System

Abstract

An object of a system according to an embodiment is to effectively utilize an unoccupied seat ticket of a sports event and increase the attendance.SOLUTION: A system according to an embodiment includes a prediction unit, a target prediction unit, and a sales prediction unit. The prediction unit analyzes the information on the opponent or the ranking and predicts an unoccupied ticket. The target prediction part predicts a target to visit based on the stay place of the subscription member on the basis of the vacant seat ticket predicted by the prediction part. The sales forecasting unit forecasts the sales of goods and food based on the visit target forecasted by the target forecasting unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately establish a method for effectively utilizing empty seats when tickets for sporting events are selling poorly, and there is room for improvement.

[0005] The system according to the embodiment aims to effectively utilize available tickets for sporting events and increase the number of spectators. [Means for solving the problem]

[0006] The system according to the embodiment includes a prediction unit, a target prediction unit, and a sales prediction unit. The prediction unit analyzes information on opponents or rankings and predicts available tickets. The target prediction unit predicts target attendees based on the locations of subscription members, based on the available tickets predicted by the prediction unit. The sales prediction unit predicts sales of merchandise and food and drink, based on the target attendees predicted by the target prediction unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively utilize available tickets for sporting events and increase the number of spectators. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention aims to revitalize the sports world by forecasting ticket demand for games with poor ticket sales and distributing remaining tickets to subscription-based members. This system utilizes a generation AI to predict available tickets based on information such as the opponents and standings, predicting target attendees based on the locations of subscription members, and providing available tickets. It also predicts sales of merchandise and food and beverages based on information such as the number of spectators, weather, and attributes of subscription members (gender, age). For example, a generation AI is used to analyze information such as the opponents and standings to predict available tickets. Next, it predicts target attendees based on the locations of subscription members, and provides available tickets. It also predicts sales of merchandise and food and beverages based on information such as the number of spectators, weather, and attributes of subscription members (gender, age). This allows the system to increase attendance, leading to increased merchandise and food and beverage sales and increased profits. This allows the system to increase attendance, leading to increased merchandise and food and beverage sales and increased profits. For example, distributing unused tickets to subscribers will increase attendance at games and fill stadiums, which will increase merchandise and food and beverage sales and improve overall revenue.

[0029] A ticket demand prediction system according to an embodiment includes a prediction unit, a target prediction unit, and a sales prediction unit. The prediction unit analyzes information about opponents or rankings and predicts available tickets. For example, the prediction unit uses a generation AI to predict available tickets based on information about opponents and rankings. The prediction unit can also analyze past match data and ticket sales history. The target prediction unit predicts target attendees based on the locations of subscription members based on the available tickets predicted by the prediction unit. For example, the target prediction unit uses a generation AI to analyze the address information and past attendance history of subscription members to identify members who are likely to attend a game. The sales prediction unit predicts sales of merchandise and food and beverages based on the target attendees predicted by the target prediction unit. For example, the sales prediction unit uses a generation AI to predict sales of merchandise and food and beverages based on information such as the number of spectators, weather, and attributes of subscription members (gender, age). This enables the ticket demand prediction system according to an embodiment to predict available tickets, target attendees, and merchandise and food and beverage sales.

[0030] The prediction unit can analyze past match data or ticket sales history in addition to opponents or rankings. The prediction unit, for example, analyzes past match data to improve the accuracy of predictions of available tickets. For example, the prediction unit can analyze match results and player performance data. The prediction unit can also analyze ticket sales history to improve the accuracy of predictions of available tickets. For example, the prediction unit can analyze the number of tickets sold and the sales period. In this way, by analyzing past match data and ticket sales history, prediction accuracy is improved.

[0031] The target prediction unit can analyze the address information and past attendance history of subscription members to identify members who are likely to attend a game. The target prediction unit can, for example, analyze the address information of subscription members to identify members who are likely to attend a game. For example, the target prediction unit can perform analysis based on postal codes and city names. The target prediction unit can also analyze past attendance history to identify members who are likely to attend a game. For example, the target prediction unit can perform analysis based on the number of attendances and the date and time of attendance. In this way, by analyzing the address information and past attendance history of subscription members, the accuracy of predicting attendance targets can be improved.

[0032] The sales forecasting unit can predict sales of merchandise and food and drink based on information on the number of spectators, weather, and attributes of subscription members. The sales forecasting unit, for example, predicts sales of merchandise and food and drink based on the number of spectators. For example, the sales forecasting unit can perform analysis based on the number of tickets sold and the actual number of attendees. The sales forecasting unit can also predict sales of merchandise and food and drink based on weather information. For example, the sales forecasting unit can perform analysis based on temperature and precipitation. Furthermore, the sales forecasting unit can predict sales of merchandise and food and drink based on attribute information (gender, age) of subscription members. For example, the sales forecasting unit can perform analysis based on gender and age. In this way, the accuracy of sales forecasts is improved by taking into account the number of spectators, weather, and attributes of subscription members.

[0033] The sales forecasting unit can also predict sales based on the purchasing history of spectators or the time of the game. The sales forecasting unit predicts sales of merchandise and food and drink, for example, based on the purchasing history of spectators. For example, the sales forecasting unit can perform analysis based on past purchased items and purchase frequency. The sales forecasting unit can also predict sales of merchandise and food and drink based on the time of the game. For example, the sales forecasting unit can perform analysis based on daytime or nighttime hours. In this way, the accuracy of sales forecasts can be further improved by taking into account the purchasing history of spectators and the time of the game.

[0034] The prediction unit can analyze past match data and ticket sales history to predict the impact of a specific event or promotion on available seats. The prediction unit, for example, analyzes past match data to predict the impact of a specific event on available seats. For example, the prediction unit can perform analysis based on match results and player performance data. The prediction unit can also analyze ticket sales history to predict the impact of a specific promotion on available seats. For example, the prediction unit can perform analysis based on the number of tickets sold and the sales period. Furthermore, the prediction unit can combine past match data and ticket sales history to comprehensively predict the impact of events and promotions. For example, the prediction unit can combine match results and player performance data with the number of tickets sold and the sales period to perform analysis. In this way, by predicting the impact of events and promotions, it is possible to identify the occurrence of available tickets in advance.

[0035] The prediction unit can predict the probability of empty tickets occurring based on the venue and time of the game. The prediction unit, for example, analyzes past data on the venue of the game to predict the probability of empty tickets occurring. For example, the prediction unit can perform analysis based on the stadium name or city name. The prediction unit can also predict the probability of empty tickets occurring based on the time of the game. For example, the prediction unit can perform analysis based on daytime or nighttime hours. Furthermore, the prediction unit can combine the venue and time of the game to comprehensively predict the probability of empty tickets occurring. For example, the prediction unit can perform analysis by combining the stadium name or city name with the daytime or nighttime hours. This improves prediction accuracy by predicting the probability of empty tickets occurring based on the venue and time of the game.

[0036] The prediction unit can predict available tickets based on the importance and attention of a match. The prediction unit, for example, analyzes the importance of a match and predicts available tickets. For example, the prediction unit can perform analysis based on the importance of league matches, final matches, etc. The prediction unit can also analyze the attention of a match and predict available tickets. For example, the prediction unit can perform analysis based on media reports and social media reactions. Furthermore, the prediction unit can combine the importance and attention of a match to comprehensively predict available tickets. For example, the prediction unit can combine and analyze the importance of league matches, final matches, etc. with media reports and social media reactions. This improves prediction accuracy by predicting available tickets based on the importance and attention of a match.

[0037] The prediction unit can predict available tickets based on traffic conditions or accessibility at the venue of the match. The prediction unit can predict available tickets based on, for example, traffic congestion information at the venue of the match. For example, the prediction unit can analyze traffic congestion information and predict the probability of available tickets. The prediction unit can also predict available tickets by taking into account the operation status of public transportation at the venue of the match. For example, the prediction unit can analyze the operation status of public transportation and predict the probability of available tickets. The prediction unit can also predict available tickets based on accessibility to the venue of the match. For example, the prediction unit can analyze accessibility to the stadium and predict the probability of available tickets. In this way, by taking traffic conditions and accessibility into account, the accuracy of available ticket predictions is improved.

[0038] The prediction unit can predict the number of available tickets by taking into account the effectiveness of promotional activities and advertising for the match. The prediction unit, for example, analyzes the effectiveness of promotional activities for the match and predicts the number of available tickets. For example, the prediction unit can perform analysis based on responses to advertising campaigns and social media. The prediction unit can also analyze the effectiveness of advertising for the match and predict the number of available tickets. For example, the prediction unit can analyze the effectiveness of advertising campaigns and predict the probability of available tickets. Furthermore, the prediction unit can combine promotional activities for the match with the effectiveness of advertising to comprehensively predict the number of available tickets. For example, the prediction unit can combine and analyze responses to advertising campaigns and social media with the effectiveness of advertising. This improves the accuracy of predictions of available tickets by taking into account the effectiveness of promotional activities and advertising.

[0039] The prediction unit can predict available tickets based on whether the game date falls on a holiday or a weekend. For example, the prediction unit predicts available tickets when the game date falls on a holiday. For example, the prediction unit can perform analysis based on national holidays. The prediction unit can also predict available tickets when the game date falls on a weekend. For example, the prediction unit can perform analysis based on Saturday or Sunday. Furthermore, the prediction unit can also perform a comprehensive prediction of available tickets by combining whether the game date falls on a holiday or a weekend. For example, the prediction unit can perform analysis by combining national holidays, Saturdays, and Sundays. In this way, by taking into account whether the game date falls on a holiday or a weekend, the accuracy of available ticket predictions is improved.

[0040] The target prediction unit can analyze the subscription member's past attendance history and predict the level of interest in a specific game or event. The target prediction unit can, for example, analyze the subscription member's past attendance history and predict the level of interest in a specific game. For example, the target prediction unit can perform analysis based on the number of attendances and the date and time of attendance. The target prediction unit can also analyze the subscription member's past attendance history and predict the level of interest in a specific event. For example, the target prediction unit can perform analysis based on the number of times an event has been attended and the date and time of attendance. Furthermore, the target prediction unit can comprehensively analyze the subscription member's past attendance history and predict the level of interest in a game or event. For example, the target prediction unit can perform analysis by combining the number of attendances and the date and time of attendance with the number of times an event has been attended and the date and time of attendance. In this way, the level of interest in a specific game or event can be predicted by analyzing the past attendance history.

[0041] The target prediction unit can analyze the social media activities of subscribers and identify targets who are likely to visit. The target prediction unit can, for example, analyze the content of subscribers' posts on social media and identify targets who are likely to visit. For example, the target prediction unit can perform analysis based on the content of the posts and the number of likes. The target prediction unit can also analyze the check-in information of subscribers on social media and identify targets who are likely to visit. For example, the target prediction unit can perform analysis based on the check-in information. Furthermore, the target prediction unit can analyze the subscribers' friendships on social media and identify targets who are likely to visit. For example, the target prediction unit can perform analysis based on friendships. In this way, by analyzing social media activities, it is possible to identify targets who are likely to visit.

[0042] The target prediction unit can predict the target visitor by taking into account the family structure and friendships of the subscription member. The target prediction unit predicts the target visitor by taking into account the family structure of the subscription member, for example. For example, the target prediction unit can perform analysis based on the number of family members and their composition. The target prediction unit can also predict the target visitor by taking into account the friendships of the subscription member. For example, the target prediction unit can perform analysis based on the number of friends and their relationships. Furthermore, the target prediction unit can combine the family structure and friendships of the subscription member to comprehensively predict the target visitor. For example, the target prediction unit can perform analysis by combining the number of family members and their composition with the number of friends and their relationships. In this way, by taking into account the family structure and friendships, the accuracy of predicting the target visitor is improved.

[0043] The target prediction unit can predict the target visitor by taking into account the occupation and lifestyle of the subscription member. The target prediction unit predicts the target visitor by taking into account, for example, the occupation of the subscription member. For example, the target prediction unit can perform analysis based on the type of occupation and working hours. The target prediction unit can also predict the target visitor by taking into account the lifestyle of the subscription member. For example, the target prediction unit can perform analysis based on lifestyle habits and hobbies. Furthermore, the target prediction unit can combine the occupation and lifestyle of the subscription member to comprehensively predict the target visitor. For example, the target prediction unit can perform analysis by combining the type of occupation and working hours with the lifestyle habits and hobbies. In this way, by taking the occupation and lifestyle into account, the accuracy of predicting the target visitor is improved.

[0044] The target prediction unit can analyze the hobbies and interests of subscription members and identify targets who are likely to visit. The target prediction unit can, for example, analyze the hobbies of subscription members and identify targets who are likely to visit. For example, the target prediction unit can perform analysis based on survey results and social media posts. The target prediction unit can also analyze the interests of subscription members and identify targets who are likely to visit. For example, the target prediction unit can perform analysis based on the content of social media posts and the number of likes. Furthermore, the target prediction unit can combine the hobbies and interests of subscription members to comprehensively identify targets who are likely to visit. For example, the target prediction unit can combine and analyze survey results and the content of social media posts with the number of likes. In this way, targets who are likely to visit can be identified by analyzing hobbies and interests.

[0045] The target prediction unit can predict the target attendees by taking into account the subscription member's past purchasing history. The target prediction unit can predict the target attendees by taking into account, for example, the subscription member's past ticket purchase history. For example, the target prediction unit can perform analysis based on purchased items and purchase frequency. The target prediction unit can also predict the target attendees by taking into account the subscription member's past merchandise purchase history. For example, the target prediction unit can perform analysis based on purchased items and purchase frequency. Furthermore, the target prediction unit can predict the target attendees by taking into account the subscription member's past food and beverage purchase history. For example, the target prediction unit can perform analysis based on purchased items and purchase frequency. In this way, by taking into account the past purchase history, the accuracy of predicting the target attendees is improved.

[0046] The sales prediction unit can analyze the purchase history of spectators and predict the sales of specific products or food and drink. The sales prediction unit can, for example, analyze the spectators' past merchandise purchase history and predict whether a specific product will sell. For example, the sales prediction unit can perform analysis based on the products purchased and the purchase frequency. The sales prediction unit can also analyze the spectators' past food and drink purchase history and predict whether a specific food and drink will sell. For example, the sales prediction unit can perform analysis based on the products purchased and the purchase frequency. Furthermore, the sales prediction unit can comprehensively analyze the spectators' past purchase history and predict the sales of specific products or food and drink. For example, the sales prediction unit can comprehensively analyze the products purchased and the purchase frequency. In this way, the sales prediction unit can predict the sales of specific products or food and drink by analyzing the purchase history.

[0047] The sales forecasting unit can predict sales of merchandise and food and drink based on the time of the game and the weather. For example, the sales forecasting unit can predict which merchandise and food and drink will sell well during a specific time period based on the time of the game. For example, the sales forecasting unit can perform analysis based on daytime and nighttime hours. The sales forecasting unit can also predict which merchandise and food and drink will sell well on sunny or rainy days based on weather information. For example, the sales forecasting unit can perform analysis based on temperature and precipitation. Furthermore, the sales forecasting unit can combine the time of the game with weather information to comprehensively predict which merchandise and food and drink will sell well. For example, the sales forecasting unit can perform analysis by combining the time of day and nighttime hours with temperature and precipitation. In this way, by predicting sales based on the time of day and weather, prediction accuracy is improved.

[0048] The sales forecasting unit can predict sales taking into account the attribute information (gender, age) of the audience. The sales forecasting unit can predict sales of specific products or food and drink taking into account the gender of the audience, for example. For example, the sales forecasting unit can perform analysis based on gender. The sales forecasting unit can also predict sales of specific products or food and drink taking into account the age of the audience. For example, the sales forecasting unit can perform analysis based on age. Furthermore, the sales forecasting unit can combine the gender and age of the audience to comprehensively predict sales of specific products or food and drink. For example, the sales forecasting unit can perform analysis by combining gender and age. In this way, the accuracy of sales forecasts is improved by taking attribute information into account.

[0049] The sales forecasting unit can predict sales taking into account the characteristics of the venue of the match and the consumption trends of the region. The sales forecasting unit, for example, predicts sales of specific products and food and drink taking into account the characteristics of the venue of the match. For example, the sales forecasting unit can perform analysis based on regional purchasing trends and consumption patterns. The sales forecasting unit can also predict sales of specific products and food and drink taking into account regional consumption trends. For example, the sales forecasting unit can perform analysis based on regional purchasing trends and consumption patterns. Furthermore, the sales forecasting unit can combine the characteristics of the venue of the match and the consumption trends of the region to comprehensively predict sales of specific products and food and drink. For example, the sales forecasting unit can perform analysis by combining the purchasing trends and consumption patterns of the region with the characteristics of the venue of the match. In this way, the accuracy of sales forecasts is improved by taking into account the characteristics of the venue and the consumption trends of the region.

[0050] The sales forecasting unit can predict sales taking into account the effectiveness of match promotional activities and advertising. For example, the sales forecasting unit can analyze the effectiveness of match promotional activities and predict sales of specific products or food and beverages. For example, the sales forecasting unit can perform analysis based on responses to advertising campaigns and social media. The sales forecasting unit can also analyze the effectiveness of match advertising and predict sales of specific products or food and beverages. For example, the sales forecasting unit can analyze the effectiveness of advertising campaigns and predict sales of specific products or food and beverages. Furthermore, the sales forecasting unit can combine match promotional activities and advertising effectiveness to comprehensively predict sales of specific products or food and beverages. For example, the sales forecasting unit can combine responses to advertising campaigns and social media with advertising effectiveness to perform analysis. This improves the accuracy of sales forecasts by taking into account the effectiveness of promotional activities and advertising.

[0051] The sales forecasting unit can predict sales taking into account whether the game date falls on a public holiday or a weekend. For example, if the game date falls on a public holiday, the sales forecasting unit predicts sales of a specific product or food and drink. For example, the sales forecasting unit can perform analysis based on national holidays. The sales forecasting unit can also predict sales of a specific product or food and drink if the game date falls on a weekend. For example, the sales forecasting unit can perform analysis based on Saturday or Sunday. Furthermore, the sales forecasting unit can also comprehensively predict sales of a specific product or food and drink by taking into account whether the game date falls on a public holiday or a weekend. For example, the sales forecasting unit can perform analysis by combining national holidays, Saturdays, and Sundays. In this way, the accuracy of sales forecasts can be improved by taking into account whether the game date falls on a public holiday or a weekend.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The target prediction unit can predict the level of interest in a particular game or event based on the subscriber's past purchase history. For example, the target prediction unit can analyze the subscriber's past ticket purchase history and merchandise purchase history to predict the level of interest in a particular game or event. The target prediction unit can also predict the level of interest in a particular game or event based on the subscriber's past attendance history. Furthermore, the target prediction unit can analyze the subscriber's social media activity to predict the level of interest in a particular game or event. In this way, the subscriber's past purchase history, attendance history, and social media activity can be analyzed to predict the level of interest in a particular game or event.

[0054] The prediction unit can predict the number of available tickets by taking into account the characteristics of the venue and the consumption trends of the region. For example, the prediction unit can analyze past data on the venue and predict the probability of available tickets. The prediction unit can also predict the probability of available tickets based on the consumption trends of the region. Furthermore, the prediction unit can combine the characteristics of the venue and the consumption trends of the region to comprehensively predict the probability of available tickets. In this way, by taking into account the characteristics of the venue and the consumption trends of the region, the accuracy of available ticket predictions is improved.

[0055] The sales forecasting unit can predict sales taking into account the effectiveness of match promotional activities and advertising. For example, the sales forecasting unit can analyze the effectiveness of match promotional activities and predict sales of specific products, food, and beverages. The sales forecasting unit can also analyze the effectiveness of advertising campaigns and predict sales of specific products, food, and beverages. Furthermore, the sales forecasting unit can combine match promotional activities and advertising effectiveness to comprehensively predict sales of specific products, food, and beverages. In this way, the accuracy of sales forecasts can be improved by taking into account the effectiveness of promotional activities and advertising.

[0056] The target prediction unit can analyze the social media activities of subscribers and identify targets who are likely to visit. For example, the target prediction unit can analyze the content of subscribers' posts on social media and identify targets who are likely to visit. The target prediction unit can also analyze subscribers' check-in information on social media and identify targets who are likely to visit. Furthermore, the target prediction unit can analyze subscribers' friendships on social media and identify targets who are likely to visit. In this way, by analyzing social media activities, it is possible to identify targets who are likely to visit.

[0057] The sales forecasting unit can predict sales taking into account the attribute information (gender, age) of the audience. For example, the sales forecasting unit can predict sales of specific products or food and drink taking into account the gender of the audience. The sales forecasting unit can also predict sales of specific products or food and drink taking into account the age of the audience. Furthermore, the sales forecasting unit can combine the gender and age of the audience to comprehensively predict sales of specific products or food and drink. In this way, by taking attribute information into account, the accuracy of sales forecasts is improved.

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

[0059] Step 1: The prediction unit analyzes information about the opponent or ranking and predicts available tickets. For example, the prediction unit uses a generation AI to predict available tickets based on information about the opponent and ranking. The prediction unit can also analyze past match data and ticket sales history. Step 2: The target prediction unit predicts target attendees based on the locations of subscribers, based on the available tickets predicted by the prediction unit. For example, the target prediction unit uses generation AI to analyze subscribers' address information and past attendance history to identify members who are likely to attend the game. Step 3: The sales forecasting department predicts merchandise and food and drink sales based on the target attendance predicted by the target forecasting department. For example, the sales forecasting department uses generative AI to predict merchandise and food and drink sales based on information such as the number of spectators, weather, and subscriber attributes (gender, age).

[0060] (Example 2) A system according to an embodiment of the present invention aims to revitalize the sports world by forecasting ticket demand for games with poor ticket sales and distributing remaining tickets to subscription-based members. This system utilizes a generation AI to predict available tickets based on information such as the opponents and standings, predicting target attendees based on the locations of subscription members, and providing available tickets. It also predicts sales of merchandise and food and beverages based on information such as the number of spectators, weather, and attributes of subscription members (gender, age). For example, a generation AI is used to analyze information such as the opponents and standings to predict available tickets. Next, it predicts target attendees based on the locations of subscription members, and provides available tickets. It also predicts sales of merchandise and food and beverages based on information such as the number of spectators, weather, and attributes of subscription members (gender, age). This allows the system to increase attendance, leading to increased merchandise and food and beverage sales and increased profits. This allows the system to increase attendance, leading to increased merchandise and food and beverage sales and increased profits. For example, distributing unused tickets to subscribers will increase attendance at games and fill stadiums, which will increase merchandise and food and beverage sales and improve overall revenue.

[0061] A ticket demand prediction system according to an embodiment includes a prediction unit, a target prediction unit, and a sales prediction unit. The prediction unit analyzes information about opponents or rankings and predicts available tickets. For example, the prediction unit uses a generation AI to predict available tickets based on information about opponents and rankings. The prediction unit can also analyze past match data and ticket sales history. The target prediction unit predicts target attendees based on the locations of subscription members based on the available tickets predicted by the prediction unit. For example, the target prediction unit uses a generation AI to analyze the address information and past attendance history of subscription members to identify members who are likely to attend a game. The sales prediction unit predicts sales of merchandise and food and beverages based on the target attendees predicted by the target prediction unit. For example, the sales prediction unit uses a generation AI to predict sales of merchandise and food and beverages based on information such as the number of spectators, weather, and attributes of subscription members (gender, age). This enables the ticket demand prediction system according to an embodiment to predict available tickets, target attendees, and merchandise and food and beverage sales.

[0062] The prediction unit can analyze past match data or ticket sales history in addition to opponents or rankings. The prediction unit, for example, analyzes past match data to improve the accuracy of predictions of available tickets. For example, the prediction unit can analyze match results and player performance data. The prediction unit can also analyze ticket sales history to improve the accuracy of predictions of available tickets. For example, the prediction unit can analyze the number of tickets sold and the sales period. In this way, by analyzing past match data and ticket sales history, prediction accuracy is improved.

[0063] The target prediction unit can analyze the address information and past attendance history of subscription members to identify members who are likely to attend a game. The target prediction unit can, for example, analyze the address information of subscription members to identify members who are likely to attend a game. For example, the target prediction unit can perform analysis based on postal codes and city names. The target prediction unit can also analyze past attendance history to identify members who are likely to attend a game. For example, the target prediction unit can perform analysis based on the number of attendances and the date and time of attendance. In this way, by analyzing the address information and past attendance history of subscription members, the accuracy of predicting attendance targets can be improved.

[0064] The sales forecasting unit can predict sales of merchandise and food and drink based on information on the number of spectators, weather, and attributes of subscription members. The sales forecasting unit, for example, predicts sales of merchandise and food and drink based on the number of spectators. For example, the sales forecasting unit can perform analysis based on the number of tickets sold and the actual number of attendees. The sales forecasting unit can also predict sales of merchandise and food and drink based on weather information. For example, the sales forecasting unit can perform analysis based on temperature and precipitation. Furthermore, the sales forecasting unit can predict sales of merchandise and food and drink based on attribute information (gender, age) of subscription members. For example, the sales forecasting unit can perform analysis based on gender and age. In this way, the accuracy of sales forecasts is improved by taking into account the number of spectators, weather, and attributes of subscription members.

[0065] The sales forecasting unit can also predict sales based on the purchasing history of spectators or the time of the game. The sales forecasting unit predicts sales of merchandise and food and drink, for example, based on the purchasing history of spectators. For example, the sales forecasting unit can perform analysis based on past purchased items and purchase frequency. The sales forecasting unit can also predict sales of merchandise and food and drink based on the time of the game. For example, the sales forecasting unit can perform analysis based on daytime or nighttime hours. In this way, the accuracy of sales forecasts can be further improved by taking into account the purchasing history of spectators and the time of the game.

[0066] The prediction unit can estimate the user's emotions and adjust the accuracy of the available ticket predictions based on the estimated user emotions. For example, if the user is excited, the prediction unit analyzes past match data in more detail to improve the prediction accuracy of the generation AI. For example, the prediction unit can analyze match results and player performance data in detail. Furthermore, if the user is feeling anxious, the prediction unit can focus on analyzing ticket sales history to improve the prediction accuracy of the generation AI. For example, the prediction unit can focus on analyzing the number of tickets sold and the sales period. Furthermore, if the user is relaxed, the prediction unit can focus on analyzing information about opponents and rankings to improve the prediction accuracy of the generation AI. For example, the prediction unit can focus on analyzing information about opponents and rankings. This allows for more accurate prediction of available tickets by adjusting the prediction accuracy based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0067] The prediction unit can analyze past match data and ticket sales history to predict the impact of a specific event or promotion on available seats. The prediction unit, for example, analyzes past match data to predict the impact of a specific event on available seats. For example, the prediction unit can perform analysis based on match results and player performance data. The prediction unit can also analyze ticket sales history to predict the impact of a specific promotion on available seats. For example, the prediction unit can perform analysis based on the number of tickets sold and the sales period. Furthermore, the prediction unit can combine past match data and ticket sales history to comprehensively predict the impact of events and promotions. For example, the prediction unit can combine match results and player performance data with the number of tickets sold and the sales period to perform analysis. In this way, by predicting the impact of events and promotions, it is possible to identify the occurrence of available tickets in advance.

[0068] The prediction unit can predict the probability of empty tickets occurring based on the venue and time of the game. The prediction unit, for example, analyzes past data on the venue of the game to predict the probability of empty tickets occurring. For example, the prediction unit can perform analysis based on the stadium name or city name. The prediction unit can also predict the probability of empty tickets occurring based on the time of the game. For example, the prediction unit can perform analysis based on daytime or nighttime hours. Furthermore, the prediction unit can combine the venue and time of the game to comprehensively predict the probability of empty tickets occurring. For example, the prediction unit can perform analysis by combining the stadium name or city name with the daytime or nighttime hours. This improves prediction accuracy by predicting the probability of empty tickets occurring based on the venue and time of the game.

[0069] The prediction unit can predict available tickets based on the importance and attention of a match. The prediction unit, for example, analyzes the importance of a match and predicts available tickets. For example, the prediction unit can perform analysis based on the importance of league matches, final matches, etc. The prediction unit can also analyze the attention of a match and predict available tickets. For example, the prediction unit can perform analysis based on media reports and social media reactions. Furthermore, the prediction unit can combine the importance and attention of a match to comprehensively predict available tickets. For example, the prediction unit can combine and analyze the importance of league matches, final matches, etc. with media reports and social media reactions. This improves prediction accuracy by predicting available tickets based on the importance and attention of a match.

[0070] The prediction unit can estimate the user's emotion and adjust the display method of the prediction result based on the estimated user emotion. For example, if the user is excited, the prediction unit can visually emphasize the prediction result and display it. For example, the prediction unit can visually emphasize the prediction result using a graph or chart. Furthermore, if the user is feeling anxious, the prediction unit can simply display the prediction result. For example, the prediction unit can simply display the prediction result using only text. Furthermore, if the user is relaxed, the prediction unit can display the prediction result in detail. For example, the prediction unit can display detailed data and analysis results. This allows the display method of the prediction result to be adjusted based on the user's emotion, making it easier for the user to view. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0071] The prediction unit can predict available tickets based on traffic conditions or accessibility at the venue of the match. The prediction unit can predict available tickets based on, for example, traffic congestion information at the venue of the match. For example, the prediction unit can analyze traffic congestion information and predict the probability of available tickets. The prediction unit can also predict available tickets by taking into account the operation status of public transportation at the venue of the match. For example, the prediction unit can analyze the operation status of public transportation and predict the probability of available tickets. The prediction unit can also predict available tickets based on accessibility to the venue of the match. For example, the prediction unit can analyze accessibility to the stadium and predict the probability of available tickets. In this way, by taking traffic conditions and accessibility into account, the accuracy of available ticket predictions is improved.

[0072] The prediction unit can predict the number of available tickets by taking into account the effectiveness of promotional activities and advertising for the match. The prediction unit, for example, analyzes the effectiveness of promotional activities for the match and predicts the number of available tickets. For example, the prediction unit can perform analysis based on responses to advertising campaigns and social media. The prediction unit can also analyze the effectiveness of advertising for the match and predict the number of available tickets. For example, the prediction unit can analyze the effectiveness of advertising campaigns and predict the probability of available tickets. Furthermore, the prediction unit can combine promotional activities for the match with the effectiveness of advertising to comprehensively predict the number of available tickets. For example, the prediction unit can combine and analyze responses to advertising campaigns and social media with the effectiveness of advertising. This improves the accuracy of predictions of available tickets by taking into account the effectiveness of promotional activities and advertising.

[0073] The prediction unit can predict available tickets based on whether the game date falls on a holiday or a weekend. For example, the prediction unit predicts available tickets when the game date falls on a holiday. For example, the prediction unit can perform analysis based on national holidays. The prediction unit can also predict available tickets when the game date falls on a weekend. For example, the prediction unit can perform analysis based on Saturday or Sunday. Furthermore, the prediction unit can also perform a comprehensive prediction of available tickets by combining whether the game date falls on a holiday or a weekend. For example, the prediction unit can perform analysis by combining national holidays, Saturdays, and Sundays. In this way, by taking into account whether the game date falls on a holiday or a weekend, the accuracy of available ticket predictions is improved.

[0074] The target prediction unit can estimate the user's emotions and adjust the accuracy of the visitor target prediction based on the estimated user emotions. For example, if the user is excited, the target prediction unit analyzes the past visit history in more detail to improve the prediction accuracy of the generation AI. For example, the target prediction unit can analyze the number of visits and the date and time of visit in detail. Furthermore, if the user is feeling anxious, the target prediction unit can focus on analyzing address information to improve the prediction accuracy of the generation AI. For example, the target prediction unit can focus on analyzing the postal code and city name. Furthermore, if the user is relaxed, the target prediction unit can focus on analyzing information about the place of stay to improve the prediction accuracy of the generation AI. For example, the target prediction unit can focus on analyzing information about the place of stay. This allows for more accurate prediction of the visitor target by adjusting the prediction accuracy based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] The target prediction unit can analyze the subscription member's past attendance history and predict the level of interest in a specific game or event. The target prediction unit can, for example, analyze the subscription member's past attendance history and predict the level of interest in a specific game. For example, the target prediction unit can perform analysis based on the number of attendances and the date and time of attendance. The target prediction unit can also analyze the subscription member's past attendance history and predict the level of interest in a specific event. For example, the target prediction unit can perform analysis based on the number of times an event has been attended and the date and time of attendance. Furthermore, the target prediction unit can comprehensively analyze the subscription member's past attendance history and predict the level of interest in a game or event. For example, the target prediction unit can perform analysis by combining the number of attendances and the date and time of attendance with the number of times an event has been attended and the date and time of attendance. In this way, the level of interest in a specific game or event can be predicted by analyzing the past attendance history.

[0076] The target prediction unit can analyze the social media activities of subscribers and identify targets who are likely to visit. The target prediction unit can, for example, analyze the content of subscribers' posts on social media and identify targets who are likely to visit. For example, the target prediction unit can perform analysis based on the content of the posts and the number of likes. The target prediction unit can also analyze the check-in information of subscribers on social media and identify targets who are likely to visit. For example, the target prediction unit can perform analysis based on the check-in information. Furthermore, the target prediction unit can analyze the subscribers' friendships on social media and identify targets who are likely to visit. For example, the target prediction unit can perform analysis based on friendships. In this way, by analyzing social media activities, it is possible to identify targets who are likely to visit.

[0077] The target prediction unit can predict the target visitor by taking into account the family structure and friendships of the subscription member. The target prediction unit predicts the target visitor by taking into account the family structure of the subscription member, for example. For example, the target prediction unit can perform analysis based on the number of family members and their composition. The target prediction unit can also predict the target visitor by taking into account the friendships of the subscription member. For example, the target prediction unit can perform analysis based on the number of friends and their relationships. Furthermore, the target prediction unit can combine the family structure and friendships of the subscription member to comprehensively predict the target visitor. For example, the target prediction unit can perform analysis by combining the number of family members and their composition with the number of friends and their relationships. In this way, by taking into account the family structure and friendships, the accuracy of predicting the target visitor is improved.

[0078] The target prediction unit can estimate the user's emotions and adjust the display method of the target prediction result based on the estimated user's emotions. For example, if the user is excited, the target prediction unit can visually emphasize and display the target prediction result. For example, the target prediction unit can visually emphasize the target prediction result using a graph or chart. Furthermore, if the user is feeling anxious, the target prediction unit can simply display the target prediction result. For example, the target prediction unit can simply display the target prediction result using only text. Furthermore, if the user is relaxed, the target prediction unit can display the target prediction result in detail. For example, the target prediction unit can display detailed data and analysis results. This allows for adjusting the display method of the target prediction result based on the user's emotions, thereby enabling a display that is easy for the user to view. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] The target prediction unit can predict the target visitor by taking into account the occupation and lifestyle of the subscription member. The target prediction unit predicts the target visitor by taking into account, for example, the occupation of the subscription member. For example, the target prediction unit can perform analysis based on the type of occupation and working hours. The target prediction unit can also predict the target visitor by taking into account the lifestyle of the subscription member. For example, the target prediction unit can perform analysis based on lifestyle habits and hobbies. Furthermore, the target prediction unit can combine the occupation and lifestyle of the subscription member to comprehensively predict the target visitor. For example, the target prediction unit can perform analysis by combining the type of occupation and working hours with the lifestyle habits and hobbies. In this way, by taking the occupation and lifestyle into account, the accuracy of predicting the target visitor is improved.

[0080] The target prediction unit can analyze the hobbies and interests of subscription members and identify targets who are likely to visit. The target prediction unit can, for example, analyze the hobbies of subscription members and identify targets who are likely to visit. For example, the target prediction unit can perform analysis based on survey results and social media posts. The target prediction unit can also analyze the interests of subscription members and identify targets who are likely to visit. For example, the target prediction unit can perform analysis based on the content of social media posts and the number of likes. Furthermore, the target prediction unit can combine the hobbies and interests of subscription members to comprehensively identify targets who are likely to visit. For example, the target prediction unit can combine and analyze survey results and the content of social media posts with the number of likes. In this way, targets who are likely to visit can be identified by analyzing hobbies and interests.

[0081] The target prediction unit can predict the target attendees by taking into account the subscription member's past purchasing history. The target prediction unit can predict the target attendees by taking into account, for example, the subscription member's past ticket purchase history. For example, the target prediction unit can perform analysis based on purchased items and purchase frequency. The target prediction unit can also predict the target attendees by taking into account the subscription member's past merchandise purchase history. For example, the target prediction unit can perform analysis based on purchased items and purchase frequency. Furthermore, the target prediction unit can predict the target attendees by taking into account the subscription member's past food and beverage purchase history. For example, the target prediction unit can perform analysis based on purchased items and purchase frequency. In this way, by taking into account the past purchase history, the accuracy of predicting the target attendees is improved.

[0082] The sales forecasting unit can estimate the user's emotions and adjust sales forecasts for merchandise and food and beverages based on the estimated user emotions. For example, if the user is excited, the sales forecasting unit analyzes the user's past purchase history in more detail to enable the generation AI to improve sales forecasts. For example, the sales forecasting unit can analyze the purchased items and purchase frequency in detail. Furthermore, if the user is feeling anxious, the generation AI can focus on analyzing the number of spectators to improve sales forecasts. For example, the sales forecasting unit can focus on analyzing the number of tickets sold and the actual number of attendees. Furthermore, if the user is relaxed, the sales forecasting unit can focus on analyzing weather information to improve sales forecasts. For example, the sales forecasting unit can focus on temperature and precipitation. This allows for more accurate sales forecasts by adjusting sales forecasts based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] The sales prediction unit can analyze the purchase history of spectators and predict the sales of specific products or food and drink. The sales prediction unit can, for example, analyze the spectators' past merchandise purchase history and predict whether a specific product will sell. For example, the sales prediction unit can perform analysis based on the products purchased and the purchase frequency. The sales prediction unit can also analyze the spectators' past food and drink purchase history and predict whether a specific food and drink will sell. For example, the sales prediction unit can perform analysis based on the products purchased and the purchase frequency. Furthermore, the sales prediction unit can comprehensively analyze the spectators' past purchase history and predict the sales of specific products or food and drink. For example, the sales prediction unit can comprehensively analyze the products purchased and the purchase frequency. In this way, the sales prediction unit can predict the sales of specific products or food and drink by analyzing the purchase history.

[0084] The sales forecasting unit can predict sales of merchandise and food and drink based on the time of the game and the weather. For example, the sales forecasting unit can predict which merchandise and food and drink will sell well during a specific time period based on the time of the game. For example, the sales forecasting unit can perform analysis based on daytime and nighttime hours. The sales forecasting unit can also predict which merchandise and food and drink will sell well on sunny or rainy days based on weather information. For example, the sales forecasting unit can perform analysis based on temperature and precipitation. Furthermore, the sales forecasting unit can combine the time of the game with weather information to comprehensively predict which merchandise and food and drink will sell well. For example, the sales forecasting unit can perform analysis by combining the time of day and nighttime hours with temperature and precipitation. In this way, by predicting sales based on the time of day and weather, prediction accuracy is improved.

[0085] The sales forecasting unit can predict sales taking into account the attribute information (gender, age) of the audience. The sales forecasting unit can predict sales of specific products or food and drink taking into account the gender of the audience, for example. For example, the sales forecasting unit can perform analysis based on gender. The sales forecasting unit can also predict sales of specific products or food and drink taking into account the age of the audience. For example, the sales forecasting unit can perform analysis based on age. Furthermore, the sales forecasting unit can combine the gender and age of the audience to comprehensively predict sales of specific products or food and drink. For example, the sales forecasting unit can perform analysis by combining gender and age. In this way, the accuracy of sales forecasts is improved by taking attribute information into account.

[0086] The sales forecasting unit can estimate the user's emotions and adjust the display method of the sales forecast results based on the estimated user emotions. For example, if the user is excited, the sales forecasting unit can visually emphasize the sales forecast results when displaying them. For example, the sales forecasting unit can visually emphasize the sales forecast results using graphs or charts. Furthermore, if the user is feeling anxious, the sales forecasting unit can simply display the sales forecast results. For example, the sales forecasting unit can simply display the sales forecast results using only text. Furthermore, if the user is relaxed, the sales forecasting unit can display the sales forecast results in detail. For example, the sales forecasting unit can display detailed data and analysis results. This allows the display method of the sales forecast results to be adjusted based on the user's emotions, thereby enabling a display that is easy for the user to view. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] The sales forecasting unit can predict sales taking into account the characteristics of the venue of the match and the consumption trends of the region. The sales forecasting unit, for example, predicts sales of specific products and food and drink taking into account the characteristics of the venue of the match. For example, the sales forecasting unit can perform analysis based on regional purchasing trends and consumption patterns. The sales forecasting unit can also predict sales of specific products and food and drink taking into account regional consumption trends. For example, the sales forecasting unit can perform analysis based on regional purchasing trends and consumption patterns. Furthermore, the sales forecasting unit can combine the characteristics of the venue of the match and the consumption trends of the region to comprehensively predict sales of specific products and food and drink. For example, the sales forecasting unit can perform analysis by combining the purchasing trends and consumption patterns of the region with the characteristics of the venue of the match. In this way, the accuracy of sales forecasts is improved by taking into account the characteristics of the venue and the consumption trends of the region.

[0088] The sales forecasting unit can predict sales taking into account the effectiveness of match promotional activities and advertising. For example, the sales forecasting unit can analyze the effectiveness of match promotional activities and predict sales of specific products or food and beverages. For example, the sales forecasting unit can perform analysis based on responses to advertising campaigns and social media. The sales forecasting unit can also analyze the effectiveness of match advertising and predict sales of specific products or food and beverages. For example, the sales forecasting unit can analyze the effectiveness of advertising campaigns and predict sales of specific products or food and beverages. Furthermore, the sales forecasting unit can combine match promotional activities and advertising effectiveness to comprehensively predict sales of specific products or food and beverages. For example, the sales forecasting unit can combine responses to advertising campaigns and social media with advertising effectiveness to perform analysis. This improves the accuracy of sales forecasts by taking into account the effectiveness of promotional activities and advertising.

[0089] The sales forecasting unit can predict sales taking into account whether the game date falls on a public holiday or a weekend. For example, if the game date falls on a public holiday, the sales forecasting unit predicts sales of a specific product or food and drink. For example, the sales forecasting unit can perform analysis based on national holidays. The sales forecasting unit can also predict sales of a specific product or food and drink if the game date falls on a weekend. For example, the sales forecasting unit can perform analysis based on Saturday or Sunday. Furthermore, the sales forecasting unit can also comprehensively predict sales of a specific product or food and drink by taking into account whether the game date falls on a public holiday or a weekend. For example, the sales forecasting unit can perform analysis by combining national holidays, Saturdays, and Sundays. In this way, the accuracy of sales forecasts can be improved by taking into account whether the game date falls on a public holiday or a weekend. === Hard Collateral 1-1 === Each of the multiple elements including the prediction unit, target prediction unit, and sales prediction unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the prediction unit is realized by at least one of the smart device 14 and the data processing device 12. For example, the prediction unit is realized by the specific processing unit 290 of the data processing device 12, and uses a generation AI to analyze information about opponents and rankings and predict available tickets. The target prediction unit is realized, for example, by the control unit 46A of the smart device 14, and predicts targets who will attend based on the locations of subscription members. The sales prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts sales of goods and food and beverages based on information such as the number of spectators, weather, and subscription member attributes (gender, age). === Hard Collateral 1-2 === Each of the multiple elements including the prediction unit, target prediction unit, and sales prediction unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the prediction unit is realized by at least one of the smart glasses 214 and the data processing device 12. For example, the prediction unit is realized by the specific processing unit 290 of the data processing device 12, and uses a generation AI to analyze information on opponents and rankings and predict available tickets. The target prediction unit is realized, for example, by the control unit 46A of the smart glasses 214, and predicts targets who will attend based on the locations of subscription members. The sales prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts sales of merchandise and food and beverages based on information such as the number of spectators, weather, and subscription member attributes (gender, age). === Hard Collateral 1-3 === Each of the multiple elements including the prediction unit, target prediction unit, and sales prediction unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the prediction unit is realized by at least one of the headset terminal 314 and the data processing device 12. For example, the prediction unit is realized by the specific processing unit 290 of the data processing device 12, and uses a generation AI to analyze information about opponents and rankings and predict available tickets. The target prediction unit is realized, for example, by the control unit 46A of the headset terminal 314, and predicts targets who will attend based on the locations of subscription members. The sales prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts sales of goods and food and drink based on information such as the number of spectators, weather, and subscription member attributes (gender, age). === Hard Collateral 1-4 === Each of the multiple elements including the prediction unit, target prediction unit, and sales prediction unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the prediction unit is realized by at least one of the robot 414 and the data processing device 12. For example, the prediction unit is realized by the specific processing unit 290 of the data processing device 12, and uses a generation AI to analyze information about opponents and rankings and predict available tickets. The target prediction unit is realized, for example, by the control unit 46A of the robot 414, and predicts targets who will attend based on the locations of subscription members. The sales prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts sales of goods and food and drink based on information such as the number of spectators, weather, and subscription member attributes (gender, age).

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

[0091] The target prediction unit can predict the level of interest in a particular game or event based on the subscriber's past purchase history. For example, the target prediction unit can analyze the subscriber's past ticket purchase history and merchandise purchase history to predict the level of interest in a particular game or event. The target prediction unit can also predict the level of interest in a particular game or event based on the subscriber's past attendance history. Furthermore, the target prediction unit can analyze the subscriber's social media activity to predict the level of interest in a particular game or event. In this way, the subscriber's past purchase history, attendance history, and social media activity can be analyzed to predict the level of interest in a particular game or event.

[0092] The prediction unit can predict the number of available tickets by taking into account the characteristics of the venue and the consumption trends of the region. For example, the prediction unit can analyze past data on the venue and predict the probability of available tickets. The prediction unit can also predict the probability of available tickets based on the consumption trends of the region. Furthermore, the prediction unit can combine the characteristics of the venue and the consumption trends of the region to comprehensively predict the probability of available tickets. In this way, by taking into account the characteristics of the venue and the consumption trends of the region, the accuracy of available ticket predictions is improved.

[0093] The sales forecasting unit can predict sales taking into account the effectiveness of match promotional activities and advertising. For example, the sales forecasting unit can analyze the effectiveness of match promotional activities and predict sales of specific products, food, and beverages. The sales forecasting unit can also analyze the effectiveness of advertising campaigns and predict sales of specific products, food, and beverages. Furthermore, the sales forecasting unit can combine match promotional activities and advertising effectiveness to comprehensively predict sales of specific products, food, and beverages. In this way, the accuracy of sales forecasts can be improved by taking into account the effectiveness of promotional activities and advertising.

[0094] The target prediction unit can analyze the social media activities of subscribers and identify targets who are likely to visit. For example, the target prediction unit can analyze the content of subscribers' posts on social media and identify targets who are likely to visit. The target prediction unit can also analyze subscribers' check-in information on social media and identify targets who are likely to visit. Furthermore, the target prediction unit can analyze subscribers' friendships on social media and identify targets who are likely to visit. In this way, by analyzing social media activities, it is possible to identify targets who are likely to visit.

[0095] The sales forecasting unit can predict sales taking into account the attribute information (gender, age) of the audience. For example, the sales forecasting unit can predict sales of specific products or food and drink taking into account the gender of the audience. The sales forecasting unit can also predict sales of specific products or food and drink taking into account the age of the audience. Furthermore, the sales forecasting unit can combine the gender and age of the audience to comprehensively predict sales of specific products or food and drink. In this way, by taking attribute information into account, the accuracy of sales forecasts is improved.

[0096] The prediction unit can estimate the user's emotions and adjust the accuracy of the available ticket predictions based on the estimated user emotions. For example, if the user is excited, the prediction unit can analyze past match data in more detail to enable the generation AI to improve prediction accuracy. In addition, if the user is feeling anxious, the prediction unit can focus on analyzing ticket sales history to enable the generation AI to improve prediction accuracy. Furthermore, if the user is relaxed, the prediction unit can analyze information about opponents and rankings to enable the generation AI to improve prediction accuracy. This allows for more accurate available ticket predictions by adjusting prediction accuracy based on the user's emotions.

[0097] The target prediction unit can estimate the user's emotions and adjust the accuracy of the visitor target prediction based on the estimated user emotions. For example, if the user is excited, the target prediction unit can analyze past visit history in more detail so that the generation AI can improve prediction accuracy. In addition, if the user is feeling anxious, the target prediction unit can focus on analyzing address information so that the generation AI can improve prediction accuracy. Furthermore, if the user is relaxed, the target prediction unit can focus on analyzing information about the user's place of stay so that the generation AI can improve prediction accuracy. This allows for more accurate prediction of visitor targets by adjusting prediction accuracy based on the user's emotions.

[0098] The sales forecasting unit can estimate the user's emotions and adjust sales forecasts for merchandise and food and drink based on the estimated user emotions. For example, if the user is excited, the sales forecasting unit can analyze past purchase history in more detail so that the generation AI can improve sales forecasts. Also, if the user is feeling anxious, the sales forecasting unit can focus on analyzing the number of spectators so that the generation AI can improve sales forecasts. Furthermore, if the user is relaxed, the sales forecasting unit can focus on analyzing weather information so that the generation AI can improve sales forecasts. This allows for more accurate sales forecasts by adjusting sales forecasts based on the user's emotions.

[0099] The prediction unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated user emotions. For example, if the user is excited, the prediction unit can display the prediction results in a visually emphasized manner. If the user is feeling anxious, the prediction unit can also display the prediction results in a simplified manner. Furthermore, if the user is relaxed, the prediction unit can also display the prediction results in detail. In this way, by adjusting the display method of the prediction results based on the user's emotions, it is possible to provide a display that is easy for the user to view.

[0100] The target prediction unit can estimate the user's emotions and adjust the display method of the target prediction result based on the estimated user's emotions. For example, if the user is excited, the target prediction unit can display the target prediction result in a visually emphasized manner. Furthermore, if the user is feeling anxious, the target prediction unit can display the target prediction result in a simplified manner. Furthermore, if the user is relaxed, the target prediction unit can display the target prediction result in detail. In this way, by adjusting the display method of the target prediction result based on the user's emotions, it is possible to provide a display that is easy for the user to view.

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

[0102] Step 1: The prediction unit analyzes information about the opponent or ranking and predicts available tickets. For example, the prediction unit uses a generation AI to predict available tickets based on information about the opponent and ranking. The prediction unit can also analyze past match data and ticket sales history. Step 2: The target prediction unit predicts target attendees based on the locations of subscribers, based on the available tickets predicted by the prediction unit. For example, the target prediction unit uses generation AI to analyze subscribers' address information and past attendance history to identify members who are likely to attend the game. Step 3: The sales forecasting department predicts merchandise and food and drink sales based on the target attendance predicted by the target forecasting department. For example, the sales forecasting department uses generative AI to predict merchandise and food and drink sales based on information such as the number of spectators, weather, and subscriber attributes (gender, age).

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

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

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] 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.

[0166] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a prediction unit that analyzes information on opponents or rankings and predicts available tickets; A target prediction unit that predicts targets who will visit based on the locations of subscribers based on the available tickets predicted by the prediction unit; a sales forecasting unit that forecasts sales of goods and food and drink based on the target visitors predicted by the target forecasting unit. A system characterized by:

2. The prediction unit In addition to opponents or rankings, past match data or ticket sales history will also be analyzed.

2. The system of claim 1.

3. The target prediction unit Analyze subscribers' address information and past attendance history to identify those most likely to attend a match 2. The system of claim 1.

4. The sales forecasting unit Predict merchandise and food sales based on audience numbers, weather, and subscriber demographics 2. The system of claim 1.

5. The sales forecasting unit Based on the purchase history of spectators or the time of the match 2. The system of claim 1.

6. The prediction unit Estimate user emotions and adjust the accuracy of vacant ticket predictions based on the estimated user emotions 2. The system of claim 1.

7. The prediction unit Analyze past match data and ticket sales history to predict the impact of specific events and promotions on seat availability 2. The system of claim 1.

8. The prediction unit Predict the probability of tickets being available based on the location and time of the match 2. The system of claim 1.

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