system

Abstract

We provide the system. [Solution] Means of collecting user game activity, A means of analyzing collected data and evaluating play style, A means of recommending similar professional players and related video data based on the evaluated play style, A means of providing information about upcoming tournaments that are tailored to user interests, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Although e-sports are widely recognized, there are problems in the growth of the market because the number of viewers is limited. In particular, there is a lack of a method for arousing the interest of professional players and tournaments in potential viewer groups. As a result, sponsors, media, and teams require further involvement. In order to solve this problem, a mechanism is required to provide appropriate relevant information based on the user's interests and play style and enhance the motivation to watch.

Means for Solving the Problems

[0005] This invention is a system that collects user game activity data and analyzes it using AI to recommend professional players and related video data best suited to the user's play style. It also includes means for providing information on upcoming tournaments tailored to the user's region and preferred game titles. The aim is to enable users to become interested in competitions and players that match their style, thereby contributing to an increase in spectator numbers.

[0006] "User" refers to individuals who engage in gaming activities or those who utilize their data on the platform.

[0007] "Game activity" refers to the totality of data such as a series of actions and results that occur when playing a game, as well as the characters used and tactics employed.

[0008] "Data" refers to the collection of information generated by the user's game activities.

[0009] "Analysis" refers to the process of evaluating and classifying users' play styles and tendencies based on collected data.

[0010] "Playstyle" refers to the unique strategies and tendencies a user exhibits when playing a game.

[0011] A "professional player" refers to a highly skilled athlete who competes in esports.

[0012] "Video data" refers to visual materials, including professional players' matches and related video content.

[0013] "Recommendation" refers to the act of selecting and presenting information or content that is deemed beneficial to the user.

[0014] "Local information" refers to information about the geographical area where the user resides.

[0015] "Game Title" refers to the name of a specific video game and is used to provide information related to that game.

[0016] "Competition Information" refers to details such as the schedule, location, and participating players of upcoming competitive events.

Brief Explanation of Drawings

[0017] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0021] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0030] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0037] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0038] This invention includes an AI-based information provision system aimed at increasing the number of e-sports spectators. This system records the user's gaming activity and analyzes the collected data via a server. The following describes the program processing of this system.

[0039] First, the device monitors the user's gaming activity and collects data. This data includes the character the user uses, tactics, in-game movements, and win / loss information. The data obtained here forms the basis for subsequent analysis.

[0040] Next, the device sends the collected data to the server. For security reasons, a secure communication protocol is applied to the data transmission.

[0041] The server analyzes the received gameplay data and uses an AI model to evaluate the user's play style. This evaluation includes the user's offensive tendencies, defensive tendencies, and balanced play style.

[0042] Based on the analysis results, the server selects professional players and match video data suitable for the user. Professional players selected are those with a play style similar to the user's.

[0043] The server also provides information about upcoming tournaments based on the user's location and preferred game titles. This tournament information includes dates, locations, participating players, and total prize money.

[0044] Finally, the device displays the information received from the server in a user-friendly format. This can be done using a dashboard or pop-up notifications to allow users to check video data and tournament information.

[0045] For example, if a user exhibits an aggressive playstyle, the AI ​​analysis will recommend recent match videos of professional players known for their aggressive playstyles. Furthermore, information on upcoming tournaments in the user's region will be provided, encouraging them to watch.

[0046] This system allows users to easily access strategies and tournament information from professional players relevant to their play style, which in turn increases their motivation to watch. In this way, it can broaden the base of esports viewership.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The device collects user gameplay data in real time or periodically. This includes information about the user's match results, characters used, playtime, scores, and tactics. This data is collected only with the user's permission.

[0050] Step 2:

[0051] The terminal collects data and sends it to the server using a secure communication protocol. To prevent unauthorized access and data leakage, an encrypted connection is used during this process.

[0052] Step 3:

[0053] The server analyzes the received gameplay data using an AI model. Here, machine learning algorithms are used to identify the user's playstyle (e.g., offensive, defensive, balanced) and skill level. This analysis is then compared to historical datasets and the playstyles of professional players.

[0054] Step 4:

[0055] Based on the analysis results, the server selects professional players and related video data suitable for the user. This selection includes match highlights of professional players and explanatory videos focusing on tactics.

[0056] Step 5:

[0057] The server selects upcoming tournament information based on the user's location and preferred game titles. This tournament information includes dates, locations, and a list of participating professional players.

[0058] Step 6:

[0059] The device displays recommended content and tournament information received from the server to the user. This is done using UI notifications and a dedicated information dashboard, which users can click to view details.

[0060] Step 7:

[0061] Users can watch videos of professional players and tournament information provided, developing an interest in watching them. Furthermore, by referring to content related to their own gameplay, they can improve their own playstyle and discover new tactics.

[0062] (Example 1)

[0063] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0064] In recent years, with the rise of esports, the need to increase spectator interest has grown. However, current systems fail to adequately provide information tailored to each user's play style, making it difficult for users to develop a deeper interest in esports. Therefore, there is a need to develop a system that can provide accurate information tailored to the characteristics of each user.

[0065] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0066] In this invention, the server includes means for monitoring the user's electronic game activity and collecting behavioral information, means for transmitting the collected information to a data center using a secure communication protocol, and means for analyzing the information received at the data center and evaluating the user's behavioral characteristics using generative artificial intelligence. This makes it possible to provide information tailored to each user's behavioral characteristics and increase interest in watching esports.

[0067] "Electronic gaming activities" refer to interactive gameplay that users engage in using computers.

[0068] "Action information" refers to data related to the user's choices, actions, and other behaviors during gameplay.

[0069] A "data center" refers to a centralized computer system for receiving and processing information.

[0070] A "secure communication protocol" refers to a means of communication for transmitting information while protecting the confidentiality and integrity of the data.

[0071] "Generative artificial intelligence" refers to artificial intelligence technology that uses machine learning algorithms to generate new information and analyze data.

[0072] "Behavioral characteristics" refer to elements that characterize a user's play style and behavioral patterns.

[0073] A "player character" refers to a person who is a professional or has a notable play style in electronic games.

[0074] "Visual resources" refers to information and content stored in video media format.

[0075] A "competitive event" refers to a competitive event in a specific game title.

[0076] This invention is an artificial intelligence-based information provision system designed to increase the number of esports viewers. The main components of the system are the user's terminal, a server, and a generative artificial intelligence model. Embodiments of the invention are described in detail below.

[0077] Device operation:

[0078] The terminal monitors the user's electronic gaming activity in real time. Specifically, it uses dedicated monitoring software to record user input and in-game actions. This software retrieves necessary data through the game client's API and collects user activity information. Furthermore, secure communication protocols such as HTTPS and TLS are applied to ensure that the collected information is safely transmitted to the data center.

[0079] Server operation:

[0080] The server receives behavioral information transmitted from the terminal. The received data is first pre-processed for analysis, and then generative artificial intelligence is used to evaluate the user's behavioral characteristics. This generative AI learns and evaluates the user's play style, such as attack and defense tendencies. Based on the evaluation results, the server selects player characters and video resources related to the user. It also uses external APIs to obtain information on competitive events based on the user's location and game titles of interest.

[0081] Providing information to users:

[0082] The user's device displays information received from the server in an intuitive and easy-to-understand format. This includes dashboard displays and pop-up notifications. This allows users to easily view match footage of recommended players and detailed information about competitive events they are interested in.

[0083] Specific example:

[0084] For example, if a user has an aggressive playstyle, AI analysis will recommend the latest match footage of professional players that match that style. Additionally, information on upcoming competitive events in the user's region, including the date, time, location, and participating players, will be presented.

[0085] Example of a prompt:

[0086] "Based on recent matches, please recommend some gameplay videos of professional players with an aggressive style."

[0087] "Please provide information on upcoming competitive events in your region."

[0088] In this way, users can easily access information related to their own play style and deepen their interest in watching esports.

[0089] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0090] Step 1:

[0091] The device monitors the user's electronic gaming activity. Specifically, it uses dedicated monitoring software to record control information entered by the user and actions within the game in real time. Inputs include user operation data and character movements within the game. From this information, the device collects user behavior data and outputs it as a dataset.

[0092] Step 2:

[0093] The terminal collects operational information and transmits it to the data center. To ensure security, the information is encrypted and transmitted using the HTTPS protocol. The input is a dataset of operational information obtained in the previous step. The output is an encrypted dataset, prepared for analysis on the server side.

[0094] Step 3:

[0095] The server decrypts the operational information received from the terminal and performs preprocessing for data analysis. The input is an encrypted dataset, which is decrypted to obtain a clean dataset. Specific operations include data formatting and removal of unnecessary information. The output is a dataset ready for analysis.

[0096] Step 4:

[0097] The server uses a generated AI model to analyze the user's behavioral characteristics. The input is the clean dataset obtained in the previous step. The server inputs this data into the AI ​​model to evaluate the user's playstyle characteristics (e.g., offensive tendencies and defensive tendencies). The output is the analysis result, which includes evaluation values ​​for various behavioral characteristics.

[0098] Step 5:

[0099] The server selects relevant information based on the user's behavioral characteristics. Specifically, it selects video resources of professional players and information on competitive events of interest. To do this, it retrieves information from databases and external APIs using prompt messages. The input is the analysis results, and the output is the selected video resources and event information.

[0100] Step 6:

[0101] The terminal receives selected information from the server and displays it to the user. Inputs include video resources of professional players and information on competitive events. The terminal visually presents this information as a dashboard or pop-up, allowing the user to intuitively understand and utilize it. Output is the visualized information presented to the user.

[0102] (Application Example 1)

[0103] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0104] With the development of electronic sports, providing spectators with a high-quality viewing experience and enhancing their motivation to watch is crucial. However, conventional technologies have not made it easy to appropriately recommend relevant content and event information according to each user's gameplay style. Therefore, there is a need to build a system that provides information optimized for individual users.

[0105] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0106] In this invention, the server includes means for collecting the user's electronic gaming activities, means for analyzing the collected information and evaluating the user's playing style, means for recommending similar professional players and related video information based on the evaluated playing style, means for providing information on upcoming events that are relevant to the user's interests, and means for visually displaying the processing results. This makes it possible to quickly and easily provide individual users with customized information on professional player strategies and events.

[0107] "Means for collecting users' electronic sports activities" refers to devices or software for systematically acquiring information about electronic sports activities conducted by users.

[0108] "Means for analyzing collected information and evaluating operating styles" refers to a system or process for analyzing collected data to identify and evaluate specific user operating methods and strategies.

[0109] "Means for recommending similar professional players and related video information" refers to a method or system for selecting and presenting information on professional players with similar operating methods and related video content to a user, based on the user's operating style.

[0110] "Means of providing information on upcoming events tailored to user interests" refers to devices or software that identify and inform users of relevant upcoming events based on their interests and past activities.

[0111] "Means for visually displaying processing results" refers to a display device or user interface that presents the results of analysis and recommendations to the user in a graphical format, providing information in an easily understandable manner.

[0112] The system for carrying out this invention consists of a user terminal, a cloud server, and a visual display device.

[0113] The user's device has the capability to monitor electronic gaming activities. Specifically, it uses Nvidia's ShadowPlay or other recording software to record specific gameplay in real time. The data collected in this process includes the character selected by the player, their behavior patterns, the tactics used, and match win / loss information. This data is transmitted to a cloud server via a secure protocol such as HTTPS to ensure security.

[0114] Upon receiving the collected data, the server analyzes it using an AI model. Specifically, it uses deep learning libraries such as TENSORFLOW® to classify the user's play style into categories such as aggressive, defensive, and balanced. Based on this classification, the server searches and selects match videos of professional players with similar styles from databases such as MongoDB. It also provides relevant upcoming event information based on the user's location and preferred game titles.

[0115] The information returned to the user's device is visually displayed in a user interface built using React Native, etc. Through the dashboard, users can easily view match videos of recommended expert players and information on events of interest. This allows users to learn strategies related to their own gameplay and increases their motivation to watch.

[0116] For example, if a user prefers an aggressive style, the server will automatically recommend match videos and related event information of professional players known for this style. An example of a prompt used in this case would be: "This user has used an aggressive style in over 50% of their last 10 matches. Please show me recent match videos and upcoming related events of players known for this playstyle."

[0117] This system will allow users to more easily access information related to their preferences and style, enriching their viewing experience.

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] The user's device records their electronic gaming activities. Specifically, it collects data in real time, such as character selection, action patterns, tactics used, and match results. Recording tools such as Nvidia's ShadowPlay are used for this purpose. The input is the user's gameplay data, and the output is a compiled record of this data.

[0121] Step 2:

[0122] The user's device sends collected game data to the server. Secure protocols such as HTTPS are used for data transmission, protecting the user's privacy. The input is organized game data, and the output is encrypted data packets.

[0123] Step 3:

[0124] The server analyzes the received data and evaluates the user's play style. In this process, an AI model using TensorFlow classifies the play style. The input is encrypted game data, and the output is the user's play style (e.g., aggressive, defensive, balanced).

[0125] Step 4:

[0126] The server selects match footage of similar professional players based on the evaluated play style. It searches MongoDB for appropriate video data and identifies the relevant content. The input is the user's play style, and the output is video data of matched professional players.

[0127] Step 5:

[0128] The server provides information on upcoming relevant events based on the user's interests and location. This information includes details of events found (e.g., dates, locations, participants) and selects those that are suitable for the user. The input is the user's profile information, and the output is information on upcoming events.

[0129] Step 6:

[0130] All information returned from the server is sent to the user's terminal and visually displayed in the user interface. A UI using React Native presents the data in a way that captures the user's attention. Input consists of video data and event information from professional players, while output is a visually organized display screen.

[0131] Step 7:

[0132] Users view and watch the presented content, or research event information. This allows them to deepen their knowledge related to their own play style and increases their motivation to watch. The input is the displayed information, and the output is the user's actions (e.g., watching, researching).

[0133] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0134] This invention combines an AI-based information provision system aimed at increasing the number of esports viewers with an emotion engine that recognizes user emotions. This system records the user's gaming activity, performs data analysis including emotion analysis on a server, and provides information on professional players and tournaments based on the results. The following describes the program processing of this system.

[0135] First, the device simultaneously collects the user's gaming activity and emotional state. This process includes the user's facial expressions, voice tone, and in-game behavior data (e.g., wins / losses, character used, play time). Emotional state is recorded through devices such as cameras and microphones.

[0136] Next, the device sends the collected data to the server. Here, both sentiment data and game performance data are transmitted securely.

[0137] Upon receiving this data, the server uses an AI model to analyze the user's emotional state and play style. The emotion engine identifies the emotional states the user exhibited during gameplay (e.g., enjoyment, frustration, excitement, etc.) and provides insights into how they impact game performance.

[0138] Based on the analysis data, the server selects the most suitable professional players, related videos, and news for the user, providing information that best matches the user's current emotional state. This selection might include, for example, showing up refreshing and lighthearted match highlights if the user is feeling frustrated.

[0139] Furthermore, the server selects and provides tournament information that it believes will be most enjoyable for the user based on their current emotional state. In this way, content selection is made that reflects the user's emotions.

[0140] Finally, the device presents the information received from the server to the user visually and audibly. This allows the user to receive information tailored to their emotional state, thereby increasing their motivation to watch the event.

[0141] For example, if the system is inferred to be experiencing stress, it can display calming content to elicit positive emotions. This process allows users to have a personalized experience tailored to their emotions, promoting the effective use of the entire system.

[0142] The following describes the processing flow.

[0143] Step 1:

[0144] The device collects the user's game activity data and emotional data. Game activity data includes win / loss results, characters used, and tactics, while emotional data includes emotional states (e.g., enjoyment, anger, frustration) determined by facial recognition via the camera and voice analysis.

[0145] Step 2:

[0146] The device sends the collected data to the server using a secure communication protocol. This protocol is encrypted to protect data privacy.

[0147] Step 3:

[0148] The server receives the data and begins AI analysis. The emotion engine analyzes the user's emotional state based on facial expressions and voice, and a machine learning algorithm evaluates the play style. This helps to understand the relationship between in-game actions and emotions.

[0149] Step 4:

[0150] Based on the analysis results, the server recommends professional players and related video content suitable for the user. This includes selecting professional player match videos tailored to the user's emotional state, as well as highlights designed to boost motivation.

[0151] Step 5:

[0152] The server selects and provides tournament information that takes into account the user's emotional state and interests. For example, if the user is relaxed, it will provide friendly tournament information that they can enjoy watching.

[0153] Step 6:

[0154] The device displays recommended content and tournament information from the server to the user. This display uses an intuitive dashboard and notification features, allowing users to easily see emotionally relevant options.

[0155] Step 7:

[0156] Based on the information users receive, they watch videos of professional players and check tournament information. In this process, they can enjoy content that resonates with their emotions, increasing their desire to watch.

[0157] (Example 2)

[0158] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0159] Traditional esports viewing support systems have faced the challenge of providing a limited user experience due to the difficulty in delivering personalized content based on the user's emotional state. As a result, this has led to decreased viewing motivation and a lack of user participation and engagement.

[0160] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0161] In this invention, the server includes means for simultaneously collecting the user's game activity and emotional state, means for analyzing the user's emotional state and play style using a generative AI model, and means for identifying appropriate professional player content using prompt messages. This makes it possible to provide personalized content that responds to the user's emotions.

[0162] "User game activity" refers to a series of actions and operations performed by a user while playing a game.

[0163] "Emotional state" refers to the type and intensity of emotions a user experiences while playing a game.

[0164] A "generative AI model" is an artificial intelligence model used for data analysis, specifically for analyzing a user's emotional state and play style.

[0165] A "promote statement" is a text statement used as an instruction or question for a generative AI model, and is used to request a specific output from the model.

[0166] A "professional player" refers to a player who possesses high skill and competitive achievements in a particular game.

[0167] "Content" refers to all data, such as information, images, and audio, presented to the user.

[0168] This invention is an information provision system for esports spectators. Specific embodiments will be described below.

[0169] First, when a user plays a game, the device simultaneously collects game activity and emotional state. The device uses camera and microphone hardware to analyze the user's facial expressions and voice tone, and records gameplay data (wins / losses, characters used, play time, etc.). This provides detailed activity data of the user.

[0170] The device sends this data to the server. The server processes this data using a generative AI model to analyze the user's emotional state. For example, it identifies how much enjoyment or frustration the user is experiencing. An emotion engine is used for this analysis.

[0171] The server selects the most suitable content for the user based on the analysis of their emotional state and game data. Specifically, it selects match highlights of professional players, related videos, and news. It also provides tournament information that the user can enjoy in their current emotional state. For example, if the user needs to relax, it will present lighthearted match highlights that will help them refresh. An example of a prompt used here would be, "Please recommend professional player matches that match my emotional state."

[0172] Ultimately, the device presents the information received from the server to the user visually and audibly. This allows the user to receive information that matches their emotions, further increasing their motivation to watch the event. Through this process, the user can obtain a personalized experience that responds to their emotions.

[0173] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0174] Step 1:

[0175] The device simultaneously collects the user's gaming activity and emotional state. Specifically, it uses a camera to capture the user's facial expressions and a microphone to record the user's voice tone. It also acquires behavioral data such as game wins and losses, selected characters, and play time. This data serves as input. The data is recorded as output, indicating the user's current emotional state and gaming behavior.

[0176] Step 2:

[0177] The device sends the collected data to the server. During this transmission process, the data is encrypted, ensuring that user sentiment data and game performance data are securely delivered to the server. The input is the collected data, and the output is the data package sent to the server.

[0178] Step 3:

[0179] The server analyzes the received data. Here, a generative AI model is used to analyze the user's emotional state and play style. The input is an encrypted data package, which outputs the identification of the user's emotional state (e.g., enjoyment, frustration) and insights into their play style. The generative AI model uses an emotion engine to identify the user's emotions.

[0180] Step 4:

[0181] The server selects the most suitable content for the user based on the analysis results. Specifically, it selects information on professional players, related videos, and news. The input is the analysis results, and the output is recommended content tailored to the user's emotional state. Prompt messages are used to identify the appropriate professional player matches.

[0182] Step 5:

[0183] The device presents information received from the server to the user visually and audibly. Input is recommended content from the server, and output is information delivered through the user's display or speakers. Specifically, it displays videos on the screen and provides additional information through audio. Through this process, the user receives content that resonates with their emotions.

[0184] (Application Example 2)

[0185] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0186] Traditional esports viewing systems have a problem where content is not adequately provided considering the emotional state of the user, resulting in a viewing experience that does not match the emotional needs of individual users and thus reducing their motivation to watch. Furthermore, if the content provided does not match the user's play style, the learning effect through viewing is limited.

[0187] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0188] In this invention, the server includes means for collecting the user's game activity and emotional state, means for analyzing the collected data and evaluating the play style and emotional state, and means for recommending similar professional players, relevant video data, and commentary based on the evaluated play style and emotional state. This makes it possible to provide content that is appropriate to the user's emotional state and to provide an optimized viewing experience for each individual user.

[0189] "User game activity" refers to data that encompasses all game-related operations and behaviors performed by the user.

[0190] "Emotional state" refers to information that indicates the type and intensity of emotions a user exhibits at a given point in time.

[0191] "Play style" refers to an evaluation of a user's unique methods and tendencies when playing a game.

[0192] A "professional player" is someone who possesses advanced skills in a particular game and makes a living by participating in competitive games.

[0193] "Video data" refers to information about videos and live footage that are generated for the purpose of being viewed by users.

[0194] "Live commentary" refers to audio or text information that explains the game's progress and strategy in real time.

[0195] "Entertainment information" refers to entertainment-related data intended to provide users with enjoyment and interest.

[0196] "Local information" refers to data about the geographical location of the user.

[0197] "Entertainment genres" is a classification that refers to a variety of types and fields of entertainment, such as movies, music, and sports.

[0198] To realize this invention, the terminal first collects the user's gaming activity and emotional state. Specifically, it uses hardware such as a camera and microphone to sense the user's facial expressions and tone of voice, and acquires behavioral data during gameplay.

[0199] Next, the device sends the collected data to the server. The server analyzes the received data using an AI model. Specifically, it uses an emotion analysis engine to identify the user's emotional state and evaluate how that emotion affects game performance. In this analysis, generative AI models play a crucial role in personalizing the user's experience.

[0200] Based on these results, the server selects and recommends professional players, relevant video data, and commentators suitable for the evaluated play style and emotional state. The recommended content is customized according to the user's current emotions. For example, if the user is feeling frustrated, it will recommend lighthearted match highlights and positive commentary to help them relax.

[0201] Ultimately, the device presents the information provided by the server to the user visually and audibly. This allows the user to receive optimized content tailored to their emotional state, resulting in a richer viewing experience.

[0202] For example, if the system determines that a user is feeling nervous during a match, it could provide a match highlight reel with relaxing background music to soothe their emotions. An example of a prompt to the generative AI model would be: "The user's emotional data indicates they are feeling nervous. Please recommend relaxing content."

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] The device uses a camera and microphone to collect data on the user's gaming activity and emotional state. Input data includes the user's facial expressions, voice tone, and in-game behavior (e.g., wins / losses, character used, play time). This data is collected and organized in real time to build the initial data set.

[0206] Step 2:

[0207] The device sends the collected data to the server as a package. The input includes a series of activity and emotion data collected in step 1, which are then sent to the server using a secure communication protocol. Data compression is also performed to improve transfer efficiency.

[0208] Step 3:

[0209] The server analyzes the received data. Using an AI model, it evaluates the emotional state and play style based on the input data. In this analysis, the emotion analysis engine classifies the user's emotions in detail and calculates how they affect game performance, obtaining this as output.

[0210] Step 4:

[0211] The server selects content based on the analysis results. Based on the evaluation data, it recommends similar professional players, relevant video data, and commentary. In this step, a generative AI model is used to select the optimal content based on the prompt message, "The user's sentiment data indicates an AA state. Please recommend appropriate content."

[0212] Step 5:

[0213] The device receives information from the server and presents it to the user. Recommended content from the server is output to the user in visual and audio formats. This allows the user to receive information optimized for their emotional state, improving their esports viewing experience.

[0214] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0215] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0216] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0217] [Second Embodiment]

[0218] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0219] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0220] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0221] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0222] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0223] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0224] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0225] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0226] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0227] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0228] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0229] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0230] This invention includes an AI-based information provision system aimed at increasing the number of e-sports spectators. This system records the user's gaming activity and analyzes the collected data via a server. The following describes the program processing of this system.

[0231] First, the device monitors the user's gaming activity and collects data. This data includes the character the user uses, tactics, in-game movements, and win / loss information. The data obtained here forms the basis for subsequent analysis.

[0232] Next, the device sends the collected data to the server. For security reasons, a secure communication protocol is applied to the data transmission.

[0233] The server analyzes the received gameplay data and uses an AI model to evaluate the user's play style. This evaluation includes the user's offensive tendencies, defensive tendencies, and balanced play style.

[0234] Based on the analysis results, the server selects professional players and match video data suitable for the user. Professional players selected are those with a play style similar to the user's.

[0235] The server also provides information about upcoming tournaments based on the user's location and preferred game titles. This tournament information includes dates, locations, participating players, and total prize money.

[0236] Finally, the device displays the information received from the server in a user-friendly format. This can be done using a dashboard or pop-up notifications to allow users to check video data and tournament information.

[0237] For example, if a user exhibits an aggressive playstyle, the AI ​​analysis will recommend recent match videos of professional players known for their aggressive playstyles. Furthermore, information on upcoming tournaments in the user's region will be provided, encouraging them to watch.

[0238] This system allows users to easily access strategies and tournament information from professional players relevant to their play style, which in turn increases their motivation to watch. In this way, it can broaden the base of esports viewership.

[0239] The following describes the processing flow.

[0240] Step 1:

[0241] The device collects user gameplay data in real time or periodically. This includes information about the user's match results, characters used, playtime, scores, and tactics. This data is collected only with the user's permission.

[0242] Step 2:

[0243] The terminal collects data and sends it to the server using a secure communication protocol. To prevent unauthorized access and data leakage, an encrypted connection is used during this process.

[0244] Step 3:

[0245] The server analyzes the received gameplay data using an AI model. Here, machine learning algorithms are used to identify the user's playstyle (e.g., offensive, defensive, balanced) and skill level. This analysis is then compared to historical datasets and the playstyles of professional players.

[0246] Step 4:

[0247] Based on the analysis results, the server selects professional players and related video data suitable for the user. This selection includes match highlights of professional players and explanatory videos focusing on tactics.

[0248] Step 5:

[0249] The server selects upcoming tournament information based on the user's location and preferred game titles. This tournament information includes dates, locations, and a list of participating professional players.

[0250] Step 6:

[0251] The device displays recommended content and tournament information received from the server to the user. This is done using UI notifications and a dedicated information dashboard, which users can click to view details.

[0252] Step 7:

[0253] Users can watch videos of professional players and tournament information provided, developing an interest in watching them. Furthermore, by referring to content related to their own gameplay, they can improve their own playstyle and discover new tactics.

[0254] (Example 1)

[0255] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0256] In recent years, with the rise of esports, the need to increase spectator interest has grown. However, current systems fail to adequately provide information tailored to each user's play style, making it difficult for users to develop a deeper interest in esports. Therefore, there is a need to develop a system that can provide accurate information tailored to the characteristics of each user.

[0257] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0258] In this invention, the server includes means for monitoring the user's electronic game activity and collecting behavioral information, means for transmitting the collected information to a data center using a secure communication protocol, and means for analyzing the information received at the data center and evaluating the user's behavioral characteristics using generative artificial intelligence. This makes it possible to provide information tailored to each user's behavioral characteristics and increase interest in watching esports.

[0259] "Electronic gaming activities" refer to interactive gameplay that users engage in using computers.

[0260] "Action information" refers to data related to the user's choices, actions, and other behaviors during gameplay.

[0261] A "data center" refers to a centralized computer system for receiving and processing information.

[0262] A "secure communication protocol" refers to a means of communication for transmitting information while protecting the confidentiality and integrity of the data.

[0263] "Generative artificial intelligence" refers to artificial intelligence technology that uses machine learning algorithms to generate new information and analyze data.

[0264] "Behavioral characteristics" refer to elements that characterize a user's play style and behavioral patterns.

[0265] A "player character" refers to a person who is a professional or has a notable play style in electronic games.

[0266] "Visual resources" refers to information and content stored in video media format.

[0267] A "competitive event" refers to a competitive event in a specific game title.

[0268] This invention is an artificial intelligence-based information provision system designed to increase the number of esports viewers. The main components of the system are the user's terminal, a server, and a generative artificial intelligence model. Embodiments of the invention are described in detail below.

[0269] Device operation:

[0270] The terminal monitors the user's electronic gaming activity in real time. Specifically, it uses dedicated monitoring software to record user input and in-game actions. This software retrieves necessary data through the game client's API and collects user activity information. Furthermore, secure communication protocols such as HTTPS and TLS are applied to ensure that the collected information is safely transmitted to the data center.

[0271] Server operation:

[0272] The server receives behavioral information transmitted from the terminal. The received data is first pre-processed for analysis, and then generative artificial intelligence is used to evaluate the user's behavioral characteristics. This generative AI learns and evaluates the user's play style, such as attack and defense tendencies. Based on the evaluation results, the server selects player characters and video resources related to the user. It also uses external APIs to obtain information on competitive events based on the user's location and game titles of interest.

[0273] Providing information to users:

[0274] The user's device displays information received from the server in an intuitive and easy-to-understand format. This includes dashboard displays and pop-up notifications. This allows users to easily view match footage of recommended players and detailed information about competitive events they are interested in.

[0275] Specific example:

[0276] For example, if a user has an aggressive playstyle, AI analysis will recommend the latest match footage of professional players that match that style. Additionally, information on upcoming competitive events in the user's region, including the date, time, location, and participating players, will be presented.

[0277] Example of a prompt:

[0278] "Based on recent matches, please recommend some gameplay videos of professional players with an aggressive style."

[0279] "Please provide information on upcoming competitive events in your region."

[0280] In this way, users can easily access information related to their own play styles and deepen their interest in watching esports.

[0281] The flow of the specific process in Example 1 will be described using FIG. 11.

[0282] Step 1:

[0283] The terminal monitors the user's electronic game activities. Specifically, it uses dedicated monitoring software to record in real time the control information input by the user and the actions within the game. The inputs include the user's operation data and the movements of the characters within the game. From this information, the terminal collects the user's motion information and outputs it as a data set.

[0284] Step 2:

[0285] The terminal transmits the motion information it has collected to the data center. To ensure security, the information is encrypted and transmitted using the HTTPS protocol. The input is the data set of the motion information obtained in the previous step. The output is the encrypted data set, which is prepared for analysis on the server side.

[0286] Step 3:

[0287] The server decrypts the motion information received from the terminal and performs preprocessing for data analysis. The input is the encrypted data set, which is decrypted to obtain a clean data set. Specific operations include data formatting and deletion of unnecessary information. The output is a data set in a state that can be analyzed.

[0288] Step 4:

[0289] The server uses a generated AI model to analyze the user's behavioral characteristics. The input is the clean dataset obtained in the previous step. The server inputs this data into the AI ​​model to evaluate the user's playstyle characteristics (e.g., offensive tendencies and defensive tendencies). The output is the analysis result, which includes evaluation values ​​for various behavioral characteristics.

[0290] Step 5:

[0291] The server selects relevant information based on the user's behavioral characteristics. Specifically, it selects video resources of professional players and information on competitive events of interest. To do this, it retrieves information from databases and external APIs using prompt messages. The input is the analysis results, and the output is the selected video resources and event information.

[0292] Step 6:

[0293] The terminal receives selected information from the server and displays it to the user. Inputs include video resources of professional players and information on competitive events. The terminal visually presents this information as a dashboard or pop-up, allowing the user to intuitively understand and utilize it. Output is the visualized information presented to the user.

[0294] (Application Example 1)

[0295] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0296] With the development of electronic sports, providing spectators with a high-quality viewing experience and enhancing their motivation to watch is crucial. However, conventional technologies have not made it easy to appropriately recommend relevant content and event information according to each user's gameplay style. Therefore, there is a need to build a system that provides information optimized for individual users.

[0297] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0298] In this invention, the server includes means for collecting the user's electronic gaming activities, means for analyzing the collected information and evaluating the user's playing style, means for recommending similar professional players and related video information based on the evaluated playing style, means for providing information on upcoming events that are relevant to the user's interests, and means for visually displaying the processing results. This makes it possible to quickly and easily provide individual users with customized information on professional player strategies and events.

[0299] "Means for collecting users' electronic sports activities" refers to devices or software for systematically acquiring information about electronic sports activities conducted by users.

[0300] "Means for analyzing collected information and evaluating operating styles" refers to a system or process for analyzing collected data to identify and evaluate specific user operating methods and strategies.

[0301] "Means for recommending similar professional players and related video information" refers to a method or system for selecting and presenting information on professional players with similar operating methods and related video content to a user, based on the user's operating style.

[0302] "Means of providing information on upcoming events tailored to user interests" refers to devices or software that identify and inform users of relevant upcoming events based on their interests and past activities.

[0303] "Means for visually displaying processing results" refers to a display device or user interface that presents the results of analysis and recommendations to the user in a graphical format, providing information in an easily understandable manner.

[0304] The system for implementing this invention consists of a user's terminal, a cloud server, and a visual display device.

[0305] The user's terminal has the function of monitoring e-sports activities. Specifically, in order to record a specific game play in real time, Nvidia's ShadowPlay or other recording software is used. The data collected in this process includes the characters selected by the player, action patterns, tactics used, win-loss information of the game, etc. These data are sent to the cloud server through a secure protocol such as HTTPS to ensure security.

[0306] When the server receives the collected data, it analyzes these data using an AI model. Specifically, using deep learning libraries such as TensorFlow, the user's play style is classified into aggressive, defensive, balanced, etc. Based on this classification result, the server searches and selects the game videos of professional players with similar styles from a database such as MongoDB. Also, according to the user's area information and interested game titles, relevant future event information is provided.

[0307] The information returned to the user's terminal is visually displayed through a user interface built using React Native or the like. The user can easily check the recommended game videos of professional players and interested event information through the dashboard. Thereby, the user can learn the strategies related to their own game operations and enhance the motivation to watch games.

[0308] As a specific example, when a certain user prefers an aggressive style, the server automatically recommends the game videos and related event information of professional players known for this style. An example of the prompt sentence used at this time is, "This user has taken an aggressive style in more than 50% of the past 10 games. Please tell me the recent game videos of players known for this play style and future related events."

[0309] This system will allow users to more easily access information related to their preferences and style, enriching their viewing experience.

[0310] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0311] Step 1:

[0312] The user's device records their electronic gaming activities. Specifically, it collects data in real time, such as character selection, action patterns, tactics used, and match results. Recording tools such as Nvidia's ShadowPlay are used for this purpose. The input is the user's gameplay data, and the output is a compiled record of this data.

[0313] Step 2:

[0314] The user's device sends collected game data to the server. Secure protocols such as HTTPS are used for data transmission, protecting the user's privacy. The input is organized game data, and the output is encrypted data packets.

[0315] Step 3:

[0316] The server analyzes the received data and evaluates the user's play style. In this process, an AI model using TensorFlow classifies the play style. The input is encrypted game data, and the output is the user's play style (e.g., aggressive, defensive, balanced).

[0317] Step 4:

[0318] The server selects match footage of similar professional players based on the evaluated play style. It searches MongoDB for appropriate video data and identifies the relevant content. The input is the user's play style, and the output is video data of matched professional players.

[0319] Step 5:

[0320] The server provides information on upcoming relevant events based on the user's interests and location. This information includes details of events found (e.g., dates, locations, participants) and selects those that are suitable for the user. The input is the user's profile information, and the output is information on upcoming events.

[0321] Step 6:

[0322] All information returned from the server is sent to the user's terminal and visually displayed in the user interface. A UI using React Native presents the data in a way that captures the user's attention. Input consists of video data and event information from professional players, while output is a visually organized display screen.

[0323] Step 7:

[0324] Users view and watch the presented content, or research event information. This allows them to deepen their knowledge related to their own play style and increases their motivation to watch. The input is the displayed information, and the output is the user's actions (e.g., watching, researching).

[0325] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0326] This invention combines an AI-based information provision system aimed at increasing the number of esports viewers with an emotion engine that recognizes user emotions. This system records the user's gaming activity, performs data analysis including emotion analysis on a server, and provides information on professional players and tournaments based on the results. The following describes the program processing of this system.

[0327] First, the device simultaneously collects the user's gaming activity and emotional state. This process includes the user's facial expressions, voice tone, and in-game behavior data (e.g., wins / losses, character used, play time). Emotional state is recorded through devices such as cameras and microphones.

[0328] Next, the device sends the collected data to the server. Here, both sentiment data and game performance data are transmitted securely.

[0329] Upon receiving this data, the server uses an AI model to analyze the user's emotional state and play style. The emotion engine identifies the emotional states the user exhibited during gameplay (e.g., enjoyment, frustration, excitement, etc.) and provides insights into how they impact game performance.

[0330] Based on the analysis data, the server selects the most suitable professional players, related videos, and news for the user, providing information that best matches the user's current emotional state. This selection might include, for example, showing up refreshing and lighthearted match highlights if the user is feeling frustrated.

[0331] Furthermore, the server selects and provides tournament information that it believes will be most enjoyable for the user based on their current emotional state. In this way, content selection is made that reflects the user's emotions.

[0332] Finally, the device presents the information received from the server to the user visually and audibly. This allows the user to receive information tailored to their emotional state, thereby increasing their motivation to watch the event.

[0333] For example, if the system is inferred to be experiencing stress, it can display calming content to elicit positive emotions. This process allows users to have a personalized experience tailored to their emotions, promoting the effective use of the entire system.

[0334] The following describes the processing flow.

[0335] Step 1:

[0336] The device collects the user's game activity data and emotional data. Game activity data includes win / loss results, characters used, and tactics, while emotional data includes emotional states (e.g., enjoyment, anger, frustration) determined by facial recognition via the camera and voice analysis.

[0337] Step 2:

[0338] The device sends the collected data to the server using a secure communication protocol. This protocol is encrypted to protect data privacy.

[0339] Step 3:

[0340] The server receives the data and begins AI analysis. The emotion engine analyzes the user's emotional state based on facial expressions and voice, and a machine learning algorithm evaluates the play style. This helps to understand the relationship between in-game actions and emotions.

[0341] Step 4:

[0342] Based on the analysis results, the server recommends professional players and related video content suitable for the user. This includes selecting professional player match videos tailored to the user's emotional state, as well as highlights designed to boost motivation.

[0343] Step 5:

[0344] The server selects and provides tournament information that takes into account the user's emotional state and interests. For example, if the user is relaxed, it will provide friendly tournament information that they can enjoy watching.

[0345] Step 6:

[0346] The device displays recommended content and tournament information from the server to the user. This display uses an intuitive dashboard and notification features, allowing users to easily see emotionally relevant options.

[0347] Step 7:

[0348] Based on the information users receive, they watch videos of professional players and check tournament information. In this process, they can enjoy content that resonates with their emotions, increasing their desire to watch.

[0349] (Example 2)

[0350] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0351] Traditional esports viewing support systems have faced the challenge of providing a limited user experience due to the difficulty in delivering personalized content based on the user's emotional state. As a result, this has led to decreased viewing motivation and a lack of user participation and engagement.

[0352] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0353] In this invention, the server includes means for simultaneously collecting the user's game activity and emotional state, means for analyzing the user's emotional state and play style using a generative AI model, and means for identifying appropriate professional player content using prompt messages. This makes it possible to provide personalized content that responds to the user's emotions.

[0354] "User game activity" refers to a series of actions and operations performed by a user while playing a game.

[0355] "Emotional state" refers to the type and intensity of emotions a user experiences while playing a game.

[0356] A "generative AI model" is an artificial intelligence model used for data analysis, specifically for analyzing a user's emotional state and play style.

[0357] A "promote statement" is a text statement used as an instruction or question for a generative AI model, and is used to request a specific output from the model.

[0358] A "professional player" refers to a player who possesses high skill and competitive achievements in a particular game.

[0359] "Content" refers to all data, such as information, images, and audio, presented to the user.

[0360] This invention is an information provision system for esports spectators. Specific embodiments will be described below.

[0361] First, when a user plays a game, the device simultaneously collects game activity and emotional state. The device uses camera and microphone hardware to analyze the user's facial expressions and voice tone, and records gameplay data (wins / losses, characters used, play time, etc.). This provides detailed activity data of the user.

[0362] The device sends this data to the server. The server processes this data using a generative AI model to analyze the user's emotional state. For example, it identifies how much enjoyment or frustration the user is experiencing. An emotion engine is used for this analysis.

[0363] The server selects the most suitable content for the user based on the analysis of their emotional state and game data. Specifically, it selects match highlights of professional players, related videos, and news. It also provides tournament information that the user can enjoy in their current emotional state. For example, if the user needs to relax, it will present lighthearted match highlights that will help them refresh. An example of a prompt used here would be, "Please recommend professional player matches that match my emotional state."

[0364] Ultimately, the device presents the information received from the server to the user visually and audibly. This allows the user to receive information that matches their emotions, further increasing their motivation to watch the event. Through this process, the user can obtain a personalized experience that responds to their emotions.

[0365] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0366] Step 1:

[0367] The device simultaneously collects the user's gaming activity and emotional state. Specifically, it uses a camera to capture the user's facial expressions and a microphone to record the user's voice tone. It also acquires behavioral data such as game wins and losses, selected characters, and play time. This data serves as input. The data is recorded as output, indicating the user's current emotional state and gaming behavior.

[0368] Step 2:

[0369] The device sends the collected data to the server. During this transmission process, the data is encrypted, ensuring that user sentiment data and game performance data are securely delivered to the server. The input is the collected data, and the output is the data package sent to the server.

[0370] Step 3:

[0371] The server analyzes the received data. Here, a generative AI model is used to analyze the user's emotional state and play style. The input is an encrypted data package, which outputs the identification of the user's emotional state (e.g., enjoyment, frustration) and insights into their play style. The generative AI model uses an emotion engine to identify the user's emotions.

[0372] Step 4:

[0373] The server selects the most suitable content for the user based on the analysis results. Specifically, it selects information on professional players, related videos, and news. The input is the analysis results, and the output is recommended content tailored to the user's emotional state. Prompt messages are used to identify the appropriate professional player matches.

[0374] Step 5:

[0375] The device presents information received from the server to the user visually and audibly. Input is recommended content from the server, and output is information delivered through the user's display or speakers. Specifically, it displays videos on the screen and provides additional information through audio. Through this process, the user receives content that resonates with their emotions.

[0376] (Application Example 2)

[0377] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0378] Traditional esports viewing systems have a problem where content is not adequately provided considering the emotional state of the user, resulting in a viewing experience that does not match the emotional needs of individual users and thus reducing their motivation to watch. Furthermore, if the content provided does not match the user's play style, the learning effect through viewing is limited.

[0379] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0380] In this invention, the server includes means for collecting the user's game activity and emotional state, means for analyzing the collected data and evaluating the play style and emotional state, and means for recommending similar professional players, relevant video data, and commentary based on the evaluated play style and emotional state. This makes it possible to provide content that is appropriate to the user's emotional state and to provide an optimized viewing experience for each individual user.

[0381] "User game activity" refers to data that encompasses all game-related operations and behaviors performed by the user.

[0382] "Emotional state" refers to information that indicates the type and intensity of emotions a user exhibits at a given point in time.

[0383] "Play style" refers to an evaluation of a user's unique methods and tendencies when playing a game.

[0384] A "professional player" is someone who possesses advanced skills in a particular game and makes a living by participating in competitive games.

[0385] "Video data" refers to information about videos and live footage that are generated for the purpose of being viewed by users.

[0386] "Live commentary" refers to audio or text information that explains the game's progress and strategy in real time.

[0387] "Entertainment information" refers to entertainment-related data intended to provide users with enjoyment and interest.

[0388] "Local information" refers to data about the geographical location of the user.

[0389] "Entertainment genres" is a classification that refers to a variety of types and fields of entertainment, such as movies, music, and sports.

[0390] To realize this invention, the terminal first collects the user's gaming activity and emotional state. Specifically, it uses hardware such as a camera and microphone to sense the user's facial expressions and tone of voice, and acquires behavioral data during gameplay.

[0391] Next, the device sends the collected data to the server. The server analyzes the received data using an AI model. Specifically, it uses an emotion analysis engine to identify the user's emotional state and evaluate how that emotion affects game performance. In this analysis, generative AI models play a crucial role in personalizing the user's experience.

[0392] Based on these results, the server selects and recommends professional players, relevant video data, and commentators suitable for the evaluated play style and emotional state. The recommended content is customized according to the user's current emotions. For example, if the user is feeling frustrated, it will recommend lighthearted match highlights and positive commentary to help them relax.

[0393] Ultimately, the device presents the information provided by the server to the user visually and audibly. This allows the user to receive optimized content tailored to their emotional state, resulting in a richer viewing experience.

[0394] For example, if the system determines that a user is feeling nervous during a match, it could provide a match highlight reel with relaxing background music to soothe their emotions. An example of a prompt to the generative AI model would be: "The user's emotional data indicates they are feeling nervous. Please recommend relaxing content."

[0395] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0396] Step 1:

[0397] The device uses a camera and microphone to collect data on the user's gaming activity and emotional state. Input data includes the user's facial expressions, voice tone, and in-game behavior (e.g., wins / losses, character used, play time). This data is collected and organized in real time to build the initial data set.

[0398] Step 2:

[0399] The device sends the collected data to the server as a package. The input includes a series of activity and emotion data collected in step 1, which are then sent to the server using a secure communication protocol. Data compression is also performed to improve transfer efficiency.

[0400] Step 3:

[0401] The server analyzes the received data. Using an AI model, it evaluates the emotional state and play style based on the input data. In this analysis, the emotion analysis engine classifies the user's emotions in detail and calculates how they affect game performance, obtaining this as output.

[0402] Step 4:

[0403] The server selects content based on the analysis results. Based on the evaluation data, it recommends similar professional players, relevant video data, and commentary. In this step, a generative AI model is used to select the optimal content based on the prompt message, "The user's sentiment data indicates an AA state. Please recommend appropriate content."

[0404] Step 5:

[0405] The device receives information from the server and presents it to the user. Recommended content from the server is output to the user in visual and audio formats. This allows the user to receive information optimized for their emotional state, improving their esports viewing experience.

[0406] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0407] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0408] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0409] [Third Embodiment]

[0410] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0411] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0412] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0413] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0414] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0415] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0416] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0417] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0418] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0419] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0420] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0421] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0422] This invention includes an AI-based information provision system aimed at increasing the number of e-sports spectators. This system records the user's gaming activity and analyzes the collected data via a server. The following describes the program processing of this system.

[0423] First, the device monitors the user's gaming activity and collects data. This data includes the character the user uses, tactics, in-game movements, and win / loss information. The data obtained here forms the basis for subsequent analysis.

[0424] Next, the device sends the collected data to the server. For security reasons, a secure communication protocol is applied to the data transmission.

[0425] The server analyzes the received gameplay data and uses an AI model to evaluate the user's play style. This evaluation includes the user's offensive tendencies, defensive tendencies, and balanced play style.

[0426] Based on the analysis results, the server selects professional players and match video data suitable for the user. Professional players selected are those with a play style similar to the user's.

[0427] The server also provides information about upcoming tournaments based on the user's location and preferred game titles. This tournament information includes dates, locations, participating players, and total prize money.

[0428] Finally, the device displays the information received from the server in a user-friendly format. This can be done using a dashboard or pop-up notifications to allow users to check video data and tournament information.

[0429] For example, if a user exhibits an aggressive playstyle, the AI ​​analysis will recommend recent match videos of professional players known for their aggressive playstyles. Furthermore, information on upcoming tournaments in the user's region will be provided, encouraging them to watch.

[0430] This system allows users to easily access strategies and tournament information from professional players relevant to their play style, which in turn increases their motivation to watch. In this way, it can broaden the base of esports viewership.

[0431] The following describes the processing flow.

[0432] Step 1:

[0433] The device collects user gameplay data in real time or periodically. This includes information about the user's match results, characters used, playtime, scores, and tactics. This data is collected only with the user's permission.

[0434] Step 2:

[0435] The terminal collects data and sends it to the server using a secure communication protocol. To prevent unauthorized access and data leakage, an encrypted connection is used during this process.

[0436] Step 3:

[0437] The server analyzes the received gameplay data using an AI model. Here, machine learning algorithms are used to identify the user's playstyle (e.g., offensive, defensive, balanced) and skill level. This analysis is then compared to historical datasets and the playstyles of professional players.

[0438] Step 4:

[0439] Based on the analysis results, the server selects professional players and related video data suitable for the user. This selection includes match highlights of professional players and explanatory videos focusing on tactics.

[0440] Step 5:

[0441] The server selects upcoming tournament information based on the user's location and preferred game titles. This tournament information includes dates, locations, and a list of participating professional players.

[0442] Step 6:

[0443] The device displays recommended content and tournament information received from the server to the user. This is done using UI notifications and a dedicated information dashboard, which users can click to view details.

[0444] Step 7:

[0445] Users can watch videos of professional players and tournament information provided, developing an interest in watching them. Furthermore, by referring to content related to their own gameplay, they can improve their own playstyle and discover new tactics.

[0446] (Example 1)

[0447] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0448] In recent years, with the rise of esports, the need to increase spectator interest has grown. However, current systems fail to adequately provide information tailored to each user's play style, making it difficult for users to develop a deeper interest in esports. Therefore, there is a need to develop a system that can provide accurate information tailored to the characteristics of each user.

[0449] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0450] In this invention, the server includes means for monitoring the user's electronic game activity and collecting behavioral information, means for transmitting the collected information to a data center using a secure communication protocol, and means for analyzing the information received at the data center and evaluating the user's behavioral characteristics using generative artificial intelligence. This makes it possible to provide information tailored to each user's behavioral characteristics and increase interest in watching esports.

[0451] "Electronic gaming activities" refer to interactive gameplay that users engage in using computers.

[0452] "Action information" refers to data related to the user's choices, actions, and other behaviors during gameplay.

[0453] A "data center" refers to a centralized computer system for receiving and processing information.

[0454] A "secure communication protocol" refers to a means of communication for transmitting information while protecting the confidentiality and integrity of the data.

[0455] "Generative artificial intelligence" refers to artificial intelligence technology that uses machine learning algorithms to generate new information and analyze data.

[0456] "Behavioral characteristics" refer to elements that characterize a user's play style and behavioral patterns.

[0457] A "player character" refers to a person who is a professional or has a notable play style in electronic games.

[0458] "Visual resources" refers to information and content stored in video media format.

[0459] A "competitive event" refers to a competitive event in a specific game title.

[0460] This invention is an artificial intelligence-based information provision system designed to increase the number of esports viewers. The main components of the system are the user's terminal, a server, and a generative artificial intelligence model. Embodiments of the invention are described in detail below.

[0461] Device operation:

[0462] The terminal monitors the user's electronic gaming activity in real time. Specifically, it uses dedicated monitoring software to record user input and in-game actions. This software retrieves necessary data through the game client's API and collects user activity information. Furthermore, secure communication protocols such as HTTPS and TLS are applied to ensure that the collected information is safely transmitted to the data center.

[0463] Server operation:

[0464] The server receives behavioral information transmitted from the terminal. The received data is first pre-processed for analysis, and then generative artificial intelligence is used to evaluate the user's behavioral characteristics. This generative AI learns and evaluates the user's play style, such as attack and defense tendencies. Based on the evaluation results, the server selects player characters and video resources related to the user. It also uses external APIs to obtain information on competitive events based on the user's location and game titles of interest.

[0465] Providing information to users:

[0466] The user's device displays information received from the server in an intuitive and easy-to-understand format. This includes dashboard displays and pop-up notifications. This allows users to easily view match footage of recommended players and detailed information about competitive events they are interested in.

[0467] Specific example:

[0468] For example, if a user has an aggressive playstyle, AI analysis will recommend the latest match footage of professional players that match that style. Additionally, information on upcoming competitive events in the user's region, including the date, time, location, and participating players, will be presented.

[0469] Example of a prompt:

[0470] "Based on recent matches, please recommend some gameplay videos of professional players with an aggressive style."

[0471] "Please provide information on upcoming competitive events in your region."

[0472] In this way, users can easily access information related to their own play style and deepen their interest in watching esports.

[0473] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0474] Step 1:

[0475] The device monitors the user's electronic gaming activity. Specifically, it uses dedicated monitoring software to record control information entered by the user and actions within the game in real time. Inputs include user operation data and character movements within the game. From this information, the device collects user behavior data and outputs it as a dataset.

[0476] Step 2:

[0477] The terminal collects operational information and transmits it to the data center. To ensure security, the information is encrypted and transmitted using the HTTPS protocol. The input is a dataset of operational information obtained in the previous step. The output is an encrypted dataset, prepared for analysis on the server side.

[0478] Step 3:

[0479] The server decrypts the operational information received from the terminal and performs preprocessing for data analysis. The input is an encrypted dataset, which is decrypted to obtain a clean dataset. Specific operations include data formatting and removal of unnecessary information. The output is a dataset ready for analysis.

[0480] Step 4:

[0481] The server uses a generated AI model to analyze the user's behavioral characteristics. The input is the clean dataset obtained in the previous step. The server inputs this data into the AI ​​model to evaluate the user's playstyle characteristics (e.g., offensive tendencies and defensive tendencies). The output is the analysis result, which includes evaluation values ​​for various behavioral characteristics.

[0482] Step 5:

[0483] The server selects relevant information based on the user's behavioral characteristics. Specifically, it selects video resources of professional players and information on competitive events of interest. To do this, it retrieves information from databases and external APIs using prompt messages. The input is the analysis results, and the output is the selected video resources and event information.

[0484] Step 6:

[0485] The terminal receives selected information from the server and displays it to the user. Inputs include video resources of professional players and information on competitive events. The terminal visually presents this information as a dashboard or pop-up, allowing the user to intuitively understand and utilize it. Output is the visualized information presented to the user.

[0486] (Application Example 1)

[0487] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0488] With the development of electronic sports, providing spectators with a high-quality viewing experience and enhancing their motivation to watch is crucial. However, conventional technologies have not made it easy to appropriately recommend relevant content and event information according to each user's gameplay style. Therefore, there is a need to build a system that provides information optimized for individual users.

[0489] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0490] In this invention, the server includes means for collecting the user's electronic gaming activities, means for analyzing the collected information and evaluating the user's playing style, means for recommending similar professional players and related video information based on the evaluated playing style, means for providing information on upcoming events that are relevant to the user's interests, and means for visually displaying the processing results. This makes it possible to quickly and easily provide individual users with customized information on professional player strategies and events.

[0491] "Means for collecting users' electronic sports activities" refers to devices or software for systematically acquiring information about electronic sports activities conducted by users.

[0492] "Means for analyzing collected information and evaluating operating styles" refers to a system or process for analyzing collected data to identify and evaluate specific user operating methods and strategies.

[0493] "Means for recommending similar professional players and related video information" refers to a method or system for selecting and presenting information on professional players with similar operating methods and related video content to a user, based on the user's operating style.

[0494] "Means of providing information on upcoming events tailored to user interests" refers to devices or software that identify and inform users of relevant upcoming events based on their interests and past activities.

[0495] "Means for visually displaying processing results" refers to a display device or user interface that presents the results of analysis and recommendations to the user in a graphical format, providing information in an easily understandable manner.

[0496] The system for carrying out this invention consists of a user terminal, a cloud server, and a visual display device.

[0497] The user's device has the capability to monitor electronic gaming activities. Specifically, it uses Nvidia's ShadowPlay or other recording software to record specific gameplay in real time. The data collected in this process includes the character selected by the player, their behavior patterns, the tactics used, and match win / loss information. This data is transmitted to a cloud server via a secure protocol such as HTTPS to ensure security.

[0498] Upon receiving the collected data, the server analyzes it using an AI model. Specifically, it uses deep learning libraries such as TensorFlow to classify the user's play style into categories such as aggressive, defensive, and balanced. Based on this classification, the server searches and selects match videos of professional players with similar styles from databases such as MongoDB. It also provides relevant upcoming event information based on the user's location and preferred game titles.

[0499] The information returned to the user's device is visually displayed in a user interface built using React Native, etc. Through the dashboard, users can easily view match videos of recommended expert players and information on events of interest. This allows users to learn strategies related to their own gameplay and increases their motivation to watch.

[0500] For example, if a user prefers an aggressive style, the server will automatically recommend match videos and related event information of professional players known for this style. An example of a prompt used in this case would be: "This user has used an aggressive style in over 50% of their last 10 matches. Please show me recent match videos and upcoming related events of players known for this playstyle."

[0501] This system will allow users to more easily access information related to their preferences and style, enriching their viewing experience.

[0502] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0503] Step 1:

[0504] The user's device records their electronic gaming activities. Specifically, it collects data in real time, such as character selection, action patterns, tactics used, and match results. Recording tools such as Nvidia's ShadowPlay are used for this purpose. The input is the user's gameplay data, and the output is a compiled record of this data.

[0505] Step 2:

[0506] The user's device sends collected game data to the server. Secure protocols such as HTTPS are used for data transmission, protecting the user's privacy. The input is organized game data, and the output is encrypted data packets.

[0507] Step 3:

[0508] The server analyzes the received data and evaluates the user's play style. In this process, an AI model using TensorFlow classifies the play style. The input is encrypted game data, and the output is the user's play style (e.g., aggressive, defensive, balanced).

[0509] Step 4:

[0510] The server selects match footage of similar professional players based on the evaluated play style. It searches MongoDB for appropriate video data and identifies the relevant content. The input is the user's play style, and the output is video data of matched professional players.

[0511] Step 5:

[0512] The server provides information on upcoming relevant events based on the user's interests and location. This information includes details of events found (e.g., dates, locations, participants) and selects those that are suitable for the user. The input is the user's profile information, and the output is information on upcoming events.

[0513] Step 6:

[0514] All information returned from the server is sent to the user's terminal and visually displayed in the user interface. A UI using React Native presents the data in a way that captures the user's attention. Input consists of video data and event information from professional players, while output is a visually organized display screen.

[0515] Step 7:

[0516] Users view and watch the presented content, or research event information. This allows them to deepen their knowledge related to their own play style and increases their motivation to watch. The input is the displayed information, and the output is the user's actions (e.g., watching, researching).

[0517] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0518] This invention combines an AI-based information provision system aimed at increasing the number of esports viewers with an emotion engine that recognizes user emotions. This system records the user's gaming activity, performs data analysis including emotion analysis on a server, and provides information on professional players and tournaments based on the results. The following describes the program processing of this system.

[0519] First, the device simultaneously collects the user's gaming activity and emotional state. This process includes the user's facial expressions, voice tone, and in-game behavior data (e.g., wins / losses, character used, play time). Emotional state is recorded through devices such as cameras and microphones.

[0520] Next, the device sends the collected data to the server. Here, both sentiment data and game performance data are transmitted securely.

[0521] Upon receiving this data, the server uses an AI model to analyze the user's emotional state and play style. The emotion engine identifies the emotional states the user exhibited during gameplay (e.g., enjoyment, frustration, excitement, etc.) and provides insights into how they impact game performance.

[0522] Based on the analysis data, the server selects the most suitable professional players, related videos, and news for the user, providing information that best matches the user's current emotional state. This selection might include, for example, showing up refreshing and lighthearted match highlights if the user is feeling frustrated.

[0523] Furthermore, the server selects and provides tournament information that it believes will be most enjoyable for the user based on their current emotional state. In this way, content selection is made that reflects the user's emotions.

[0524] Finally, the device presents the information received from the server to the user visually and audibly. This allows the user to receive information tailored to their emotional state, thereby increasing their motivation to watch the event.

[0525] For example, if the system is inferred to be experiencing stress, it can display calming content to elicit positive emotions. This process allows users to have a personalized experience tailored to their emotions, promoting the effective use of the entire system.

[0526] The following describes the processing flow.

[0527] Step 1:

[0528] The device collects the user's game activity data and emotional data. Game activity data includes win / loss results, characters used, and tactics, while emotional data includes emotional states (e.g., enjoyment, anger, frustration) determined by facial recognition via the camera and voice analysis.

[0529] Step 2:

[0530] The device sends the collected data to the server using a secure communication protocol. This protocol is encrypted to protect data privacy.

[0531] Step 3:

[0532] The server receives the data and begins AI analysis. The emotion engine analyzes the user's emotional state based on facial expressions and voice, and a machine learning algorithm evaluates the play style. This helps to understand the relationship between in-game actions and emotions.

[0533] Step 4:

[0534] Based on the analysis results, the server recommends professional players and related video content suitable for the user. This includes selecting professional player match videos tailored to the user's emotional state, as well as highlights designed to boost motivation.

[0535] Step 5:

[0536] The server selects and provides tournament information that takes into account the user's emotional state and interests. For example, if the user is relaxed, it will provide friendly tournament information that they can enjoy watching.

[0537] Step 6:

[0538] The device displays recommended content and tournament information from the server to the user. This display uses an intuitive dashboard and notification features, allowing users to easily see emotionally relevant options.

[0539] Step 7:

[0540] Based on the information users receive, they watch videos of professional players and check tournament information. In this process, they can enjoy content that resonates with their emotions, increasing their desire to watch.

[0541] (Example 2)

[0542] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0543] Traditional esports viewing support systems have faced the challenge of providing a limited user experience due to the difficulty in delivering personalized content based on the user's emotional state. As a result, this has led to decreased viewing motivation and a lack of user participation and engagement.

[0544] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0545] In this invention, the server includes means for simultaneously collecting the user's game activity and emotional state, means for analyzing the user's emotional state and play style using a generative AI model, and means for identifying appropriate professional player content using prompt messages. This makes it possible to provide personalized content that responds to the user's emotions.

[0546] "User game activity" refers to a series of actions and operations performed by a user while playing a game.

[0547] "Emotional state" refers to the type and intensity of emotions a user experiences while playing a game.

[0548] A "generative AI model" is an artificial intelligence model used for data analysis, specifically for analyzing a user's emotional state and play style.

[0549] A "promote statement" is a text statement used as an instruction or question for a generative AI model, and is used to request a specific output from the model.

[0550] A "professional player" refers to a player who possesses high skill and competitive achievements in a particular game.

[0551] "Content" refers to all data, such as information, images, and audio, presented to the user.

[0552] This invention is an information provision system for esports spectators. Specific embodiments will be described below.

[0553] First, when a user plays a game, the device simultaneously collects game activity and emotional state. The device uses camera and microphone hardware to analyze the user's facial expressions and voice tone, and records gameplay data (wins / losses, characters used, play time, etc.). This provides detailed activity data of the user.

[0554] The device sends this data to the server. The server processes this data using a generative AI model to analyze the user's emotional state. For example, it identifies how much enjoyment or frustration the user is experiencing. An emotion engine is used for this analysis.

[0555] The server selects the most suitable content for the user based on the analysis of their emotional state and game data. Specifically, it selects match highlights of professional players, related videos, and news. It also provides tournament information that the user can enjoy in their current emotional state. For example, if the user needs to relax, it will present lighthearted match highlights that will help them refresh. An example of a prompt used here would be, "Please recommend professional player matches that match my emotional state."

[0556] Ultimately, the device presents the information received from the server to the user visually and audibly. This allows the user to receive information that matches their emotions, further increasing their motivation to watch the event. Through this process, the user can obtain a personalized experience that responds to their emotions.

[0557] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0558] Step 1:

[0559] The device simultaneously collects the user's gaming activity and emotional state. Specifically, it uses a camera to capture the user's facial expressions and a microphone to record the user's voice tone. It also acquires behavioral data such as game wins and losses, selected characters, and play time. This data serves as input. The data is recorded as output, indicating the user's current emotional state and gaming behavior.

[0560] Step 2:

[0561] The device sends the collected data to the server. During this transmission process, the data is encrypted, ensuring that user sentiment data and game performance data are securely delivered to the server. The input is the collected data, and the output is the data package sent to the server.

[0562] Step 3:

[0563] The server analyzes the received data. Here, a generative AI model is used to analyze the user's emotional state and play style. The input is an encrypted data package, which outputs the identification of the user's emotional state (e.g., enjoyment, frustration) and insights into their play style. The generative AI model uses an emotion engine to identify the user's emotions.

[0564] Step 4:

[0565] The server selects the most suitable content for the user based on the analysis results. Specifically, it selects information on professional players, related videos, and news. The input is the analysis results, and the output is recommended content tailored to the user's emotional state. Prompt messages are used to identify the appropriate professional player matches.

[0566] Step 5:

[0567] The device presents information received from the server to the user visually and audibly. Input is recommended content from the server, and output is information delivered through the user's display or speakers. Specifically, it displays videos on the screen and provides additional information through audio. Through this process, the user receives content that resonates with their emotions.

[0568] (Application Example 2)

[0569] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0570] Traditional esports viewing systems have a problem where content is not adequately provided considering the emotional state of the user, resulting in a viewing experience that does not match the emotional needs of individual users and thus reducing their motivation to watch. Furthermore, if the content provided does not match the user's play style, the learning effect through viewing is limited.

[0571] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0572] In this invention, the server includes means for collecting the user's game activity and emotional state, means for analyzing the collected data and evaluating the play style and emotional state, and means for recommending similar professional players, relevant video data, and commentary based on the evaluated play style and emotional state. This makes it possible to provide content that is appropriate to the user's emotional state and to provide an optimized viewing experience for each individual user.

[0573] "User game activity" refers to data that encompasses all game-related operations and behaviors performed by the user.

[0574] "Emotional state" refers to information that indicates the type and intensity of emotions a user exhibits at a given point in time.

[0575] "Play style" refers to an evaluation of a user's unique methods and tendencies when playing a game.

[0576] A "professional player" is someone who possesses advanced skills in a particular game and makes a living by participating in competitive games.

[0577] "Video data" refers to information about videos and live footage that are generated for the purpose of being viewed by users.

[0578] "Live commentary" refers to audio or text information that explains the game's progress and strategy in real time.

[0579] "Entertainment information" refers to entertainment-related data intended to provide users with enjoyment and interest.

[0580] "Local information" refers to data about the geographical location of the user.

[0581] "Entertainment genres" is a classification that refers to a variety of types and fields of entertainment, such as movies, music, and sports.

[0582] To realize this invention, the terminal first collects the user's gaming activity and emotional state. Specifically, it uses hardware such as a camera and microphone to sense the user's facial expressions and tone of voice, and acquires behavioral data during gameplay.

[0583] Next, the device sends the collected data to the server. The server analyzes the received data using an AI model. Specifically, it uses an emotion analysis engine to identify the user's emotional state and evaluate how that emotion affects game performance. In this analysis, generative AI models play a crucial role in personalizing the user's experience.

[0584] Based on these results, the server selects and recommends professional players, relevant video data, and commentators suitable for the evaluated play style and emotional state. The recommended content is customized according to the user's current emotions. For example, if the user is feeling frustrated, it will recommend lighthearted match highlights and positive commentary to help them relax.

[0585] Ultimately, the device presents the information provided by the server to the user visually and audibly. This allows the user to receive optimized content tailored to their emotional state, resulting in a richer viewing experience.

[0586] For example, if the system determines that a user is feeling nervous during a match, it could provide a match highlight reel with relaxing background music to soothe their emotions. An example of a prompt to the generative AI model would be: "The user's emotional data indicates they are feeling nervous. Please recommend relaxing content."

[0587] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0588] Step 1:

[0589] The device uses a camera and microphone to collect data on the user's gaming activity and emotional state. Input data includes the user's facial expressions, voice tone, and in-game behavior (e.g., wins / losses, character used, play time). This data is collected and organized in real time to build the initial data set.

[0590] Step 2:

[0591] The device sends the collected data to the server as a package. The input includes a series of activity and emotion data collected in step 1, which are then sent to the server using a secure communication protocol. Data compression is also performed to improve transfer efficiency.

[0592] Step 3:

[0593] The server analyzes the received data. Using an AI model, it evaluates the emotional state and play style based on the input data. In this analysis, the emotion analysis engine classifies the user's emotions in detail and calculates how they affect game performance, obtaining this as output.

[0594] Step 4:

[0595] The server selects content based on the analysis results. Based on the evaluation data, it recommends similar professional players, relevant video data, and commentary. In this step, a generative AI model is used to select the optimal content based on the prompt message, "The user's sentiment data indicates an AA state. Please recommend appropriate content."

[0596] Step 5:

[0597] The device receives information from the server and presents it to the user. Recommended content from the server is output to the user in visual and audio formats. This allows the user to receive information optimized for their emotional state, improving their esports viewing experience.

[0598] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0599] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0600] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0601] [Fourth Embodiment]

[0602] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0603] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0604] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0605] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0606] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0607] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0608] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0609] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0610] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0611] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0612] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0613] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0614] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0615] This invention includes an AI-based information provision system aimed at increasing the number of e-sports spectators. This system records the user's gaming activity and analyzes the collected data via a server. The following describes the program processing of this system.

[0616] First, the device monitors the user's gaming activity and collects data. This data includes the character the user uses, tactics, in-game movements, and win / loss information. The data obtained here forms the basis for subsequent analysis.

[0617] Next, the device sends the collected data to the server. For security reasons, a secure communication protocol is applied to the data transmission.

[0618] The server analyzes the received gameplay data and uses an AI model to evaluate the user's play style. This evaluation includes the user's offensive tendencies, defensive tendencies, and balanced play style.

[0619] Based on the analysis results, the server selects professional players and match video data suitable for the user. Professional players selected are those with a play style similar to the user's.

[0620] The server also provides information about upcoming tournaments based on the user's location and preferred game titles. This tournament information includes dates, locations, participating players, and total prize money.

[0621] Finally, the device displays the information received from the server in a user-friendly format. This can be done using a dashboard or pop-up notifications to allow users to check video data and tournament information.

[0622] For example, if a user exhibits an aggressive playstyle, the AI ​​analysis will recommend recent match videos of professional players known for their aggressive playstyles. Furthermore, information on upcoming tournaments in the user's region will be provided, encouraging them to watch.

[0623] This system allows users to easily access strategies and tournament information from professional players relevant to their play style, which in turn increases their motivation to watch. In this way, it can broaden the base of esports viewership.

[0624] The following describes the processing flow.

[0625] Step 1:

[0626] The device collects user gameplay data in real time or periodically. This includes information about the user's match results, characters used, playtime, scores, and tactics. This data is collected only with the user's permission.

[0627] Step 2:

[0628] The terminal collects data and sends it to the server using a secure communication protocol. To prevent unauthorized access and data leakage, an encrypted connection is used during this process.

[0629] Step 3:

[0630] The server analyzes the received gameplay data using an AI model. Here, machine learning algorithms are used to identify the user's playstyle (e.g., offensive, defensive, balanced) and skill level. This analysis is then compared to historical datasets and the playstyles of professional players.

[0631] Step 4:

[0632] Based on the analysis results, the server selects professional players and related video data suitable for the user. This selection includes match highlights of professional players and explanatory videos focusing on tactics.

[0633] Step 5:

[0634] The server selects upcoming tournament information based on the user's location and preferred game titles. This tournament information includes dates, locations, and a list of participating professional players.

[0635] Step 6:

[0636] The device displays recommended content and tournament information received from the server to the user. This is done using UI notifications and a dedicated information dashboard, which users can click to view details.

[0637] Step 7:

[0638] Users can watch videos of professional players and tournament information provided, developing an interest in watching them. Furthermore, by referring to content related to their own gameplay, they can improve their own playstyle and discover new tactics.

[0639] (Example 1)

[0640] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0641] In recent years, with the rise of esports, the need to increase spectator interest has grown. However, current systems fail to adequately provide information tailored to each user's play style, making it difficult for users to develop a deeper interest in esports. Therefore, there is a need to develop a system that can provide accurate information tailored to the characteristics of each user.

[0642] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0643] In this invention, the server includes means for monitoring the user's electronic game activity and collecting behavioral information, means for transmitting the collected information to a data center using a secure communication protocol, and means for analyzing the information received at the data center and evaluating the user's behavioral characteristics using generative artificial intelligence. This makes it possible to provide information tailored to each user's behavioral characteristics and increase interest in watching esports.

[0644] "Electronic gaming activities" refer to interactive gameplay that users engage in using computers.

[0645] "Action information" refers to data related to the user's choices, actions, and other behaviors during gameplay.

[0646] A "data center" refers to a centralized computer system for receiving and processing information.

[0647] A "secure communication protocol" refers to a means of communication for transmitting information while protecting the confidentiality and integrity of the data.

[0648] "Generative artificial intelligence" refers to artificial intelligence technology that uses machine learning algorithms to generate new information and analyze data.

[0649] "Behavioral characteristics" refer to elements that characterize a user's play style and behavioral patterns.

[0650] A "player character" refers to a person who is a professional or has a notable play style in electronic games.

[0651] "Visual resources" refers to information and content stored in video media format.

[0652] A "competitive event" refers to a competitive event in a specific game title.

[0653] This invention is an artificial intelligence-based information provision system designed to increase the number of esports viewers. The main components of the system are the user's terminal, a server, and a generative artificial intelligence model. Embodiments of the invention are described in detail below.

[0654] Device operation:

[0655] The terminal monitors the user's electronic gaming activity in real time. Specifically, it uses dedicated monitoring software to record user input and in-game actions. This software retrieves necessary data through the game client's API and collects user activity information. Furthermore, secure communication protocols such as HTTPS and TLS are applied to ensure that the collected information is safely transmitted to the data center.

[0656] Server operation:

[0657] The server receives behavioral information transmitted from the terminal. The received data is first pre-processed for analysis, and then generative artificial intelligence is used to evaluate the user's behavioral characteristics. This generative AI learns and evaluates the user's play style, such as attack and defense tendencies. Based on the evaluation results, the server selects player characters and video resources related to the user. It also uses external APIs to obtain information on competitive events based on the user's location and game titles of interest.

[0658] Providing information to users:

[0659] The user's device displays information received from the server in an intuitive and easy-to-understand format. This includes dashboard displays and pop-up notifications. This allows users to easily view match footage of recommended players and detailed information about competitive events they are interested in.

[0660] Specific example:

[0661] For example, if a user has an aggressive playstyle, AI analysis will recommend the latest match footage of professional players that match that style. Additionally, information on upcoming competitive events in the user's region, including the date, time, location, and participating players, will be presented.

[0662] Example of a prompt:

[0663] "Based on recent matches, please recommend some gameplay videos of professional players with an aggressive style."

[0664] "Please provide information on upcoming competitive events in your region."

[0665] In this way, users can easily access information related to their own play style and deepen their interest in watching esports.

[0666] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0667] Step 1:

[0668] The device monitors the user's electronic gaming activity. Specifically, it uses dedicated monitoring software to record control information entered by the user and actions within the game in real time. Inputs include user operation data and character movements within the game. From this information, the device collects user behavior data and outputs it as a dataset.

[0669] Step 2:

[0670] The terminal collects operational information and transmits it to the data center. To ensure security, the information is encrypted and transmitted using the HTTPS protocol. The input is a dataset of operational information obtained in the previous step. The output is an encrypted dataset, prepared for analysis on the server side.

[0671] Step 3:

[0672] The server decrypts the operational information received from the terminal and performs preprocessing for data analysis. The input is an encrypted dataset, which is decrypted to obtain a clean dataset. Specific operations include data formatting and removal of unnecessary information. The output is a dataset ready for analysis.

[0673] Step 4:

[0674] The server uses a generated AI model to analyze the user's behavioral characteristics. The input is the clean dataset obtained in the previous step. The server inputs this data into the AI ​​model to evaluate the user's playstyle characteristics (e.g., offensive tendencies and defensive tendencies). The output is the analysis result, which includes evaluation values ​​for various behavioral characteristics.

[0675] Step 5:

[0676] The server selects relevant information based on the user's behavioral characteristics. Specifically, it selects video resources of professional players and information on competitive events of interest. To do this, it retrieves information from databases and external APIs using prompt messages. The input is the analysis results, and the output is the selected video resources and event information.

[0677] Step 6:

[0678] The terminal receives selected information from the server and displays it to the user. Inputs include video resources of professional players and information on competitive events. The terminal visually presents this information as a dashboard or pop-up, allowing the user to intuitively understand and utilize it. Output is the visualized information presented to the user.

[0679] (Application Example 1)

[0680] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0681] With the development of electronic sports, providing spectators with a high-quality viewing experience and enhancing their motivation to watch is crucial. However, conventional technologies have not made it easy to appropriately recommend relevant content and event information according to each user's gameplay style. Therefore, there is a need to build a system that provides information optimized for individual users.

[0682] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0683] In this invention, the server includes means for collecting the user's electronic gaming activities, means for analyzing the collected information and evaluating the user's playing style, means for recommending similar professional players and related video information based on the evaluated playing style, means for providing information on upcoming events that are relevant to the user's interests, and means for visually displaying the processing results. This makes it possible to quickly and easily provide individual users with customized information on professional player strategies and events.

[0684] "Means for collecting users' electronic sports activities" refers to devices or software for systematically acquiring information about electronic sports activities conducted by users.

[0685] "Means for analyzing collected information and evaluating operating styles" refers to a system or process for analyzing collected data to identify and evaluate specific user operating methods and strategies.

[0686] "Means for recommending similar professional players and related video information" refers to a method or system for selecting and presenting information on professional players with similar operating methods and related video content to a user, based on the user's operating style.

[0687] "Means of providing information on upcoming events tailored to user interests" refers to devices or software that identify and inform users of relevant upcoming events based on their interests and past activities.

[0688] "Means for visually displaying processing results" refers to a display device or user interface that presents the results of analysis and recommendations to the user in a graphical format, providing information in an easily understandable manner.

[0689] The system for carrying out this invention consists of a user terminal, a cloud server, and a visual display device.

[0690] The user's device has the capability to monitor electronic gaming activities. Specifically, it uses Nvidia's ShadowPlay or other recording software to record specific gameplay in real time. The data collected in this process includes the character selected by the player, their behavior patterns, the tactics used, and match win / loss information. This data is transmitted to a cloud server via a secure protocol such as HTTPS to ensure security.

[0691] Upon receiving the collected data, the server analyzes it using an AI model. Specifically, it uses deep learning libraries such as TensorFlow to classify the user's play style into categories such as aggressive, defensive, and balanced. Based on this classification, the server searches and selects match videos of professional players with similar styles from databases such as MongoDB. It also provides relevant upcoming event information based on the user's location and preferred game titles.

[0692] The information returned to the user's device is visually displayed in a user interface built using React Native, etc. Through the dashboard, users can easily view match videos of recommended expert players and information on events of interest. This allows users to learn strategies related to their own gameplay and increases their motivation to watch.

[0693] For example, if a user prefers an aggressive style, the server will automatically recommend match videos and related event information of professional players known for this style. An example of a prompt used in this case would be: "This user has used an aggressive style in over 50% of their last 10 matches. Please show me recent match videos and upcoming related events of players known for this playstyle."

[0694] This system will allow users to more easily access information related to their preferences and style, enriching their viewing experience.

[0695] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0696] Step 1:

[0697] The user's device records their electronic gaming activities. Specifically, it collects data in real time, such as character selection, action patterns, tactics used, and match results. Recording tools such as Nvidia's ShadowPlay are used for this purpose. The input is the user's gameplay data, and the output is a compiled record of this data.

[0698] Step 2:

[0699] The user's device sends collected game data to the server. Secure protocols such as HTTPS are used for data transmission, protecting the user's privacy. The input is organized game data, and the output is encrypted data packets.

[0700] Step 3:

[0701] The server analyzes the received data and evaluates the user's play style. In this process, an AI model using TensorFlow classifies the play style. The input is encrypted game data, and the output is the user's play style (e.g., aggressive, defensive, balanced).

[0702] Step 4:

[0703] The server selects match footage of similar professional players based on the evaluated play style. It searches MongoDB for appropriate video data and identifies the relevant content. The input is the user's play style, and the output is video data of matched professional players.

[0704] Step 5:

[0705] The server provides information on upcoming relevant events based on the user's interests and location. This information includes details of events found (e.g., dates, locations, participants) and selects those that are suitable for the user. The input is the user's profile information, and the output is information on upcoming events.

[0706] Step 6:

[0707] All information returned from the server is sent to the user's terminal and visually displayed in the user interface. A UI using React Native presents the data in a way that captures the user's attention. Input consists of video data and event information from professional players, while output is a visually organized display screen.

[0708] Step 7:

[0709] Users view and watch the presented content, or research event information. This allows them to deepen their knowledge related to their own play style and increases their motivation to watch. The input is the displayed information, and the output is the user's actions (e.g., watching, researching).

[0710] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0711] This invention combines an AI-based information provision system aimed at increasing the number of esports viewers with an emotion engine that recognizes user emotions. This system records the user's gaming activity, performs data analysis including emotion analysis on a server, and provides information on professional players and tournaments based on the results. The following describes the program processing of this system.

[0712] First, the device simultaneously collects the user's gaming activity and emotional state. This process includes the user's facial expressions, voice tone, and in-game behavior data (e.g., wins / losses, character used, play time). Emotional state is recorded through devices such as cameras and microphones.

[0713] Next, the device sends the collected data to the server. Here, both sentiment data and game performance data are transmitted securely.

[0714] Upon receiving this data, the server uses an AI model to analyze the user's emotional state and play style. The emotion engine identifies the emotional states the user exhibited during gameplay (e.g., enjoyment, frustration, excitement, etc.) and provides insights into how they impact game performance.

[0715] Based on the analysis data, the server selects the most suitable professional players, related videos, and news for the user, providing information that best matches the user's current emotional state. This selection might include, for example, showing up refreshing and lighthearted match highlights if the user is feeling frustrated.

[0716] Furthermore, the server selects and provides tournament information that it believes will be most enjoyable for the user based on their current emotional state. In this way, content selection is made that reflects the user's emotions.

[0717] Finally, the device presents the information received from the server to the user visually and audibly. This allows the user to receive information tailored to their emotional state, thereby increasing their motivation to watch the event.

[0718] For example, if the system is inferred to be experiencing stress, it can display calming content to elicit positive emotions. This process allows users to have a personalized experience tailored to their emotions, promoting the effective use of the entire system.

[0719] The following describes the processing flow.

[0720] Step 1:

[0721] The device collects the user's game activity data and emotional data. Game activity data includes win / loss results, characters used, and tactics, while emotional data includes emotional states (e.g., enjoyment, anger, frustration) determined by facial recognition via the camera and voice analysis.

[0722] Step 2:

[0723] The device sends the collected data to the server using a secure communication protocol. This protocol is encrypted to protect data privacy.

[0724] Step 3:

[0725] The server receives the data and begins AI analysis. The emotion engine analyzes the user's emotional state based on facial expressions and voice, and a machine learning algorithm evaluates the play style. This helps to understand the relationship between in-game actions and emotions.

[0726] Step 4:

[0727] Based on the analysis results, the server recommends professional players and related video content suitable for the user. This includes selecting professional player match videos tailored to the user's emotional state, as well as highlights designed to boost motivation.

[0728] Step 5:

[0729] The server selects and provides tournament information that takes into account the user's emotional state and interests. For example, if the user is relaxed, it will provide friendly tournament information that they can enjoy watching.

[0730] Step 6:

[0731] The device displays recommended content and tournament information from the server to the user. This display uses an intuitive dashboard and notification features, allowing users to easily see emotionally relevant options.

[0732] Step 7:

[0733] Based on the information users receive, they watch videos of professional players and check tournament information. In this process, they can enjoy content that resonates with their emotions, increasing their desire to watch.

[0734] (Example 2)

[0735] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0736] Traditional esports viewing support systems have faced the challenge of providing a limited user experience due to the difficulty in delivering personalized content based on the user's emotional state. As a result, this has led to decreased viewing motivation and a lack of user participation and engagement.

[0737] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0738] In this invention, the server includes means for simultaneously collecting the user's game activity and emotional state, means for analyzing the user's emotional state and play style using a generative AI model, and means for identifying appropriate professional player content using prompt messages. This makes it possible to provide personalized content that responds to the user's emotions.

[0739] "User game activity" refers to a series of actions and operations performed by a user while playing a game.

[0740] "Emotional state" refers to the type and intensity of emotions a user experiences while playing a game.

[0741] A "generative AI model" is an artificial intelligence model used for data analysis, specifically for analyzing a user's emotional state and play style.

[0742] A "promote statement" is a text statement used as an instruction or question for a generative AI model, and is used to request a specific output from the model.

[0743] A "professional player" refers to a player who possesses high skill and competitive achievements in a particular game.

[0744] "Content" refers to all data, such as information, images, and audio, presented to the user.

[0745] This invention is an information provision system for esports spectators. Specific embodiments will be described below.

[0746] First, when a user plays a game, the device simultaneously collects game activity and emotional state. The device uses camera and microphone hardware to analyze the user's facial expressions and voice tone, and records gameplay data (wins / losses, characters used, play time, etc.). This provides detailed activity data of the user.

[0747] The device sends this data to the server. The server processes this data using a generative AI model to analyze the user's emotional state. For example, it identifies how much enjoyment or frustration the user is experiencing. An emotion engine is used for this analysis.

[0748] The server selects the most suitable content for the user based on the analysis of their emotional state and game data. Specifically, it selects match highlights of professional players, related videos, and news. It also provides tournament information that the user can enjoy in their current emotional state. For example, if the user needs to relax, it will present lighthearted match highlights that will help them refresh. An example of a prompt used here would be, "Please recommend professional player matches that match my emotional state."

[0749] Ultimately, the device presents the information received from the server to the user visually and audibly. This allows the user to receive information that matches their emotions, further increasing their motivation to watch the event. Through this process, the user can obtain a personalized experience that responds to their emotions.

[0750] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0751] Step 1:

[0752] The device simultaneously collects the user's gaming activity and emotional state. Specifically, it uses a camera to capture the user's facial expressions and a microphone to record the user's voice tone. It also acquires behavioral data such as game wins and losses, selected characters, and play time. This data serves as input. The data is recorded as output, indicating the user's current emotional state and gaming behavior.

[0753] Step 2:

[0754] The device sends the collected data to the server. During this transmission process, the data is encrypted, ensuring that user sentiment data and game performance data are securely delivered to the server. The input is the collected data, and the output is the data package sent to the server.

[0755] Step 3:

[0756] The server analyzes the received data. Here, a generative AI model is used to analyze the user's emotional state and play style. The input is an encrypted data package, which outputs the identification of the user's emotional state (e.g., enjoyment, frustration) and insights into their play style. The generative AI model uses an emotion engine to identify the user's emotions.

[0757] Step 4:

[0758] The server selects the most suitable content for the user based on the analysis results. Specifically, it selects information on professional players, related videos, and news. The input is the analysis results, and the output is recommended content tailored to the user's emotional state. Prompt messages are used to identify the appropriate professional player matches.

[0759] Step 5:

[0760] The device presents information received from the server to the user visually and audibly. Input is recommended content from the server, and output is information delivered through the user's display or speakers. Specifically, it displays videos on the screen and provides additional information through audio. Through this process, the user receives content that resonates with their emotions.

[0761] (Application Example 2)

[0762] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0763] Traditional esports viewing systems have a problem where content is not adequately provided considering the emotional state of the user, resulting in a viewing experience that does not match the emotional needs of individual users and thus reducing their motivation to watch. Furthermore, if the content provided does not match the user's play style, the learning effect through viewing is limited.

[0764] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0765] In this invention, the server includes means for collecting the user's game activity and emotional state, means for analyzing the collected data and evaluating the play style and emotional state, and means for recommending similar professional players, relevant video data, and commentary based on the evaluated play style and emotional state. This makes it possible to provide content that is appropriate to the user's emotional state and to provide an optimized viewing experience for each individual user.

[0766] "User game activity" refers to data that encompasses all game-related operations and behaviors performed by the user.

[0767] "Emotional state" refers to information that indicates the type and intensity of emotions a user exhibits at a given point in time.

[0768] "Play style" refers to an evaluation of a user's unique methods and tendencies when playing a game.

[0769] A "professional player" is someone who possesses advanced skills in a particular game and makes a living by participating in competitive games.

[0770] "Video data" refers to information about videos and live footage that are generated for the purpose of being viewed by users.

[0771] "Live commentary" refers to audio or text information that explains the game's progress and strategy in real time.

[0772] "Entertainment information" refers to entertainment-related data intended to provide users with enjoyment and interest.

[0773] "Local information" refers to data about the geographical location of the user.

[0774] "Entertainment genres" is a classification that refers to a variety of types and fields of entertainment, such as movies, music, and sports.

[0775] To realize this invention, the terminal first collects the user's gaming activity and emotional state. Specifically, it uses hardware such as a camera and microphone to sense the user's facial expressions and tone of voice, and acquires behavioral data during gameplay.

[0776] Next, the device sends the collected data to the server. The server analyzes the received data using an AI model. Specifically, it uses an emotion analysis engine to identify the user's emotional state and evaluate how that emotion affects game performance. In this analysis, generative AI models play a crucial role in personalizing the user's experience.

[0777] Based on these results, the server selects and recommends professional players, relevant video data, and commentators suitable for the evaluated play style and emotional state. The recommended content is customized according to the user's current emotions. For example, if the user is feeling frustrated, it will recommend lighthearted match highlights and positive commentary to help them relax.

[0778] Ultimately, the device presents the information provided by the server to the user visually and audibly. This allows the user to receive optimized content tailored to their emotional state, resulting in a richer viewing experience.

[0779] For example, if the system determines that a user is feeling nervous during a match, it could provide a match highlight reel with relaxing background music to soothe their emotions. An example of a prompt to the generative AI model would be: "The user's emotional data indicates they are feeling nervous. Please recommend relaxing content."

[0780] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0781] Step 1:

[0782] The device uses a camera and microphone to collect data on the user's gaming activity and emotional state. Input data includes the user's facial expressions, voice tone, and in-game behavior (e.g., wins / losses, character used, play time). This data is collected and organized in real time to build the initial data set.

[0783] Step 2:

[0784] The device sends the collected data to the server as a package. The input includes a series of activity and emotion data collected in step 1, which are then sent to the server using a secure communication protocol. Data compression is also performed to improve transfer efficiency.

[0785] Step 3:

[0786] The server analyzes the received data. Using an AI model, it evaluates the emotional state and play style based on the input data. In this analysis, the emotion analysis engine classifies the user's emotions in detail and calculates how they affect game performance, obtaining this as output.

[0787] Step 4:

[0788] The server selects content based on the analysis results. Based on the evaluation data, it recommends similar professional players, relevant video data, and commentary. In this step, a generative AI model is used to select the optimal content based on the prompt message, "The user's sentiment data indicates an AA state. Please recommend appropriate content."

[0789] Step 5:

[0790] The device receives information from the server and presents it to the user. Recommended content from the server is output to the user in visual and audio formats. This allows the user to receive information optimized for their emotional state, improving their esports viewing experience.

[0791] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0792] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0793] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0794] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0795] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0796] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0797] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0798] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0799] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0800] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0801] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0802] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0803] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0804] 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.

[0805] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0806] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0807] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0808] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0809] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0810] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0811] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0812] The following is further disclosed regarding the embodiments described above.

[0813] (Claim 1)

[0814] Means of collecting user game activity,

[0815] A means of analyzing collected data and evaluating play style,

[0816] A means of recommending similar professional players and related video data based on the evaluated play style,

[0817] A means of providing information about upcoming tournaments that are tailored to user interests,

[0818] A system that includes this.

[0819] (Claim 2)

[0820] The system according to claim 1, characterized in that the recommendation means selects information on professional players and video data of past tournaments based on the user's play style.

[0821] (Claim 3)

[0822] The system according to claim 1, characterized in that the means provided selects and displays details of upcoming tournaments based on the user's regional information and game titles of interest.

[0823] "Example 1"

[0824] (Claim 1)

[0825] A means for monitoring a user's electronic game activity and collecting activity information,

[0826] The collected information is transmitted to a data center using a secure communication protocol,

[0827] A means of analyzing information received at a data center and evaluating user behavioral characteristics using generative artificial intelligence,

[0828] A means of recommending similar player characters and related video resources based on evaluated behavioral characteristics,

[0829] A means of providing users with detailed information about upcoming competitive events that match their interests,

[0830] A system that includes this.

[0831] (Claim 2)

[0832] The system according to claim 1, characterized in that the recommendation method selects player information and video resources of past competitive events based on the user's behavioral characteristics.

[0833] (Claim 3)

[0834] The system according to claim 1, characterized in that the means of providing information is to select and display details of upcoming competitive events based on the user's regional information and the electronic game titles they are interested in.

[0835] "Application Example 1"

[0836] (Claim 1)

[0837] A means of collecting users' electronic sports activity,

[0838] A means for analyzing the collected information and evaluating the operating style,

[0839] A means of recommending similar professional players and related video information based on the evaluated playing style,

[0840] A means of providing information on upcoming events tailored to user interests,

[0841] A means of visually displaying the processing results,

[0842] A system that includes this.

[0843] (Claim 2)

[0844] The system according to claim 1, characterized in that the recommended means selects information on professional players and video information of past events based on the user's operating style.

[0845] (Claim 3)

[0846] The system according to claim 1, characterized in that the means provided selects and displays detailed information about upcoming events based on the user's regional information and preferred electronic sports titles.

[0847] "Example 2 of combining an emotion engine"

[0848] (Claim 1)

[0849] A means of simultaneously collecting the user's game activity and emotional state,

[0850] A means of sending the collected data to the server,

[0851] A method for analyzing a user's emotional state and play style using a server-generated AI model,

[0852] A means of selecting professional players and related content based on the analysis results,

[0853] Means for presenting selected information to the user visually and aurally,

[0854] A system that includes this.

[0855] (Claim 2)

[0856] The system according to claim 1, characterized in that the server selects tournament information that the user can enjoy based on the user's emotional state.

[0857] (Claim 3)

[0858] The system according to claim 1, characterized in that the selection means uses prompt statements to identify professional player content suitable for the user.

[0859] "Application example 2 when combining with an emotional engine"

[0860] (Claim 1)

[0861] A means of collecting user game activity and emotional state,

[0862] A means of analyzing collected data and evaluating play style and emotional state,

[0863] Based on the evaluated play style and emotional state, a means of recommending similar professional players, relevant video data, and commentary is provided.

[0864] A means of providing information on upcoming tournaments and entertainment events that align with users' interests and emotions,

[0865] A system that includes this.

[0866] (Claim 2)

[0867] The system according to claim 1, characterized in that the recommendation means selects information on professional players, video data of past tournaments, and commentary that aligns with the user's emotions, based on the user's play style and emotional state.

[0868] (Claim 3)

[0869] The system according to claim 1, characterized in that the means provided selects and displays detailed information about upcoming tournaments and related events based on the user's regional information, preferred entertainment genres, and emotional state. [Explanation of Symbols]

[0870] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of collecting user game activity, A means of analyzing collected data and evaluating play style, A means of recommending similar professional players and related video data based on the evaluated play style, A means of providing information about upcoming tournaments that are tailored to user interests, A system that includes this.

2. The system according to claim 1, characterized in that the recommendation means selects information on professional players and video data of past tournaments based on the user's play style.

3. The system according to claim 1, characterized in that the means provided selects and displays detailed information about upcoming tournaments based on the user's regional information and game titles of interest.

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