System

Abstract

An object of a system according to an embodiment is to support optimal matching between an E-sports team or a player and a sponsor company.SOLUTION: A system according to an embodiment includes a player analysis unit, a sponsor analysis unit, a matching proposal unit, an effect measurement unit, and a return analysis unit. The player analysis unit analyzes the performance and popularity of the player. A sponsor analysis part analyzes the needs of the sponsor. The matching proposal unit proposes optimal matching based on the data analyzed by the player analysis unit and the sponsor analysis unit. The effect measurement unit measures an effect of sponsorship. The return analysis component analyzes sponsorship returns.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 faced the challenge of making it difficult to efficiently match e-sports teams and players with sponsoring companies in an optimal manner.

[0005] The system according to the embodiment aims to support optimal matching between E-sports teams or players and sponsoring companies. [Means for solving the problem]

[0006] The system according to the embodiment includes a player analysis unit, a sponsor analysis unit, a match suggestion unit, an effect measurement unit, and a return analysis unit. The player analysis unit analyzes the achievements and popularity of players. The sponsor analysis unit analyzes the needs of sponsors. The match suggestion unit suggests optimal matches based on the data analyzed by the player analysis unit and the sponsor analysis unit. The effect measurement unit measures the effect of sponsorship. The return analysis unit analyzes the return of sponsorship. [Effects of the Invention]

[0007] The system according to the embodiment can support optimal matching of E-sports teams or players with sponsoring companies. [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) An AI system according to an embodiment of the present invention supports matching of esports teams and players with corporate sponsors. This system analyzes players' performance and popularity, analyzes sponsors' needs, and supports contracts with optimal sponsors. It also aims to increase sponsor satisfaction by measuring the effectiveness of sponsorships and analyzing returns. This enables esports teams and players to increase sponsorship contracts and boost their income. Effective sponsorship also enables sponsors to improve their brand recognition and sales.

[0029] The AI ​​system according to the embodiment includes a player analysis unit, a sponsor analysis unit, a match suggestion unit, an effectiveness measurement unit, and a return analysis unit. The player analysis unit analyzes a player's performance and popularity. For example, the player analysis unit uses a generation AI to collect and analyze data such as a player's past match results and performance, the number of followers on social media, and their engagement rate. The generation AI performs analysis based on the player's performance data and social media data. The sponsor analysis unit analyzes the sponsor's needs. For example, the sponsor analysis unit uses a generation AI to collect and analyze data such as the sponsor company's marketing strategy, target demographic, and past sponsorship results. The generation AI performs analysis based on the sponsor company's marketing data and past sponsorship data. The match suggestion unit proposes optimal matches based on the data analyzed by the player analysis unit and the sponsor analysis unit. For example, the match suggestion unit uses a generation AI to propose optimal matches between sponsors and players based on the analyzed player's performance, popularity, and sponsor needs. The generation AI proposes matches based on the analysis results of the player and sponsor. The effectiveness measurement unit measures the effectiveness of the sponsorship. For example, the effect measurement unit uses the generation AI to collect and analyze data such as sales data during the sponsorship period, changes in brand awareness, and engagement rates on social media. The generation AI performs analysis based on the sales data and social media data during the sponsorship period. The return analysis unit analyzes the returns from the sponsorship. For example, the return analysis unit uses the generation AI to compare the sponsor company's investment amount with the returns obtained. The generation AI analyzes the returns based on data on the sponsor company's investment amount and the returns obtained. As a result, the AI ​​system according to the embodiment enables e-sports teams and players to increase sponsorship contracts and improve their income. Furthermore, sponsor companies can improve their brand awareness and sales by conducting effective sponsorship.

[0030] The player analysis unit can analyze a player's in-game playing style and strategy to evaluate their uniqueness and appeal. The player analysis unit, for example, uses generative AI to analyze a player's in-game playing style. For example, it extracts player characteristics, such as offensive or defensive playing style, and evaluates their uniqueness. The player analysis unit also analyzes a player's strategy and evaluates their judgment and reaction speed in specific situations. For example, it analyzes their leadership in team matches and their tactical decisions in individual matches. The player analysis unit also analyzes a player's movements in the game to evaluate their appeal. For example, it evaluates plays that attract spectators and their ability to create highlight scenes. This allows the uniqueness and appeal of players to be evaluated, making it possible to propose attractive players to sponsoring companies.

[0031] The player analysis unit can analyze a player's past interviews and media appearances to evaluate their personality and brand value. For example, the player analysis unit analyzes a player's past interviews to evaluate their personality. For example, it extracts statements that emphasize the importance of leadership and team play. The player analysis unit also analyzes the frequency and content of media appearances to evaluate a player's brand value. For example, it evaluates based on the number and content of television appearances and magazine interviews. The player analysis unit also analyzes a player's statements and posts on social media to evaluate their personality and brand value. For example, it evaluates their interactions with fans and social contribution activities. In this way, by evaluating a player's personality and brand value, it is possible to propose suitable players to sponsoring companies.

[0032] The player analysis unit can analyze a player's health condition and fitness data to evaluate the consistency of their performance. The player analysis unit, for example, analyzes a player's health condition and evaluates the consistency of their performance. For example, it compares health data before and after a match to evaluate fluctuations in performance. The player analysis unit also analyzes fitness data to evaluate a player's physical strength and endurance. For example, it makes an evaluation based on training data and heart rate during a match. The player analysis unit also evaluates the consistency of a player's performance based on the health condition and fitness data. For example, it evaluates injury risk and recovery ability. In this way, by evaluating the consistency of a player's performance, it is possible to propose reliable players to sponsor companies.

[0033] The player analysis unit can analyze a player's training data and evaluate their growth rate and potential. The player analysis unit, for example, analyzes a player's training data and evaluates their growth rate. For example, it evaluates the growth rate based on the frequency and intensity of training. The player analysis unit also evaluates a player's potential based on the training data. For example, it evaluates the improvement of a specific skill or the acquisition of a new tactic. The player analysis unit also analyzes the training data and evaluates a player's growth rate and potential. For example, it evaluates based on the results of training and the rate of goal achievement. In this way, by evaluating a player's growth rate and potential, it is possible to suggest promising players to sponsoring companies.

[0034] The sponsor analysis unit can analyze the success factors of sponsor companies' advertising campaigns and propose effective sponsorship strategies. For example, the sponsor analysis unit uses generative AI to analyze data from sponsor companies' past advertising campaigns and identify the success factors. For example, it evaluates the selection of target audiences and advertising media. The sponsor analysis unit also proposes effective sponsorship strategies based on the success factors of advertising campaigns. For example, it proposes collaborations with specific events or athletes. The sponsor analysis unit also analyzes data from sponsor companies' advertising campaigns to identify the success factors and proposes sponsorship strategies based on them. For example, it evaluates the timing of advertising and the content of messages. This makes it possible to propose effective sponsorship strategies to sponsor companies.

[0035] The sponsor analysis unit can analyze the sponsorship activities of the sponsoring company's competitors and identify points of differentiation. The sponsor analysis unit, for example, analyzes the sponsorship activities of the sponsoring company's competitors and identifies points of differentiation. For example, it evaluates the events and athletes supported by the competitors. The sponsor analysis unit also analyzes data on the sponsorship activities of competitors and identifies points of differentiation. For example, it evaluates the age groups and regions that competitors target. The sponsor analysis unit also analyzes the sponsorship activities of competitors and identifies points where the sponsoring company can differentiate itself. For example, it proposes benefits and services that competitors do not offer. This allows the sponsoring company to identify points of differentiation from its competitors and propose effective sponsorship strategies.

[0036] The sponsor analysis unit can analyze product reviews and customer feedback of the sponsor company to evaluate its brand image. The sponsor analysis unit, for example, analyzes product reviews of the sponsor company to evaluate its brand image. For example, it evaluates the ratio of positive and negative reviews. The sponsor analysis unit also analyzes customer feedback to evaluate the brand image of the sponsor company. For example, it evaluates based on customer satisfaction and repeat purchase rates. The sponsor analysis unit also evaluates the brand image of the sponsor company based on product reviews and customer feedback. For example, it analyzes evaluations of product quality and service. This makes it possible to evaluate the brand image of the sponsor company and propose an effective sponsorship strategy.

[0037] The sponsor analysis unit can analyze the social media activities of sponsor companies and evaluate the engagement rate. The sponsor analysis unit, for example, analyzes the social media activities of sponsor companies and evaluates the engagement rate. For example, it evaluates based on the number of likes and shares on posts. The sponsor analysis unit also analyzes the number of followers and comments on social media and evaluates the engagement rate of sponsor companies. For example, it evaluates the rate of increase in followers and the content of comments. The sponsor analysis unit also evaluates the engagement rate based on the social media activities of sponsor companies. For example, it analyzes responses to specific campaigns and events. This makes it possible to evaluate the engagement rate of the sponsor company's social media activities and propose effective sponsorship strategies.

[0038] The matching proposal unit can analyze the degree of match between the brand value of the player and the sponsoring company and propose the optimal match. The matching proposal unit, for example, uses generative AI to analyze the degree of match between the brand value of the player and the sponsoring company. For example, it evaluates whether the player's image and values ​​match the sponsoring company's brand. The matching proposal unit also proposes the optimal match based on the degree of match in brand value. For example, it proposes a match if the player's fan base and the sponsoring company's target audience match. The matching proposal unit also analyzes the brand value of the player and the sponsoring company and proposes a match if there is a high degree of match. For example, it makes a proposal if the player's activities and message match the sponsoring company's brand strategy. This makes it possible to analyze the degree of match between the brand value of the player and the sponsoring company and propose the optimal match.

[0039] The matching proposal unit can analyze past collaboration cases between athletes and sponsoring companies and identify success factors. The matching proposal unit, for example, analyzes past collaboration cases between athletes and sponsoring companies and identifies success factors. For example, it evaluates the success factors of a particular event or campaign. The matching proposal unit also analyzes data from collaboration cases and identifies success factors. For example, it evaluates the performance of athletes and the marketing strategies of sponsoring companies. The matching proposal unit also identifies success factors based on past collaboration cases and uses them for future matching. For example, it extracts common points between successful collaborations and reflects them in proposals. In this way, by analyzing past collaboration cases and identifying success factors, it can be used for future matching.

[0040] The matching proposal unit can analyze sponsorship examples from different sports and entertainment fields and propose applications to e-sports. For example, the matching proposal unit analyzes sponsorship examples from different sports fields and proposes applications to e-sports. For example, it applies sponsorship strategies for soccer or basketball to e-sports. The matching proposal unit also analyzes sponsorship examples from the entertainment field and proposes applications to e-sports. For example, it applies sponsorship strategies for music festivals or movies to e-sports. The matching proposal unit also proposes applications to e-sports based on sponsorship examples from different fields. For example, it proposes a sponsorship strategy based on successful examples from other industries. This makes it possible to analyze sponsorship examples from different fields and propose applications to e-sports.

[0041] The matching proposal unit can analyze the regional relevance between players and sponsor companies and propose region-specific sponsorship. The matching proposal unit, for example, analyzes the regional relevance between players and sponsor companies and proposes region-specific sponsorship. For example, it evaluates the player's hometown or area of ​​activity and the sponsor company's market area. The matching proposal unit also proposes region-specific sponsorship based on the regional relevance. For example, it proposes a sponsorship strategy that utilizes local events and local media. The matching proposal unit also analyzes the regional relevance between players and sponsor companies and proposes region-specific sponsorship. For example, it makes proposals based on the local fan base and market needs. In this way, it is possible to analyze the regional relevance between players and sponsor companies and propose region-specific sponsorship.

[0042] The effect measurement unit can use the generation AI to analyze consumer behavior data during the sponsorship period and evaluate changes in purchasing intent. The effect measurement unit, for example, uses the generation AI to analyze consumer behavior data during the sponsorship period and evaluate changes in purchasing intent. For example, it compares sales data before and after sponsorship. The effect measurement unit also evaluates the effectiveness of the sponsorship based on the consumer behavior data. For example, it evaluates increases in sales of specific products or services. The effect measurement unit also analyzes consumer behavior data during the sponsorship period and evaluates changes in purchasing intent. For example, it evaluates based on consumer purchasing history and online shopping data. In this way, it is possible to analyze consumer behavior data during the sponsorship period and evaluate changes in purchasing intent.

[0043] The effect measurement unit can analyze the amount of media exposure during the sponsorship period and evaluate the improvement in brand awareness. The effect measurement unit, for example, analyzes the amount of media exposure during the sponsorship period and evaluates the improvement in brand awareness. For example, the evaluation is made based on the number of television and radio broadcasts and viewer ratings. The effect measurement unit also evaluates the effectiveness of the sponsorship based on the amount of media exposure. For example, the number of newspaper and magazine articles and the number of times it is featured in online media. The effect measurement unit also analyzes the amount of media exposure during the sponsorship period and evaluates the improvement in brand awareness. For example, the evaluation is made based on the number of mentions on social media and the engagement rate. In this way, the amount of media exposure during the sponsorship period can be analyzed and the improvement in brand awareness can be evaluated.

[0044] The effect measurement unit can analyze fluctuations in market share during the sponsorship period and evaluate the economic impact. The effect measurement unit, for example, analyzes fluctuations in market share during the sponsorship period and evaluates the economic impact. For example, it evaluates fluctuations in market share before and after sponsorship. The effect measurement unit also evaluates the economic impact of sponsorship based on market share data. For example, it evaluates increases or decreases in share in specific markets or regions. The effect measurement unit also analyzes fluctuations in market share during the sponsorship period and evaluates the economic impact. For example, it compares shares with competitors and evaluates overall market trends. In this way, it is possible to analyze fluctuations in market share during the sponsorship period and evaluate the economic impact.

[0045] The return analysis unit can use the generation AI to analyze long-term sales data of sponsorships and evaluate sustainable returns. The return analysis unit, for example, uses the generation AI to analyze long-term sales data of sponsorships and evaluate sustainable returns. For example, it evaluates sales data after the sponsorship period has ended. The return analysis unit also evaluates sustainable returns of sponsorships based on the long-term sales data. For example, it evaluates increases in sales of specific products or services. The return analysis unit also analyzes long-term sales data of sponsorships and evaluates sustainable returns. For example, it evaluates based on brand awareness and engagement rates after the sponsorship has ended. In this way, it is possible to analyze long-term sales data of sponsorships and evaluate sustainable returns.

[0046] The return analysis unit can analyze the number of new customers acquired as a result of the sponsorship and evaluate the expansion of the customer base. The return analysis unit, for example, analyzes the number of new customers acquired as a result of the sponsorship and evaluates the expansion of the customer base. For example, it evaluates the number of new customers during the sponsorship period. The return analysis unit also evaluates the expansion of the customer base from the sponsorship based on the number of new customers acquired. For example, it evaluates the repeat rate and recommendation intention of new customers. The return analysis unit also analyzes the number of new customers acquired as a result of the sponsorship and evaluates the expansion of the customer base. For example, it evaluates based on the purchase history and online shopping data of new customers. In this way, it is possible to analyze the number of new customers acquired as a result of the sponsorship and evaluate the expansion of the customer base.

[0047] The return analysis unit can analyze the effects of other marketing activities influenced by the sponsorship and evaluate the synergy effects. The return analysis unit, for example, analyzes the effects of other marketing activities influenced by the sponsorship and evaluates the synergy effects. For example, it evaluates the effectiveness of advertising campaigns and promotional activities. The return analysis unit also evaluates the synergy effects of the sponsorship based on data from other marketing activities. For example, it evaluates the click-through rate and conversion rate of advertisements. The return analysis unit also analyzes the effects of other marketing activities influenced by the sponsorship and evaluates the synergy effects. For example, it evaluates the synergy effects between the sponsorship and other marketing activities. This makes it possible to analyze the effects of other marketing activities influenced by the sponsorship and evaluate the synergy effects.

[0048] The return analysis department can analyze performance data of employees affected by sponsorship and evaluate internal productivity improvements. The return analysis department, for example, analyzes performance data of employees affected by sponsorship and evaluates internal productivity improvements. For example, it evaluates employee work efficiency and results. The return analysis department also evaluates internal productivity improvements from sponsorship based on employee performance data. For example, it evaluates employee motivation and teamwork. The return analysis department also analyzes performance data of employees affected by sponsorship and evaluates internal productivity improvements. For example, it evaluates employee work improvements and skill improvements. In this way, it is possible to analyze performance data of employees affected by sponsorship and evaluate internal productivity improvements.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The player analysis unit can analyze a player's in-game playing style and strategy to evaluate their uniqueness and appeal. For example, it extracts a player's characteristics, such as an offensive or defensive playing style, and evaluates their uniqueness. It also analyzes a player's strategy to evaluate their judgment and reaction speed in specific situations. For example, it analyzes their leadership in team matches and their tactical decisions in individual matches. It also analyzes a player's movements in the game to evaluate their appeal. For example, it evaluates plays that attract spectators and their ability to create highlight scenes. By evaluating a player's uniqueness and appeal, it is possible to propose attractive players to sponsoring companies.

[0051] The player analysis unit can analyze a player's past interviews and media appearances to evaluate their personality and brand value. For example, it can analyze a player's past interviews to evaluate their personality. For example, it can extract statements that emphasize the importance of leadership and team play. It can also analyze the frequency and content of media appearances to evaluate a player's brand value. For example, it can evaluate based on the number and content of television appearances and magazine interviews. It can also analyze a player's statements and posts on social media to evaluate their personality and brand value. For example, it can evaluate interactions with fans and social contribution activities. By evaluating a player's personality and brand value, it can propose suitable players to sponsoring companies.

[0052] The player analysis unit can analyze a player's health and fitness data to evaluate the consistency of their performance. For example, it can compare health data before and after a match to evaluate fluctuations in performance. It can also analyze fitness data to evaluate a player's physical strength and endurance. For example, it can evaluate based on training data and heart rate during a match. It can also evaluate the consistency of a player's performance based on the health and fitness data. For example, it can evaluate injury risk and recovery ability. By evaluating the consistency of a player's performance, it can propose reliable players to sponsor companies.

[0053] The player analysis unit can analyze a player's training data and evaluate their growth rate and potential. For example, it can evaluate their growth rate based on the frequency and intensity of training. It can also evaluate a player's potential based on the training data. For example, it can evaluate the improvement of a specific skill or the acquisition of a new tactic. It can also analyze the training data and evaluate a player's growth rate and potential. For example, it can evaluate based on the results of training and the rate at which goals are achieved. By evaluating a player's growth rate and potential, it can propose promising players to sponsoring companies.

[0054] The sponsor analysis unit can analyze the success factors of sponsor companies' advertising campaigns and propose effective sponsorship strategies. For example, it can analyze data from sponsor companies' past advertising campaigns to identify the success factors. For example, it can evaluate the selection of target audiences and advertising media. It can also propose effective sponsorship strategies based on the success factors of advertising campaigns. For example, it can propose collaborations with specific events or athletes. It can also analyze data from sponsor companies' advertising campaigns to identify the success factors and propose sponsorship strategies based on those factors. For example, it can evaluate the timing of advertising and the content of messages. This makes it possible to propose effective sponsorship strategies to sponsor companies.

[0055] The sponsor analysis unit can analyze the sponsorship activities of the sponsoring company's competitors and identify points of differentiation. For example, it can analyze the sponsorship activities of the sponsoring company's competitors and identify points of differentiation. For example, it can evaluate the events and athletes supported by competitors. It can also analyze the sponsorship activity data of competitors and identify points of differentiation. For example, it can evaluate the age groups and regions that competitors target. It can also analyze the sponsorship activities of competitors and identify points where the sponsoring company can differentiate itself. For example, it can propose benefits and services that competitors do not offer. This allows it to identify points of differentiation for the sponsoring company from its competitors and propose effective sponsorship strategies.

[0056] The sponsor analysis unit can analyze product reviews and customer feedback of sponsor companies to evaluate their brand image. For example, it can analyze product reviews of sponsor companies to evaluate their brand image. For example, it can evaluate the ratio of positive and negative reviews. It can also analyze customer feedback to evaluate the brand image of sponsor companies. For example, it can evaluate based on customer satisfaction and repeat purchase rates. It can also evaluate the brand image of sponsor companies based on product reviews and customer feedback. For example, it can analyze evaluations of product quality and service. This makes it possible to evaluate the brand image of sponsor companies and propose effective sponsorship strategies.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The player analysis unit analyzes the player's performance and popularity. For example, the player analysis unit uses generative AI to collect and analyze data such as the player's past match results and performance, as well as the number of followers and engagement rate on social media. Step 2: The sponsor analysis unit analyzes the sponsor's needs. For example, the sponsor analysis unit uses generative AI to collect and analyze data such as the sponsor's marketing strategy, target demographic, and past sponsorship results. Step 3: The matching suggestion unit proposes optimal matches based on the data analyzed by the player analysis unit and the sponsor analysis unit. For example, the matching suggestion unit uses a generation AI to propose optimal matches between sponsors and players based on the analyzed player's track record, popularity, and sponsor needs. Step 4: The effectiveness measurement department measures the effectiveness of the sponsorship. For example, the effectiveness measurement department uses generative AI to collect and analyze data such as sales data, changes in brand awareness, and engagement rates on social media during the sponsorship period. Step 5: The return analysis unit analyzes the sponsorship returns. For example, the return analysis unit uses the generation AI to compare the sponsor company's investment amount with the return obtained.

[0059] (Example 2) An AI system according to an embodiment of the present invention supports matching of esports teams and players with corporate sponsors. This system analyzes players' performance and popularity, analyzes sponsors' needs, and supports contracts with optimal sponsors. It also aims to increase sponsor satisfaction by measuring the effectiveness of sponsorships and analyzing returns. This enables esports teams and players to increase sponsorship contracts and boost their income. Effective sponsorship also enables sponsors to improve their brand recognition and sales.

[0060] The AI ​​system according to the embodiment includes a player analysis unit, a sponsor analysis unit, a match suggestion unit, an effectiveness measurement unit, and a return analysis unit. The player analysis unit analyzes a player's performance and popularity. For example, the player analysis unit uses a generation AI to collect and analyze data such as a player's past match results and performance, the number of followers on social media, and their engagement rate. The generation AI performs analysis based on the player's performance data and social media data. The sponsor analysis unit analyzes the sponsor's needs. For example, the sponsor analysis unit uses a generation AI to collect and analyze data such as the sponsor company's marketing strategy, target demographic, and past sponsorship results. The generation AI performs analysis based on the sponsor company's marketing data and past sponsorship data. The match suggestion unit proposes optimal matches based on the data analyzed by the player analysis unit and the sponsor analysis unit. For example, the match suggestion unit uses a generation AI to propose optimal matches between sponsors and players based on the analyzed player's performance, popularity, and sponsor needs. The generation AI proposes matches based on the analysis results of the player and sponsor. The effectiveness measurement unit measures the effectiveness of the sponsorship. For example, the effect measurement unit uses the generation AI to collect and analyze data such as sales data during the sponsorship period, changes in brand awareness, and engagement rates on social media. The generation AI performs analysis based on the sales data and social media data during the sponsorship period. The return analysis unit analyzes the returns from the sponsorship. For example, the return analysis unit uses the generation AI to compare the sponsor company's investment amount with the returns obtained. The generation AI analyzes the returns based on data on the sponsor company's investment amount and the returns obtained. As a result, the AI ​​system according to the embodiment enables e-sports teams and players to increase sponsorship contracts and improve their income. Furthermore, sponsor companies can improve their brand awareness and sales by conducting effective sponsorship.

[0061] The player analysis unit can analyze a player's in-game playing style and strategy to evaluate their uniqueness and appeal. The player analysis unit, for example, uses generative AI to analyze a player's in-game playing style. For example, it extracts player characteristics, such as offensive or defensive playing style, and evaluates their uniqueness. The player analysis unit also analyzes a player's strategy and evaluates their judgment and reaction speed in specific situations. For example, it analyzes their leadership in team matches and their tactical decisions in individual matches. The player analysis unit also analyzes a player's movements in the game to evaluate their appeal. For example, it evaluates plays that attract spectators and their ability to create highlight scenes. This allows the uniqueness and appeal of players to be evaluated, making it possible to propose attractive players to sponsoring companies.

[0062] The player analysis unit can analyze a player's past interviews and media appearances to evaluate their personality and brand value. For example, the player analysis unit analyzes a player's past interviews to evaluate their personality. For example, it extracts statements that emphasize the importance of leadership and team play. The player analysis unit also analyzes the frequency and content of media appearances to evaluate a player's brand value. For example, it evaluates based on the number and content of television appearances and magazine interviews. The player analysis unit also analyzes a player's statements and posts on social media to evaluate their personality and brand value. For example, it evaluates their interactions with fans and social contribution activities. In this way, by evaluating a player's personality and brand value, it is possible to propose suitable players to sponsoring companies.

[0063] The player analysis unit can use the emotion estimation function to analyze fans' emotional reactions to a player's social media posts and evaluate their popularity. The player analysis unit, for example, uses the emotion estimation function to analyze fans' emotional reactions to a player's social media posts. For example, it evaluates the ratio of positive and negative reactions. The player analysis unit also analyzes the content of comments and retweets on a player's posts to evaluate fans' emotional reactions. For example, it extracts supportive messages and critical comments. The player analysis unit also analyzes engagement rates on social media to evaluate a player's popularity. For example, it evaluates based on the number of likes and shares on a post. This allows for an accurate evaluation of a player's popularity, making it possible to propose attractive players to sponsoring companies.

[0064] The player analysis unit can analyze a player's health condition and fitness data to evaluate the consistency of their performance. The player analysis unit, for example, analyzes a player's health condition and evaluates the consistency of their performance. For example, it compares health data before and after a match to evaluate fluctuations in performance. The player analysis unit also analyzes fitness data to evaluate a player's physical strength and endurance. For example, it makes an evaluation based on training data and heart rate during a match. The player analysis unit also evaluates the consistency of a player's performance based on the health condition and fitness data. For example, it evaluates injury risk and recovery ability. In this way, by evaluating the consistency of a player's performance, it is possible to propose reliable players to sponsor companies.

[0065] The player analysis unit can analyze a player's training data and evaluate their growth rate and potential. The player analysis unit, for example, analyzes a player's training data and evaluates their growth rate. For example, it evaluates the growth rate based on the frequency and intensity of training. The player analysis unit also evaluates a player's potential based on the training data. For example, it evaluates the improvement of a specific skill or the acquisition of a new tactic. The player analysis unit also analyzes the training data and evaluates a player's growth rate and potential. For example, it evaluates based on the results of training and the rate of goal achievement. In this way, by evaluating a player's growth rate and potential, it is possible to suggest promising players to sponsoring companies.

[0066] The player analysis unit can use the emotion estimation function to analyze a player's emotional state during a match and evaluate their performance under pressure. The player analysis unit, for example, uses the emotion estimation function to analyze a player's emotional state during a match. For example, it evaluates the degree of tension and concentration. The player analysis unit also evaluates their performance under pressure based on their emotional state during a match. For example, it evaluates their judgment and reaction speed in important situations. The player analysis unit also analyzes a player's emotional state during a match based on the emotion estimation data and evaluates their performance under pressure. For example, it evaluates changes in emotions depending on the flow of the match and the situation. In this way, by evaluating a player's performance under pressure, it is possible to propose reliable players to sponsoring companies.

[0067] The sponsor analysis unit can analyze the success factors of sponsor companies' advertising campaigns and propose effective sponsorship strategies. For example, the sponsor analysis unit uses generative AI to analyze data from sponsor companies' past advertising campaigns and identify the success factors. For example, it evaluates the selection of target audiences and advertising media. The sponsor analysis unit also proposes effective sponsorship strategies based on the success factors of advertising campaigns. For example, it proposes collaborations with specific events or athletes. The sponsor analysis unit also analyzes data from sponsor companies' advertising campaigns to identify the success factors and proposes sponsorship strategies based on them. For example, it evaluates the timing of advertising and the content of messages. This makes it possible to propose effective sponsorship strategies to sponsor companies.

[0068] The sponsor analysis unit can analyze the sponsorship activities of the sponsoring company's competitors and identify points of differentiation. The sponsor analysis unit, for example, analyzes the sponsorship activities of the sponsoring company's competitors and identifies points of differentiation. For example, it evaluates the events and athletes supported by the competitors. The sponsor analysis unit also analyzes data on the sponsorship activities of competitors and identifies points of differentiation. For example, it evaluates the age groups and regions that competitors target. The sponsor analysis unit also analyzes the sponsorship activities of competitors and identifies points where the sponsoring company can differentiate itself. For example, it proposes benefits and services that competitors do not offer. This allows the sponsoring company to identify points of differentiation from its competitors and propose effective sponsorship strategies.

[0069] The sponsor analysis unit can use the emotion estimation function to analyze the emotional responses of the sponsor company's target demographic and propose optimal marketing messages. The sponsor analysis unit, for example, uses the emotion estimation function to analyze the emotional responses of the sponsor company's target demographic. For example, it evaluates positive and negative responses to advertisements and campaigns. The sponsor analysis unit also proposes optimal marketing messages based on the emotional responses of the target demographic. For example, it proposes messages and content with high emotion scores. The sponsor analysis unit also analyzes the emotional responses of the sponsor company's target demographic based on the emotion estimation data and proposes optimal marketing messages. For example, it proposes advertisements and campaigns that appeal to the emotions of the target demographic. This makes it possible to propose optimal marketing messages to sponsor companies based on the emotional responses of the target demographic.

[0070] The sponsor analysis unit can analyze product reviews and customer feedback of the sponsor company to evaluate its brand image. The sponsor analysis unit, for example, analyzes product reviews of the sponsor company to evaluate its brand image. For example, it evaluates the ratio of positive and negative reviews. The sponsor analysis unit also analyzes customer feedback to evaluate the brand image of the sponsor company. For example, it evaluates based on customer satisfaction and repeat purchase rates. The sponsor analysis unit also evaluates the brand image of the sponsor company based on product reviews and customer feedback. For example, it analyzes evaluations of product quality and service. This makes it possible to evaluate the brand image of the sponsor company and propose an effective sponsorship strategy.

[0071] The sponsor analysis unit can analyze the social media activities of sponsor companies and evaluate the engagement rate. The sponsor analysis unit, for example, analyzes the social media activities of sponsor companies and evaluates the engagement rate. For example, it evaluates based on the number of likes and shares on posts. The sponsor analysis unit also analyzes the number of followers and comments on social media and evaluates the engagement rate of sponsor companies. For example, it evaluates the rate of increase in followers and the content of comments. The sponsor analysis unit also evaluates the engagement rate based on the social media activities of sponsor companies. For example, it analyzes responses to specific campaigns and events. This makes it possible to evaluate the engagement rate of the sponsor company's social media activities and propose effective sponsorship strategies.

[0072] The sponsor analysis unit uses the emotion estimation function to analyze the emotional state of employees of the sponsor company and can propose improvements to the corporate culture and internal communication. The sponsor analysis unit, for example, uses the emotion estimation function to analyze the emotional state of employees of the sponsor company. For example, it evaluates workplace stress and satisfaction. The sponsor analysis unit also proposes improvements to the corporate culture and internal communication based on the emotional state of employees. For example, it proposes improvement measures for departments or teams with low emotion scores. The sponsor analysis unit also analyzes the emotional state of employees of the sponsor company based on the emotion estimation data and proposes improvements to the corporate culture and internal communication. For example, it proposes training programs and team building activities based on the employees' emotions. In this way, it is possible to analyze the emotional state of employees of the sponsor company and propose improvements to the corporate culture and internal communication.

[0073] The matching proposal unit can analyze the degree of match between the brand value of the player and the sponsoring company and propose the optimal match. The matching proposal unit, for example, uses generative AI to analyze the degree of match between the brand value of the player and the sponsoring company. For example, it evaluates whether the player's image and values ​​match the sponsoring company's brand. The matching proposal unit also proposes the optimal match based on the degree of match in brand value. For example, it proposes a match if the player's fan base and the sponsoring company's target audience match. The matching proposal unit also analyzes the brand value of the player and the sponsoring company and proposes a match if there is a high degree of match. For example, it makes a proposal if the player's activities and message match the sponsoring company's brand strategy. This makes it possible to analyze the degree of match between the brand value of the player and the sponsoring company and propose the optimal match.

[0074] The matching proposal unit can analyze past collaboration cases between athletes and sponsoring companies and identify success factors. The matching proposal unit, for example, analyzes past collaboration cases between athletes and sponsoring companies and identifies success factors. For example, it evaluates the success factors of a particular event or campaign. The matching proposal unit also analyzes data from collaboration cases and identifies success factors. For example, it evaluates the performance of athletes and the marketing strategies of sponsoring companies. The matching proposal unit also identifies success factors based on past collaboration cases and uses them for future matching. For example, it extracts common points between successful collaborations and reflects them in proposals. In this way, by analyzing past collaboration cases and identifying success factors, it can be used for future matching.

[0075] The matching suggestion unit can use the emotion estimation function to analyze the emotional reactions of the fan bases of the players and sponsor companies, and propose matches with a high degree of empathy. The matching suggestion unit, for example, uses the emotion estimation function to analyze the emotional reactions of the fan bases of the players and sponsor companies. For example, it evaluates the emotional reactions to the player's posts and the sponsor company's advertisements. The matching suggestion unit also proposes matches with a high degree of empathy based on the emotional reactions of the fan base. For example, it proposes combinations of players and sponsor companies with many positive emotional reactions. The matching suggestion unit also analyzes the emotional reactions of the fan bases of the players and sponsor companies based on the emotion estimation data, and proposes matches with a high degree of empathy. For example, it proposes advertisements and campaigns that appeal to the emotions of the fan base. In this way, it is possible to analyze the emotional reactions of the fan bases of the players and sponsor companies, and propose matches with a high degree of empathy.

[0076] The matching proposal unit can analyze sponsorship examples from different sports and entertainment fields and propose applications to e-sports. For example, the matching proposal unit analyzes sponsorship examples from different sports fields and proposes applications to e-sports. For example, it applies sponsorship strategies for soccer or basketball to e-sports. The matching proposal unit also analyzes sponsorship examples from the entertainment field and proposes applications to e-sports. For example, it applies sponsorship strategies for music festivals or movies to e-sports. The matching proposal unit also proposes applications to e-sports based on sponsorship examples from different fields. For example, it proposes a sponsorship strategy based on successful examples from other industries. This makes it possible to analyze sponsorship examples from different fields and propose applications to e-sports.

[0077] The matching proposal unit can analyze the regional relevance between players and sponsor companies and propose region-specific sponsorship. The matching proposal unit, for example, analyzes the regional relevance between players and sponsor companies and proposes region-specific sponsorship. For example, it evaluates the player's hometown or area of ​​activity and the sponsor company's market area. The matching proposal unit also proposes region-specific sponsorship based on the regional relevance. For example, it proposes a sponsorship strategy that utilizes local events and local media. The matching proposal unit also analyzes the regional relevance between players and sponsor companies and proposes region-specific sponsorship. For example, it makes proposals based on the local fan base and market needs. In this way, it is possible to analyze the regional relevance between players and sponsor companies and propose region-specific sponsorship.

[0078] The matching proposal unit can use the emotion estimation function to analyze the emotional reactions of a collaboration event between an athlete and a sponsor company, and identify factors that contribute to the success of the event. The matching proposal unit, for example, uses the emotion estimation function to analyze the emotional reactions of a collaboration event between an athlete and a sponsor company. For example, it evaluates the emotional reactions of event participants. The matching proposal unit also identifies factors that contribute to the success of the collaboration event based on the emotional reactions of the collaboration event. For example, it extracts factors that contribute to events with a high number of positive emotional reactions. The matching proposal unit also analyzes the emotional reactions of a collaboration event between an athlete and a sponsor company based on the emotion estimation data, and identifies factors that contribute to the success. For example, it evaluates the impact that the content and production of the event have on emotions. This makes it possible to analyze the emotional reactions of a collaboration event between an athlete and a sponsor company, and identify factors that contribute to the success of the event.

[0079] The effect measurement unit can use the generation AI to analyze consumer behavior data during the sponsorship period and evaluate changes in purchasing intent. The effect measurement unit, for example, uses the generation AI to analyze consumer behavior data during the sponsorship period and evaluate changes in purchasing intent. For example, it compares sales data before and after sponsorship. The effect measurement unit also evaluates the effectiveness of the sponsorship based on the consumer behavior data. For example, it evaluates increases in sales of specific products or services. The effect measurement unit also analyzes consumer behavior data during the sponsorship period and evaluates changes in purchasing intent. For example, it evaluates based on consumer purchasing history and online shopping data. In this way, it is possible to analyze consumer behavior data during the sponsorship period and evaluate changes in purchasing intent.

[0080] The effect measurement unit can analyze the amount of media exposure during the sponsorship period and evaluate the improvement in brand awareness. The effect measurement unit, for example, analyzes the amount of media exposure during the sponsorship period and evaluates the improvement in brand awareness. For example, the evaluation is made based on the number of television and radio broadcasts and viewer ratings. The effect measurement unit also evaluates the effectiveness of the sponsorship based on the amount of media exposure. For example, the number of newspaper and magazine articles and the number of times it is featured in online media. The effect measurement unit also analyzes the amount of media exposure during the sponsorship period and evaluates the improvement in brand awareness. For example, the evaluation is made based on the number of mentions on social media and the engagement rate. In this way, the amount of media exposure during the sponsorship period can be analyzed and the improvement in brand awareness can be evaluated.

[0081] The effect measurement unit can use the emotion estimation function to analyze consumers' emotional responses during the sponsorship period and evaluate brand loyalty. The effect measurement unit, for example, uses the emotion estimation function to analyze consumers' emotional responses during the sponsorship period. For example, it evaluates the proportion of positive and negative emotional responses. The effect measurement unit also evaluates brand loyalty based on consumers' emotional responses. For example, it evaluates the repeat purchase rate and recommendation intention of consumers with high emotion scores. The effect measurement unit also analyzes consumers' emotional responses during the sponsorship period based on emotion estimation data and evaluates brand loyalty. For example, it evaluates the impact of emotional changes on purchasing behavior. This makes it possible to analyze consumers' emotional responses during the sponsorship period and evaluate brand loyalty.

[0082] The effect measurement unit can analyze fluctuations in market share during the sponsorship period and evaluate the economic impact. The effect measurement unit, for example, analyzes fluctuations in market share during the sponsorship period and evaluates the economic impact. For example, it evaluates fluctuations in market share before and after sponsorship. The effect measurement unit also evaluates the economic impact of sponsorship based on market share data. For example, it evaluates increases or decreases in share in specific markets or regions. The effect measurement unit also analyzes fluctuations in market share during the sponsorship period and evaluates the economic impact. For example, it compares shares with competitors and evaluates overall market trends. In this way, it is possible to analyze fluctuations in market share during the sponsorship period and evaluate the economic impact.

[0083] The effect measurement unit can use the emotion estimation function to analyze the emotional state of employees during the sponsorship period and evaluate the improvement in internal motivation. The effect measurement unit, for example, uses the emotion estimation function to analyze the emotional state of employees during the sponsorship period. For example, it evaluates workplace stress and satisfaction. The effect measurement unit also evaluates the improvement in internal motivation of sponsorship based on the emotional state of employees. For example, it evaluates the performance and productivity of employees with high emotion scores. The effect measurement unit also analyzes the emotional state of employees during the sponsorship period based on the emotion estimation data and evaluates the improvement in internal motivation. For example, it evaluates the impact of emotional changes on work. In this way, it is possible to analyze the emotional state of employees during the sponsorship period and evaluate the improvement in internal motivation.

[0084] The return analysis unit can use the generation AI to analyze long-term sales data of sponsorships and evaluate sustainable returns. The return analysis unit, for example, uses the generation AI to analyze long-term sales data of sponsorships and evaluate sustainable returns. For example, it evaluates sales data after the sponsorship period has ended. The return analysis unit also evaluates sustainable returns of sponsorships based on the long-term sales data. For example, it evaluates increases in sales of specific products or services. The return analysis unit also analyzes long-term sales data of sponsorships and evaluates sustainable returns. For example, it evaluates based on brand awareness and engagement rates after the sponsorship has ended. In this way, it is possible to analyze long-term sales data of sponsorships and evaluate sustainable returns.

[0085] The return analysis unit can analyze the number of new customers acquired as a result of the sponsorship and evaluate the expansion of the customer base. The return analysis unit, for example, analyzes the number of new customers acquired as a result of the sponsorship and evaluates the expansion of the customer base. For example, it evaluates the number of new customers during the sponsorship period. The return analysis unit also evaluates the expansion of the customer base from the sponsorship based on the number of new customers acquired. For example, it evaluates the repeat rate and recommendation intention of new customers. The return analysis unit also analyzes the number of new customers acquired as a result of the sponsorship and evaluates the expansion of the customer base. For example, it evaluates based on the purchase history and online shopping data of new customers. In this way, it is possible to analyze the number of new customers acquired as a result of the sponsorship and evaluate the expansion of the customer base.

[0086] The return analysis unit can use the emotion estimation function to analyze the emotional reactions of customers influenced by the sponsorship and evaluate customer satisfaction. The return analysis unit, for example, uses the emotion estimation function to analyze the emotional reactions of customers influenced by the sponsorship. For example, it evaluates the proportion of positive and negative emotional reactions. The return analysis unit also evaluates customer satisfaction with the sponsorship based on the customer's emotional reactions. For example, it evaluates the repeat rate and recommendation intention of customers with high emotion scores. The return analysis unit also analyzes the emotional reactions of customers influenced by the sponsorship based on the emotion estimation data and evaluates customer satisfaction. For example, it evaluates the impact of emotional changes on purchasing behavior. In this way, it is possible to analyze the emotional reactions of customers influenced by the sponsorship and evaluate customer satisfaction.

[0087] The return analysis unit can analyze the effects of other marketing activities influenced by the sponsorship and evaluate the synergy effects. The return analysis unit, for example, analyzes the effects of other marketing activities influenced by the sponsorship and evaluates the synergy effects. For example, it evaluates the effectiveness of advertising campaigns and promotional activities. The return analysis unit also evaluates the synergy effects of the sponsorship based on data from other marketing activities. For example, it evaluates the click-through rate and conversion rate of advertisements. The return analysis unit also analyzes the effects of other marketing activities influenced by the sponsorship and evaluates the synergy effects. For example, it evaluates the synergy effects between the sponsorship and other marketing activities. This makes it possible to analyze the effects of other marketing activities influenced by the sponsorship and evaluate the synergy effects.

[0088] The return analysis department can analyze performance data of employees affected by sponsorship and evaluate internal productivity improvements. The return analysis department, for example, analyzes performance data of employees affected by sponsorship and evaluates internal productivity improvements. For example, it evaluates employee work efficiency and results. The return analysis department also evaluates internal productivity improvements from sponsorship based on employee performance data. For example, it evaluates employee motivation and teamwork. The return analysis department also analyzes performance data of employees affected by sponsorship and evaluates internal productivity improvements. For example, it evaluates employee work improvements and skill improvements. In this way, it is possible to analyze performance data of employees affected by sponsorship and evaluate internal productivity improvements.

[0089] The return analysis unit can use the emotion estimation function to analyze the emotional reactions of shareholders affected by sponsorship and evaluate fluctuations in stock prices. The return analysis unit, for example, uses the emotion estimation function to analyze the emotional reactions of shareholders affected by sponsorship. For example, it evaluates emotional reactions at shareholders' meetings and investor events. The return analysis unit also evaluates stock price fluctuations due to sponsorship based on the emotional reactions of shareholders. For example, it evaluates the investment intentions and stock price fluctuations of shareholders with high emotion scores. The return analysis unit also analyzes the emotional reactions of shareholders affected by sponsorship based on the emotion estimation data and evaluates stock price fluctuations. For example, it evaluates the impact of emotional changes on stock prices. This makes it possible to analyze the emotional reactions of shareholders affected by sponsorship and evaluate stock price fluctuations.

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

[0091] The player analysis unit can analyze a player's in-game playing style and strategy to evaluate their uniqueness and appeal. For example, it extracts a player's characteristics, such as an offensive or defensive playing style, and evaluates their uniqueness. It also analyzes a player's strategy to evaluate their judgment and reaction speed in specific situations. For example, it analyzes their leadership in team matches and their tactical decisions in individual matches. It also analyzes a player's movements in the game to evaluate their appeal. For example, it evaluates plays that attract spectators and their ability to create highlight scenes. By evaluating a player's uniqueness and appeal, it is possible to propose attractive players to sponsoring companies.

[0092] The player analysis unit can analyze a player's past interviews and media appearances to evaluate their personality and brand value. For example, it can analyze a player's past interviews to evaluate their personality. For example, it can extract statements that emphasize the importance of leadership and team play. It can also analyze the frequency and content of media appearances to evaluate a player's brand value. For example, it can evaluate based on the number and content of television appearances and magazine interviews. It can also analyze a player's statements and posts on social media to evaluate their personality and brand value. For example, it can evaluate interactions with fans and social contribution activities. By evaluating a player's personality and brand value, it can propose suitable players to sponsoring companies.

[0093] The player analysis unit uses the emotion estimation function to analyze fans' emotional reactions to players' social media posts and evaluate their popularity. For example, it evaluates the ratio of positive and negative reactions. It also analyzes the content of comments and retweets on players' posts to evaluate fans' emotional reactions. For example, it extracts supportive messages and critical comments. It also analyzes engagement rates on social media to evaluate players' popularity. For example, it evaluates based on the number of likes and shares on posts. This allows for accurate evaluation of players' popularity and makes it possible to propose attractive players to sponsoring companies.

[0094] The player analysis unit can analyze a player's health and fitness data to evaluate the consistency of their performance. For example, it can compare health data before and after a match to evaluate fluctuations in performance. It can also analyze fitness data to evaluate a player's physical strength and endurance. For example, it can evaluate based on training data and heart rate during a match. It can also evaluate the consistency of a player's performance based on the health and fitness data. For example, it can evaluate injury risk and recovery ability. By evaluating the consistency of a player's performance, it can propose reliable players to sponsor companies.

[0095] The player analysis unit can analyze a player's training data and evaluate their growth rate and potential. For example, it can evaluate their growth rate based on the frequency and intensity of training. It can also evaluate a player's potential based on the training data. For example, it can evaluate the improvement of a specific skill or the acquisition of a new tactic. It can also analyze the training data and evaluate a player's growth rate and potential. For example, it can evaluate based on the results of training and the rate at which goals are achieved. By evaluating a player's growth rate and potential, it can propose promising players to sponsoring companies.

[0096] The player analysis unit uses the emotion estimation function to analyze a player's emotional state during a match and evaluate their performance under pressure. For example, it evaluates the level of tension and concentration. It also evaluates their performance under pressure based on their emotional state during the match. For example, it evaluates their judgment and reaction speed in important situations. It also analyzes a player's emotional state during a match based on the emotion estimation data and evaluates their performance under pressure. For example, it evaluates changes in their emotions depending on the flow of the match and the situation. This allows it to evaluate a player's performance under pressure and recommend reliable players to sponsoring companies.

[0097] The sponsor analysis unit can analyze the success factors of sponsor companies' advertising campaigns and propose effective sponsorship strategies. For example, it can analyze data from sponsor companies' past advertising campaigns to identify the success factors. For example, it can evaluate the selection of target audiences and advertising media. It can also propose effective sponsorship strategies based on the success factors of advertising campaigns. For example, it can propose collaborations with specific events or athletes. It can also analyze data from sponsor companies' advertising campaigns to identify the success factors and propose sponsorship strategies based on those factors. For example, it can evaluate the timing of advertising and the content of messages. This makes it possible to propose effective sponsorship strategies to sponsor companies.

[0098] The sponsor analysis unit can analyze the sponsorship activities of the sponsoring company's competitors and identify points of differentiation. For example, it can analyze the sponsorship activities of the sponsoring company's competitors and identify points of differentiation. For example, it can evaluate the events and athletes supported by competitors. It can also analyze the sponsorship activity data of competitors and identify points of differentiation. For example, it can evaluate the age groups and regions that competitors target. It can also analyze the sponsorship activities of competitors and identify points where the sponsoring company can differentiate itself. For example, it can propose benefits and services that competitors do not offer. This allows it to identify points of differentiation for the sponsoring company from its competitors and propose effective sponsorship strategies.

[0099] The sponsor analysis unit can use the emotion estimation function to analyze the emotional responses of the sponsor company's target audience and propose optimal marketing messages. For example, the emotion estimation function is used to analyze the emotional responses of the sponsor company's target audience. For example, positive and negative responses to advertisements and campaigns are evaluated. The unit also proposes optimal marketing messages based on the emotional responses of the target audience. For example, messages and content with high emotion scores are proposed. The unit also analyzes the emotional responses of the sponsor company's target audience based on the emotion estimation data and proposes optimal marketing messages. For example, advertisements and campaigns that appeal to the emotions of the target audience are proposed. This makes it possible to propose optimal marketing messages to sponsor companies based on the emotional responses of the target audience.

[0100] The sponsor analysis unit can analyze product reviews and customer feedback of sponsor companies to evaluate their brand image. For example, it can analyze product reviews of sponsor companies to evaluate their brand image. For example, it can evaluate the ratio of positive and negative reviews. It can also analyze customer feedback to evaluate the brand image of sponsor companies. For example, it can evaluate based on customer satisfaction and repeat purchase rates. It can also evaluate the brand image of sponsor companies based on product reviews and customer feedback. For example, it can analyze evaluations of product quality and service. This makes it possible to evaluate the brand image of sponsor companies and propose effective sponsorship strategies.

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

[0102] Step 1: The player analysis unit analyzes the player's performance and popularity. For example, the player analysis unit uses generative AI to collect and analyze data such as the player's past match results and performance, as well as the number of followers and engagement rate on social media. Step 2: The sponsor analysis unit analyzes the sponsor's needs. For example, the sponsor analysis unit uses generative AI to collect and analyze data such as the sponsor's marketing strategy, target demographic, and past sponsorship results. Step 3: The matching suggestion unit proposes optimal matches based on the data analyzed by the player analysis unit and the sponsor analysis unit. For example, the matching suggestion unit uses a generation AI to propose optimal matches between sponsors and players based on the analyzed player's track record, popularity, and sponsor needs. Step 4: The effectiveness measurement department measures the effectiveness of the sponsorship. For example, the effectiveness measurement department uses generative AI to collect and analyze data such as sales data, changes in brand awareness, and engagement rates on social media during the sponsorship period. Step 5: The return analysis unit analyzes the sponsorship returns. For example, the return analysis unit uses the generation AI to compare the sponsor company's investment amount with the return obtained.

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

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

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

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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. [Explanation of symbols]

[0170] 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 player analysis department that analyzes players' achievements and popularity, a sponsor analysis department that analyzes the needs of sponsors; a matching suggestion unit that suggests optimal matching based on the data analyzed by the player analysis unit and the sponsor analysis unit; An effectiveness measurement department that measures the effectiveness of sponsorships; A return analysis unit that analyzes the return on sponsorship. A system characterized by:

2. The player analysis unit Analyze players' in-game playing styles and strategies to evaluate their uniqueness and appeal 2. The system of claim 1.

3. The player analysis unit Analyze players' past interviews and media appearances to evaluate their personalities and brand value 2. The system of claim 1.

4. The player analysis unit Analyzing fans' emotional responses to players' social media posts and assessing their popularity 2. The system of claim 1.

5. The player analysis unit Analyze player health and fitness data to assess performance consistency 2. The system of claim 1.

6. The player analysis unit Analyze players' training data to evaluate their growth rate and potential 2. The system of claim 1.

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