schedule information using web scraping or an API. The collection unit, for example, collects schedule information using web scraping technology. For example, the collection unit analyzes the webpage of a specific ticket sales site and extracts schedule information. The collection unit can also collect schedule information using an API. For example, the collection unit acquires schedule information using the API of a specific ticket sales site. This allows the collection unit to collect schedule information efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the schedule information acquired using web scraping technology into a generation AI and have the generation AI analyze the schedule information.

[0031] The notification unit can notify the user using email or push notification. The notification unit can notify the user using email, for example. For example, the notification unit can send schedule information to the user's email address. The notification unit can also notify the user using push notification. For example, the notification unit can send a push notification to the user's smartphone. This allows the user to receive the notification in a timely manner. Some or all of the above-mentioned processing in the notification unit can be performed using AI, for example, or can be performed without using AI. For example, the notification unit can input the schedule information collected by the collection unit into a generation AI and cause the generation AI to generate the notification content.

[0032] The calendar unit can display the user's schedule in calendar format. For example, the calendar unit displays the user's schedule in calendar format. The calendar unit can display the schedule in a format such as a monthly display, a weekly display, or a daily display. For example, the calendar unit displays the user's schedule in a monthly display, allowing the schedule of each event to be visually confirmed. This allows the user to visually check the schedule. Some or all of the above-mentioned processing in the calendar unit may be performed, for example, using AI or may be performed without using AI. For example, the calendar unit can input the schedule information collected by the collection unit to a generation AI and cause the generation AI to generate a calendar display.

[0033] The lottery result management unit can automatically obtain the lottery result and notify the user. For example, the lottery result management unit automatically obtains the lottery result and notifies the user. The lottery result management unit can also update the schedule based on the lottery result. For example, the lottery result management unit notifies the user when the lottery result is announced, and updates the schedule so that the user can proceed with ticket purchase procedures if they are successful. This allows the user to quickly check the lottery result. Some or all of the above-mentioned processing in the lottery result management unit may be performed using AI, for example, or may be performed without using AI. For example, the lottery result management unit can input the lottery result to the generation AI and have the generation AI analyze the lottery result.

[0034] The reception unit can accept information such as the artist name, event name, date, and venue. The reception unit, for example, accepts the artist name and event name entered by the user. The reception unit can also accept information such as the date and venue. For example, the reception unit accepts the artist name entered by the user and stores it in the system. The reception unit can also accept the event name entered by the user and store it in the system. This allows the user to enter detailed ticket information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the information entered by the user into a generation AI and have the generation AI analyze the information.

[0035] The reception unit can analyze the user's past input history and suggest an efficient input method. The reception unit, for example, analyzes the user's past input history and suggests the optimal input method. For example, it automatically displays artist names and event names that the user has frequently input in the past as candidates. It can also prioritize suggestions of input methods (voice, text, etc.) that the user has used in the past. It can also predict and suggest artist names and event names to be used during a specific time period from the user's past input history. This makes it possible to provide an efficient input method based on the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history to a generation AI and have the generation AI suggest the optimal input method.

[0036] The reception unit can automatically complete input candidates based on events of current interest to the user during input. The reception unit, for example, automatically completes input candidates based on events of current interest to the user during input. For example, the reception unit automatically completes related event names based on event information recently searched by the user. The reception unit can also automatically complete input candidates based on events in which the user expressed interest on social media. The reception unit can also automatically complete related event names based on information on events the user has previously attended. This makes it possible to provide input candidates based on the user's interests. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input data on events of interest to the user to a generation AI and cause the generation AI to automatically complete input candidates.

[0037] The reception unit can prioritize displaying highly relevant event information based on the user's geographical location information at the time of input. For example, the reception unit prioritizes displaying highly relevant event information taking into account the user's geographical location information at the time of input. For example, events close to the user's current location can be displayed with priority. Related events can also be displayed based on the user's past location information. Events with good transportation access can also be displayed with priority based on the user's location information. This makes it possible to provide event information based on the user's location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to a generation AI and cause the generation AI to display highly relevant event information.

[0038] The reception unit can analyze the user's social media activity at the time of input and suggest related event information. For example, the reception unit can analyze the user's social media activity at the time of input and suggest related event information. For example, the reception unit can suggest event information for artists the user follows on social media. It can also suggest event information for posts the user has "liked" on social media. It can also analyze the content of the user's social media posts and suggest related event information. This makes it possible to provide event information based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to suggest related event information.

[0039] The collection unit can optimize the collection algorithm by referring to past collected data during collection. The collection unit, for example, optimizes the collection algorithm by referring to past collected data during collection. For example, the collection unit selects the most efficient collection method based on past collected data. Schedule information for collection during a specific time period can also be optimized from past collected data. Past collected data can also be analyzed to improve the collection algorithm. This allows the collection algorithm to be optimized based on past data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past collected data into a generation AI and cause the generation AI to optimize the collection algorithm.

[0040] The collection unit can adjust the collection timing based on the update frequency of each ticket sales site during collection. For example, the collection unit adjusts the collection timing based on the update frequency of each ticket sales site during collection. For example, the collection unit prioritizes collecting schedule information from sites with high update frequencies. The collection timing can also be adjusted periodically for sites with low update frequencies. The update frequency of each site can also be analyzed to set the optimal collection timing. This makes it possible to provide the optimal collection timing based on the update frequency of each site. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input update frequency data of each ticket sales site into a generation AI and have the generation AI adjust the collection timing.

[0041] The collection unit can prioritize collecting highly relevant schedule information based on the user's geographical location information during collection. For example, the collection unit prioritizes collecting highly relevant schedule information taking the user's geographical location information into consideration during collection. For example, the collection unit prioritizes collecting schedule information for events close to the user's current location. Related schedule information can also be collected based on the user's past location information. Schedule information for events with good transportation access can also be collected preferentially based on the user's location information. This makes it possible to provide schedule information based on the user's location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant schedule information.

[0042] The collection unit can analyze the user's social media activities and collect related schedule information at the time of collection. For example, the collection unit can analyze the user's social media activities and collect related schedule information at the time of collection. For example, the collection unit can collect schedule information of artists the user follows on social media. The collection unit can also collect schedule information of events the user has "liked" on social media. The collection unit can also analyze the content of the user's social media posts and collect related schedule information. This makes it possible to provide schedule information based on the user's social media activities. Some or all of the above-described processing in the collection unit can be performed using AI, for example, or can be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related schedule information.

[0043] The notification unit can adjust the level of detail of the notification based on the importance of the notification at the time of notification. The notification unit, for example, adjusts the level of detail of the notification based on the importance of the notification at the time of notification. For example, an important notification can be provided with detailed information. Also, a low-importance notification can be provided with concise information. The level of detail of the notification can also be adjusted according to the importance of the notification. This makes it possible to provide a level of detail according to the importance of the notification. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input notification importance data to a generation AI and cause the generation AI to adjust the level of detail of the notification.

[0044] The notification unit can apply different notification algorithms depending on the notification category when making a notification. For example, the notification unit applies different notification algorithms depending on the notification category when making a notification. For example, a notification regarding the start time of an event can enhance the reminder function. A notification regarding the start of ticket sales can also be an immediate notification. A notification regarding lottery results can also be a notification including detailed information. This makes it possible to provide the optimal notification method depending on the notification category. Some or all of the above-mentioned processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input notification category data into a generation AI and have the generation AI apply the notification algorithm.

[0045] The notification unit can select the optimal notification method based on the user's device information at the time of notification. For example, the notification unit selects the optimal notification method by taking the user's device information into consideration at the time of notification. For example, if the user is using a smartphone, a push notification can be sent. Also, if the user is using a tablet, an email notification can be sent. Also, if the user is using a smartwatch, a vibration notification can be sent. This makes it possible to provide the optimal notification method according to the user's device. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's device information into a generation AI and have the generation AI select the optimal notification method.

[0046] The notification unit can analyze the user's social media activity and provide relevant notifications at the time of notification. For example, the notification unit can analyze the user's social media activity and provide relevant notifications at the time of notification. For example, the notification unit can provide notifications about events of artists the user follows on social media. It can also provide notifications about events the user has "liked" on social media. It can also analyze the content of the user's social media posts and provide relevant notifications. This makes it possible to provide notifications based on the user's social media activity. Some or all of the above-mentioned processing in the notification unit can be performed using, or without, AI, for example. For example, the notification unit can input the user's social media data into a generation AI and cause the generation AI to generate relevant notifications.

[0047] The calendar unit can select the optimal display method by referring to the user's past schedule history when displaying the calendar. For example, when displaying the calendar, the calendar unit selects the optimal display method by referring to the user's past schedule history. For example, the calendar unit may preferentially provide calendar display methods that the user has frequently used in the past. The calendar unit can also select the optimal display method for a specific time period from the user's past schedule history. The calendar unit can also analyze the user's past schedule history and suggest the optimal calendar display method. This makes it possible to provide the optimal calendar display method based on the user's past schedule history. Some or all of the above-described processing in the calendar unit may be performed using, for example, AI, or may be performed without using AI. For example, the calendar unit can input the user's past schedule history data into a generation AI and have the generation AI select the optimal display method.

[0048] The calendar unit can customize the display content based on the user's current schedule when displaying the calendar. For example, the calendar unit customizes the display content based on the user's current schedule when displaying the calendar. For example, important events can be highlighted based on the user's current schedule. Free time can also be displayed based on the user's current schedule. Related events can also be displayed based on the user's current schedule. This makes it possible to provide a customized display based on the user's current schedule. Some or all of the above-described processing in the calendar unit may be performed using AI, for example, or may be performed without using AI. For example, the calendar unit can input the user's current schedule data into a generation AI and have the generation AI customize the display content.

[0049] The calendar unit can select the optimal display method based on the user's device information when displaying the calendar. For example, when displaying the calendar, the calendar unit selects the optimal display method taking into account the user's device information. For example, if the user is using a smartphone, a calendar display tailored to the screen size can be provided. Also, if the user is using a tablet, a calendar display optimized for a large screen can be provided. Also, if the user is using a smartwatch, a calendar display that is concise and highly visible can be provided. This makes it possible to provide the optimal calendar display method tailored to the user's device. Some or all of the above-described processing in the calendar unit may be performed using, for example, AI, or may be performed without using AI. For example, the calendar unit can input the user's device information into the generation AI and have the generation AI select the optimal display method.

[0050] The calendar unit can analyze the user's social media activity and display related schedule information when displaying the calendar. For example, the calendar unit can analyze the user's social media activity and display related schedule information when displaying the calendar. For example, the calendar unit can display event schedules for artists the user follows on social media. It can also display schedules for events the user has "liked" on social media. It can also analyze the content of the user's social media posts and display related schedule information. This makes it possible to provide schedule information based on the user's social media activity. Some or all of the above-described processing in the calendar unit can be performed using, or without, AI. For example, the calendar unit can input the user's social media data into a generation AI and cause the generation AI to display related schedule information.

[0051] The winning / losing management unit can select the optimal display method by referring to the user's past winning / losing history when displaying the winning / losing results. For example, the winning / losing management unit selects the optimal display method by referring to the user's past winning / losing history when displaying the winning / losing results. For example, the optimal display method is selected based on information about events in which the user has won in the past. The optimal display method for a specific time period can also be selected from the user's past winning / losing history. The user's past winning / losing history can also be analyzed and the optimal display method can be suggested. This makes it possible to provide the optimal display method based on the user's past winning / losing history. Some or all of the above-described processing in the winning / losing management unit can be performed using, for example, AI, or can be performed without using AI. For example, the winning / losing management unit can input the user's past winning / losing history data into the generation AI and have the generation AI select the optimal display method.

[0052] The winning / losing management unit can customize the display content based on the user's current schedule when displaying the winning / losing results. The winning / losing management unit customizes the display content based on the user's current schedule when displaying the winning / losing results, for example. For example, the winning / losing management unit can display the next step if the user is successful based on the user's current schedule. It can also display alternatives if the user is unsuccessful based on the user's current schedule. It can also display related event information based on the user's current schedule. This makes it possible to provide a customized display based on the user's current schedule. Some or all of the above-mentioned processing in the winning / losing management unit may be performed using AI, for example, or may be performed without using AI. For example, the winning / losing management unit can input the user's current schedule data into the generation AI and have the generation AI customize the display content.

[0053] The winning / losing management unit can select the optimal display method based on the user's device information when displaying the winning / losing results. For example, the winning / losing management unit selects the optimal display method by taking the user's device information into consideration when displaying the winning / losing results. For example, if the user is using a smartphone, a display method tailored to the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Also, if the user is using a smartwatch, a display method that is simple and highly visible can be provided. This makes it possible to provide the optimal display method tailored to the user's device. Some or all of the above-mentioned processing in the winning / losing management unit may be performed using, for example, AI, or may be performed without using AI. For example, the winning / losing management unit can input the user's device information into the generation AI and have the generation AI select the optimal display method.

[0054] The lottery result management unit can analyze the user's social media activity and display related lottery result information when displaying the lottery result. The lottery result management unit can, for example, analyze the user's social media activity and display related lottery result information when displaying the lottery result. For example, it can display lottery result information for artists the user follows on social media. It can also display lottery result information for events that the user has "liked" on social media. It can also analyze the content of the user's social media posts and display related lottery result information. This makes it possible to provide lottery result information based on the user's social media activity. Some or all of the above-mentioned processing in the lottery result management unit can be performed using AI, for example, or without AI. For example, the lottery result management unit can input the user's social media data into a generation AI and have the generation AI display related lottery result information.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The reception unit can analyze the user's past purchase history and prioritize displaying related event information. For example, related events can be suggested based on information about artists and events for which the user has previously purchased tickets. Related events can also be displayed taking into consideration the genres and regions of events the user has previously attended. Furthermore, the reception unit can infer from the user's past purchase history that the user is a fan of a particular artist or event, and prioritize displaying information related to that artist or event. This makes it possible to provide personalized event information based on the user's past purchase history.

[0057] The collection unit can analyze the user's social media activities and collect related schedule information. For example, it can collect schedule information about artists the user follows on social media. It can also collect schedule information about events the user has "liked" on social media. It can also analyze the content of the user's social media posts and collect related schedule information. This makes it possible to provide schedule information based on the user's social media activities.

[0058] The reception unit can prioritize displaying highly relevant event information based on the user's geographical location information. For example, events close to the user's current location can be prioritized. Related events can also be displayed based on the user's past location information. Furthermore, events with good transportation access can be prioritized based on the user's location information. This makes it possible to provide event information based on the user's location information.

[0059] The collection unit can adjust the collection timing based on the update frequency of each ticket sales site when collecting data. For example, it can prioritize collecting schedule information from sites with high update frequencies. It can also periodically adjust the collection timing for sites with low update frequencies. It can also analyze the update frequency of each site and set the optimal collection timing. This makes it possible to provide the optimal collection timing based on the update frequency of each site.

[0060] When notifying, the notification unit can select the optimal notification method based on the user's device information. For example, if the user is using a smartphone, a push notification can be sent. If the user is using a tablet, an email notification can be sent. Furthermore, if the user is using a smartwatch, a vibration notification can be sent. This makes it possible to provide the optimal notification method according to the user's device.

[0061] When displaying a calendar, the calendar unit can select the optimal display method by referring to the user's past schedule history. For example, it can provide a calendar display method that the user has frequently used in the past with priority. It can also select the optimal display method for a specific time period from the user's past schedule history. It can also analyze the user's past schedule history and suggest the optimal calendar display method. This makes it possible to provide the optimal calendar display method based on the user's past schedule history.

[0062] When displaying the lottery results, the lottery result management unit can select the optimal display method by referring to the user's past lottery result history. For example, the optimal display method can be selected based on information about events in which the user has won in the past. The optimal display method for a specific time period can also be selected from the user's past lottery result history. Furthermore, the unit can analyze the user's past lottery result history and suggest the optimal display method. This makes it possible to provide the optimal display method based on the user's past lottery result history.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The reception unit receives ticket information from the user. Ticket information from the user includes the artist name, event name, event date, venue, etc. For example, the reception unit receives information about the artist name, event name, event date, and venue entered by the user. Step 2: The collection unit collects schedule information from each ticket sales site based on the information received by the reception unit. The collection unit collects schedule information using web scraping technology or an API. For example, the collection unit obtains schedule information using the API of a specific ticket sales site. Step 3: The notification unit notifies the user based on the schedule information collected by the collection unit. The notification unit notifies the user by email or push notification. For example, the schedule information is sent to the user's email address. Step 4: The calendar section provides a calendar function for schedule management. The calendar section displays the user's schedule in a calendar format, and displays the schedule in a format such as a monthly view, a weekly view, or a daily view. For example, the user's schedule can be displayed in a monthly view, allowing the user to visually check the schedule of each event. Step 5: The lottery results management unit manages the lottery results. The lottery results management unit automatically obtains the lottery results and notifies the user. It also updates the schedule based on the lottery results. For example, when the lottery results are announced, the unit notifies the user, and updates the schedule so that if the user wins, they can proceed with the ticket purchase procedure.

[0065] (Example 2) A ticket acquisition support system according to an embodiment of the present invention allows users to input information about tickets they wish to acquire, automatically collect schedule information from various ticket sales sites, and, in the case of lottery sales, manage the schedule of lottery results. This system allows users to centrally manage their ticket acquisition schedules and efficiently acquire tickets. For example, users input information about the tickets they wish to acquire, such as the artist name, event name, date, and venue. This information is then entered into the system. The system then automatically collects advance and general sales schedules from fan clubs, credit card companies, and ticket sales companies (ePlus, Ticket Pia, Lawson Ticket, etc.). The system collects schedule information from various ticket sales sites and provides it to users. For example, the system collects information about when fan club advance sales begin, when credit card company advance sales begin, when ticket sales companies advance sales begin, and when general sales begin. Furthermore, in the case of lottery sales, the system also manages the schedule when the results of the lottery sales are announced. The system notifies users of the date and time when the lottery sales results will be announced and updates the schedule based on the results. For example, when the results of a lottery sale are announced, the user is notified, and if they win, their schedule is updated to allow them to proceed with the ticket purchase procedure. This system allows users to centrally manage their ticket acquisition schedules and acquire tickets efficiently. For example, it can save users the trouble of checking multiple ticket sales sites, allowing them to smoothly acquire tickets without missing the results of the lottery sale. In this way, the ticket acquisition support system allows users to centrally manage their ticket acquisition schedules and acquire tickets efficiently.

[0066] A ticket acquisition support system according to an embodiment includes a reception unit, a collection unit, a notification unit, a calendar unit, and a ticket lottery management unit. The reception unit receives ticket information from a user. The ticket information from the user includes, but is not limited to, the artist name, event name, event date, and venue. The reception unit receives, for example, the artist name and event name entered by the user. The reception unit can also receive information on the event date and venue. The collection unit collects schedule information from each ticket sales site based on the information received by the reception unit. The collection unit collects schedule information using, for example, web scraping technology. The collection unit can also use an API to collect schedule information. For example, the collection unit acquires schedule information using the API of a specific ticket sales site. The notification unit notifies the user based on the schedule information collected by the collection unit. The notification unit notifies the user using, for example, email. The notification unit can also notify the user using push notification. For example, the notification unit sends schedule information to the user's email address. The calendar unit provides a calendar function for schedule management. The calendar unit, for example, displays the user's schedule in calendar format. The calendar unit can display the schedule in formats such as monthly, weekly, and daily. For example, the calendar unit displays the user's schedule in monthly format, allowing the user to visually check the schedule for each event. The lottery result management unit manages the lottery results. For example, the lottery result management unit automatically obtains the lottery result and notifies the user. The lottery result management unit can also update the schedule based on the lottery result. For example, the lottery result management unit notifies the user when the lottery result is announced, and updates the schedule so that the user can proceed with ticket purchase procedures if they are successful. In this way, the ticket acquisition support system according to the embodiment allows users to centrally manage their ticket acquisition schedules and efficiently acquire tickets.

[0067] The collection unit can collect schedule information using web scraping or an API. The collection unit, for example, collects schedule information using web scraping technology. For example, the collection unit analyzes the webpage of a specific ticket sales site and extracts schedule information. The collection unit can also collect schedule information using an API. For example, the collection unit acquires schedule information using the API of a specific ticket sales site. This allows the collection unit to collect schedule information efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the schedule information acquired using web scraping technology into a generation AI and have the generation AI analyze the schedule information.

[0068] The notification unit can notify the user using email or push notification. The notification unit can notify the user using email, for example. For example, the notification unit can send schedule information to the user's email address. The notification unit can also notify the user using push notification. For example, the notification unit can send a push notification to the user's smartphone. This allows the user to receive the notification in a timely manner. Some or all of the above-mentioned processing in the notification unit can be performed using AI, for example, or can be performed without using AI. For example, the notification unit can input the schedule information collected by the collection unit into a generation AI and cause the generation AI to generate the notification content.

[0069] The calendar unit can display the user's schedule in calendar format. For example, the calendar unit displays the user's schedule in calendar format. The calendar unit can display the schedule in a format such as a monthly display, a weekly display, or a daily display. For example, the calendar unit displays the user's schedule in a monthly display, allowing the schedule of each event to be visually confirmed. This allows the user to visually check the schedule. Some or all of the above-mentioned processing in the calendar unit may be performed, for example, using AI or may be performed without using AI. For example, the calendar unit can input the schedule information collected by the collection unit to a generation AI and cause the generation AI to generate a calendar display.

[0070] The lottery result management unit can automatically obtain the lottery result and notify the user. For example, the lottery result management unit automatically obtains the lottery result and notifies the user. The lottery result management unit can also update the schedule based on the lottery result. For example, the lottery result management unit notifies the user when the lottery result is announced, and updates the schedule so that the user can proceed with ticket purchase procedures if they are successful. This allows the user to quickly check the lottery result. Some or all of the above-mentioned processing in the lottery result management unit may be performed using AI, for example, or may be performed without using AI. For example, the lottery result management unit can input the lottery result to the generation AI and have the generation AI analyze the lottery result.

[0071] The reception unit can accept information such as the artist name, event name, date, and venue. The reception unit, for example, accepts the artist name and event name entered by the user. The reception unit can also accept information such as the date and venue. For example, the reception unit accepts the artist name entered by the user and stores it in the system. The reception unit can also accept the event name entered by the user and store it in the system. This allows the user to enter detailed ticket information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the information entered by the user into a generation AI and have the generation AI analyze the information.

[0072] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated user emotions. For example, the reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated user emotions. For example, if the user is excited, the reception unit can provide a brightly colored interface to make inputting more enjoyable. If the user is stressed, the reception unit can provide a simple interface to minimize input steps. If the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. This allows the reception unit to provide an interface that matches the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using AI, or without AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0073] The reception unit can analyze the user's past input history and suggest an efficient input method. The reception unit, for example, analyzes the user's past input history and suggests the optimal input method. For example, it automatically displays artist names and event names that the user has frequently input in the past as candidates. It can also prioritize suggestions of input methods (voice, text, etc.) that the user has used in the past. It can also predict and suggest artist names and event names to be used during a specific time period from the user's past input history. This makes it possible to provide an efficient input method based on the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history to a generation AI and have the generation AI suggest the optimal input method.

[0074] The reception unit can automatically complete input candidates based on events of current interest to the user during input. The reception unit, for example, automatically completes input candidates based on events of current interest to the user during input. For example, the reception unit automatically completes related event names based on event information recently searched by the user. The reception unit can also automatically complete input candidates based on events in which the user expressed interest on social media. The reception unit can also automatically complete related event names based on information on events the user has previously attended. This makes it possible to provide input candidates based on the user's interests. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input data on events of interest to the user to a generation AI and cause the generation AI to automatically complete input candidates.

[0075] The reception unit can estimate the user's emotions and prioritize inputs based on the estimated user emotions. For example, the reception unit can estimate the user's emotions and prioritize inputs based on the estimated user emotions. For example, if the user is in a hurry, the reception unit can prioritize input of important information (such as the artist name and event name). Alternatively, if the user is relaxed, the reception unit can prioritize input of detailed information (such as the date and venue). Alternatively, if the user is excited, the reception unit can simplify the input procedure and quickly complete the input. This allows inputs to be prioritized according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using an AI, for example, or without an AI. For example, the reception unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0076] The reception unit can prioritize displaying highly relevant event information based on the user's geographical location information at the time of input. For example, the reception unit prioritizes displaying highly relevant event information taking into account the user's geographical location information at the time of input. For example, events close to the user's current location can be displayed with priority. Related events can also be displayed based on the user's past location information. Events with good transportation access can also be displayed with priority based on the user's location information. This makes it possible to provide event information based on the user's location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to a generation AI and cause the generation AI to display highly relevant event information.

[0077] The reception unit can analyze the user's social media activity at the time of input and suggest related event information. For example, the reception unit can analyze the user's social media activity at the time of input and suggest related event information. For example, the reception unit can suggest event information for artists the user follows on social media. It can also suggest event information for posts the user has "liked" on social media. It can also analyze the content of the user's social media posts and suggest related event information. This makes it possible to provide event information based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to suggest related event information.

[0078] The collection unit can estimate the user's emotions and adjust the priority of the schedule information to be collected based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and adjust the priority of the schedule information to be collected based on the estimated user emotions. For example, if the user is excited, schedule information for popular events can be collected preferentially. Also, if the user is stressed, simple schedule information can be collected preferentially. Also, if the user is relaxed, detailed schedule information can be collected preferentially. This makes it possible to provide a priority order of schedule information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using an AI, for example, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0079] The collection unit can optimize the collection algorithm by referring to past collected data during collection. The collection unit, for example, optimizes the collection algorithm by referring to past collected data during collection. For example, the collection unit selects the most efficient collection method based on past collected data. Schedule information for collection during a specific time period can also be optimized from past collected data. Past collected data can also be analyzed to improve the collection algorithm. This allows the collection algorithm to be optimized based on past data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past collected data into a generation AI and cause the generation AI to optimize the collection algorithm.

[0080] The collection unit can adjust the collection timing based on the update frequency of each ticket sales site during collection. For example, the collection unit adjusts the collection timing based on the update frequency of each ticket sales site during collection. For example, the collection unit prioritizes collecting schedule information from sites with high update frequencies. The collection timing can also be adjusted periodically for sites with low update frequencies. The update frequency of each site can also be analyzed to set the optimal collection timing. This makes it possible to provide the optimal collection timing based on the update frequency of each site. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input update frequency data of each ticket sales site into a generation AI and have the generation AI adjust the collection timing.

[0081] The collection unit can estimate the user's emotions and adjust the display method of the collected results based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and adjust the display method of the collected results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to provide a display method that suits the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using an AI, for example, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0082] The collection unit can prioritize collecting highly relevant schedule information based on the user's geographical location information during collection. For example, the collection unit prioritizes collecting highly relevant schedule information taking the user's geographical location information into consideration during collection. For example, the collection unit prioritizes collecting schedule information for events close to the user's current location. Related schedule information can also be collected based on the user's past location information. Schedule information for events with good transportation access can also be collected preferentially based on the user's location information. This makes it possible to provide schedule information based on the user's location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant schedule information.

[0083] The collection unit can analyze the user's social media activities and collect related schedule information at the time of collection. For example, the collection unit can analyze the user's social media activities and collect related schedule information at the time of collection. For example, the collection unit can collect schedule information of artists the user follows on social media. The collection unit can also collect schedule information of events the user has "liked" on social media. The collection unit can also analyze the content of the user's social media posts and collect related schedule information. This makes it possible to provide schedule information based on the user's social media activities. Some or all of the above-described processing in the collection unit can be performed using AI, for example, or can be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related schedule information.

[0084] The notification unit can estimate the user's emotion and adjust the notification expression method based on the estimated user's emotion. For example, the notification unit can estimate the user's emotion and adjust the notification expression method based on the estimated user's emotion. For example, if the user is nervous, the notification can be made in a calm expression. If the user is relaxed, the notification can be made in a cheerful expression. If the user is in a hurry, the notification can be made in a quick and concise expression. This makes it possible to provide a notification method that suits the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the notification unit can be performed using an AI, for example, or without an AI. For example, the notification unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0085] The notification unit can adjust the level of detail of the notification based on the importance of the notification at the time of notification. The notification unit, for example, adjusts the level of detail of the notification based on the importance of the notification at the time of notification. For example, an important notification can be provided with detailed information. Also, a low-importance notification can be provided with concise information. The level of detail of the notification can also be adjusted according to the importance of the notification. This makes it possible to provide a level of detail according to the importance of the notification. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input notification importance data to a generation AI and cause the generation AI to adjust the level of detail of the notification.

[0086] The notification unit can apply different notification algorithms depending on the notification category when making a notification. For example, the notification unit applies different notification algorithms depending on the notification category when making a notification. For example, a notification regarding the start time of an event can enhance the reminder function. A notification regarding the start of ticket sales can also be an immediate notification. A notification regarding lottery results can also be a notification including detailed information. This makes it possible to provide the optimal notification method depending on the notification category. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input notification category data into a generation AI and have the generation AI apply the notification algorithm.

[0087] The notification unit can estimate the user's emotion and adjust the timing of the notification based on the estimated user's emotion. For example, the notification unit can delay the notification if the user is relaxed. Alternatively, if the user is in a hurry, the notification can be provided immediately. Alternatively, if the user is excited, the notification can be provided earlier. This allows the notification timing to be tailored to the user's emotion. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the notification unit can be performed using an AI, or can be performed without an AI. For example, the notification unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0088] The notification unit can select the optimal notification method based on the user's device information at the time of notification. For example, the notification unit selects the optimal notification method by taking the user's device information into consideration at the time of notification. For example, if the user is using a smartphone, a push notification can be sent. Also, if the user is using a tablet, an email notification can be sent. Also, if the user is using a smartwatch, a vibration notification can be sent. This makes it possible to provide the optimal notification method according to the user's device. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's device information into a generation AI and have the generation AI select the optimal notification method.

[0089] The notification unit can analyze the user's social media activity and provide relevant notifications at the time of notification. For example, the notification unit can analyze the user's social media activity and provide relevant notifications at the time of notification. For example, the notification unit can provide notifications about events of artists the user follows on social media. It can also provide notifications about events the user has "liked" on social media. It can also analyze the content of the user's social media posts and provide relevant notifications. This makes it possible to provide notifications based on the user's social media activity. Some or all of the above-mentioned processing in the notification unit can be performed using, or without, AI, for example. For example, the notification unit can input the user's social media data into a generation AI and cause the generation AI to generate relevant notifications.

[0090] The calendar unit can estimate a user's emotions and adjust the calendar display method based on the estimated user emotions. For example, the calendar unit can estimate a user's emotions and adjust the calendar display method based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible calendar display can be provided. If the user is relaxed, a calendar display including detailed information can be provided. If the user is in a hurry, a calendar display focusing on the main points can be provided. This makes it possible to provide a calendar display method that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the calendar unit can be performed using AI, for example, or without AI. For example, the calendar unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0091] The calendar unit can select the optimal display method by referring to the user's past schedule history when displaying the calendar. For example, when displaying the calendar, the calendar unit selects the optimal display method by referring to the user's past schedule history. For example, the calendar unit may preferentially provide calendar display methods that the user has frequently used in the past. The calendar unit can also select the optimal display method for a specific time period from the user's past schedule history. The calendar unit can also analyze the user's past schedule history and suggest the optimal calendar display method. This makes it possible to provide the optimal calendar display method based on the user's past schedule history. Some or all of the above-described processing in the calendar unit may be performed using, for example, AI, or may be performed without using AI. For example, the calendar unit can input the user's past schedule history data into a generation AI and have the generation AI select the optimal display method.

[0092] The calendar unit can customize the display content based on the user's current schedule when displaying the calendar. For example, the calendar unit customizes the display content based on the user's current schedule when displaying the calendar. For example, important events can be highlighted based on the user's current schedule. Free time can also be displayed based on the user's current schedule. Related events can also be displayed based on the user's current schedule. This makes it possible to provide a customized display based on the user's current schedule. Some or all of the above-described processing in the calendar unit may be performed using AI, for example, or may be performed without using AI. For example, the calendar unit can input the user's current schedule data into a generation AI and have the generation AI customize the display content.

[0093] The calendar unit can estimate a user's emotions and adjust calendar operation procedures based on the estimated user emotions. For example, the calendar unit can estimate a user's emotions and adjust calendar operation procedures based on the estimated user emotions. For example, if the user is nervous, it can provide simple and intuitive operation procedures. If the user is relaxed, it can provide detailed operation procedures. If the user is in a hurry, it can provide procedures that allow quick operation. This makes it possible to provide operation procedures that correspond to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the calendar unit can be performed using AI, for example, or without AI. For example, the calendar unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0094] The calendar unit can select the optimal display method based on the user's device information when displaying the calendar. For example, when displaying the calendar, the calendar unit selects the optimal display method taking into account the user's device information. For example, if the user is using a smartphone, a calendar display tailored to the screen size can be provided. Also, if the user is using a tablet, a calendar display optimized for a large screen can be provided. Also, if the user is using a smartwatch, a calendar display that is concise and highly visible can be provided. This makes it possible to provide the optimal calendar display method tailored to the user's device. Some or all of the above-described processing in the calendar unit may be performed using, for example, AI, or may be performed without using AI. For example, the calendar unit can input the user's device information into the generation AI and have the generation AI select the optimal display method.

[0095] The calendar unit can analyze the user's social media activity and display related schedule information when displaying the calendar. For example, the calendar unit can analyze the user's social media activity and display related schedule information when displaying the calendar. For example, the calendar unit can display event schedules for artists the user follows on social media. It can also display schedules for events the user has "liked" on social media. It can also analyze the content of the user's social media posts and display related schedule information. This makes it possible to provide schedule information based on the user's social media activity. Some or all of the above-described processing in the calendar unit can be performed using, or without, AI. For example, the calendar unit can input the user's social media data into a generation AI and cause the generation AI to display related schedule information.

[0096] The winning / loss management unit can estimate the user's emotions and adjust the display method of the winning / loss results based on the estimated user emotions. For example, the winning / loss management unit can estimate the user's emotions and adjust the display method of the winning / loss results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to provide a display method of the winning / loss results that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the winning / loss management unit can be performed using AI, or without AI. For example, the winning / loss management unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0097] The winning / losing management unit can select the optimal display method by referring to the user's past winning / losing history when displaying the winning / losing results. For example, the winning / losing management unit selects the optimal display method by referring to the user's past winning / losing history when displaying the winning / losing results. For example, the optimal display method is selected based on information about events in which the user has won in the past. The optimal display method for a specific time period can also be selected from the user's past winning / losing history. The user's past winning / losing history can also be analyzed and the optimal display method can be suggested. This makes it possible to provide the optimal display method based on the user's past winning / losing history. Some or all of the above-described processing in the winning / losing management unit can be performed using, for example, AI, or can be performed without using AI. For example, the winning / losing management unit can input the user's past winning / losing history data into the generation AI and have the generation AI select the optimal display method.

[0098] The winning / losing management unit can customize the display content based on the user's current schedule when displaying the winning / losing results. The winning / losing management unit customizes the display content based on the user's current schedule when displaying the winning / losing results, for example. For example, the winning / losing management unit can display the next step if the user is successful based on the user's current schedule. It can also display alternatives if the user is unsuccessful based on the user's current schedule. It can also display related event information based on the user's current schedule. This makes it possible to provide a customized display based on the user's current schedule. Some or all of the above-mentioned processing in the winning / losing management unit may be performed using AI, for example, or may be performed without using AI. For example, the winning / losing management unit can input the user's current schedule data into the generation AI and have the generation AI customize the display content.

[0099] The winning / losing management unit can estimate the user's emotions and adjust the timing of notification of the winning / losing result based on the estimated user emotions. The winning / losing management unit, for example, estimates the user's emotions and adjusts the timing of notification of the winning / losing result based on the estimated user emotions. For example, if the user is relaxed, the notification can be delayed. If the user is in a hurry, the notification can be immediate. If the user is excited, the notification can be advanced. This makes it possible to provide notification timing that matches the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the winning / losing management unit can be performed using AI, for example, or without AI. For example, the winning / losing management unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0100] The winning / losing management unit can select the optimal display method based on the user's device information when displaying the winning / losing results. For example, the winning / losing management unit selects the optimal display method by taking the user's device information into consideration when displaying the winning / losing results. For example, if the user is using a smartphone, a display method tailored to the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Also, if the user is using a smartwatch, a display method that is simple and highly visible can be provided. This makes it possible to provide the optimal display method tailored to the user's device. Some or all of the above-mentioned processing in the winning / losing management unit may be performed using, for example, AI, or may be performed without using AI. For example, the winning / losing management unit can input the user's device information into the generation AI and have the generation AI select the optimal display method.

[0101] The lottery result management unit can analyze the user's social media activity and display related lottery result information when displaying the lottery result. The lottery result management unit can, for example, analyze the user's social media activity and display related lottery result information when displaying the lottery result. For example, it can display lottery result information for artists the user follows on social media. It can also display lottery result information for events that the user has "liked" on social media. It can also analyze the content of the user's social media posts and display related lottery result information. This makes it possible to provide lottery result information based on the user's social media activity. Some or all of the above-mentioned processing in the lottery result management unit can be performed using AI, for example, or without AI. For example, the lottery result management unit can input the user's social media data into a generation AI and have the generation AI display related lottery result information. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, collection unit, notification unit, calendar unit, and lottery result management unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives ticket information from the user. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects schedule information from each ticket sales site. The notification unit is realized by the control unit 46A of the smart device 14 and notifies the user based on the collected schedule information. The calendar unit is realized by the control unit 46A of the smart device 14 and displays the user's schedule in calendar format. The lottery result management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the lottery results and updates the schedule. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, collection unit, notification unit, calendar unit, and lottery result management unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives ticket information from the user. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects schedule information from each ticket sales site. The notification unit is realized by the control unit 46A of the smart glasses 214 and notifies the user based on the collected schedule information. The calendar unit is realized by the control unit 46A of the smart glasses 214 and displays the user's schedule in calendar format. The lottery result management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the lottery results and updates the schedule. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, collection unit, notification unit, calendar unit, and lottery result management unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives ticket information from the user. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects schedule information from each ticket sales site. The notification unit is realized by the control unit 46A of the headset type terminal 314 and notifies the user based on the collected schedule information. The calendar unit is realized by the control unit 46A of the headset type terminal 314 and displays the user's schedule in calendar format. The lottery result management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the lottery results and updates the schedule. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, collection unit, notification unit, calendar unit, and lottery result management unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives ticket information from users. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects schedule information from each ticket sales site. The notification unit is realized by the control unit 46A of the robot 414 and notifies the user based on the collected schedule information. The calendar unit is realized by the control unit 46A of the robot 414 and displays the user's schedule in calendar format. The lottery result management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the lottery results and updates the schedule.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The reception unit can analyze the user's past purchase history and prioritize displaying related event information. For example, related events can be suggested based on information about artists and events for which the user has previously purchased tickets. Related events can also be displayed taking into consideration the genres and regions of events the user has previously attended. Furthermore, the reception unit can infer from the user's past purchase history that the user is a fan of a particular artist or event, and prioritize displaying information related to that artist or event. This makes it possible to provide personalized event information based on the user's past purchase history.

[0104] The collection unit can analyze the user's social media activities and collect related schedule information. For example, it can collect schedule information about artists the user follows on social media. It can also collect schedule information about events the user has "liked" on social media. It can also analyze the content of the user's social media posts and collect related schedule information. This makes it possible to provide schedule information based on the user's social media activities.

[0105] The notification unit can estimate the user's emotions and adjust the notification expression method based on the estimated user's emotions. For example, if the user is nervous, the notification can be made in a calm manner. If the user is relaxed, the notification can be made in a cheerful manner. Furthermore, if the user is in a hurry, the notification can be made in a quick and concise manner. This makes it possible to provide a notification method that suits the user's emotions.

[0106] The calendar unit can estimate the user's emotions and adjust the calendar display method based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible calendar display can be provided. If the user is relaxed, a calendar display including detailed information can be provided. Furthermore, if the user is in a hurry, a calendar display that focuses on the main points can be provided. In this way, a calendar display method that corresponds to the user's emotions can be provided.

[0107] The lottery result management unit can estimate the user's emotions and adjust the display method of the lottery result based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, a lottery result display method that corresponds to the user's emotions can be provided.

[0108] The reception unit can prioritize displaying highly relevant event information based on the user's geographical location information. For example, events close to the user's current location can be prioritized. Related events can also be displayed based on the user's past location information. Furthermore, events with good transportation access can be prioritized based on the user's location information. This makes it possible to provide event information based on the user's location information.

[0109] The collection unit can adjust the collection timing based on the update frequency of each ticket sales site when collecting data. For example, it can prioritize collecting schedule information from sites with high update frequencies. It can also periodically adjust the collection timing for sites with low update frequencies. It can also analyze the update frequency of each site and set the optimal collection timing. This makes it possible to provide the optimal collection timing based on the update frequency of each site.

[0110] When notifying, the notification unit can select the optimal notification method based on the user's device information. For example, if the user is using a smartphone, a push notification can be sent. If the user is using a tablet, an email notification can be sent. Furthermore, if the user is using a smartwatch, a vibration notification can be sent. This makes it possible to provide the optimal notification method according to the user's device.

[0111] When displaying a calendar, the calendar unit can select the optimal display method by referring to the user's past schedule history. For example, it can provide a calendar display method that the user has frequently used in the past with priority. It can also select the optimal display method for a specific time period from the user's past schedule history. It can also analyze the user's past schedule history and suggest the optimal calendar display method. This makes it possible to provide the optimal calendar display method based on the user's past schedule history.

[0112] When displaying the lottery results, the lottery result management unit can select the optimal display method by referring to the user's past lottery result history. For example, the optimal display method can be selected based on information about events in which the user has won in the past. The optimal display method for a specific time period can also be selected from the user's past lottery result history. Furthermore, the unit can analyze the user's past lottery result history and suggest the optimal display method. This makes it possible to provide the optimal display method based on the user's past lottery result history.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The reception unit receives ticket information from the user. Ticket information from the user includes the artist name, event name, event date, venue, etc. For example, the reception unit receives information about the artist name, event name, event date, and venue entered by the user. Step 2: The collection unit collects schedule information from each ticket sales site based on the information received by the reception unit. The collection unit collects schedule information using web scraping technology or an API. For example, the collection unit obtains schedule information using the API of a specific ticket sales site. Step 3: The notification unit notifies the user based on the schedule information collected by the collection unit. The notification unit notifies the user by email or push notification. For example, the schedule information is sent to the user's email address. Step 4: The calendar section provides a calendar function for schedule management. The calendar section displays the user's schedule in a calendar format, and displays the schedule in a format such as a monthly view, a weekly view, or a daily view. For example, the user's schedule can be displayed in a monthly view, allowing the user to visually check the schedule of each event. Step 5: The lottery results management unit manages the lottery results. The lottery results management unit automatically obtains the lottery results and notifies the user. It also updates the schedule based on the lottery results. For example, when the lottery results are announced, the unit notifies the user, and updates the schedule so that if the user wins, they can proceed with the ticket purchase procedure.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0117] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 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.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0145] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0155] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0157] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0158] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0159] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0160] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0161] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0162] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0163] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0170] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0171] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0172] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0176] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0177] 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.

[0178] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0179] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0180] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0181] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0183] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0184] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0185] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0186] [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives ticket information from a user; a collection unit that collects schedule information from each ticket sales site based on the information received by the reception unit; a notification unit that notifies a user based on the schedule information collected by the collection unit; A calendar section that provides a calendar function for schedule management; and a lottery sales management unit that manages the lottery sales results. A system characterized by:

2. The collecting unit Use web scraping or APIs to gather schedule information 2. The system of claim 1.

3. The notification unit Notify users via email or push notification 2. The system of claim 1.

4. The calendar unit is View a user's schedule in a calendar format 2. The system of claim 1.

5. The winning / losing management department Automatically obtain lottery results and notify users 2. The system of claim 1.

6. The reception unit Accepts artist name, event name, date, and venue information 2. The system of claim 1.

7. The reception unit Estimate user emotions and adjust the design of the input interface based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyzes the user's input history and suggests efficient input methods 2. The system of claim 1.

9. The reception unit As you type, auto-complete suggestions based on the user's current interest events 2. The system of claim 1.

10. The reception unit Estimate the user's emotions and prioritize inputs based on the estimated user emotions.

2. The system of claim 1.

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