system

Abstract

The system according to this embodiment aims to analyze the user's interests and predict and notify them of events that they may be interested in in the future. [Solution] The system according to the embodiment comprises an analysis unit, a prediction unit, a data collection unit, and a notification unit. The analysis unit analyzes the user's interests. The prediction unit predicts events that the user is likely to be interested in in the future based on the data obtained by the analysis unit. The data collection unit collects event information in real time. The notification unit notifies the user of the start time of sales and the number of remaining items based on the information collected by the data collection unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to provide event information that the user is interested in at an appropriate timing, and there are problems of overlooking and insufficient personalization.

[0005] The system according to the embodiment aims to analyze the user's interests, predict and notify events that the user is likely to be interested in in the future.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a prediction unit, a data collection unit, and a notification unit. The analysis unit analyzes the user's interests. The prediction unit predicts events that the user is likely to be interested in in the future based on the data obtained by the analysis unit. The data collection unit collects event information in real time. The notification unit notifies the user of the start time of sales and the number of remaining items based on the information collected by the data collection unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's interests and predict and notify them of events that they may be interested in in the future. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The event prediction system according to an embodiment of the present invention is a system that analyzes a user's interests and behavioral history to predict events that the user is likely to be interested in in the future and notifies them in real time. The event prediction system is a mechanism that analyzes a user's interests and behavioral history to predict events that the user is likely to be interested in in the future and notifies them in real time. Conventional manual event searches and notification settings have problems such as missing event information and a lack of personalization, but the event prediction system solves these problems. First, a generating AI analyzes the user's past event participation history and social media data to identify the user's interests. For example, it collects data on music concerts and plays the user has attended in the past, and posts that the user has "liked" on social media, and the generating AI analyzes them. Next, based on this data, the generating AI predicts events that the user is likely to be interested in in the future. For example, it predicts similar events that will be held in the future based on the genre and artist of music concerts the user has attended in the past. Furthermore, the generating AI collects event information in real time and notifies the user of the start time of sales and the number of remaining tickets. For example, it collects information in real time from ticket sales sites, and the generating AI analyzes it to extract important information. As a result, users can participate in events that interest them in a timely manner and prevent missing out on tickets. This system is expected to improve users' quality of life and revitalize the event industry. Users will be able to participate in events that interest them in a timely manner, enriching their daily lives and increasing their enjoyment. Event organizers will also see increased profits due to higher attendance, and are expected to continue providing excellent content. Furthermore, by utilizing generative AI and data analysis, it will be possible to provide personalized experiences and build a sustainable event ecosystem. As a result, the event prediction system will analyze users' interests, predict events they are likely to be interested in in the future, and notify them in real time, preventing them from missing out on event information.

[0029] The event prediction system according to this embodiment comprises an analysis unit, a prediction unit, a data collection unit, and a notification unit. The analysis unit analyzes the user's interests. For example, the analysis unit analyzes the user's past event participation history and social media data. For example, the analysis unit collects data on music concerts and plays the user has attended in the past, posts the user has "liked" on social media, etc., and a generating AI analyzes them. The prediction unit predicts events that the user is likely to be interested in in the future based on the data obtained by the analysis unit. For example, the prediction unit predicts similar events to be held in the future based on the genre and artists of music concerts the user has attended in the past. For example, the prediction unit uses a generating AI to predict events that the user is likely to be interested in in the future based on this data. The data collection unit collects event information in real time. For example, the data collection unit collects information in real time from ticket sales sites, etc., and a generating AI analyzes it to extract important information. For example, the data collection unit uses a generating AI to collect event information in real time and notifies the user of the start time of sales and the number of remaining tickets. The notification unit notifies the user of the start time of sales and the number of remaining tickets based on the information collected by the data collection unit. The notification unit, for example, allows users to participate in events they are interested in in a timely manner and prevents them from missing out on tickets. Thus, the event prediction system according to this embodiment can prevent users from missing event information by analyzing their interests, predicting events they are likely to be interested in in the future, and notifying them in real time.

[0030] The analytics unit analyzes user interests. For example, it analyzes users' past event participation history and social media data. Specifically, it collects data on music concerts and theatrical performances that users have attended in the past, and posts they have "liked" on social media, and a generative AI analyzes this data. The generative AI uses natural language processing technology to analyze the content of social media posts and extract users' interests and concerns. For example, it analyzes the content and comments on posts that users have "liked" to identify their interests in specific genres or artists. It also analyzes what kinds of events users have attended based on their past event participation history to understand their interest trends. Furthermore, the generative AI can analyze users' interests over time to identify changes in interests and new areas of interest. This allows the analytics unit to analyze users' interests in detail and use this information to predict future interests. It is also important for the analytics unit to take measures such as anonymizing data and implementing security measures to protect user privacy. For example, by deleting personally identifiable information and encrypting the data, analysis can be performed while protecting user privacy. This allows the analysis unit to analyze user interests with high accuracy and provide useful data to the prediction and notification units.

[0031] The prediction unit predicts events that users are likely to be interested in in the future, based on data obtained by the analysis unit. For example, the prediction unit predicts similar events to be held in the future based on the genre and artists of music concerts that the user has attended in the past. Specifically, the generative AI uses this data to predict events that users are likely to be interested in in the future. The generative AI uses machine learning algorithms to learn the user's interest patterns and predict future interests. For example, using data on the genre and artists of events the user has attended in the past as input, the generative AI generates a list of events to be held in the future. The generative AI can also consider the user's changing interests and predict interest in new genres and artists. Furthermore, the prediction unit can improve the accuracy of its predictions by linking with external event information databases and obtaining the latest event information. For example, it can obtain music festival and theater performance schedules in real time and predict events that match the user's interests. As a result, the prediction unit can predict events that users are likely to be interested in in the future with high accuracy and provide users with appropriate event information.

[0032] The data collection unit gathers event information in real time. For example, it collects information in real time from ticket sales websites, and the generating AI analyzes it to extract important information. Specifically, the generating AI collects event information in real time and notifies users of the start time of sales and the number of remaining tickets. The generating AI uses web scraping technology to collect the latest event information from ticket sales websites and event organizer websites. For example, it automatically obtains information such as the date and time of the event, location, ticket sales start time, and the number of remaining tickets, and stores it in a database. Furthermore, the generating AI analyzes the collected information and extracts important information. For example, it analyzes popular events and ticket sales status and prioritizes notifying users of information that is important to them. The data collection unit also collects event information from social media and news sites, enabling it to grasp the latest trends and trending events. As a result, the data collection unit can collect the latest event information in real time and provide users with quick and accurate information. Furthermore, the data collection unit can link the collected data with the analysis unit and prediction unit to improve the accuracy and efficiency of the entire system.

[0033] The notification unit notifies users of the start time of ticket sales and the number of remaining tickets based on the information collected by the data collection unit. For example, the notification unit can enable users to participate in events they are interested in in a timely manner and prevent them from missing out on tickets. Specifically, it sends real-time notifications to users' smartphones and email addresses. For example, it notifies users via push notifications or email when the ticket sales start time for an event is approaching. It also sends immediate notifications when the number of remaining tickets becomes low, ensuring that users do not miss the opportunity to purchase tickets. Furthermore, the notification unit can customize notification methods according to user settings. For example, users can set it to receive notifications for specific events preferentially or adjust the frequency of notifications. This allows users to receive notifications tailored to their interests and needs. The notification unit can also collect user feedback and improve the content and timing of notifications. For example, it analyzes user behavior after receiving notifications and evaluates the effectiveness of the notifications. This allows the notification unit to provide users with important information at the optimal time and maximize their opportunities to participate in events.

[0034] The optimization unit can analyze user behavior patterns and send notifications at the optimal timing. For example, the optimization unit analyzes the user's past behavior data and behavior by time of day to determine the optimal timing. For example, the optimization unit sends notifications at the optimal timing based on the user's behavior history and response rates by time of day. This maximizes the effectiveness of notifications by sending them at the optimal timing based on the user's behavior patterns. Some or all of the above processing in the optimization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the optimization unit can input user behavior data into a generation AI and have the generation AI execute the optimal notification timing.

[0035] The customization unit can provide customized event lists and notifications for each user. For example, the customization unit generates customized event lists based on the user's interests and past participation history. For example, the customization unit provides customized notifications based on the user's interests and past responses. By providing customized event lists and notifications for each user, it is possible to provide information optimized to the user's interests. Some or all of the above processing in the customization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the customization unit can input user interest data into a generation AI and have the generation AI execute a customized event list.

[0036] The analysis unit can analyze a user's past event participation history and social media data. For example, the analysis unit collects a user's past event participation history and social media data, and the generating AI analyzes it. For example, the analysis unit collects data on music concerts and plays the user has attended in the past, posts that the user has "liked" on social media, etc., and the generating AI analyzes it. By analyzing a user's past event participation history and social media data, the user's interests can be identified. Some or all of the above processing in the analysis unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the analysis unit can input a user's social media data into the generating AI and have the generating AI perform an analysis to identify the user's interests.

[0037] The prediction unit can predict events that users are likely to be interested in in the future, based on the data obtained by the analysis unit. For example, the prediction unit uses data obtained by the analysis unit to have a generative AI predict events that users are likely to be interested in in the future. For example, the prediction unit predicts similar events to be held in the future based on the genre and artists of music concerts that the user has attended in the past. In this way, by predicting events that users are likely to be interested in in the future based on the data obtained by the analysis unit, the prediction unit can provide users with optimal event information. Some or all of the above processing in the prediction unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the prediction unit can input data obtained by the analysis unit into a generative AI and have the generative AI perform predictions of events that users are likely to be interested in in the future.

[0038] The data collection unit can collect information from ticket sales websites in real time. For example, the data collection unit collects information from ticket sales websites in real time, and the generating AI analyzes it to extract important information. For example, the data collection unit uses the generating AI to collect event information in real time and notify users of the start time of sales and the number of remaining tickets. This allows the system to provide the latest event information by collecting information from ticket sales websites in real time. Some or all of the above-described processes in the data collection unit may be performed using the generating AI or not. For example, the data collection unit can input information from ticket sales websites into the generating AI and have the generating AI perform real-time information collection.

[0039] The notification unit can notify users of the start time of sales and the number of remaining items based on the information collected by the collection unit. For example, the notification unit uses a generating AI to notify users of the start time of sales and the number of remaining items based on the information collected by the collection unit. For example, the notification unit uses a generating AI to collect event information in real time and notify users of the start time of sales and the number of remaining items. This allows users to participate in events in a timely manner by notifying them of the start time of sales and the number of remaining items based on the information collected by the collection unit. Some or all of the above processing in the notification unit may be performed using a generating AI or not using a generating AI. For example, the notification unit can input information collected by the collection unit into a generating AI and have the generating AI execute notifications of the start time of sales and the number of remaining items.

[0040] The analysis unit can classify a user's past event participation history in detail and extract specific patterns. For example, the analysis unit can classify the user's past event participation history by genre and analyze their interest trends. For example, the analysis unit can classify the user's past event participation history by location and analyze their regional interests. The analysis unit can also classify the user's past event participation history by time of day and analyze their interest trends at specific time slots. In this way, by classifying the user's past event participation history in detail and extracting specific patterns, the analysis unit can analyze the user's interest trends. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's event participation history data into a generative AI and have the generative AI perform the extraction of specific patterns.

[0041] The analysis unit can perform sentiment analysis of user posts during social media data analysis to detect changes in interest. For example, the analysis unit can detect positive emotions from user posts and identify events of interest. For example, the analysis unit can detect negative emotions from user posts and exclude events of no interest. The analysis unit can also detect neutral emotions from user posts and track changes in interest. This allows for the detection of changes in interest by performing sentiment analysis of user posts during social media data analysis. Sentiment estimation is achieved using sentiment estimation functions, such as sentiment engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using generative AI or not. For example, the analysis unit can input user social media data into a generative AI and have the generative AI perform sentiment analysis.

[0042] The analysis unit can analyze event participation trends by region, taking into account the user's geographical location information. For example, the analysis unit can prioritize analyzing nearby event information based on the user's current location. For example, the analysis unit can analyze event participation trends in a specific region based on the user's past location information. The analysis unit can also analyze event information in frequently visited regions based on the user's travel history. By analyzing event participation trends by region, taking into account the user's geographical location information, it is possible to provide event information optimized for each region. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the user's geographical location information into a generation AI and have the generation AI perform the analysis of event participation trends by region.

[0043] The analysis unit can analyze a user's purchase history and investigate its correlation with their event participation history. For example, the analysis unit can analyze events related to products purchased by the user. For example, the analysis unit can analyze events related to a specific brand or artist from the user's purchase history. The analysis unit can also compare the user's purchase history with their event participation history to analyze their interests. In this way, by analyzing the user's purchase history and investigating its correlation with their event participation history, the analysis unit can analyze the user's interests. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's purchase history data into a generative AI and have the generative AI perform an investigation into its correlation with their event participation history.

[0044] The prediction unit can predict events that a user might be interested in during a particular season or period, based on their past event participation history. For example, the prediction unit can classify events a user has previously attended by season and predict their interest trends. For example, the prediction unit can predict events held during similar periods based on events a user has attended during a particular period. The prediction unit can also predict events that a user might be interested in during a particular season or period, based on their past event participation history. This allows the system to provide users with optimal event information by predicting events that a user might be interested in during a particular season or period based on their past event participation history. Some or all of the above processing in the prediction unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the prediction unit can input the user's event participation history data into a generative AI and have the generative AI predict events that a user might be interested in during a particular season or period.

[0045] The prediction unit can track changes in user interests in real time and dynamically adjust its prediction algorithm. For example, the prediction unit can analyze the user's social media posts in real time to track changes in interests. For example, the prediction unit can update the user's event participation history in real time and adjust its prediction algorithm. The prediction unit can also analyze the user's purchase history in real time to track changes in interests. This allows the prediction unit to provide more appropriate event information by tracking changes in user interests in real time and dynamically adjusting the prediction algorithm. Some or all of the above processing in the prediction unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the prediction unit can input user interest data into a generative AI and have the generative AI perform dynamic adjustments to the prediction algorithm.

[0046] The prediction unit can predict events that a user might be interested in by considering the event participation history of their friends and followers. For example, the prediction unit can predict events that a user might be interested in based on events that their friends have attended. For example, the prediction unit can predict events that a user might be interested in based on events that their followers have attended. The prediction unit can also analyze the event participation history of the user's friends and followers to predict events that a user might be interested in. This allows the system to provide the user with optimal event information by considering the event participation history of the user's friends and followers to predict events that a user might be interested in. Some or all of the above processing in the prediction unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the prediction unit can input the event participation history data of the user's friends and followers into a generative AI and have the generative AI perform the prediction of events that a user might be interested in.

[0047] The prediction unit can predict relevant events based on the user's occupation and lifestyle. For example, the prediction unit can predict events related to the user's occupation. For example, the prediction unit can predict events related to the user's lifestyle. The prediction unit can also predict events that the user might be interested in, based on their occupation and lifestyle. This allows the system to provide the user with optimal event information by predicting relevant events based on their occupation and lifestyle. Some or all of the above processing in the prediction unit may be performed using generative AI, or it may be performed without generative AI. For example, the prediction unit can input the user's occupation and lifestyle data into a generative AI and have the generative AI perform the prediction of relevant events.

[0048] The data collection unit analyzes the update frequency of ticket sales websites and collects information at the optimal time. For example, the data collection unit analyzes the update frequency of ticket sales websites and collects information during the time period when updates occur most frequently. For example, the data collection unit analyzes the update patterns of ticket sales websites and collects information at the optimal time. The data collection unit can also analyze the update history of ticket sales websites and collect information at the most efficient time. By analyzing the update frequency of ticket sales websites and collecting information at the optimal time, the latest event information can be provided. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input update data from ticket sales websites into a generation AI and have the generation AI perform information collection at the optimal time.

[0049] The data collection unit can evaluate the reliability of event information and prioritize the collection of highly reliable information. For example, the data collection unit can evaluate the source of the event information and prioritize the collection of highly reliable information. For example, the data collection unit can analyze the content of the event information and prioritize the collection of highly reliable information. The data collection unit can also analyze the past history of the event information and prioritize the collection of highly reliable information. By evaluating the reliability of event information and prioritizing the collection of highly reliable information, accurate event information can be provided. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input event information reliability data into a generation AI and have the generation AI perform a reliability evaluation.

[0050] The data collection unit can collect event information from social media and improve its accuracy by comparing it with information from the official website. For example, the data collection unit can improve accuracy by comparing event information collected from social media with information from the official website. For example, the data collection unit can improve accuracy by analyzing the content of social media posts and comparing them with information from the official website. The data collection unit can also improve accuracy by collecting event information from social media and integrating it with information from the official website. In this way, accurate event information can be provided by collecting event information from social media and improving its accuracy by comparing it with information from the official website. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input social media event information into a generative AI and have the generative AI perform a comparison with information from the official website.

[0051] The data collection unit can collect event information from news articles and blogs related to the user's interests. For example, the data collection unit can analyze news articles related to the user's interests and collect event information. For example, the data collection unit can analyze blogs related to the user's interests and collect event information. The data collection unit can also analyze online media related to the user's interests and collect event information. This allows the system to provide the user with the most relevant event information by collecting event information from news articles and blogs related to the user's interests. Some or all of the above-described processes in the data collection unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the data collection unit can input news article and blog data into a generative AI and have the generative AI perform the collection of event information.

[0052] The notification unit can customize notification content based on the user's past responses to deliver effective notifications. For example, the notification unit can send customized notifications based on notifications that the user has previously responded to favorably. For example, the notification unit can avoid sending notifications that the user has previously ignored to deliver effective notifications. The notification unit can also analyze the user's past responses and customize and send the most suitable notification content. In this way, by customizing notification content based on the user's past responses, effective notifications can be delivered. Some or all of the above processing in the notification unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the notification unit can input the user's past response data into a generative AI and have the generative AI perform the customization of notification content.

[0053] The notification unit can dynamically adjust the frequency of notifications to match the user's schedule. For example, the notification unit can analyze the user's calendar information and adjust the notification frequency accordingly. For example, the notification unit can dynamically adjust the optimal notification frequency based on the user's past notification history. The notification unit can also analyze the user's real-time schedule and adjust the notification frequency accordingly. By dynamically adjusting the notification frequency to match the user's schedule, notifications can be sent at a more appropriate time. Some or all of the above-described processes in the notification unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the notification unit can input the user's schedule data into a generative AI and have the generative AI perform the adjustment of the notification frequency.

[0054] The notification unit can select the optimal notification method by considering the user's device information when sending a notification. For example, if the user is using a smartphone, the notification unit will prioritize sending push notifications. For example, if the user is using a tablet, the notification unit will send notifications optimized for the larger screen. Furthermore, if the user is using a smartwatch, the notification unit can send concise and highly visible notifications. In this way, by selecting the optimal notification method by considering the user's device information, more effective notifications can be sent. Some or all of the above processing in the notification unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the notification unit can input the user's device information into a generative AI and have the generative AI select the optimal notification method.

[0055] The notification unit can include information about events the user's friends and followers plan to attend in its notifications. For example, the notification unit can include information about events the user's friends plan to attend in the notification. For example, the notification unit can include information about events the user's followers plan to attend in the notification. The notification unit can also analyze the information about events the user's friends and followers plan to attend and reflect it in the notification. This allows the notification unit to send notifications that are of interest to the user by including information about events the user's friends and followers plan to attend in the notification. Some or all of the above processing in the notification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the notification unit can input information about events the user's friends and followers plan to attend into a generation AI and have the generation AI perform the task of reflecting it in the notification.

[0056] The optimization unit can analyze the user's past notification history and learn the optimal notification timing. For example, the optimization unit learns the optimal notification timing based on notification timings in which the user has previously responded favorably. For example, the optimization unit learns the optimal notification timing by avoiding notification timings that the user has previously ignored. The optimization unit can also learn the optimal notification timing by analyzing the user's past notification history. By analyzing the user's past notification history and learning the optimal notification timing, it is possible to send more effective notifications. Some or all of the above processing in the optimization unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the optimization unit can input the user's notification history data into a generative AI and have the generative AI perform the learning of the optimal notification timing.

[0057] The optimization unit can analyze user behavior patterns in detail and select the optimal notification method. For example, the optimization unit can analyze user behavior patterns and select the optimal notification method. For example, the optimization unit can select the optimal notification method based on the user's past behavior patterns. The optimization unit can also analyze the user's real-time behavior patterns and select the optimal notification method. By analyzing user behavior patterns in detail and selecting the optimal notification method, more effective notifications can be sent. Some or all of the above processing in the optimization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the optimization unit can input user behavior pattern data into a generation AI and have the generation AI select the optimal notification method.

[0058] The optimization unit can select the optimal notification method by considering the user's device information. For example, if the user is using a smartphone, the optimization unit will prioritize sending push notifications. For example, if the user is using a tablet, the optimization unit will send notifications optimized for the larger screen. Furthermore, if the user is using a smartwatch, the optimization unit can also send concise and highly visible notifications. In this way, by selecting the optimal notification method by considering the user's device information, more effective notifications can be sent. Some or all of the above processing in the optimization unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the optimization unit can input the user's device information into a generative AI and have the generative AI select the optimal notification method.

[0059] The optimization unit can analyze the user's schedule information and determine the optimal notification timing. For example, the optimization unit can analyze the user's calendar information and determine the optimal notification timing according to the schedule. For example, the optimization unit can determine the optimal notification timing based on the user's past schedule information. The optimization unit can also analyze the user's real-time schedule and determine the optimal notification timing. By analyzing the user's schedule information and determining the optimal notification timing, more effective notifications can be sent. Some or all of the above processing in the optimization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the optimization unit can input the user's schedule information into a generation AI and have the generation AI perform the determination of the optimal notification timing.

[0060] The customization unit can analyze a user's past event participation history in detail and generate a customized event list. For example, the customization unit can classify the events the user has attended in the past by genre and generate a customized event list. For example, the customization unit can classify the events the user has attended by location and generate a customized event list. Furthermore, the customization unit can classify the events the user has attended by time slot and generate a customized event list. In this way, by analyzing a user's past event participation history in detail and generating a customized event list, it is possible to provide the user with event information that is best suited to them. Some or all of the above processing in the customization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the customization unit can input the user's event participation history data into a generation AI and have the generation AI execute the generation of a customized event list.

[0061] The customization unit can track changes in user interests in real time and dynamically adjust the customization content. For example, the customization unit can analyze the user's social media posts in real time to track changes in interests. For example, the customization unit can update the user's event participation history in real time and adjust the customization content. The customization unit can also analyze the user's purchase history in real time to track changes in interests. This allows for the provision of more appropriate event information by tracking changes in user interests in real time and dynamically adjusting the customization content. Some or all of the above processing in the customization unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the customization unit can input user interest data into a generative AI and have the generative AI perform dynamic adjustments to the customization content.

[0062] The customization unit can generate a customized event list by considering the event participation history of the user's friends and followers. For example, the customization unit can generate a customized event list based on events attended by the user's friends. For example, the customization unit can generate a customized event list based on events attended by the user's followers. The customization unit can also analyze the event participation history of the user's friends and followers and generate a customized event list. This allows the system to provide the user with the most suitable event information by generating a customized event list that takes into account the event participation history of the user's friends and followers. Some or all of the above processing in the customization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the customization unit can input the event participation history data of the user's friends and followers into a generation AI and have the generation AI generate a customized event list.

[0063] The customization unit can generate a customized event list based on the user's occupation and lifestyle. For example, the customization unit can generate a customized event list based on events related to the user's occupation. For example, the customization unit can generate a customized event list based on events related to the user's lifestyle. The customization unit can also generate a customized event list based on the user's occupation and lifestyle. This allows the system to provide the user with event information that is best suited to them by generating a customized event list based on their occupation and lifestyle. Some or all of the above-described processes in the customization unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the customization unit can input the user's occupation and lifestyle data into a generation AI and have the generation AI generate a customized event list.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] The event prediction system can analyze not only user interests and behavioral history, but also user purchase history. For example, it collects data on products and services that users have purchased in the past, and the analysis unit identifies user interests based on this data. The prediction unit can then suggest relevant events based on the user's purchase history. For instance, if a user frequently purchases products from a particular brand, the system can suggest events hosted by that brand or related exhibitions. Similarly, if a user purchases books of a specific genre, the system can suggest lectures or autograph sessions related to that genre. This allows the system to provide optimal event information based on the user's purchase history.

[0066] The event prediction system can analyze not only the user's interests and behavioral history, but also their geographical location. For example, it can collect the user's current location and past travel history, and the analysis unit can identify the user's interests based on this data. The prediction unit can then suggest nearby events based on the user's geographical location. For instance, it can suggest events held in areas the user frequently visits or events easily accessible from the user's current location. Furthermore, if the user is traveling, it can suggest events held at their travel destination. This allows the system to provide optimal event information based on the user's geographical location.

[0067] The event prediction system can analyze not only user interests and behavioral history, but also user activity on social media. For example, it collects posts that users "like" and content they share on social media, and the analysis unit identifies user interests based on this data. The prediction unit can then suggest relevant events based on the user's social media activity. For instance, if a user "likes" a post by a specific artist, the system can suggest a live event by that artist. Similarly, if a user shares a post on a specific topic, the system can suggest seminars or workshops related to that topic. This allows the system to provide optimal event information based on the user's social media activity.

[0068] The event prediction system can analyze not only the user's interests and behavioral history, but also the event participation history of the user's friends and followers. For example, it collects data on events attended by the user's friends and followers, and the analysis unit uses this data to identify the user's interests. The prediction unit can then suggest relevant events based on the event participation history of the user's friends and followers. For instance, by suggesting events attended by the user's friends, it can make it easier for the user to find events they can attend with their friends. It can also help the user discover new interests by suggesting events that their followers are interested in. This allows the system to provide optimal event information based on the event participation history of the user's friends and followers.

[0069] The event prediction system can analyze not only the user's interests and behavioral history, but also their occupation and lifestyle. For example, it can collect data related to the user's occupation and lifestyle, and the analysis unit can identify the user's interests based on this data. The prediction unit can then suggest relevant events based on the user's occupation and lifestyle. For instance, if the user is engaged in a creative profession, it can suggest art exhibitions or design workshops. Similarly, if the user has an active lifestyle, it can suggest sports events or outdoor activities. This allows the system to provide optimal event information based on the user's occupation and lifestyle.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The analysis unit analyzes the user's interests. For example, the analysis unit analyzes the user's past event participation history and social media data. Specifically, it collects data on music concerts and theatrical performances the user has attended in the past, posts that the user has "liked" on social media, etc., and the generating AI analyzes this data. Step 2: The prediction unit predicts events that the user might be interested in in the future based on the data obtained by the analysis unit. For example, the prediction unit predicts similar events that will be held in the future based on the genre and artists of music concerts the user has attended in the past. The generative AI then uses this data to predict events that the user might be interested in in the future. Step 3: The data collection unit collects event information in real time. The data collection unit collects information in real time from sources such as ticket sales websites, and the generating AI analyzes it to extract important information. Specifically, the generating AI collects event information in real time and notifies users of the start time of sales and the number of remaining tickets. Step 4: The notification unit notifies users of the start time of sales and the number of remaining tickets based on the information collected by the collection unit. The notification unit can, for example, enable users to participate in events they are interested in in a timely manner and prevent them from missing out on tickets.

[0072] (Example of form 2) The event prediction system according to an embodiment of the present invention is a system that analyzes a user's interests and behavioral history to predict events that the user is likely to be interested in in the future and notifies them in real time. The event prediction system is a mechanism that analyzes a user's interests and behavioral history to predict events that the user is likely to be interested in in the future and notifies them in real time. Conventional manual event searches and notification settings have problems such as missing event information and a lack of personalization, but the event prediction system solves these problems. First, a generating AI analyzes the user's past event participation history and social media data to identify the user's interests. For example, it collects data on music concerts and plays the user has attended in the past, and posts that the user has "liked" on social media, and the generating AI analyzes them. Next, based on this data, the generating AI predicts events that the user is likely to be interested in in the future. For example, it predicts similar events that will be held in the future based on the genre and artist of music concerts the user has attended in the past. Furthermore, the generating AI collects event information in real time and notifies the user of the start time of sales and the number of remaining tickets. For example, it collects information in real time from ticket sales sites, and the generating AI analyzes it to extract important information. As a result, users can participate in events that interest them in a timely manner and prevent missing out on tickets. This system is expected to improve users' quality of life and revitalize the event industry. Users will be able to participate in events that interest them in a timely manner, enriching their daily lives and increasing their enjoyment. Event organizers will also see increased profits due to higher attendance, and are expected to continue providing excellent content. Furthermore, by utilizing generative AI and data analysis, it will be possible to provide personalized experiences and build a sustainable event ecosystem. As a result, the event prediction system will analyze users' interests, predict events they are likely to be interested in in the future, and notify them in real time, preventing them from missing out on event information.

[0073] The event prediction system according to this embodiment comprises an analysis unit, a prediction unit, a data collection unit, and a notification unit. The analysis unit analyzes the user's interests. For example, the analysis unit analyzes the user's past event participation history and social media data. For example, the analysis unit collects data on music concerts and plays the user has attended in the past, posts the user has "liked" on social media, etc., and a generating AI analyzes them. The prediction unit predicts events that the user is likely to be interested in in the future based on the data obtained by the analysis unit. For example, the prediction unit predicts similar events to be held in the future based on the genre and artists of music concerts the user has attended in the past. For example, the prediction unit uses a generating AI to predict events that the user is likely to be interested in in the future based on this data. The data collection unit collects event information in real time. For example, the data collection unit collects information in real time from ticket sales sites, etc., and a generating AI analyzes it to extract important information. For example, the data collection unit uses a generating AI to collect event information in real time and notifies the user of the start time of sales and the number of remaining tickets. The notification unit notifies the user of the start time of sales and the number of remaining tickets based on the information collected by the data collection unit. The notification unit, for example, allows users to participate in events they are interested in in a timely manner and prevents them from missing out on tickets. Thus, the event prediction system according to this embodiment can prevent users from missing event information by analyzing their interests, predicting events they are likely to be interested in in the future, and notifying them in real time.

[0074] The analytics unit analyzes user interests. For example, it analyzes users' past event participation history and social media data. Specifically, it collects data on music concerts and theatrical performances that users have attended in the past, and posts they have "liked" on social media, and a generative AI analyzes this data. The generative AI uses natural language processing technology to analyze the content of social media posts and extract users' interests and concerns. For example, it analyzes the content and comments on posts that users have "liked" to identify their interests in specific genres or artists. It also analyzes what kinds of events users have attended based on their past event participation history to understand their interest trends. Furthermore, the generative AI can analyze users' interests over time to identify changes in interests and new areas of interest. This allows the analytics unit to analyze users' interests in detail and use this information to predict future interests. It is also important for the analytics unit to take measures such as anonymizing data and implementing security measures to protect user privacy. For example, by deleting personally identifiable information and encrypting the data, analysis can be performed while protecting user privacy. This allows the analysis unit to analyze user interests with high accuracy and provide useful data to the prediction and notification units.

[0075] The prediction unit predicts events that users are likely to be interested in in the future, based on data obtained by the analysis unit. For example, the prediction unit predicts similar events to be held in the future based on the genre and artists of music concerts that the user has attended in the past. Specifically, the generative AI uses this data to predict events that users are likely to be interested in in the future. The generative AI uses machine learning algorithms to learn the user's interest patterns and predict future interests. For example, using data on the genre and artists of events the user has attended in the past as input, the generative AI generates a list of events to be held in the future. The generative AI can also consider the user's changing interests and predict interest in new genres and artists. Furthermore, the prediction unit can improve the accuracy of its predictions by linking with external event information databases and obtaining the latest event information. For example, it can obtain music festival and theater performance schedules in real time and predict events that match the user's interests. As a result, the prediction unit can predict events that users are likely to be interested in in the future with high accuracy and provide users with appropriate event information.

[0076] The data collection unit gathers event information in real time. For example, it collects information in real time from ticket sales websites, and the generating AI analyzes it to extract important information. Specifically, the generating AI collects event information in real time and notifies users of the start time of sales and the number of remaining tickets. The generating AI uses web scraping technology to collect the latest event information from ticket sales websites and event organizer websites. For example, it automatically obtains information such as the date and time of the event, location, ticket sales start time, and the number of remaining tickets, and stores it in a database. Furthermore, the generating AI analyzes the collected information and extracts important information. For example, it analyzes popular events and ticket sales status and prioritizes notifying users of information that is important to them. The data collection unit also collects event information from social media and news sites, enabling it to grasp the latest trends and trending events. As a result, the data collection unit can collect the latest event information in real time and provide users with quick and accurate information. Furthermore, the data collection unit can link the collected data with the analysis unit and prediction unit to improve the accuracy and efficiency of the entire system.

[0077] The notification unit notifies users of the start time of ticket sales and the number of remaining tickets based on the information collected by the data collection unit. For example, the notification unit can enable users to participate in events they are interested in in a timely manner and prevent them from missing out on tickets. Specifically, it sends real-time notifications to users' smartphones and email addresses. For example, it notifies users via push notifications or email when the ticket sales start time for an event is approaching. It also sends immediate notifications when the number of remaining tickets becomes low, ensuring that users do not miss the opportunity to purchase tickets. Furthermore, the notification unit can customize notification methods according to user settings. For example, users can set it to receive notifications for specific events preferentially or adjust the frequency of notifications. This allows users to receive notifications tailored to their interests and needs. The notification unit can also collect user feedback and improve the content and timing of notifications. For example, it analyzes user behavior after receiving notifications and evaluates the effectiveness of the notifications. This allows the notification unit to provide users with important information at the optimal time and maximize their opportunities to participate in events.

[0078] The optimization unit can analyze user behavior patterns and send notifications at the optimal timing. For example, the optimization unit analyzes the user's past behavior data and behavior by time of day to determine the optimal timing. For example, the optimization unit sends notifications at the optimal timing based on the user's behavior history and response rates by time of day. This maximizes the effectiveness of notifications by sending them at the optimal timing based on the user's behavior patterns. Some or all of the above processing in the optimization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the optimization unit can input user behavior data into a generation AI and have the generation AI execute the optimal notification timing.

[0079] The customization unit can provide customized event lists and notifications for each user. For example, the customization unit generates customized event lists based on the user's interests and past participation history. For example, the customization unit provides customized notifications based on the user's interests and past responses. By providing customized event lists and notifications for each user, it is possible to provide information optimized to the user's interests. Some or all of the above processing in the customization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the customization unit can input user interest data into a generation AI and have the generation AI execute a customized event list.

[0080] The analysis unit can analyze a user's past event participation history and social media data. For example, the analysis unit collects a user's past event participation history and social media data, and the generating AI analyzes it. For example, the analysis unit collects data on music concerts and plays the user has attended in the past, posts that the user has "liked" on social media, etc., and the generating AI analyzes it. By analyzing a user's past event participation history and social media data, the user's interests can be identified. Some or all of the above processing in the analysis unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the analysis unit can input a user's social media data into the generating AI and have the generating AI perform an analysis to identify the user's interests.

[0081] The prediction unit can predict events that users are likely to be interested in in the future, based on the data obtained by the analysis unit. For example, the prediction unit uses data obtained by the analysis unit to have a generative AI predict events that users are likely to be interested in in the future. For example, the prediction unit predicts similar events to be held in the future based on the genre and artists of music concerts that the user has attended in the past. In this way, by predicting events that users are likely to be interested in in the future based on the data obtained by the analysis unit, the prediction unit can provide users with optimal event information. Some or all of the above processing in the prediction unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the prediction unit can input data obtained by the analysis unit into a generative AI and have the generative AI perform predictions of events that users are likely to be interested in in the future.

[0082] The data collection unit can collect information from ticket sales websites in real time. For example, the data collection unit collects information from ticket sales websites in real time, and the generating AI analyzes it to extract important information. For example, the data collection unit uses the generating AI to collect event information in real time and notify users of the start time of sales and the number of remaining tickets. This allows the system to provide the latest event information by collecting information from ticket sales websites in real time. Some or all of the above-described processes in the data collection unit may be performed using the generating AI or not. For example, the data collection unit can input information from ticket sales websites into the generating AI and have the generating AI perform real-time information collection.

[0083] The notification unit can notify users of the start time of sales and the number of remaining items based on the information collected by the collection unit. For example, the notification unit uses a generating AI to notify users of the start time of sales and the number of remaining items based on the information collected by the collection unit. For example, the notification unit uses a generating AI to collect event information in real time and notify users of the start time of sales and the number of remaining items. This allows users to participate in events in a timely manner by notifying them of the start time of sales and the number of remaining items based on the information collected by the collection unit. Some or all of the above processing in the notification unit may be performed using a generating AI or not using a generating AI. For example, the notification unit can input information collected by the collection unit into a generating AI and have the generating AI execute notifications of the start time of sales and the number of remaining items.

[0084] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. The analysis unit estimates emotions using methods such as facial expression analysis, text analysis, and voice analysis. For example, if the user is excited, the analysis unit's generative AI performs a detailed analysis and provides more event information. If the user is stressed, the analysis unit can also have the generative AI reduce the accuracy of the analysis and provide only concise information. Furthermore, if the user is relaxed, the analysis unit can have the generative AI provide balanced information with moderate analysis accuracy. This allows for the provision of more appropriate event information by adjusting the accuracy of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may 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 processes in the analysis unit may be performed using or without the generative AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform the analysis accuracy adjustment.

[0085] The analysis unit can classify a user's past event participation history in detail and extract specific patterns. For example, the analysis unit can classify the user's past event participation history by genre and analyze their interest trends. For example, the analysis unit can classify the user's past event participation history by location and analyze their regional interests. The analysis unit can also classify the user's past event participation history by time of day and analyze their interest trends at specific time slots. In this way, by classifying the user's past event participation history in detail and extracting specific patterns, the analysis unit can analyze the user's interest trends. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's event participation history data into a generative AI and have the generative AI perform the extraction of specific patterns.

[0086] The analysis unit can perform sentiment analysis of user posts during social media data analysis to detect changes in interest. For example, the analysis unit can detect positive emotions from user posts and identify events of interest. For example, the analysis unit can detect negative emotions from user posts and exclude events of no interest. The analysis unit can also detect neutral emotions from user posts and track changes in interest. This allows for the detection of changes in interest by performing sentiment analysis of user posts during social media data analysis. Sentiment estimation is achieved using sentiment estimation functions, such as sentiment engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using generative AI or not. For example, the analysis unit can input user social media data into a generative AI and have the generative AI perform sentiment analysis.

[0087] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated emotions. The analysis unit estimates emotions using methods such as facial expression analysis, text analysis, and voice analysis. For example, if the user is excited, the analysis unit can have the generating AI prioritize displaying events of high interest. Similarly, if the user is stressed, the analysis unit can have the generating AI prioritize displaying events that promote relaxation. Furthermore, if the user is relaxed, the analysis unit can have the generating AI prioritize displaying balanced events. This allows for the provision of more appropriate event information by prioritizing analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI may 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 processes in the analysis unit may be performed using or without a generating AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI determine the priority of analysis results.

[0088] The analysis unit can analyze event participation trends by region, taking into account the user's geographical location information. For example, the analysis unit can prioritize analyzing nearby event information based on the user's current location. For example, the analysis unit can analyze event participation trends in a specific region based on the user's past location information. The analysis unit can also analyze event information in frequently visited regions based on the user's travel history. By analyzing event participation trends by region, taking into account the user's geographical location information, it is possible to provide event information optimized for each region. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the user's geographical location information into a generation AI and have the generation AI perform the analysis of event participation trends by region.

[0089] The analysis unit can analyze a user's purchase history and investigate its correlation with their event participation history. For example, the analysis unit can analyze events related to products purchased by the user. For example, the analysis unit can analyze events related to a specific brand or artist from the user's purchase history. The analysis unit can also compare the user's purchase history with their event participation history to analyze their interests. In this way, by analyzing the user's purchase history and investigating its correlation with their event participation history, the analysis unit can analyze the user's interests. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's purchase history data into a generative AI and have the generative AI perform an investigation into its correlation with their event participation history.

[0090] The prediction unit can estimate the user's emotions and adjust the accuracy of the prediction based on the estimated emotions. The prediction unit estimates emotions using methods such as facial expression analysis, text analysis, and voice analysis of the user. For example, if the user is excited, the prediction unit's generative AI can make a detailed prediction and suggest more events. Also, if the user is stressed, the prediction unit can have the generative AI reduce the accuracy of the prediction and provide concise event information. Also, if the user is relaxed, the prediction unit can have the generative AI provide balanced event information with moderate prediction accuracy. In this way, by adjusting the accuracy of the prediction based on the user's emotions, more appropriate event information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is 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 processing in the prediction unit may be performed using the generative AI or not. For example, the prediction unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the prediction accuracy.

[0091] The prediction unit can predict events that a user might be interested in during a particular season or period, based on their past event participation history. For example, the prediction unit can classify events a user has previously attended by season and predict their interest trends. For example, the prediction unit can predict events held during similar periods based on events a user has attended during a particular period. The prediction unit can also predict events that a user might be interested in during a particular season or period, based on their past event participation history. This allows the system to provide users with optimal event information by predicting events that a user might be interested in during a particular season or period based on their past event participation history. Some or all of the above processing in the prediction unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the prediction unit can input the user's event participation history data into a generative AI and have the generative AI predict events that a user might be interested in during a particular season or period.

[0092] The prediction unit can track changes in user interests in real time and dynamically adjust its prediction algorithm. For example, the prediction unit can analyze the user's social media posts in real time to track changes in interests. For example, the prediction unit can update the user's event participation history in real time and adjust its prediction algorithm. The prediction unit can also analyze the user's purchase history in real time to track changes in interests. This allows the prediction unit to provide more appropriate event information by tracking changes in user interests in real time and dynamically adjusting the prediction algorithm. Some or all of the above processing in the prediction unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the prediction unit can input user interest data into a generative AI and have the generative AI perform dynamic adjustments to the prediction algorithm.

[0093] The prediction unit can estimate the user's emotions and adjust how the prediction results are displayed based on the estimated emotions. The prediction unit estimates emotions using methods such as facial expression analysis, text analysis, and voice analysis of the user. For example, if the user is excited, the prediction unit's generating AI can display detailed prediction results. The prediction unit can also display concise prediction results if the user is stressed. The prediction unit can also display balanced prediction results if the user is relaxed. By adjusting how the prediction results are displayed based on the user's emotions, more appropriate event information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the prediction unit may be performed using the generating AI or not. For example, the prediction unit can input user emotion data into the generating AI and have the generating AI adjust how the prediction results are displayed.

[0094] The prediction unit can predict events that a user might be interested in by considering the event participation history of their friends and followers. For example, the prediction unit can predict events that a user might be interested in based on events that their friends have attended. For example, the prediction unit can predict events that a user might be interested in based on events that their followers have attended. The prediction unit can also analyze the event participation history of the user's friends and followers to predict events that a user might be interested in. This allows the system to provide the user with optimal event information by considering the event participation history of the user's friends and followers to predict events that a user might be interested in. Some or all of the above processing in the prediction unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the prediction unit can input the event participation history data of the user's friends and followers into a generative AI and have the generative AI perform the prediction of events that a user might be interested in.

[0095] The prediction unit can predict relevant events based on the user's occupation and lifestyle. For example, the prediction unit can predict events related to the user's occupation. For example, the prediction unit can predict events related to the user's lifestyle. The prediction unit can also predict events that the user might be interested in, based on their occupation and lifestyle. This allows the system to provide the user with optimal event information by predicting relevant events based on their occupation and lifestyle. Some or all of the above processing in the prediction unit may be performed using generative AI, or it may be performed without generative AI. For example, the prediction unit can input the user's occupation and lifestyle data into a generative AI and have the generative AI perform the prediction of relevant events.

[0096] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. The data collection unit estimates emotions using methods such as facial expression analysis, text analysis, and voice analysis. For example, if the user is excited, the data collection unit's generative AI will prioritize collecting detailed information. If the user is stressed, the data collection unit's generative AI can prioritize collecting concise information. If the user is relaxed, the data collection unit's generative AI can prioritize collecting balanced information. By prioritizing the information to collect based on the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using the generative AI or not. For example, the data collection unit can input user emotion data into the generative AI and have the generative AI perform the priority determination of information to collect.

[0097] The data collection unit analyzes the update frequency of ticket sales websites and collects information at the optimal time. For example, the data collection unit analyzes the update frequency of ticket sales websites and collects information during the time period when updates occur most frequently. For example, the data collection unit analyzes the update patterns of ticket sales websites and collects information at the optimal time. The data collection unit can also analyze the update history of ticket sales websites and collect information at the most efficient time. By analyzing the update frequency of ticket sales websites and collecting information at the optimal time, the latest event information can be provided. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input update data from ticket sales websites into a generation AI and have the generation AI perform information collection at the optimal time.

[0098] The data collection unit can evaluate the reliability of event information and prioritize the collection of highly reliable information. For example, the data collection unit can evaluate the source of the event information and prioritize the collection of highly reliable information. For example, the data collection unit can analyze the content of the event information and prioritize the collection of highly reliable information. The data collection unit can also analyze the past history of the event information and prioritize the collection of highly reliable information. By evaluating the reliability of event information and prioritizing the collection of highly reliable information, accurate event information can be provided. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input event information reliability data into a generation AI and have the generation AI perform a reliability evaluation.

[0099] The data collection unit can estimate the user's emotions and filter the information it collects based on those emotions. The unit estimates emotions using methods such as facial expression analysis, text analysis, and voice analysis. For example, if the user is excited, the data collection unit can use a generative AI to filter and provide detailed information. If the user is stressed, the data collection unit can use a generative AI to filter and provide concise information. If the user is relaxed, the data collection unit can use a generative AI to filter and provide balanced information. This allows for the provision of more appropriate information by filtering the collected information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may 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 data collection unit may be performed using or without a generative AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform information filtering.

[0100] The data collection unit can collect event information from social media and improve its accuracy by comparing it with information from the official website. For example, the data collection unit can improve accuracy by comparing event information collected from social media with information from the official website. For example, the data collection unit can improve accuracy by analyzing the content of social media posts and comparing them with information from the official website. The data collection unit can also improve accuracy by collecting event information from social media and integrating it with information from the official website. In this way, accurate event information can be provided by collecting event information from social media and improving its accuracy by comparing it with information from the official website. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input social media event information into a generative AI and have the generative AI perform a comparison with information from the official website.

[0101] The data collection unit can collect event information from news articles and blogs related to the user's interests. For example, the data collection unit can analyze news articles related to the user's interests and collect event information. For example, the data collection unit can analyze blogs related to the user's interests and collect event information. The data collection unit can also analyze online media related to the user's interests and collect event information. This allows the system to provide the user with the most relevant event information by collecting event information from news articles and blogs related to the user's interests. Some or all of the above-described processes in the data collection unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the data collection unit can input news article and blog data into a generative AI and have the generative AI perform the collection of event information.

[0102] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. The notification unit estimates emotions using methods such as facial expression analysis, text analysis, and voice analysis of the user. For example, if the user is excited, the notification unit's generating AI can send a notification immediately. The notification unit can also send a notification when the user is calm if they are stressed. The notification unit can also send a notification at an appropriate time if the user is relaxed. By adjusting the timing of notifications based on the user's emotions, notifications can be sent at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the notification unit may be performed using the generating AI or not using the generating AI. For example, the notification unit can input user emotion data into the generating AI and have the generating AI perform the notification timing adjustment.

[0103] The notification unit can customize notification content based on the user's past responses to deliver effective notifications. For example, the notification unit can send customized notifications based on notifications that the user has previously responded to favorably. For example, the notification unit can avoid sending notifications that the user has previously ignored to deliver effective notifications. The notification unit can also analyze the user's past responses and customize and send the most suitable notification content. In this way, by customizing notification content based on the user's past responses, effective notifications can be delivered. Some or all of the above processing in the notification unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the notification unit can input the user's past response data into a generative AI and have the generative AI perform the customization of notification content.

[0104] The notification unit can dynamically adjust the frequency of notifications to match the user's schedule. For example, the notification unit can analyze the user's calendar information and adjust the notification frequency accordingly. For example, the notification unit can dynamically adjust the optimal notification frequency based on the user's past notification history. The notification unit can also analyze the user's real-time schedule and adjust the notification frequency accordingly. By dynamically adjusting the notification frequency to match the user's schedule, notifications can be sent at a more appropriate time. Some or all of the above-described processes in the notification unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the notification unit can input the user's schedule data into a generative AI and have the generative AI perform the adjustment of the notification frequency.

[0105] The notification unit can estimate the user's emotions and determine notification priorities based on those emotions. The notification unit estimates emotions using methods such as facial expression analysis, text analysis, and voice analysis. For example, if the user is excited, the notification unit's generative AI can prioritize sending important notifications. Similarly, if the user is stressed, the generative AI can prioritize sending relaxing notifications. Furthermore, if the user is relaxed, the generative AI can prioritize sending balanced notifications. This allows for the delivery of more appropriate notifications by prioritizing them based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the notification unit may be performed using or without the generative AI. For example, the notification unit can input user emotion data into the generative AI and have the generative AI determine notification priorities.

[0106] The notification unit can select the optimal notification method by considering the user's device information when sending a notification. For example, if the user is using a smartphone, the notification unit will prioritize sending push notifications. For example, if the user is using a tablet, the notification unit will send notifications optimized for the larger screen. Furthermore, if the user is using a smartwatch, the notification unit can send concise and highly visible notifications. In this way, by selecting the optimal notification method by considering the user's device information, more effective notifications can be sent. Some or all of the above processing in the notification unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the notification unit can input the user's device information into a generative AI and have the generative AI select the optimal notification method.

[0107] The notification unit can include information about events the user's friends and followers plan to attend in its notifications. For example, the notification unit can include information about events the user's friends plan to attend in the notification. For example, the notification unit can include information about events the user's followers plan to attend in the notification. The notification unit can also analyze the information about events the user's friends and followers plan to attend and reflect it in the notification. This allows the notification unit to send notifications that are of interest to the user by including information about events the user's friends and followers plan to attend in the notification. Some or all of the above processing in the notification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the notification unit can input information about events the user's friends and followers plan to attend into a generation AI and have the generation AI perform the task of reflecting it in the notification.

[0108] The optimization unit can estimate the user's emotions and adjust the optimization algorithm based on the estimated emotions. The optimization unit estimates emotions using methods such as facial expression analysis, text analysis, and voice analysis. For example, if the user is excited, the optimization unit's generative AI performs detailed optimization, providing more event information. If the user is stressed, the optimization unit can also have the generative AI reduce the optimization accuracy, providing only concise information. Furthermore, if the user is relaxed, the optimization unit can have the generative AI provide balanced information with moderate optimization accuracy. This allows for the provision of more appropriate event information by adjusting the optimization algorithm based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may 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 processes in the optimization unit may be performed using or without the generative AI. For example, the optimization unit can input user emotion data into the generative AI and have the generative AI adjust the optimization algorithm.

[0109] The optimization unit can analyze the user's past notification history and learn the optimal notification timing. For example, the optimization unit learns the optimal notification timing based on notification timings in which the user has previously responded favorably. For example, the optimization unit learns the optimal notification timing by avoiding notification timings that the user has previously ignored. The optimization unit can also learn the optimal notification timing by analyzing the user's past notification history. By analyzing the user's past notification history and learning the optimal notification timing, it is possible to send more effective notifications. Some or all of the above processing in the optimization unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the optimization unit can input the user's notification history data into a generative AI and have the generative AI perform the learning of the optimal notification timing.

[0110] The optimization unit can analyze user behavior patterns in detail and select the optimal notification method. For example, the optimization unit can analyze user behavior patterns and select the optimal notification method. For example, the optimization unit can select the optimal notification method based on the user's past behavior patterns. The optimization unit can also analyze the user's real-time behavior patterns and select the optimal notification method. By analyzing user behavior patterns in detail and selecting the optimal notification method, more effective notifications can be sent. Some or all of the above processing in the optimization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the optimization unit can input user behavior pattern data into a generation AI and have the generation AI select the optimal notification method.

[0111] The optimization unit can estimate the user's emotions and determine optimization priorities based on the estimated emotions. The optimization unit estimates emotions using methods such as facial expression analysis, text analysis, and voice analysis of the user. For example, if the user is excited, the optimization unit can have the generative AI prioritize important optimizations. Also, if the user is stressed, the optimization unit can have the generative AI prioritize optimizations that promote relaxation. Also, if the user is relaxed, the optimization unit can have the generative AI prioritize balanced optimizations. By determining optimization priorities based on the user's emotions, more appropriate event information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the optimization unit may be performed using the generative AI or not. For example, the optimization unit can input user emotion data into the generative AI and have the generative AI perform the determination of optimization priorities.

[0112] The optimization unit can select the optimal notification method by considering the user's device information. For example, if the user is using a smartphone, the optimization unit will prioritize sending push notifications. For example, if the user is using a tablet, the optimization unit will send notifications optimized for the larger screen. Furthermore, if the user is using a smartwatch, the optimization unit can also send concise and highly visible notifications. In this way, by selecting the optimal notification method by considering the user's device information, more effective notifications can be sent. Some or all of the above processing in the optimization unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the optimization unit can input the user's device information into a generative AI and have the generative AI select the optimal notification method.

[0113] The optimization unit can analyze the user's schedule information and determine the optimal notification timing. For example, the optimization unit can analyze the user's calendar information and determine the optimal notification timing according to the schedule. For example, the optimization unit can determine the optimal notification timing based on the user's past schedule information. The optimization unit can also analyze the user's real-time schedule and determine the optimal notification timing. By analyzing the user's schedule information and determining the optimal notification timing, more effective notifications can be sent. Some or all of the above processing in the optimization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the optimization unit can input the user's schedule information into a generation AI and have the generation AI perform the determination of the optimal notification timing.

[0114] The customization unit can estimate the user's emotions and adjust the customization content based on the estimated emotions. The customization unit estimates emotions using methods such as facial expression analysis, text analysis, and voice analysis. For example, if the user is excited, the customization unit's generative AI can perform detailed customization and provide more event information. If the user is stressed, the customization unit can also have the generative AI reduce the accuracy of the customization and provide only concise information. Furthermore, if the user is relaxed, the customization unit can have the generative AI provide balanced information with a moderate level of customization accuracy. This allows for the provision of more appropriate event information by adjusting the customization content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the customization unit may be performed using or without the generative AI. For example, the customization unit can input user emotion data into the generative AI and have the generative AI adjust the customization content.

[0115] The customization unit can analyze a user's past event participation history in detail and generate a customized event list. For example, the customization unit can classify the events the user has attended in the past by genre and generate a customized event list. For example, the customization unit can classify the events the user has attended by location and generate a customized event list. Furthermore, the customization unit can classify the events the user has attended by time slot and generate a customized event list. In this way, by analyzing a user's past event participation history in detail and generating a customized event list, it is possible to provide the user with event information that is best suited to them. Some or all of the above processing in the customization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the customization unit can input the user's event participation history data into a generation AI and have the generation AI execute the generation of a customized event list.

[0116] The customization unit can track changes in user interests in real time and dynamically adjust the customization content. For example, the customization unit can analyze the user's social media posts in real time to track changes in interests. For example, the customization unit can update the user's event participation history in real time and adjust the customization content. The customization unit can also analyze the user's purchase history in real time to track changes in interests. This allows for the provision of more appropriate event information by tracking changes in user interests in real time and dynamically adjusting the customization content. Some or all of the above processing in the customization unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the customization unit can input user interest data into a generative AI and have the generative AI perform dynamic adjustments to the customization content.

[0117] The customization unit can estimate the user's emotions and determine the priority of customizations based on those emotions. The customization unit estimates emotions using methods such as facial expression analysis, text analysis, and voice analysis. For example, if the user is excited, the customization unit's generative AI will prioritize important customizations. Similarly, if the user is stressed, the customization unit can prioritize relaxing customizations. Furthermore, if the user is relaxed, the customization unit can prioritize balanced customizations. This allows for the provision of more appropriate event information by prioritizing customizations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the customization unit may be performed using or without a generative AI. For example, the customization unit can input user emotion data into a generative AI and have the generative AI determine the priority of customizations.

[0118] The customization unit can generate a customized event list by considering the event participation history of the user's friends and followers. For example, the customization unit can generate a customized event list based on events attended by the user's friends. For example, the customization unit can generate a customized event list based on events attended by the user's followers. The customization unit can also analyze the event participation history of the user's friends and followers and generate a customized event list. This allows the system to provide the user with the most suitable event information by generating a customized event list that takes into account the event participation history of the user's friends and followers. Some or all of the above processing in the customization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the customization unit can input the event participation history data of the user's friends and followers into a generation AI and have the generation AI generate a customized event list.

[0119] The customization unit can generate a customized event list based on the user's occupation and lifestyle. For example, the customization unit can generate a customized event list based on events related to the user's occupation. For example, the customization unit can generate a customized event list based on events related to the user's lifestyle. The customization unit can also generate a customized event list based on the user's occupation and lifestyle. This allows the system to provide the user with event information that is best suited to them by generating a customized event list based on their occupation and lifestyle. Some or all of the above-described processes in the customization unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the customization unit can input the user's occupation and lifestyle data into a generation AI and have the generation AI generate a customized event list.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] The event prediction system can analyze not only user interests and behavioral history, but also user health data. For example, it collects heart rate and exercise data from the user's fitness tracker or smartwatch, and the analysis unit uses this data to understand the user's health status. The prediction unit can then suggest relaxing or active events based on the user's health status. For instance, if the user is feeling stressed, the prediction unit can suggest yoga or meditation events, while if the user is feeling energetic, it can suggest sports events or outdoor activities. This allows the system to provide optimal event information based on the user's health status.

[0122] The event prediction system can analyze not only user interests and behavioral history, but also user purchase history. For example, it collects data on products and services that users have purchased in the past, and the analysis unit identifies user interests based on this data. The prediction unit can then suggest relevant events based on the user's purchase history. For instance, if a user frequently purchases products from a particular brand, the system can suggest events hosted by that brand or related exhibitions. Similarly, if a user purchases books of a specific genre, the system can suggest lectures or autograph sessions related to that genre. This allows the system to provide optimal event information based on the user's purchase history.

[0123] The event prediction system can analyze not only the user's interests and behavioral history, but also their geographical location. For example, it can collect the user's current location and past travel history, and the analysis unit can identify the user's interests based on this data. The prediction unit can then suggest nearby events based on the user's geographical location. For instance, it can suggest events held in areas the user frequently visits or events easily accessible from the user's current location. Furthermore, if the user is traveling, it can suggest events held at their travel destination. This allows the system to provide optimal event information based on the user's geographical location.

[0124] The event prediction system can analyze not only user interests and behavioral history, but also user activity on social media. For example, it collects posts that users "like" and content they share on social media, and the analysis unit identifies user interests based on this data. The prediction unit can then suggest relevant events based on the user's social media activity. For instance, if a user "likes" a post by a specific artist, the system can suggest a live event by that artist. Similarly, if a user shares a post on a specific topic, the system can suggest seminars or workshops related to that topic. This allows the system to provide optimal event information based on the user's social media activity.

[0125] The event prediction system can analyze not only the user's interests and behavioral history, but also the event participation history of the user's friends and followers. For example, it collects data on events attended by the user's friends and followers, and the analysis unit uses this data to identify the user's interests. The prediction unit can then suggest relevant events based on the event participation history of the user's friends and followers. For instance, by suggesting events attended by the user's friends, it can make it easier for the user to find events they can attend with their friends. It can also help the user discover new interests by suggesting events that their followers are interested in. This allows the system to provide optimal event information based on the event participation history of the user's friends and followers.

[0126] An event prediction system can estimate a user's emotions in addition to their interests and behavioral history, and provide event information based on those estimated emotions. For example, the analysis unit can estimate emotions using facial expression analysis and voice analysis, suggesting relaxing events if the user is relaxed, and energetic events if the user is excited. For instance, if the user is stressed, the prediction unit can suggest relaxation events or yoga classes. Similarly, if the user is having fun, the prediction unit can suggest entertainment events or concerts. This allows the system to provide optimal event information based on the user's emotions.

[0127] The event prediction system can analyze not only the user's interests and behavioral history, but also their occupation and lifestyle. For example, it can collect data related to the user's occupation and lifestyle, and the analysis unit can identify the user's interests based on this data. The prediction unit can then suggest relevant events based on the user's occupation and lifestyle. For instance, if the user is engaged in a creative profession, it can suggest art exhibitions or design workshops. Similarly, if the user has an active lifestyle, it can suggest sports events or outdoor activities. This allows the system to provide optimal event information based on the user's occupation and lifestyle.

[0128] The event prediction system can estimate the user's emotions in addition to their interests and behavioral history, and adjust the timing of notifications based on those emotions. For example, the analysis unit can estimate emotions using facial expression and voice analysis, sending notifications when the user is relaxed and delaying notifications when the user is stressed. For instance, if the user is relaxed, the notification unit can send detailed event information, while if the user is stressed, it can send a concise notification. Furthermore, if the user is excited, the notification unit can send a notification immediately. This allows notifications to be sent at the optimal time based on the user's emotions.

[0129] The event prediction system can estimate the user's emotions in addition to their interests and behavioral history, and prioritize events based on those emotions. For example, the analysis unit can estimate emotions using facial expression and voice analysis, prioritizing energetic events when the user is excited and relaxation events when the user is relaxed. For instance, if the user is stressed, the prediction unit can prioritize relaxation events, and if the user is having fun, it can prioritize entertainment events. Furthermore, if the user has neutral emotions, a balanced list of events can be displayed. This allows the system to provide optimal event information based on the user's emotions.

[0130] The event prediction system can estimate the user's emotions in addition to their interests and behavioral history, and filter event information based on those estimated emotions. For example, the analysis unit can estimate emotions using facial expression analysis and voice analysis, providing detailed event information when the user is relaxed and concise event information when the user is stressed. For instance, if the user is excited, the prediction unit can provide a lot of event information, while if the user is relaxed, it can provide balanced event information. Furthermore, if the user is stressed, the prediction unit can provide only the most important event information. This allows the system to provide optimal event information based on the user's emotions.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The analysis unit analyzes the user's interests. For example, the analysis unit analyzes the user's past event participation history and social media data. Specifically, it collects data on music concerts and theatrical performances the user has attended in the past, posts that the user has "liked" on social media, etc., and the generating AI analyzes this data. Step 2: The prediction unit predicts events that the user might be interested in in the future based on the data obtained by the analysis unit. For example, the prediction unit predicts similar events that will be held in the future based on the genre and artists of music concerts the user has attended in the past. The generative AI then uses this data to predict events that the user might be interested in in the future. Step 3: The data collection unit collects event information in real time. The data collection unit collects information in real time from sources such as ticket sales websites, and the generating AI analyzes it to extract important information. Specifically, the generating AI collects event information in real time and notifies users of the start time of sales and the number of remaining tickets. Step 4: The notification unit notifies users of the start time of sales and the number of remaining tickets based on the information collected by the collection unit. The notification unit can, for example, enable users to participate in events they are interested in in a timely manner and prevent them from missing out on tickets.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0135] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the analysis unit, prediction unit, collection unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The prediction unit is implemented by the specific processing unit 290 of the data processing device 12. The collection unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The notification unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the analysis unit, prediction unit, collection unit, and notification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing device 12. The collection unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The notification unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0161] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0162] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0163] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0168] Each of the multiple elements described above, including the analysis unit, prediction unit, collection unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12. The collection unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The notification unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0173] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0175] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0176] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0177] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0178] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0179] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0180] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0181] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0183] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0185] Each of the multiple elements described above, including the analysis unit, prediction unit, collection unit, and notification unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12. The collection unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The notification unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0186] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0188] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0189] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0190] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0194] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0195] 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.

[0196] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0197] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0198] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0199] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0201] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0202] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0203] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0204] (Note 1) An analysis unit that analyzes user interests, A prediction unit predicts events that may be of interest in the future based on the data obtained by the analysis unit, A collection unit that collects event information in real time, The system includes a notification unit that notifies the user of the start time of sales and the number of remaining items based on the information collected by the aforementioned collection unit. A system characterized by the following features. (Note 2) It includes an optimization unit that analyzes user behavior patterns and sends notifications at the optimal time. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a customization section that provides customized event lists and notifications for each user. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Analyze users' past event participation history and social media data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The prediction unit, Based on the data obtained by the analysis unit, we predict events that users are likely to be interested in in the future. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Gather information in real time from ticket sales websites. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned notification unit, Based on the information collected by the data collection department, the start time of sales and the number of remaining items will be notified. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, The system categorizes users' past event participation history in detail and extracts specific patterns. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, When analyzing social media data, sentiment analysis of user posts is performed to detect changes in interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, Analyze event participation trends by region, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, Analyze users' purchase history and investigate its correlation with their event participation history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The prediction unit, It estimates the user's emotions and adjusts the accuracy of the prediction based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The prediction unit, Based on past event participation history, we predict events that you might be interested in during specific seasons or periods. The system described in Appendix 1, characterized by the features described herein. (Note 16) The prediction unit, Track user interest changes in real time and dynamically adjust predictive algorithms. The system described in Appendix 1, characterized by the features described herein. (Note 17) The prediction unit, It estimates the user's emotions and adjusts how the prediction results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The prediction unit, By considering the event participation history of the user's friends and followers, we predict events that they might be interested in. The system described in Appendix 1, characterized by the features described herein. (Note 19) The prediction unit, Predict relevant events based on the user's occupation and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned collection unit is We analyze the update frequency of ticket sales websites and collect information at the optimal time. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned collection unit is Evaluate the reliability of event information and prioritize collecting highly reliable information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned collection unit is It estimates the user's emotions and filters the information collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned collection unit is We collect event information from social media and compare it with information from the official website to improve accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned collection unit is We collect event information from news articles and blogs related to the user's interests. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, Customize notification content based on the user's past responses to deliver effective notifications. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, Dynamically adjust the frequency of notifications to match the user's schedule. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, When sending notifications, the system selects the most suitable notification method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, The notification content should include information about the user's friends and followers who are planning to attend. The system described in Appendix 1, characterized by the features described herein. (Note 32) The optimization unit, It estimates the user's emotions and adjusts the optimization algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The optimization unit, The system analyzes the user's past notification history to learn the optimal notification timing. The system described in Appendix 1, characterized by the features described herein. (Note 34) The optimization unit, We analyze user behavior patterns in detail and select the optimal notification method. The system described in Appendix 1, characterized by the features described herein. (Note 35) The optimization unit, It estimates user emotions and determines optimization priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The optimization unit, The optimal notification method is selected based on the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 37) The optimization unit, The system analyzes the user's schedule information to determine the optimal notification timing. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned customization unit is It estimates the user's emotions and adjusts the customization based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned customization unit is It analyzes the user's past event participation history in detail and generates a customized event list. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned customization unit is Track user interests in real time and dynamically adjust customizations. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned customization unit is It estimates the user's emotions and determines the priority of customization based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned customization unit is A customized event list is generated, taking into account the event participation history of the user's friends and followers. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned customization unit is Generate a customized event list based on the user's occupation and lifestyle. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An analysis unit that analyzes user interests, A prediction unit predicts events that may be of interest in the future based on the data obtained by the analysis unit, A collection unit that collects event information in real time, The system includes a notification unit that notifies the user of the start time of sales and the number of remaining items based on the information collected by the aforementioned collection unit. A system characterized by the following features.

2. It includes an optimization unit that analyzes user behavior patterns and sends notifications at the optimal time. The system according to feature 1.

3. It includes a customization section that provides customized event lists and notifications for each user. The system according to feature 1.

4. The aforementioned analysis unit, Analyze users' past event participation history and social media data. The system according to feature 1.

5. The prediction unit, Based on the data obtained by the aforementioned analysis unit, the system predicts events that users are likely to be interested in in the future. The system according to feature 1.

6. The aforementioned collection unit is Gather information in real time from ticket sales websites. The system according to feature 1.

7. The aforementioned notification unit, Based on the information collected by the aforementioned collection unit, the start time of sales and the number of remaining items will be notified. The system according to feature 1.

8. The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system according to feature 1.

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