System

Abstract

An object of a system according to an embodiment is to predict a game result with high accuracy and allow a user to obtain a profit by betting.SOLUTION: A system includes a collection part, an analysis part, a prediction part, a provision part, a betting part, and a learning part. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The prediction unit predicts a match result based on the data analyzed by the analysis unit. The providing unit provides the user with the prediction result obtained by the prediction unit. The betting unit allows the user to make a bet based on the prediction result provided by the providing unit. The learning unit updates the prediction model based on a result of betting performed by the betting unit and newly collected data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has a problem in that it has low accuracy in predicting match outcomes, making it difficult for users to make profits from betting.

[0005] The system according to the embodiment aims to predict the outcome of a match with high accuracy and enable users to make a profit from betting. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a prediction unit, a provision unit, a betting unit, and a learning unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The prediction unit predicts the outcome of a match based on the data analyzed by the analysis unit. The provision unit provides the prediction results obtained by the prediction unit to a user. The betting unit allows the user to place a bet based on the prediction results provided by the provision unit. The learning unit updates the prediction model based on the results of bets placed by the betting unit and newly collected data. [Effects of the Invention]

[0007] The system according to the embodiment can predict the outcome of a match with high accuracy, allowing users to make profits from betting. [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 sports betting system according to an embodiment of the present invention uses a generation AI to predict the outcome of a sports game, allowing users to place bets based on the predictions and maximize profits with a high probability of winning. In the sports betting system, the generation AI analyzes a wealth of data to predict game outcomes. Users then place bets based on the generation AI's suggestions. The generation AI learns in real time and optimizes strategies. For example, the sports betting system collects and analyzes a variety of data, such as past game results, player performance data, and weather information. The generation AI then predicts game outcomes based on the collected data. For example, if the generation AI predicts that a specific team will win the next game, users can bet on that team. Furthermore, the generation AI updates its prediction model based on the results of users' bets and newly collected data to improve its accuracy. This allows the sports betting system to combine reliability and profit, bringing unprecedented excitement to sports betting. This allows users to place bets based on the generation AI's highly accurate predictions and maximize profits with a high probability of winning. Furthermore, the generation AI learns in real time and always provides optimal strategies, allowing users to bet with peace of mind. For example, by placing bets based on the predictions provided by the generating AI, users can earn profits with a high probability of winning.

[0029] A sports betting system according to an embodiment includes a collection unit, an analysis unit, a prediction unit, a provision unit, a betting unit, and a learning unit. The collection unit collects data. Examples of the data include, but are not limited to, past game results, player performance data, and weather information. The collection unit collects data from, for example, a public database on the Internet. The collection unit can also collect real-time data using a sensor or an API. For example, the collection unit acquires weather information at a game venue in real time. The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, but is not limited to, a machine learning algorithm. For example, the analysis unit predicts the outcome of a next game based on past game results. The analysis unit can also analyze player performance data and evaluate the player's condition. The prediction unit predicts the game outcome based on the data analyzed by the analysis unit. The prediction is performed using, for example, but is not limited to, a statistical model or a machine learning model. For example, the prediction unit calculates the probability that a specific team will win. The prediction unit can also predict player performance. The provision unit provides the prediction results obtained by the prediction unit to a user. The provision may be performed, for example, through a web application or a mobile application, but is not limited to such examples. For example, the providing unit notifies the user of a predicted match result. The providing unit may also provide the user with a predicted player performance. The betting unit allows the user to place a bet based on the predicted result provided by the providing unit. The betting may be performed, for example, through an online betting platform, but is not limited to such examples. For example, the betting unit may support the user to bet on a specific team. The betting unit may also support the user to bet on player performance. The learning unit updates the prediction model based on the results of bets placed by the betting unit and newly collected data. The learning may be performed, for example, using a machine learning algorithm, but is not limited to such examples. For example, the learning unit improves the prediction model based on the results of users' bets. The learning unit may also update the prediction model based on newly collected data.As a result, the sports betting system according to the embodiment improves the accuracy and efficiency of sports betting by linking the processes of data collection, analysis, prediction, provision, betting, and learning.

[0030] The collection unit can collect data including past game results, player performance data, and weather information. The collection unit, for example, collects past game results. The past game results include game scores, game dates and times, and game locations. The collection unit can also collect player performance data. The player performance data includes player points, player assists, and player playing time. The collection unit can also collect weather information. The weather information includes temperature, precipitation, wind speed, and the like. By collecting a variety of data, the accuracy of game result predictions can be improved. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can automate data collection using AI when collecting data from public databases on the Internet.

[0031] The analysis unit can analyze the collected data and predict the outcome of a match. The analysis unit, for example, analyzes the collected data. The analysis is performed using, for example, a machine learning algorithm, but is not limited to such an example. For example, the analysis unit predicts the outcome of the next match based on the results of past matches. The analysis unit can also analyze player performance data and evaluate the condition of the players. Furthermore, the analysis unit can analyze weather information and evaluate its impact on the match. In this way, by analyzing the collected data, the prediction accuracy of the match outcome is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze data using a machine learning algorithm and predict the outcome of a match.

[0032] The prediction unit can predict the match result based on the analyzed data. The prediction unit predicts the match result based on the analyzed data, for example. The prediction is performed using, for example, a statistical model or a machine learning model, but is not limited to these examples. For example, the prediction unit calculates the probability that a specific team will win. The prediction unit can also predict player performance. Furthermore, the prediction unit can predict the match result based on weather information. As a result, by predicting the match result based on the analyzed data, prediction accuracy is improved. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can predict the match result using a machine learning model.

[0033] The providing unit can provide the prediction results to the user. The providing unit provides the prediction results to the user, for example. The provision can be performed, for example, through a web application or a mobile application, but is not limited to such examples. For example, the providing unit notifies the user of a predicted match result. The providing unit can also provide the user with a predicted player performance. Furthermore, the providing unit can also provide a prediction result based on weather information. By providing the prediction result to the user, the user can be provided with reference information when placing a bet. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can automatically notify the user of the prediction result using AI.

[0034] The betting unit allows users to place bets based on the provided prediction results. The betting unit allows users to place bets based on, for example, the provided prediction results. Bets are placed, for example, through an online betting platform, but are not limited to such examples. For example, the betting unit supports users to bet on specific teams. The betting unit can also support users to bet on player performance. Furthermore, the betting unit can support betting based on weather information. This allows users to place bets based on the provided prediction results, thereby improving the accuracy of betting. Some or all of the above-mentioned processing in the betting unit may be performed using, for example, AI, or may be performed without using AI. For example, the betting unit can support users' bets using AI.

[0035] The learning unit can update the prediction model based on betting results or newly collected data. The learning unit updates the prediction model based on, for example, betting results or newly collected data. Learning is performed using, for example, a machine learning algorithm, but is not limited to such an example. For example, the learning unit improves the prediction model based on the user's betting results. The learning unit can also update the prediction model based on newly collected data. Furthermore, the learning unit can also update the prediction model based on data collected in real time. In this way, by updating the prediction model based on betting results or newly collected data, prediction accuracy is improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can update the prediction model using a machine learning algorithm.

[0036] The collection unit can prioritize collection of specific conditions when collecting past match results and player performance data. For example, if a specific player is injured, the collection unit prioritizes collection of the player's past performance data. The collection unit can also prioritize collection of information regarding the player's health condition before a match. Furthermore, if a specific player returns to the game, the collection unit can prioritize collection of the player's latest performance data. In this way, by prioritizing collection of specific conditions, more important data can be collected efficiently. Some or all of the above-mentioned processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input injury information of a specific player into AI and cause the AI ​​to collect related performance data.

[0037] When collecting weather information, the collection unit can filter the data based on the geographical characteristics of the match venue. For example, if the match venue is located at a high altitude, the collection unit can collect weather information taking into account fluctuations in air pressure and temperature. Furthermore, if the match venue is located on the coast, the collection unit can also prioritize collecting data on wind speed and humidity. Furthermore, if the match venue is located in an urban area, the collection unit can also take into account meteorological conditions specific to the city when collecting weather information. In this way, by collecting weather information taking into account the geographical characteristics of the match venue, more accurate weather data can be obtained. Some or all of the above-described processing by the collection unit can be performed using, or without, AI, for example. For example, the collection unit can input the geographical characteristics of the match venue into AI and have the AI ​​filter the weather information.

[0038] When collecting data, the collection unit can prioritize collecting highly relevant data by referring to the user's past betting history. For example, the collection unit prioritizes collecting data on matches on which the user has bet in the past. Furthermore, if the user tends to bet on a particular team, the collection unit can prioritize collecting data on that team. Furthermore, if the user is paying attention to a particular player, the collection unit can prioritize collecting data on that player. In this way, by referring to the user's past betting history, highly relevant data can be efficiently collected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past betting history into AI and cause the AI ​​to collect highly relevant data.

[0039] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user lives in a specific area, the collection unit can prioritize collecting match data for that area. Also, if the user is traveling, the collection unit can prioritize collecting match data related to the user's current location. Furthermore, if the user is interested in a specific city, the collection unit can prioritize collecting match data for that city. This allows for efficient collection of highly relevant data by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI and cause the AI ​​to collect related data.

[0040] When collecting data, the collection unit can analyze the user's social media activity and collect relevant data. For example, the collection unit can prioritize collecting data on teams the user follows on social media. The collection unit can also prioritize collecting data on players mentioned by the user on social media. Furthermore, the collection unit can collect match data that the user is likely to be interested in from the user's social media activity. This allows for efficient collection of highly relevant data by analyzing the user's social media activity. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media activity into AI and cause the AI ​​to collect relevant data.

[0041] When collecting data, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit preferentially uses a data collection method that the user has previously rated highly. The collection unit can also improve a data collection method that the user has previously expressed dissatisfaction with. Furthermore, the collection unit can also try out a new data collection method based on the user's feedback. This allows the collection method to be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback into AI and have the AI ​​customize the collection method.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to AI and have the AI ​​adjust the level of detail of the analysis.

[0043] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a statistical analysis algorithm to past match result data. The analysis unit can also apply a machine learning algorithm to player performance data. Furthermore, the analysis unit can apply a weather forecasting algorithm to weather information. By applying different analysis algorithms depending on the data category, analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category into AI and have the AI ​​apply an appropriate analysis algorithm.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit refers to analysis results that the user has previously rated highly. The analysis unit can also improve analysis results that the user has previously expressed dissatisfaction with. Furthermore, the analysis unit can also try new analysis methods based on the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into AI and have the AI ​​improve the accuracy of the analysis.

[0045] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone data that was submitted earlier. Furthermore, the analysis unit can adjust the analysis schedule according to the time of submission. This enables efficient analysis by determining the priority of analysis based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data submission into AI and have the AI ​​determine the priority of analysis.

[0046] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can determine the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI and have the AI ​​adjust the order of analysis.

[0047] During analysis, the analysis unit can adjust the use of analytical terminology according to the user's level of expertise. For example, the analysis unit uses detailed terminology for users with high levels of expertise. The analysis unit can also explain the analysis results in simpler terms for users with low levels of expertise. Furthermore, the analysis unit can adjust the way the analysis results are expressed according to the user's level of expertise. This makes it possible to provide analysis results that are easier to understand by adjusting the use of analytical terminology according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise into AI and have the AI ​​use analytical terminology.

[0048] The prediction unit can adjust the level of detail of the prediction based on the importance of the data when making a prediction. For example, the prediction unit performs a detailed prediction for data with high importance. The prediction unit can also perform a simplified prediction for data with low importance. Furthermore, the prediction unit can determine the priority of the prediction according to the importance of the data. This enables efficient prediction by adjusting the level of detail of the prediction based on the importance of the data. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the importance of the data into AI and have the AI ​​adjust the level of detail of the prediction.

[0049] The prediction unit can apply different prediction algorithms depending on the data category when making predictions. For example, the prediction unit applies a statistical prediction algorithm to past match result data. The prediction unit can also apply a machine learning prediction algorithm to player performance data. Furthermore, the prediction unit can apply a weather prediction algorithm to weather information. In this way, applying different prediction algorithms depending on the data category improves prediction accuracy. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the data category into AI and cause the AI ​​to apply an appropriate prediction algorithm.

[0050] When making predictions, the prediction unit can improve the accuracy of the prediction by referring to the user's past prediction results. For example, the prediction unit refers to prediction results that the user has previously rated highly. The prediction unit can also improve prediction results that the user has previously expressed dissatisfaction with. Furthermore, the prediction unit can also try new prediction methods based on the user's past prediction results. In this way, by referring to the user's past prediction results, the accuracy of the predictions is improved. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the user's past prediction results into AI and have the AI ​​improve the accuracy of the predictions.

[0051] The prediction unit can determine the priority of predictions based on the time of data submission when making predictions. For example, the prediction unit prioritizes the most recent data for prediction. The prediction unit can also postpone data that has been submitted earlier. Furthermore, the prediction unit can adjust the prediction schedule according to the time of submission. This enables efficient predictions by determining the priority of predictions based on the time of data submission. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the time of data submission into AI and have the AI ​​determine the priority of predictions.

[0052] The prediction unit can adjust the order of predictions based on the relevance of the data during prediction. For example, the prediction unit prioritizes highly relevant data for prediction. The prediction unit can also postpone less relevant data. Furthermore, the prediction unit can determine the order of predictions according to the relevance of the data. This enables efficient prediction by adjusting the order of predictions based on the relevance of the data. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the relevance of the data into AI and have the AI ​​adjust the order of predictions.

[0053] The prediction unit can adjust the use of technical terminology in the prediction according to the user's level of expertise when making a prediction. For example, the prediction unit uses detailed technical terminology for users with high levels of expertise. The prediction unit can also explain the prediction result in simpler terms to users with low levels of expertise. Furthermore, the prediction unit can adjust the way the prediction result is expressed according to the user's level of expertise. This makes it possible to provide prediction results that are easier to understand by adjusting the use of technical terminology in the prediction according to the user's level of expertise. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the user's level of expertise into AI and have the AI ​​use technical terminology in the prediction.

[0054] The providing unit can adjust the level of detail of the provided information based on the importance of the prediction result when providing the information. For example, the providing unit provides detailed information for a prediction result with high importance. The providing unit can also provide simplified information for a prediction result with low importance. Furthermore, the providing unit can determine the priority of the provided information according to the importance of the prediction result. This enables efficient provision by adjusting the level of detail of the provided information based on the importance of the prediction result. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the importance of the prediction result to AI and cause the AI ​​to adjust the level of detail of the provided information.

[0055] The providing unit can apply different providing algorithms depending on the category of the prediction result when providing the results. For example, the providing unit applies a statistical providing algorithm to predict the result of a match. The providing unit can also apply a machine learning algorithm to predict the performance of a player. The providing unit can also apply a weather forecasting algorithm to predict the weather. In this way, by applying different providing algorithms depending on the category of the prediction result, the accuracy of the results provided is improved. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the category of the prediction result into AI and cause the AI ​​to apply an appropriate providing algorithm.

[0056] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the information. For example, the providing unit refers to provision results that the user has previously given high ratings. The providing unit can also improve provision results that the user has previously expressed dissatisfaction with. Furthermore, the providing unit can also try new provision methods based on the user's past provision results. In this way, the accuracy of the provision is improved by referring to the user's past provision results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past provision results into AI and have the AI ​​improve the accuracy of the provision.

[0057] The providing unit can determine the priority of provision based on the submission time of the prediction results at the time of provision. For example, the providing unit provides the most recent prediction results preferentially. The providing unit can also postpone prediction results that have been submitted earlier. Furthermore, the providing unit can adjust the provision schedule according to the submission time. This enables efficient provision by determining the priority of provision based on the submission time of the prediction results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the submission time of the prediction results into AI and have the AI ​​determine the priority of provision.

[0058] The providing unit can adjust the order of provision based on the relevance of the prediction results when providing them. For example, the providing unit can provide highly relevant prediction results preferentially. The providing unit can also postpone less relevant prediction results. Furthermore, the providing unit can determine the order of provision based on the relevance of the prediction results. This enables efficient provision by adjusting the order of provision based on the relevance of the prediction results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the relevance of the prediction results to AI and cause the AI ​​to adjust the order of provision.

[0059] The providing unit can adjust the use of technical terminology provided during provision according to the user's level of expertise. For example, the providing unit uses detailed technical terminology for a user with high level of expertise. The providing unit can also explain the provided results in simple terms for a user with low level of expertise. Furthermore, the providing unit can adjust the way in which the provided results are expressed according to the user's level of expertise. This allows for the provision of results that are easier to understand by adjusting the use of technical terminology provided according to the user's level of expertise. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's level of expertise into AI and have the AI ​​execute the use of technical terminology provided.

[0060] The betting unit can adjust the level of detail of a bet based on the importance of a prediction result when placing a bet. For example, the betting unit can provide detailed information for a highly important prediction result. The betting unit can also provide simplified information for a less important prediction result. Furthermore, the betting unit can determine the priority of a bet according to the importance of the prediction result. This allows for efficient betting by adjusting the level of detail of a bet based on the importance of the prediction result. Some or all of the above-described processing in the betting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the betting unit can input the importance of a prediction result to an AI and have the AI ​​adjust the level of detail of a bet.

[0061] When placing a bet, the betting unit can apply different betting algorithms depending on the category of the predicted result. For example, the betting unit can apply a statistical betting algorithm to predict the outcome of a match. The betting unit can also apply a machine learning algorithm to predict player performance. Furthermore, the betting unit can apply a weather forecasting algorithm to predict the weather. In this way, by applying different betting algorithms depending on the category of the predicted result, the accuracy of betting is improved. Some or all of the above-mentioned processing in the betting unit may be performed using, for example, AI, or may be performed without using AI. For example, the betting unit can input the category of the predicted result into AI and cause the AI ​​to apply an appropriate betting algorithm.

[0062] When placing a bet, the betting unit can improve the accuracy of the bet by referring to the user's past betting results. For example, the betting unit refers to betting results that the user has previously rated highly. The betting unit can also improve betting results that the user has previously expressed dissatisfaction with. Furthermore, the betting unit can try new betting methods based on the user's past betting results. In this way, by referring to the user's past betting results, the accuracy of the bet is improved. Some or all of the above-mentioned processing in the betting unit may be performed using, for example, AI, or may be performed without using AI. For example, the betting unit can input the user's past betting results into AI and have the AI ​​improve the accuracy of the bet.

[0063] At the time of betting, the betting unit can determine the priority of bets based on the time of submission of prediction results. For example, the betting unit prioritizes the most recent prediction results for betting. The betting unit can also postpone prediction results that have been submitted earlier. Furthermore, the betting unit can adjust the betting schedule according to the submission time. This enables efficient betting by determining the priority of bets based on the time of submission of prediction results. Some or all of the above-mentioned processing in the betting unit may be performed using, for example, AI, or may be performed without using AI. For example, the betting unit can input the time of submission of prediction results to AI and have the AI ​​determine the priority of bets.

[0064] The betting unit can adjust the order of bets based on the relevance of the prediction results when placing a bet. For example, the betting unit prioritizes highly relevant prediction results for betting. The betting unit can also postpone less relevant prediction results. Furthermore, the betting unit can determine the order of bets according to the relevance of the prediction results. This enables efficient betting by adjusting the order of bets based on the relevance of the prediction results. Some or all of the above-described processing in the betting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the betting unit can input the relevance of the prediction results to an AI and have the AI ​​adjust the order of bets.

[0065] The betting unit can adjust the use of betting terminology depending on the user's level of expertise when placing a bet. For example, the betting unit uses detailed terminology for users with high levels of expertise. The betting unit can also explain betting information in simple terms to users with low levels of expertise. Furthermore, the betting unit can adjust the way betting information is expressed depending on the user's level of expertise. This makes it possible to provide betting information that is easier to understand by adjusting the use of betting terminology depending on the user's level of expertise. Some or all of the above-described processing in the betting unit may be performed using, for example, AI, or may be performed without using AI. For example, the betting unit can input the user's level of expertise into AI and have the AI ​​execute the use of betting terminology.

[0066] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit, for example, selects an optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data and adjust algorithm parameters. Furthermore, the learning unit can also try out new learning algorithms based on past learning data. This makes it possible to optimize the learning algorithm by referring to past learning data. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into AI and have the AI ​​optimize the learning algorithm.

[0067] During learning, the learning unit can analyze fluctuations in the user's betting history and adjust the update frequency of the learning data. For example, if the user's betting history fluctuates frequently, the learning unit can increase the update frequency of the learning data. Also, if the user's betting history is stable, the learning unit can decrease the update frequency of the learning data. Furthermore, the learning unit can analyze the fluctuation pattern of the user's betting history and determine the optimal update frequency. In this way, by analyzing the fluctuations in the user's betting history, the update frequency of the learning data can be optimized. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input fluctuations in the user's betting history into AI and cause the AI ​​to adjust the update frequency of the learning data.

[0068] During learning, the learning unit can weight the learning data based on the time of submission of the betting history. For example, the learning unit sets a high weight for the most recent betting history. The learning unit can also set a low weight for older betting history. Furthermore, the learning unit can adjust the weighting of the learning data according to the time of submission. This enables efficient learning by weighting the learning data based on the time of submission of the betting history. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the time of submission of the betting history to the AI ​​and have the AI ​​perform weighting of the learning data.

[0069] During learning, the learning unit can adjust the learning algorithm by reflecting user feedback. For example, the learning unit preferentially uses learning algorithms that users have given high ratings to. The learning unit can also improve learning algorithms that users have expressed dissatisfaction with. Furthermore, the learning unit can also try out new learning algorithms based on user feedback. This improves the accuracy of the learning algorithm by reflecting user feedback. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input user feedback into AI and have the AI ​​adjust the learning algorithm.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The sports betting system may further include a trend analysis unit that analyzes a user's past betting history and identifies the user's betting tendencies. For example, if a user tends to bet on a particular team or player, the trend analysis unit customizes prediction results based on that tendency. The trend analysis unit may also identify betting patterns that have shown a high winning rate for the user in the past and suggest new bets based on those patterns. Furthermore, the trend analysis unit may analyze fluctuations in the user's betting frequency and amount and provide advice for risk management. This may improve user satisfaction and winning rates by providing individually optimized prediction results based on the user's betting history.

[0072] The collection unit can analyze the user's social media activity and prioritize collection of data related to games and players in which the user is interested. For example, data on teams and players frequently mentioned by the user on social media can be collected. The collection unit can also analyze the content of posts from sports-related accounts followed by the user to collect related game data. Furthermore, the collection unit can take into account the time periods during which the user is active on social media and prioritize collection of game data related to those time periods. This makes it possible to collect data based on the user's interests and provide more personalized prediction results.

[0073] The prediction unit can adjust the prediction model by referring to the user's past betting results. For example, the prediction model can be optimized based on prediction results that the user has previously rated highly. It can also improve prediction results that the user has previously expressed dissatisfaction with. Furthermore, the prediction unit can try out new prediction algorithms based on the user's past betting results. In this way, by referring to the user's past betting results, the accuracy of predictions can be improved and user satisfaction can be increased.

[0074] The betting unit may include a trend analysis unit that analyzes the user's past betting history and identifies the user's betting tendencies. For example, if the user tends to bet on a particular team or player, the trend analysis unit customizes betting suggestions based on that tendency. The trend analysis unit may also identify betting patterns that have previously shown the user a high winning rate and provide new betting suggestions based on those patterns. Furthermore, the trend analysis unit may analyze fluctuations in the user's betting frequency and amount and provide advice for risk management. This may improve user satisfaction and win rates by providing individually optimized betting suggestions based on the user's betting history.

[0075] The collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user lives in a specific area, it can prioritize collecting match data for that area. In addition, if the user is traveling, the collection unit can prioritize collecting match data related to the user's current location. Furthermore, if the user is interested in a specific city, the collection unit can prioritize collecting match data for that city. In this way, highly relevant data can be efficiently collected by taking into account the user's geographical location information.

[0076] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, a statistical analysis algorithm can be applied to past match result data. The analysis unit can also apply a machine learning algorithm to player performance data. Furthermore, the analysis unit can apply a weather forecasting algorithm to weather information. In this way, by applying different analysis algorithms depending on the data category, the accuracy of analysis can be improved.

[0077] The processing flow of the first embodiment will be briefly explained below.

[0078] Step 1: The collection unit collects data. This data includes past match results, player performance data, weather information, and more. The collection unit collects data from public databases on the Internet. It can also collect real-time data using sensors and APIs. For example, it can obtain weather information at a match venue in real time. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using machine learning algorithms. For example, it can predict the outcome of the next game based on the results of past games, or analyze player performance data to evaluate the condition of the players. Step 3: The prediction unit predicts the outcome of the match based on the data analyzed by the analysis unit. Predictions are made using statistical models or machine learning models. For example, they calculate the probability that a particular team will win or predict the performance of a player. Step 4: The providing unit provides the prediction results obtained by the prediction unit to the user. The provision is performed through a web application or a mobile application. For example, the providing unit notifies the user of the predicted match results or provides predictions of player performance. Step 5: The betting unit allows the user to place a bet based on the prediction results provided by the provider. The bet is placed through an online betting platform, which, for example, supports users to bet on a specific team or on the performance of a player. Step 6: The learning unit updates the prediction model based on the results of bets placed by the betting unit and newly collected data. Learning is performed using a machine learning algorithm. For example, the prediction model may be improved based on the results of users' bets, or the prediction model may be updated based on newly collected data.

[0079] (Example 2) A sports betting system according to an embodiment of the present invention uses a generation AI to predict the outcome of a sports game, allowing users to place bets based on the predictions and maximize profits with a high probability of winning. In the sports betting system, the generation AI analyzes a wealth of data to predict game outcomes. Users then place bets based on the generation AI's suggestions. The generation AI learns in real time and optimizes strategies. For example, the sports betting system collects and analyzes a variety of data, such as past game results, player performance data, and weather information. The generation AI then predicts game outcomes based on the collected data. For example, if the generation AI predicts that a specific team will win the next game, users can bet on that team. Furthermore, the generation AI updates its prediction model based on the results of users' bets and newly collected data to improve its accuracy. This allows the sports betting system to combine reliability and profit, bringing unprecedented excitement to sports betting. This allows users to place bets based on the generation AI's highly accurate predictions and maximize profits with a high probability of winning. Furthermore, the generation AI learns in real time and always provides optimal strategies, allowing users to bet with peace of mind. For example, by placing bets based on the predictions provided by the generating AI, users can earn profits with a high probability of winning.

[0080] A sports betting system according to an embodiment includes a collection unit, an analysis unit, a prediction unit, a provision unit, a betting unit, and a learning unit. The collection unit collects data. Examples of the data include, but are not limited to, past game results, player performance data, and weather information. The collection unit collects data from, for example, a public database on the Internet. The collection unit can also collect real-time data using a sensor or an API. For example, the collection unit acquires weather information at a game venue in real time. The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, but is not limited to, a machine learning algorithm. For example, the analysis unit predicts the outcome of a next game based on past game results. The analysis unit can also analyze player performance data and evaluate the player's condition. The prediction unit predicts the game outcome based on the data analyzed by the analysis unit. The prediction is performed using, for example, but is not limited to, a statistical model or a machine learning model. For example, the prediction unit calculates the probability that a specific team will win. The prediction unit can also predict player performance. The provision unit provides the prediction results obtained by the prediction unit to a user. The provision may be performed, for example, through a web application or a mobile application, but is not limited to such examples. For example, the providing unit notifies the user of a predicted match result. The providing unit may also provide the user with a predicted player performance. The betting unit allows the user to place a bet based on the predicted result provided by the providing unit. The betting may be performed, for example, through an online betting platform, but is not limited to such examples. For example, the betting unit may support the user to bet on a specific team. The betting unit may also support the user to bet on player performance. The learning unit updates the prediction model based on the results of bets placed by the betting unit and newly collected data. The learning may be performed, for example, using a machine learning algorithm, but is not limited to such examples. For example, the learning unit improves the prediction model based on the results of users' bets. The learning unit may also update the prediction model based on newly collected data.As a result, the sports betting system according to the embodiment improves the accuracy and efficiency of sports betting by linking the processes of data collection, analysis, prediction, provision, betting, and learning.

[0081] The collection unit can collect data including past game results, player performance data, and weather information. The collection unit, for example, collects past game results. The past game results include game scores, game dates and times, and game locations. The collection unit can also collect player performance data. The player performance data includes player points, player assists, and player playing time. The collection unit can also collect weather information. The weather information includes temperature, precipitation, wind speed, and the like. By collecting a variety of data, the accuracy of game result predictions can be improved. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can automate data collection using AI when collecting data from public databases on the Internet.

[0082] The analysis unit can analyze the collected data and predict the outcome of a match. The analysis unit, for example, analyzes the collected data. The analysis is performed using, for example, a machine learning algorithm, but is not limited to such an example. For example, the analysis unit predicts the outcome of the next match based on the results of past matches. The analysis unit can also analyze player performance data and evaluate the condition of the players. Furthermore, the analysis unit can analyze weather information and evaluate its impact on the match. In this way, by analyzing the collected data, the prediction accuracy of the match outcome is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze data using a machine learning algorithm and predict the outcome of a match.

[0083] The prediction unit can predict the match result based on the analyzed data. The prediction unit predicts the match result based on the analyzed data, for example. The prediction is performed using, for example, a statistical model or a machine learning model, but is not limited to these examples. For example, the prediction unit calculates the probability that a specific team will win. The prediction unit can also predict player performance. Furthermore, the prediction unit can predict the match result based on weather information. As a result, by predicting the match result based on the analyzed data, prediction accuracy is improved. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can predict the match result using a machine learning model.

[0084] The providing unit can provide the prediction results to the user. The providing unit provides the prediction results to the user, for example. The provision can be performed, for example, through a web application or a mobile application, but is not limited to such examples. For example, the providing unit notifies the user of a predicted match result. The providing unit can also provide the user with a predicted player performance. Furthermore, the providing unit can also provide a prediction result based on weather information. By providing the prediction result to the user, the user can be provided with reference information when placing a bet. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can automatically notify the user of the prediction result using AI.

[0085] The betting unit allows users to place bets based on the provided prediction results. The betting unit allows users to place bets based on, for example, the provided prediction results. Bets are placed, for example, through an online betting platform, but are not limited to such examples. For example, the betting unit supports users to bet on specific teams. The betting unit can also support users to bet on player performance. Furthermore, the betting unit can support betting based on weather information. This allows users to place bets based on the provided prediction results, thereby improving the accuracy of betting. Some or all of the above-mentioned processing in the betting unit may be performed using, for example, AI, or may be performed without using AI. For example, the betting unit can support users' bets using AI.

[0086] The learning unit can update the prediction model based on betting results or newly collected data. The learning unit updates the prediction model based on, for example, betting results or newly collected data. Learning is performed using, for example, a machine learning algorithm, but is not limited to such an example. For example, the learning unit improves the prediction model based on the user's betting results. The learning unit can also update the prediction model based on newly collected data. Furthermore, the learning unit can also update the prediction model based on data collected in real time. In this way, by updating the prediction model based on betting results or newly collected data, prediction accuracy is improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can update the prediction model using a machine learning algorithm.

[0087] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The collection unit, for example, estimates the user's emotions. Emotion estimation is performed, for example, using an emotion analysis algorithm, but is not limited to such an example. For example, the collection unit analyzes the user's facial expressions and voice to estimate emotions. The collection unit can also analyze the user's biometric data to estimate emotions. Furthermore, the collection unit can analyze the user's social media posts to estimate emotions. Next, the collection unit adjusts the timing of data collection based on the estimated user emotions. For example, if the user is excited, data can be collected in real time and analyzed immediately. Alternatively, if the user is relaxed, data can be collected at regular intervals and analyzed. Furthermore, if the user is stressed, the frequency of data collection can be reduced to reduce the user's burden. In this way, by adjusting the timing of data collection based on the user's emotions, data can be collected at more appropriate times. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0088] The collection unit can prioritize collection of specific conditions when collecting past match results and player performance data. For example, if a specific player is injured, the collection unit prioritizes collection of the player's past performance data. The collection unit can also prioritize collection of information regarding the player's health condition before a match. Furthermore, if a specific player returns to the game, the collection unit can prioritize collection of the player's latest performance data. In this way, by prioritizing collection of specific conditions, more important data can be collected efficiently. Some or all of the above-mentioned processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input injury information of a specific player into AI and cause the AI ​​to collect related performance data.

[0089] When collecting weather information, the collection unit can filter the data based on the geographical characteristics of the match venue. For example, if the match venue is located at a high altitude, the collection unit can collect weather information taking into account fluctuations in air pressure and temperature. Furthermore, if the match venue is located on the coast, the collection unit can also prioritize collecting data on wind speed and humidity. Furthermore, if the match venue is located in an urban area, the collection unit can also take into account meteorological conditions specific to the city when collecting weather information. In this way, by collecting weather information taking into account the geographical characteristics of the match venue, more accurate weather data can be obtained. Some or all of the above-described processing by the collection unit can be performed using, or without, AI, for example. For example, the collection unit can input the geographical characteristics of the match venue into AI and have the AI ​​filter the weather information.

[0090] When collecting data, the collection unit can prioritize collecting highly relevant data by referring to the user's past betting history. For example, the collection unit prioritizes collecting data on matches on which the user has bet in the past. Furthermore, if the user tends to bet on a particular team, the collection unit can prioritize collecting data on that team. Furthermore, if the user is paying attention to a particular player, the collection unit can prioritize collecting data on that player. In this way, by referring to the user's past betting history, highly relevant data can be efficiently collected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past betting history into AI and cause the AI ​​to collect highly relevant data.

[0091] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions. Emotion estimation is performed using, for example, an emotion analysis algorithm, but is not limited to, such examples. For example, the collection unit analyzes the user's facial expressions and voice to estimate emotions. The collection unit can also analyze the user's biometric data to estimate emotions. Furthermore, the collection unit can analyze the user's social media posts to estimate emotions. Next, the collection unit determines the priority of data to be collected based on the estimated user emotions. For example, if the user is excited, data immediately before a game can be collected preferentially. Alternatively, if the user is relaxed, data from past games can be collected preferentially. Furthermore, if the user is stressed, data of less importance can be postponed. In this way, by determining the priority of data based on the user's emotions, more appropriate data can be collected. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0092] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user lives in a specific area, the collection unit can prioritize collecting match data for that area. Also, if the user is traveling, the collection unit can prioritize collecting match data related to the user's current location. Furthermore, if the user is interested in a specific city, the collection unit can prioritize collecting match data for that city. This allows for efficient collection of highly relevant data by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI and cause the AI ​​to collect related data.

[0093] When collecting data, the collection unit can analyze the user's social media activity and collect relevant data. For example, the collection unit can prioritize collecting data on teams the user follows on social media. The collection unit can also prioritize collecting data on players mentioned by the user on social media. Furthermore, the collection unit can collect match data that the user is likely to be interested in from the user's social media activity. This allows for efficient collection of highly relevant data by analyzing the user's social media activity. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media activity into AI and cause the AI ​​to collect relevant data.

[0094] When collecting data, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit preferentially uses a data collection method that the user has previously rated highly. The collection unit can also improve a data collection method that the user has previously expressed dissatisfaction with. Furthermore, the collection unit can also try out a new data collection method based on the user's feedback. This allows the collection method to be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback into AI and have the AI ​​customize the collection method.

[0095] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions. Emotion estimation is performed, for example, using an emotion analysis algorithm, but is not limited to such examples. For example, the analysis unit can analyze the user's facial expressions and voice to estimate emotions. The analysis unit can also analyze the user's biometric data to estimate emotions. Furthermore, the analysis unit can analyze the user's social media posts to estimate emotions. Next, the analysis unit adjusts the presentation of the analysis based on the estimated user's emotions. For example, if the user is excited, the analysis results can be displayed using visually stimulating graphs and charts. If the user is relaxed, detailed text analysis can be provided. Furthermore, if the user is stressed, simple and concise analysis results can be displayed. This allows for more appropriate analysis results to be provided by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0096] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to AI and have the AI ​​adjust the level of detail of the analysis.

[0097] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a statistical analysis algorithm to past match result data. The analysis unit can also apply a machine learning algorithm to player performance data. Furthermore, the analysis unit can apply a weather forecasting algorithm to weather information. By applying different analysis algorithms depending on the data category, analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category into AI and have the AI ​​apply an appropriate analysis algorithm.

[0098] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit refers to analysis results that the user has previously rated highly. The analysis unit can also improve analysis results that the user has previously expressed dissatisfaction with. Furthermore, the analysis unit can also try new analysis methods based on the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into AI and have the AI ​​improve the accuracy of the analysis.

[0099] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. Emotion estimation is performed using, for example, an emotion analysis algorithm, but is not limited to, such examples. For example, the analysis unit analyzes the user's facial expressions and voice to estimate the emotion. The analysis unit can also analyze the user's biometric data to estimate the emotion. Furthermore, the analysis unit can analyze the user's social media posts to estimate the emotion. Next, the analysis unit adjusts the length of the analysis based on the estimated user's emotion. For example, if the user is excited, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is stressed, the analysis unit can provide a simple and highly visible analysis result. Thus, by adjusting the length of the analysis based on the user's emotion, more appropriate analysis results can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0100] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone data that was submitted earlier. Furthermore, the analysis unit can adjust the analysis schedule according to the time of submission. This enables efficient analysis by determining the priority of analysis based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data submission into AI and have the AI ​​determine the priority of analysis.

[0101] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can determine the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI and have the AI ​​adjust the order of analysis.

[0102] During analysis, the analysis unit can adjust the use of analytical terminology according to the user's level of expertise. For example, the analysis unit uses detailed terminology for users with high levels of expertise. The analysis unit can also explain the analysis results in simpler terms for users with low levels of expertise. Furthermore, the analysis unit can adjust the way the analysis results are expressed according to the user's level of expertise. This makes it possible to provide analysis results that are easier to understand by adjusting the use of analytical terminology according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise into AI and have the AI ​​use analytical terminology.

[0103] The prediction unit can estimate the user's emotion and adjust the way the prediction is expressed based on the estimated user's emotion. The prediction unit, for example, estimates the user's emotion. Emotion estimation is performed, for example, using an emotion analysis algorithm, but is not limited to such an example. For example, the prediction unit analyzes the user's facial expressions and voice to estimate the emotion. The prediction unit can also analyze the user's biometric data to estimate the emotion. Furthermore, the prediction unit can analyze the user's social media posts to estimate the emotion. Next, the prediction unit adjusts the way the prediction is expressed based on the estimated user's emotion. For example, if the user is excited, the prediction result can be displayed using visually stimulating graphs and charts. If the user is relaxed, a detailed text prediction can be provided. Furthermore, if the user is stressed, a simple and to-the-point prediction can be displayed. This allows for more appropriate prediction results to be provided by adjusting the way the prediction is expressed based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the prediction unit may be performed using AI, or may be performed without using AI. For example, the prediction unit may input user facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0104] The prediction unit can adjust the level of detail of the prediction based on the importance of the data when making a prediction. For example, the prediction unit performs a detailed prediction for data with high importance. The prediction unit can also perform a simplified prediction for data with low importance. Furthermore, the prediction unit can determine the priority of the prediction according to the importance of the data. This enables efficient prediction by adjusting the level of detail of the prediction based on the importance of the data. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the importance of the data into AI and have the AI ​​adjust the level of detail of the prediction.

[0105] The prediction unit can apply different prediction algorithms depending on the data category when making predictions. For example, the prediction unit applies a statistical prediction algorithm to past match result data. The prediction unit can also apply a machine learning prediction algorithm to player performance data. Furthermore, the prediction unit can apply a weather prediction algorithm to weather information. In this way, applying different prediction algorithms depending on the data category improves prediction accuracy. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the data category into AI and cause the AI ​​to apply an appropriate prediction algorithm.

[0106] When making predictions, the prediction unit can improve the accuracy of the prediction by referring to the user's past prediction results. For example, the prediction unit refers to prediction results that the user has previously rated highly. The prediction unit can also improve prediction results that the user has previously expressed dissatisfaction with. Furthermore, the prediction unit can also try new prediction methods based on the user's past prediction results. In this way, by referring to the user's past prediction results, the accuracy of the predictions is improved. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the user's past prediction results into AI and have the AI ​​improve the accuracy of the predictions.

[0107] The prediction unit can estimate the user's emotion and adjust the length of the prediction based on the estimated user's emotion. The prediction unit, for example, estimates the user's emotion. Emotion estimation is performed using, for example, an emotion analysis algorithm, but is not limited to, such examples. For example, the prediction unit analyzes the user's facial expressions and voice to estimate the emotion. The prediction unit can also analyze the user's biometric data to estimate the emotion. Furthermore, the prediction unit can analyze the user's social media posts to estimate the emotion. Next, the prediction unit adjusts the length of the prediction based on the estimated user's emotion. For example, if the user is excited, a short and concise prediction result can be provided. If the user is relaxed, a detailed prediction result can be provided. Furthermore, if the user is stressed, a simple and highly visible prediction result can be provided. Thus, by adjusting the length of the prediction based on the user's emotion, more appropriate prediction results can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0108] The prediction unit can determine the priority of predictions based on the time of data submission when making predictions. For example, the prediction unit prioritizes the most recent data for prediction. The prediction unit can also postpone data that has been submitted earlier. Furthermore, the prediction unit can adjust the prediction schedule according to the time of submission. This enables efficient predictions by determining the priority of predictions based on the time of data submission. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the time of data submission into AI and have the AI ​​determine the priority of predictions.

[0109] The prediction unit can adjust the order of predictions based on the relevance of the data during prediction. For example, the prediction unit prioritizes highly relevant data for prediction. The prediction unit can also postpone less relevant data. Furthermore, the prediction unit can determine the order of predictions according to the relevance of the data. This enables efficient prediction by adjusting the order of predictions based on the relevance of the data. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the relevance of the data into AI and have the AI ​​adjust the order of predictions.

[0110] The prediction unit can adjust the use of technical terminology in the prediction according to the user's level of expertise when making a prediction. For example, the prediction unit uses detailed technical terminology for users with high levels of expertise. The prediction unit can also explain the prediction result in simpler terms to users with low levels of expertise. Furthermore, the prediction unit can adjust the way the prediction result is expressed according to the user's level of expertise. This makes it possible to provide prediction results that are easier to understand by adjusting the use of technical terminology in the prediction according to the user's level of expertise. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the user's level of expertise into AI and have the AI ​​use technical terminology in the prediction.

[0111] The providing unit can estimate the user's emotion and adjust the presentation style based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion. Emotion estimation can be performed using, for example, an emotion analysis algorithm, but is not limited to such an example. For example, the providing unit can analyze the user's facial expressions and voice to estimate the emotion. The providing unit can also analyze the user's biometric data to estimate the emotion. Furthermore, the providing unit can analyze the user's social media posts to estimate the emotion. Next, the providing unit adjusts the presentation style based on the estimated user's emotion. For example, if the user is excited, the system can provide a prediction result using visually stimulating graphs and charts. If the user is relaxed, the system can provide a detailed text prediction. Furthermore, if the user is stressed, the system can provide a simple and concise prediction. This allows the system to adjust the presentation style based on the user's emotion, thereby providing more appropriate presentation results. Emotion estimation can be achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0112] The providing unit can adjust the level of detail of the provided information based on the importance of the prediction result when providing the information. For example, the providing unit provides detailed information for a prediction result with high importance. The providing unit can also provide simplified information for a prediction result with low importance. Furthermore, the providing unit can determine the priority of the provided information according to the importance of the prediction result. This enables efficient provision by adjusting the level of detail of the provided information based on the importance of the prediction result. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the importance of the prediction result to AI and cause the AI ​​to adjust the level of detail of the provided information.

[0113] The providing unit can apply different providing algorithms depending on the category of the prediction result when providing the results. For example, the providing unit applies a statistical providing algorithm to predict the result of a match. The providing unit can also apply a machine learning algorithm to predict the performance of a player. The providing unit can also apply a weather forecasting algorithm to predict the weather. In this way, by applying different providing algorithms depending on the category of the prediction result, the accuracy of the results provided is improved. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the category of the prediction result into AI and cause the AI ​​to apply an appropriate providing algorithm.

[0114] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the information. For example, the providing unit refers to provision results that the user has previously given high ratings. The providing unit can also improve provision results that the user has previously expressed dissatisfaction with. Furthermore, the providing unit can also try new provision methods based on the user's past provision results. In this way, the accuracy of the provision is improved by referring to the user's past provision results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past provision results into AI and have the AI ​​improve the accuracy of the provision.

[0115] The providing unit can estimate the user's emotion and adjust the length of the provided information based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion. Emotion estimation is performed using, for example, an emotion analysis algorithm, but is not limited to, such an example. For example, the providing unit analyzes the user's facial expressions and voice to estimate the emotion. The providing unit can also analyze the user's biometric data to estimate the emotion. Furthermore, the providing unit can analyze the user's social media posts to estimate the emotion. Next, the providing unit adjusts the length of the provided information based on the estimated user's emotion. For example, if the user is excited, a short and concise result can be provided. On the other hand, if the user is relaxed, a detailed result can be provided. Furthermore, if the user is stressed, a simple and highly visible result can be provided. In this way, by adjusting the length of the provided information based on the user's emotion, more appropriate results can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's facial expression data to the generating AI and cause the generating AI to estimate the emotion.

[0116] The providing unit can determine the priority of provision based on the submission time of the prediction results at the time of provision. For example, the providing unit provides the most recent prediction results preferentially. The providing unit can also postpone prediction results that have been submitted earlier. Furthermore, the providing unit can adjust the provision schedule according to the submission time. This enables efficient provision by determining the priority of provision based on the submission time of the prediction results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the submission time of the prediction results into AI and have the AI ​​determine the priority of provision.

[0117] The providing unit can adjust the order of provision based on the relevance of the prediction results when providing them. For example, the providing unit can provide highly relevant prediction results preferentially. The providing unit can also postpone less relevant prediction results. Furthermore, the providing unit can determine the order of provision based on the relevance of the prediction results. This enables efficient provision by adjusting the order of provision based on the relevance of the prediction results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the relevance of the prediction results to AI and cause the AI ​​to adjust the order of provision.

[0118] The providing unit can adjust the use of technical terminology provided during provision according to the user's level of expertise. For example, the providing unit uses detailed technical terminology for a user with high level of expertise. The providing unit can also explain the provided results in simple terms for a user with low level of expertise. Furthermore, the providing unit can adjust the way in which the provided results are expressed according to the user's level of expertise. This allows for the provision of results that are easier to understand by adjusting the use of technical terminology provided according to the user's level of expertise. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's level of expertise into AI and have the AI ​​execute the use of technical terminology provided.

[0119] The betting unit can estimate the user's emotions and adjust the manner in which the bet is expressed based on the estimated user's emotions. The betting unit, for example, estimates the user's emotions. Emotion estimation can be performed, for example, using an emotion analysis algorithm, but is not limited to such examples. For example, the betting unit can analyze the user's facial expressions and voice to estimate emotions. The betting unit can also analyze the user's biometric data to estimate emotions. Furthermore, the betting unit can analyze the user's social media posts to estimate emotions. Next, the betting unit adjusts the manner in which the bet is expressed based on the estimated user's emotions. For example, if the user is excited, visually stimulating graphs and charts can be used to provide betting information. If the user is relaxed, detailed text information can be provided. Furthermore, if the user is stressed, simple and to the point betting information can be provided. In this way, by adjusting the manner in which the bet is expressed based on the user's emotions, more appropriate betting information can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the betting unit may be performed using AI, or may be performed without using AI. For example, the betting unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0120] The betting unit can adjust the level of detail of a bet based on the importance of a prediction result when placing a bet. For example, the betting unit can provide detailed information for a highly important prediction result. The betting unit can also provide simplified information for a less important prediction result. Furthermore, the betting unit can determine the priority of a bet according to the importance of the prediction result. This allows for efficient betting by adjusting the level of detail of a bet based on the importance of the prediction result. Some or all of the above-described processing in the betting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the betting unit can input the importance of a prediction result to an AI and have the AI ​​adjust the level of detail of a bet.

[0121] When placing a bet, the betting unit can apply different betting algorithms depending on the category of the predicted result. For example, the betting unit can apply a statistical betting algorithm to predict the outcome of a match. The betting unit can also apply a machine learning algorithm to predict player performance. Furthermore, the betting unit can apply a weather forecasting algorithm to predict the weather. In this way, by applying different betting algorithms depending on the category of the predicted result, the accuracy of betting is improved. Some or all of the above-mentioned processing in the betting unit may be performed using, for example, AI, or may be performed without using AI. For example, the betting unit can input the category of the predicted result into AI and cause the AI ​​to apply an appropriate betting algorithm.

[0122] When placing a bet, the betting unit can improve the accuracy of the bet by referring to the user's past betting results. For example, the betting unit refers to betting results that the user has previously rated highly. The betting unit can also improve betting results that the user has previously expressed dissatisfaction with. Furthermore, the betting unit can try new betting methods based on the user's past betting results. In this way, by referring to the user's past betting results, the accuracy of the bet is improved. Some or all of the above-mentioned processing in the betting unit may be performed using, for example, AI, or may be performed without using AI. For example, the betting unit can input the user's past betting results into AI and have the AI ​​improve the accuracy of the bet.

[0123] The betting unit can estimate the user's emotions and adjust the betting length based on the estimated user's emotions. The betting unit, for example, estimates the user's emotions. Emotion estimation is performed using, for example, an emotion analysis algorithm, but is not limited to such examples. For example, the betting unit analyzes the user's facial expressions and voice to estimate emotions. The betting unit can also analyze the user's biometric data to estimate emotions. Furthermore, the betting unit can analyze the user's social media posts to estimate emotions. Next, the betting unit adjusts the betting length based on the estimated user's emotions. For example, if the user is excited, the betting unit can provide short and concise betting information. If the user is relaxed, the betting unit can provide detailed betting information. Furthermore, if the user is stressed, the betting unit can provide simple and highly visible betting information. In this way, by adjusting the betting length based on the user's emotions, more appropriate betting information can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the betting unit may be performed using, for example, AI, or may be performed without using AI. For example, the betting unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0124] At the time of betting, the betting unit can determine the priority of bets based on the time of submission of prediction results. For example, the betting unit prioritizes the most recent prediction results for betting. The betting unit can also postpone prediction results that have been submitted earlier. Furthermore, the betting unit can adjust the betting schedule according to the submission time. This enables efficient betting by determining the priority of bets based on the time of submission of prediction results. Some or all of the above-mentioned processing in the betting unit may be performed using, for example, AI, or may be performed without using AI. For example, the betting unit can input the time of submission of prediction results to AI and have the AI ​​determine the priority of bets.

[0125] The betting unit can adjust the order of bets based on the relevance of the prediction results when placing a bet. For example, the betting unit prioritizes highly relevant prediction results for betting. The betting unit can also postpone less relevant prediction results. Furthermore, the betting unit can determine the order of bets according to the relevance of the prediction results. This enables efficient betting by adjusting the order of bets based on the relevance of the prediction results. Some or all of the above-described processing in the betting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the betting unit can input the relevance of the prediction results to an AI and have the AI ​​adjust the order of bets.

[0126] The betting unit can adjust the use of betting terminology depending on the user's level of expertise when placing a bet. For example, the betting unit uses detailed terminology for users with high levels of expertise. The betting unit can also explain betting information in simple terms to users with low levels of expertise. Furthermore, the betting unit can adjust the way betting information is expressed depending on the user's level of expertise. This makes it possible to provide betting information that is easier to understand by adjusting the use of betting terminology depending on the user's level of expertise. Some or all of the above-described processing in the betting unit may be performed using, for example, AI, or may be performed without using AI. For example, the betting unit can input the user's level of expertise into AI and have the AI ​​execute the use of betting terminology.

[0127] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. The learning unit, for example, estimates the user's emotions. Emotion estimation is performed using, for example, an emotion analysis algorithm, but is not limited to such an example. For example, the learning unit analyzes the user's facial expressions and voice to estimate emotions. The learning unit can also analyze the user's biometric data to estimate emotions. Furthermore, the learning unit can analyze the user's social media posts to estimate emotions. Next, the learning unit selects training data based on the estimated user emotions. For example, if the user is excited, data collected in real time can be prioritized for learning. Also, if the user is relaxed, past data can be prioritized for learning. Furthermore, if the user is stressed, data of less importance can be postponed. In this way, by selecting training data based on the user's emotions, more appropriate data can be used for learning. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit may be performed using AI, or may be performed without using AI. For example, the learning unit may input user facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0128] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit, for example, selects an optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data and adjust algorithm parameters. Furthermore, the learning unit can also try out new learning algorithms based on past learning data. This makes it possible to optimize the learning algorithm by referring to past learning data. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into AI and have the AI ​​optimize the learning algorithm.

[0129] During learning, the learning unit can analyze fluctuations in the user's betting history and adjust the update frequency of the learning data. For example, if the user's betting history fluctuates frequently, the learning unit can increase the update frequency of the learning data. Also, if the user's betting history is stable, the learning unit can decrease the update frequency of the learning data. Furthermore, the learning unit can analyze the fluctuation pattern of the user's betting history and determine the optimal update frequency. In this way, by analyzing the fluctuations in the user's betting history, the update frequency of the learning data can be optimized. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input fluctuations in the user's betting history into AI and cause the AI ​​to adjust the update frequency of the learning data.

[0130] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. The learning unit, for example, estimates the user's emotions. Emotion estimation is performed using, for example, an emotion analysis algorithm, but is not limited to such examples. For example, the learning unit analyzes the user's facial expressions and voice to estimate emotions. The learning unit can also analyze the user's biometric data to estimate emotions. Furthermore, the learning unit can analyze the user's social media posts to estimate emotions. Next, the learning unit adjusts the frequency of learning based on the estimated user emotions. For example, if the user is excited, the learning frequency can be increased. If the user is relaxed, the learning frequency can be kept normal. Furthermore, if the user is stressed, the learning frequency can be decreased. In this way, by adjusting the learning frequency based on the user's emotions, learning can be performed at a more appropriate frequency. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit may input user facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0131] During learning, the learning unit can weight the learning data based on the time of submission of the betting history. For example, the learning unit sets a high weight for the most recent betting history. The learning unit can also set a low weight for older betting history. Furthermore, the learning unit can adjust the weighting of the learning data according to the time of submission. This enables efficient learning by weighting the learning data based on the time of submission of the betting history. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the time of submission of the betting history to the AI ​​and have the AI ​​perform weighting of the learning data.

[0132] During learning, the learning unit can adjust the learning algorithm by reflecting user feedback. For example, the learning unit preferentially uses learning algorithms that users have given high ratings to. The learning unit can also improve learning algorithms that users have expressed dissatisfaction with. Furthermore, the learning unit can also try out new learning algorithms based on user feedback. This improves the accuracy of the learning algorithm by reflecting user feedback. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input user feedback into AI and have the AI ​​adjust the learning algorithm. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, prediction unit, provision unit, betting unit, and learning 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 collection unit collects data using the camera 42 or a sensor of the smart device 14, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and predicts the outcome of a match based on the analyzed data. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the prediction results to the user. The betting unit is realized, for example, by the control unit 46A of the smart device 14 and supports the user in placing bets. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and updates the prediction model based on betting results and newly collected data. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, prediction unit, provision unit, betting unit, and learning 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 collection unit collects data using the camera 42 or a sensor of the smart glasses 214, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and predicts the outcome of a match based on the analyzed data. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the prediction result to the user. The betting unit is realized, for example, by the control unit 46A of the smart glasses 214 and supports the user in placing a bet. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and updates the prediction model based on the betting results and newly collected data. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, prediction unit, provision unit, betting unit, and learning unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects data using the camera 42 or a sensor of the headset-type terminal 314, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and predicts the outcome of a match based on the analyzed data. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides the prediction results to the user. The betting unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and supports the user in placing bets. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and updates the prediction model based on betting results and newly collected data. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, prediction unit, provision unit, betting unit, and learning unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data using the camera 42 or sensors of the robot 414, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and predicts the outcome of a match based on the analyzed data. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the prediction results to the user. The betting unit is realized, for example, by the control unit 46A of the robot 414 and supports the user in placing bets. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and updates the prediction model based on betting results and newly collected data.

[0133] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0134] The sports betting system may further include a trend analysis unit that analyzes a user's past betting history and identifies the user's betting tendencies. For example, if a user tends to bet on a particular team or player, the trend analysis unit customizes prediction results based on that tendency. The trend analysis unit may also identify betting patterns that have shown a high winning rate for the user in the past and suggest new bets based on those patterns. Furthermore, the trend analysis unit may analyze fluctuations in the user's betting frequency and amount and provide advice for risk management. This may improve user satisfaction and winning rates by providing individually optimized prediction results based on the user's betting history.

[0135] The collection unit can analyze the user's social media activity and prioritize collection of data related to games and players in which the user is interested. For example, data on teams and players frequently mentioned by the user on social media can be collected. The collection unit can also analyze the content of posts from sports-related accounts followed by the user to collect related game data. Furthermore, the collection unit can take into account the time periods during which the user is active on social media and prioritize collection of game data related to those time periods. This makes it possible to collect data based on the user's interests and provide more personalized prediction results.

[0136] The analysis unit can estimate the user's emotions and adjust the way the analysis results are presented based on the estimated user emotions. For example, if the user is excited, the analysis results can be displayed using visually stimulating graphs and charts. If the user is relaxed, detailed text analysis can be provided. Furthermore, if the user is stressed, simple and to the point analysis results can be displayed. In this way, by adjusting the way the analysis results are presented based on the user's emotions, more appropriate analysis results can be provided.

[0137] The prediction unit can adjust the prediction model by referring to the user's past betting results. For example, the prediction model can be optimized based on prediction results that the user has previously rated highly. It can also improve prediction results that the user has previously expressed dissatisfaction with. Furthermore, the prediction unit can try out new prediction algorithms based on the user's past betting results. In this way, by referring to the user's past betting results, the accuracy of predictions can be improved and user satisfaction can be increased.

[0138] The providing unit can estimate the user's emotions and adjust the presentation style based on the estimated user's emotions. For example, if the user is excited, the prediction result can be provided using visually stimulating graphs and charts. If the user is relaxed, a detailed text prediction can be provided. Furthermore, if the user is stressed, a simple and to-the-point prediction can be provided. In this way, by adjusting the presentation style based on the user's emotions, more appropriate presentation results can be provided.

[0139] The betting unit may include a trend analysis unit that analyzes the user's past betting history and identifies the user's betting tendencies. For example, if the user tends to bet on a particular team or player, the trend analysis unit customizes betting suggestions based on that tendency. The trend analysis unit may also identify betting patterns that have previously shown the user a high winning rate and provide new betting suggestions based on those patterns. Furthermore, the trend analysis unit may analyze fluctuations in the user's betting frequency and amount and provide advice for risk management. This may improve user satisfaction and win rates by providing individually optimized betting suggestions based on the user's betting history.

[0140] The learning unit can estimate the user's emotions and select learning data based on the estimated user emotions. For example, if the user is excited, data collected in real time can be used preferentially for learning. Also, if the user is relaxed, past data can be used preferentially for learning. Furthermore, if the user is feeling stressed, data of less importance can be postponed. In this way, by selecting learning data based on the user's emotions, more appropriate data can be used for learning.

[0141] The collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user lives in a specific area, it can prioritize collecting match data for that area. In addition, if the user is traveling, the collection unit can prioritize collecting match data related to the user's current location. Furthermore, if the user is interested in a specific city, the collection unit can prioritize collecting match data for that city. In this way, highly relevant data can be efficiently collected by taking into account the user's geographical location information.

[0142] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, a statistical analysis algorithm can be applied to past match result data. The analysis unit can also apply a machine learning algorithm to player performance data. Furthermore, the analysis unit can apply a weather forecasting algorithm to weather information. In this way, by applying different analysis algorithms depending on the data category, the accuracy of analysis can be improved.

[0143] The prediction unit can estimate the user's emotions and adjust the way the prediction is presented based on the estimated user's emotions. For example, if the user is excited, the prediction result can be displayed using visually stimulating graphs and charts. If the user is relaxed, a detailed text prediction can be provided. Furthermore, if the user is stressed, a simple and to-the-point prediction can be displayed. This allows the prediction unit to provide more appropriate prediction results by adjusting the way the prediction is presented based on the user's emotions.

[0144] The processing flow of the second embodiment will be briefly explained below.

[0145] Step 1: The collection unit collects data. This data includes past match results, player performance data, weather information, and more. The collection unit collects data from public databases on the Internet. It can also collect real-time data using sensors and APIs. For example, it can obtain weather information at a match venue in real time. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using machine learning algorithms. For example, it can predict the outcome of the next game based on the results of past games, or analyze player performance data to evaluate the condition of the players. Step 3: The prediction unit predicts the outcome of the match based on the data analyzed by the analysis unit. Predictions are made using statistical models or machine learning models. For example, they calculate the probability that a particular team will win or predict the performance of a player. Step 4: The providing unit provides the prediction results obtained by the prediction unit to the user. The provision is performed through a web application or a mobile application. For example, the providing unit notifies the user of the predicted match results or provides predictions of player performance. Step 5: The betting unit allows the user to place a bet based on the prediction results provided by the provider. The bet is placed through an online betting platform, which, for example, supports users to bet on a specific team or on the performance of a player. Step 6: The learning unit updates the prediction model based on the results of bets placed by the betting unit and newly collected data. Learning is performed using a machine learning algorithm. For example, the prediction model may be improved based on the results of users' bets, or the prediction model may be updated based on newly collected data.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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 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.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0167] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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 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.

[0180] 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.

[0181] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0182] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0183] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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).

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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 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.

[0197] 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.

[0198] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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).

[0203] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0204] 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."

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] [Explanation of symbols]

[0218] 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 collection unit that collects data; an analysis unit that analyzes the data collected by the collection unit; a prediction unit that predicts a match result based on the data analyzed by the analysis unit; a providing unit that provides a user with the prediction result obtained by the prediction unit; a betting unit where a user places a bet based on the prediction result provided by the providing unit; a learning unit that updates the prediction model based on the results of bets made by the betting unit and newly collected data; Equipped with A system characterized by:

2. The collecting unit Collect data including past match results, player performance data, and weather information 2. The system of claim 1.

3. The analysis unit Analyze the collected data and predict the outcome of the match 2. The system of claim 1.

4. The prediction unit Predicting match results based on analyzed data 2. The system of claim 1.

5. The providing unit Providing prediction results to users 2. The system of claim 1.

6. The betting section 2. The system of claim 1, wherein a user places a bet based on the provided prediction result.

7. The learning unit Update your predictive models based on betting results and newly collected data 2. The system of claim 1.

8. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

Images