system

Abstract

We provide the system. [Solution] A means for capturing video data and user input data in a game, A means for formatting and analyzing the captured data, Means including a generation model that generates commentary text based on the analyzed data, A means for outputting the generated commentary text as audio, 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, the market for digital games has expanded rapidly, and many players have come to enjoy games online or offline. However, due to the shortage of game commentators and cost issues, there is a problem that it is difficult to provide immersive commentary for all players. In particular, it is even more difficult to realize personalized commentary according to the skill levels and preferences of individual players. As a result, there may be a sense of dissatisfaction in specific game tournaments or individual game experiences.

Means for Solving the Problems

[0005] This invention provides a system for capturing video data and user input data in games, and for formatting and analyzing them. Based on the analyzed data, a generative AI model is used to generate commentary in real time, and this commentary is output as audio, allowing users to experience an immersive gaming experience even when playing alone. Furthermore, a function to customize the commentary content based on the user's play style and knowledge level enables personalized commentary. In addition, the system optimizes the protocol and encoding method for data transfer, providing a means to minimize latency during streaming. This makes it possible to provide a stable, high-quality commentary experience.

[0006] "Video data" refers to the visual information displayed on the screen during gameplay, represented in digital format.

[0007] "User input data" refers to information about key inputs, mouse and controller actions that indicate the actions a user performs within the game.

[0008] "Capture" refers to the process of acquiring video, audio, and operation information as digital data in real time.

[0009] "Formatting" refers to the process of converting and filtering acquired data into a format that is easy to analyze.

[0010] "Analysis" is the process of scrutinizing captured data and extracting and evaluating useful information.

[0011] A "generative model" is an AI algorithm that generates a specific output (in this case, commentary text) based on collected data.

[0012] "Commentary text" refers to descriptive or explanatory text generated based on the situation in the game or the user's actions.

[0013] "Outputting as audio" refers to the process of reproducing the generated commentary text in an audible form using speech synthesis technology.

[0014] "Personalization" refers to adjusting the output content (in this case, the commentary) to match the individual preferences and characteristics of the user.

[0015] "Streaming distribution" is a technology that enables viewing and use of data by continuously sending and receiving data in real time. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This 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 combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the language used in the following description will be explained.

[0019] In the following embodiments, a numbered 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 one 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.

[0020] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention relates to a system that automatically generates commentary based on game video data and user input data in order to improve the user's gaming experience. This system analyzes game data captured in real time and enables the output of personalized commentary as audio, tailored to the user's play style and knowledge level.

[0038] Overall system configuration

[0039] The system primarily consists of the user's terminal, a central server, and an audio output device. The user's terminal captures data during gameplay and sends it to the central server. The server analyzes this data and uses an AI generative model to create commentary text. This commentary text is then sent from the server to the user's terminal, which outputs it as audible data using speech synthesis technology.

[0040] Specific operation of the device

[0041] The user's device has the capability to capture video data and user input data during gameplay. This captured data is periodically sent to the server. During capture, the data is compressed whenever possible to minimize communication traffic. This process is achieved by utilizing APIs built into the game or a separate capture program.

[0042] Specific server operation

[0043] The server organizes and formats the received data packets to remove noise and extract important information. This includes, for example, the player's location, score, enemy presence, and remaining time in the game. Then, based on the analyzed data, a generative AI model creates commentary. This commentary is customized according to the user's play style and knowledge level. For example, beginners will find more explanations of controls, while advanced players will find more strategic advice.

[0044] How to perform audio output

[0045] The commentary text sent from the server to the terminal is converted into audio data by a speech synthesis engine on the terminal. This audio is overlaid on the game's video as needed, allowing the user to listen to the commentary in real time. This process allows the user to enjoy an immersive gaming experience with commentary, even when playing alone.

[0046] Specific example

[0047] For example, consider a situation where a user is playing a shooting game. If they are in a close battle and facing the last enemy, the device captures the situation and sends the data to the server. The server generates commentary such as, "Only one left, aim carefully now!" and sends this as audio to the device, allowing the user to further experience the tension of the game through the commentary.

[0048] The following describes the processing flow.

[0049] Step 1:

[0050] When a user starts a game, the device begins capturing gameplay footage and user input data. This process is carried out using in-game APIs or a dedicated capture program, and data is collected at regular intervals.

[0051] Step 2:

[0052] The terminal combines captured video data and operation data into a single packet and sends it to the server in real time. If possible, compression technology is used to minimize the data size and improve communication efficiency.

[0053] Step 3:

[0054] The server receives the data sent from the terminal and decodes it. Next, it formats the data, removing noise and extracting important elements such as the player's location and actions.

[0055] Step 4:

[0056] The server inputs the formatted data into an AI generation model. This model recognizes the current state of the game and generates personalized commentary based on the user's past play data and profile.

[0057] Step 5:

[0058] The server sends the generated live report to the terminal. During the transmission process, the optimal protocol is selected to minimize communication delays.

[0059] Step 6:

[0060] The terminal uses a speech synthesis engine to convert the live commentary sent from the server into speech. This speech is then provided to the user in real time, synchronized with the gameplay video.

[0061] Step 7:

[0062] Users can enjoy a more immersive experience by listening to the generated commentary while playing the game. If user feedback is received, the system uses this feedback to improve the accuracy of the generative model.

[0063] (Example 1)

[0064] 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."

[0065] In modern digital entertainment, there is a demand for users to be able to have an immersive experience even when working independently. However, conventional technologies do not adequately utilize visual information and user interaction information, making it difficult to provide personalized audio commentary in real time. Furthermore, delays associated with information transmission can impair the quality of the experience, and there is a lack of means to resolve this.

[0066] 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.

[0067] In this invention, the server includes means for acquiring visual information and user operation information in digital entertainment, means for formatting the acquired information and extracting important information, and means including an artificial intelligence model that generates explanatory text based on the extracted information. This enables users to receive high-quality, personalized audio commentary in real time, even when working independently.

[0068] "Digital entertainment" refers to all forms of entertainment provided using computers and electronic devices, and specifically includes games and video content.

[0069] "Visual information" refers to the data of screens and images displayed within digital content, and includes information that users can visually recognize.

[0070] "User operation information" refers to input data generated when a user interacts with digital content, and includes instructions given via controllers, keyboards, mice, etc.

[0071] "Means of acquisition" refers to devices and methods for collecting necessary information, and includes functions that combine software and hardware.

[0072] "Methods for formatting and extracting important information" refers to techniques that process collected data to remove unnecessary information and select the data necessary for a specific purpose.

[0073] An "artificial intelligence model" refers to a computational model that uses machine learning techniques to perform specific tasks, possessing functions such as data analysis, prediction, and generation.

[0074] "Generating explanatory text" refers to the process of automatically creating explanatory text in natural language based on data.

[0075] "Means of converting to audio and outputting it" refers to technology that converts text data into audio data and plays it back through speakers or other devices.

[0076] This invention provides a system that allows users to enjoy digital entertainment with a sense of realism, even when alone. The system comprises a user terminal, a central server, and an audio output device.

[0077] The user's device acquires visual and user interaction information in real time during gameplay. For this purpose, the device can utilize in-game APIs or dedicated capture software. The acquired information is compressed and efficiently transmitted to the server. For example, in a shooting game, when the user is facing an enemy, their actions and the situation are captured.

[0078] The server formats the information received from the terminal, removes noise, and extracts important information. After this analysis, a generative AI model is used to generate explanatory text. In generating this explanatory text, the user's operating habits and knowledge level are taken into consideration, and novel content is generated. For example, for beginner users, specific instructions such as "An enemy is approaching, please be careful" are included.

[0079] The generated explanatory text is sent from the server to the user's terminal. On the user's terminal, the explanatory text is converted into speech by a speech synthesis engine and played back via an audio output device. This allows the user to listen to live game commentary in real time and enjoy an immersive experience.

[0080] For example, if a user is playing a tense shooting game in real time, the device instantly captures the situation and sends it to the server. The server generates commentary such as, "Only one left, aim carefully now!" and plays it back on the device as audio, allowing the user to become more deeply immersed in the game.

[0081] Example prompt: "Generate real-time game commentary that takes into account the user's play style and situation. For example, I want to portray a tense moment in a shooting game."

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The user's device acquires visual and user operation information in real time during gameplay. It receives in-game screen display data and user control operation data as input. Based on this, the capture program packages the data into a specific format and performs compression. Compressed data packets are generated as output.

[0085] Step 2:

[0086] The terminal sends compressed data packets to the server. It receives compressed visual and operational information as input and transfers the data to the server via a communication protocol. To minimize data loss and delay, it uses the most efficient transfer technology possible. As output, the data safely reaches the server.

[0087] Step 3:

[0088] The server decompresses and formats the data packets received from the terminal. It receives compressed data as input, decompresses it, and performs data formatting to remove noise. A filtering algorithm is used to extract important information. The output is a clean, analyzable dataset.

[0089] Step 4:

[0090] The server feeds the formatted data into a generating AI model to generate explanatory text. The input dataset includes player location information, score information, and enemy movements. The generating AI model analyzes this data and automatically generates grammatically correct and contextually appropriate explanations. The output provides explanatory text tailored to the user's play style and knowledge level.

[0091] Step 5:

[0092] The server sends the generated explanatory text to the user's terminal. It receives the generated explanatory text as input and sends it to the terminal using the appropriate communication protocol. The explanatory text arrives at the terminal as output.

[0093] Step 6:

[0094] The user's device converts the received explanatory text into audio and outputs it through the speaker. It receives explanatory text sent from the server as input and converts it into audio data using a speech synthesis engine. The audio is played in harmony with the game's background sounds. As output, the user can listen to the audio commentary in real time.

[0095] (Application Example 1)

[0096] 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."

[0097] There is a need to enhance immersion for both viewers and players by providing personalized audio commentary in real time alongside a visual game experience. However, conventional systems suffer from problems such as delays in generating commentary language and multi-media distribution, which detract from the user experience. In particular, there is a challenge in providing personalized commentary that is tailored to the user's operating style and knowledge level.

[0098] 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.

[0099] In this invention, the server includes means for acquiring visual data and user input data, means for processing and analyzing the acquired data, and means including a generative model for generating commentary language expressions based on the analyzed data. This makes it possible to generate personalized commentary in real time according to the user's play style and provide it with minimal latency.

[0100] "Visual data" refers to all visual information obtained from the game, including video footage of the user's gameplay.

[0101] "User input data" refers to data that includes control information and actions provided by the user during gameplay.

[0102] "Means of acquisition" refers to the processes and mechanisms for collecting visual data and user input data.

[0103] "Means of processing and analysis" refers to methods for converting acquired data into a usable format and extracting necessary information.

[0104] "Live commentary" refers to text-based descriptions of the game's progress that are later converted into audio.

[0105] A "generative model" is a machine learning algorithm that automatically creates commentary language expressions based on user gameplay data.

[0106] "Means of outputting as audio" refers to the technology and devices that convert the generated commentary language expression into an audio format.

[0107] "Methods for integrating and distributing across multiple media" refers to the process of delivering generated audio and video content to viewers as a single piece of content.

[0108] The system implementing this invention consists mainly of a user terminal, a central server, and a viewer device. When a user plays a game, the user terminal acquires visual data and user input data in real time. This data is transmitted to the server while reducing communication traffic using compression technology.

[0109] The server processes received visual and user input data, performing analysis while removing noise. This involves applying advanced data processing algorithms and machine learning techniques to extract the user's gameplay characteristics. The server utilizes a cloud computing platform to enable rapid data processing.

[0110] Based on the analyzed data, a generative AI model generates commentary language expressions. This generation process takes into account the user's play style and knowledge level to provide personalized commentary. The generated commentary language expressions are converted into audio data using a speech synthesis engine. Google® Cloud Text-to-Speech API is used for speech synthesis to generate natural and immersive audio.

[0111] The converted audio data is sent back to the user's device, integrated with the game's visual data as a multimedia product, and then delivered to the viewer's device. This process allows viewers to experience gameplay with live commentary in real time.

[0112] As a concrete example, if a user is playing a soccer game, real-time commentary such as "It's a one-on-one with the goalkeeper! He's scored a magnificent goal!" will be generated. In this case, an example of a prompt to the generating AI model would be, "The user is playing a soccer game, and it's a shooting scene. Please include the player's name and the result of the shot in the commentary."

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The user terminal acquires visual data and user input data during gameplay. It takes in real-time visual information output from the game and user operation data as input, and preprocesses this data to compress it and reduce communication traffic. The output is compressed composite data.

[0116] Step 2:

[0117] The user terminal sends compressed visual data and user input data to the server. It receives compressed data as input, and the output is the proper transmission of data. The server stores the received data and prepares it for the next analysis.

[0118] Step 3:

[0119] The server processes and analyzes the received visual data and user input data. The input is data stored on the server, which undergoes data analysis to remove noise and structure it. The output is clean data that has been noise-free and analyzed.

[0120] Step 4:

[0121] The server generates commentary language expressions using a generative AI model based on the analyzed data. The input is clean data, which the AI ​​model interprets to generate appropriate text for commentary. The output is the generated commentary language expression.

[0122] Step 5:

[0123] The server sends the generated commentary language expression to the user terminal. The input is the commentary text generated on the server, and the output is the completion of transmission to the user terminal.

[0124] Step 6:

[0125] The user terminal converts the received commentary text into speech data using a speech synthesis engine. The input is commentary text, and the synthesis process generates natural-sounding speech data. The output is speech data.

[0126] Step 7:

[0127] The user terminal integrates audio and visual data and delivers it to the viewer's device. Input consists of audio data and game visual data, which are combined as multimedia content. Output is real-time game footage with commentary.

[0128] 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.

[0129] This invention is implemented as a system incorporating emotion recognition technology to highly personalize the user experience and enhance the sense of realism in a gaming environment. This system detects the user's emotional state and dynamically adjusts the content of the commentary based on that information.

[0130] System Configuration

[0131] This system primarily consists of the user's terminal, a central server, an emotion engine, and an audio output device. The emotion engine recognizes the user's emotions in real time and optimizes the user experience based on this. The server plays a central role in processing the received data and generating commentary that aligns with the emotion recognition.

[0132] The role of the emotional engine

[0133] The emotion engine infers the user's emotional state based on behavioral patterns derived from the speed and precision of user actions and interactions, as well as data obtained from biosensors (e.g., heart rate monitors and skin electrical activity sensors) as needed. For example, if a user suddenly speeds up their movements in a game, this behavior can be associated with stress or excitement.

[0134] Generating and adjusting commentary text

[0135] The server combines game data sent from the terminal with emotion data provided by the emotion engine and uses an AI generation model to generate commentary appropriate to the situation. This commentary is adjusted in real time according to the user's emotional state. For example, when the user is relaxed, the comments are delivered in a calm tone, and when the user is stressed, the comments are concise and provide only the necessary information.

[0136] Audio output

[0137] The terminal uses a speech synthesis engine to convert live commentary sent from the server into speech, and provides it to the user in real time, synchronized with the gameplay video. This allows for appropriate interventions tailored to the user's emotional state, improving immersion in the game.

[0138] Specific example

[0139] For example, imagine a scenario where a user is concentrating intensely on a challenging task. If the emotion engine detects the user's tension, the device will offer advice such as, "Calm down and use your remaining time wisely." This allows the user to receive emotionally-driven support while remaining deeply immersed in the game. By combining emotion recognition with AI-generated responses in this way, it is possible to provide an unprecedentedly personalized gaming experience.

[0140] The following describes the processing flow.

[0141] Step 1:

[0142] The device captures video data and user input data at the start of the game. Simultaneously, an emotion engine installed in the device receives data from sensors that capture the user's biometric data. This data is transmitted to the server in real time.

[0143] Step 2:

[0144] The server decodes the received video and operation data and performs the necessary formatting. This process removes noise while extracting important in-game information such as the player's position, score, and elapsed time. Simultaneously, emotional data provided by the emotion engine is also analyzed.

[0145] Step 3:

[0146] The server provides formatted game data and emotion data to an AI generation model, which then generates commentary tailored to specific game situations. At this stage, comments are added or emphasized based on the user's emotional state.

[0147] Step 4:

[0148] The server sends the generated commentary text to the terminal and prepares it for audio conversion. During transmission, the optimal protocol is used to minimize communication delays.

[0149] Step 5:

[0150] The terminal receives the commentary text sent from the server and converts it into speech using a speech synthesis engine. At this time, adjustments are made to synchronize the audio with the game video.

[0151] Step 6:

[0152] Users continue playing the game while listening to audio commentary output from their device. Because the commentary is based on emotion recognition, users can receive appropriate support in response to changes in their emotions during gameplay.

[0153] Step 7:

[0154] The server will collect user gameplay experience and emotional feedback, which will be used to optimize future commentary generation algorithms. This data will also be used to improve the user experience in the long term.

[0155] (Example 2)

[0156] 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".

[0157] There is a need for highly personalized gaming experiences and adaptive feedback that responds to user emotions. However, conventional technologies have struggled to detect user emotional states in real time and dynamically adjust the experience based on them. To solve this problem, optimal commentary generation and audio output that respond to changes in user emotions are necessary.

[0158] 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.

[0159] In this invention, the server includes means for detecting the user's emotional state, means for analyzing the data to dynamically adjust the user's experience based on the detected emotional state, and means for generating commentary text using a generative AI model based on the analyzed data. This enables the provision of adaptive feedback that responds to the user's emotions and a highly personalized gaming experience.

[0160] "User emotional state" refers to the emotional responses and psychological states that a user exhibits in specific situations, including stress, excitement, and relaxation.

[0161] "Dynamic adjustment" refers to a system changing its response in real time based on input information, adapting the user experience as needed.

[0162] A "generative AI model" refers to a technology or method that uses artificial intelligence to generate new content, such as commentary, from specific data.

[0163] "Commentary text" refers to explanatory text that describes the situation in the game in real time and is provided to the user as audio or text.

[0164] "Outputting as audio" refers to providing the generated text data to the user in audio format through speech synthesis technology.

[0165] This invention implements a system for providing a highly personalized gaming experience that responds to the user's emotions. The system comprises a user terminal, a central server, an emotion engine, and an audio output device.

[0166] The user's device collects operation information and biosensor data during gameplay. Specifically, it monitors heart rate and skin electrical activity using a variety of sensors. This makes it possible to detect the user's emotional state in real time.

[0167] The server receives data sent from the terminal and analyzes it in conjunction with the emotion engine. The emotion engine infers the emotional state based on the user's operation patterns and biometric information. This analysis result is sent to a generative AI model, which then generates appropriate commentary based on its output.

[0168] The generative AI model runs on a server and receives specific prompt messages as input to generate appropriate commentary. For example, a prompt message might be "Generate commentary appropriate for when the user's emotional state is excited."

[0169] The device uses a speech synthesis engine to convert live commentary received from the server into speech and provides it to the user in real time within the game. This allows the user to receive appropriate support and feedback based on their emotional state through voice. This method enables the user to become more deeply immersed in the game.

[0170] For example, if a user is playing a stage in a game where the difficulty gradually increases, the emotion engine might detect this tension. In that case, the device would provide specific voice advice such as, "Concentrate and calmly proceed to the next step." This enables a real-time experience that is tailored to the user's actions and emotions.

[0171] The above describes specific embodiments for carrying out this invention.

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1:

[0174] During gameplay, the user collects operational information and biosensor data through their device. Inputs include the user's controller input, heart rate, and skin electrical activity. The device transmits this data to the emotion engine in real time. The output generates raw data that reflects the user's actions.

[0175] Step 2:

[0176] The device sends the received data to the emotion engine, which analyzes the emotional state. The input to the emotion engine is the operation information and biosensor data collected in step 1. By analyzing the data, the system infers the user's emotional state. For example, a sudden change in operation speed may indicate stress or excitement. This result is output as an emotional state profile.

[0177] Step 3:

[0178] The server runs a generative AI model based on the emotional state profile received from the emotion engine. The input includes the current game state and the user's emotional state profile. The server sends prompts to the generative AI model to generate appropriate commentary. An example prompt might be, "Generate commentary suitable for when the user's emotional state is relaxed." The output of this step is the generated commentary.

[0179] Step 4:

[0180] The terminal uses a speech synthesis engine to convert the generated commentary text sent from the server into speech. The input is the commentary text generated in step 3. The speech synthesis engine converts this text into speech that is easy for the user to understand. The output is real-time audio feedback synchronized with the game video. Through this audio, the user can receive adaptive feedback that matches their emotional state.

[0181] Step 5:

[0182] Users can continue playing the game based on voice feedback. Adaptive voice support allows users to become more immersed in the game and concentrate more effectively. This step is crucial as the ultimate outcome of the entire system. In summary, a highly personalized experience is provided to the user.

[0183] (Application Example 2)

[0184] 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".

[0185] Traditional virtual stores have struggled to provide real-time purchasing support that reflects the emotional state of users, resulting in decreased purchasing efficiency. In particular, they have been unable to provide appropriate information when users show interest in a specific product or when they are hesitant about making a purchase, leading to decreased immersion and satisfaction.

[0186] 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.

[0187] In this invention, the server includes a device for acquiring video data and user input data, a device for detecting the user's emotional state and dynamically adjusting text based on that state, and a device for processing and evaluating the acquired data. This makes it possible to provide a personalized purchase assistant that responds to the user's emotions in real time.

[0188] "Visual data" refers to information obtained from visual media and is a visual representation stored in digital or analog format.

[0189] "User input data" refers to information provided by users through their devices, and is digital data that reflects their actions and choices.

[0190] A "device" is a set of interconnected hardware or software elements designed to perform a specific function.

[0191] "Processing" refers to the process of transforming, organizing, or modifying acquired data to suit a specific purpose.

[0192] "Evaluation" is the process of analyzing data and judging its content and value.

[0193] A "generative engine" is software or an algorithm used to create content based on data.

[0194] Sound is a vibration that travels through the air or other media, and is perceived through the sense of hearing.

[0195] An "information processing system" is a computer-based system designed to acquire, analyze, transform, and output data.

[0196] "Emotional state" refers to characteristics and data that indicate a user's mental and emotional state.

[0197] "Dynamic adjustment" refers to a process of flexibly changing or optimizing according to the situation and the user's condition.

[0198] "Personalization" refers to the act of customizing or personalizing something to suit the specific characteristics and needs of a particular user.

[0199] "Distribution" is the process of supplying content or data to multiple receiving devices.

[0200] A "transmission method" refers to the technical techniques or protocols used to transmit data from one point to another.

[0201] An "encoding method" is an algorithm or technique designed to compress or transform data to make it easier to transmit.

[0202] To realize this invention, it is necessary to build a system in which a virtual store provides a personalized assistant that responds to the user's emotions. The central elements of this system are an emotion recognition device, a data evaluation device, a generation engine, and a voice output device.

[0203] The server acquires video data and user input data from the user's smart glasses. This involves using hardware devices such as cameras and sensors. The acquired data is processed and analyzed by a data evaluation device to identify the user's emotional state. This evaluation utilizes open-source emotion recognition libraries (e.g., OpenCV and TENSORFLOW®).

[0204] Once the user's emotional state is identified, the generation engine uses AI to generate information in real time. Specifically, a generation AI model (e.g., GPT-3®) is used to generate appropriate sentences based on the prompt. These sentences are then spoken by a speech output device and provided to the user. Throughout this entire process, the user receives a personalized voice assistant tailored to their emotions, allowing them to receive appropriate support in selecting and purchasing products.

[0205] For example, if a user stands in front of a specific product shelf for an extended period, the emotion recognition device detects that the user is "interested." Based on this, the generation engine generates a message such as, "This product is currently on sale and offers excellent performance. Please consider it," and the voice output device delivers this message to the user as sound. Another example of a prompt for the generation AI model would be a question like, "Explain the features of the latest PC in an easy-to-understand way to an interested buyer."

[0206] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0207] Step 1:

[0208] The server acquires video data and user input data from the user's smart glasses. The input data includes the user's facial expressions and gestures indicating interest. This data is acquired in real time by cameras and other sensors. The server records this raw data.

[0209] Step 2:

[0210] The server analyzes the user's emotional state using the acquired video data. Open-source emotion recognition libraries (such as OpenCV or TensorFlow) are used for this purpose. These libraries analyze facial expressions and body movements to estimate emotions such as interest, confusion, relaxation, and stress. The output is the analyzed emotion data.

[0211] Step 3:

[0212] The server calls a generative AI model based on the analyzed sentiment data to create an appropriate prompt. For example, a prompt such as "Explain the features of the latest PC clearly to an interested buyer" might be generated. In this case, the input is sentiment data, and the output is the prompt.

[0213] Step 4:

[0214] The server uses a generative AI model (e.g., GPT-3) to generate natural language assistant messages based on the prompt. This AI, having learned from a large amount of text data, has the capability to produce clear and persuasive sentences. The input is the prompt, and the output is the generated assistant message.

[0215] Step 5:

[0216] The terminal sends the generated assistant message to the voice output device and provides it to the user as synthesized speech. The text is converted into speech using speech synthesis software, which the user can then hear. Here, the input is the generated text message, and the output is the voice message.

[0217] Step 6:

[0218] Users listen to voice messages and use that information to select products and make purchasing decisions. By modifying their actions based on the information they receive, the purchasing experience is improved. The user's responses are then re-entered into the system as data for the next processing cycle.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] [Second Embodiment]

[0223] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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".

[0235] This invention relates to a system that automatically generates commentary based on game video data and user input data in order to improve the user's gaming experience. This system analyzes game data captured in real time and enables the output of personalized commentary as audio, tailored to the user's play style and knowledge level.

[0236] Overall system configuration

[0237] The system primarily consists of the user's terminal, a central server, and an audio output device. The user's terminal captures data during gameplay and sends it to the central server. The server analyzes this data and uses an AI generative model to create commentary text. This commentary text is then sent from the server to the user's terminal, which outputs it as audible data using speech synthesis technology.

[0238] Specific operation of the device

[0239] The user's device has the capability to capture video data and user input data during gameplay. This captured data is periodically sent to the server. During capture, the data is compressed whenever possible to minimize communication traffic. This process is achieved by utilizing APIs built into the game or a separate capture program.

[0240] Specific server operation

[0241] The server organizes and formats the received data packets to remove noise and extract important information. This includes, for example, the player's location, score, enemy presence, and remaining time in the game. Then, based on the analyzed data, a generative AI model creates commentary. This commentary is customized according to the user's play style and knowledge level. For example, beginners will find more explanations of controls, while advanced players will find more strategic advice.

[0242] How to perform audio output

[0243] The commentary text sent from the server to the terminal is converted into audio data by a speech synthesis engine on the terminal. This audio is overlaid on the game's video as needed, allowing the user to listen to the commentary in real time. This process allows the user to enjoy an immersive gaming experience with commentary, even when playing alone.

[0244] Specific example

[0245] For example, consider a situation where a user is playing a shooting game. If they are in a close battle and facing the last enemy, the device captures the situation and sends the data to the server. The server generates commentary such as, "Only one left, aim carefully now!" and sends this as audio to the device, allowing the user to further experience the tension of the game through the commentary.

[0246] The following describes the processing flow.

[0247] Step 1:

[0248] When a user starts a game, the device begins capturing gameplay footage and user input data. This process is carried out using in-game APIs or a dedicated capture program, and data is collected at regular intervals.

[0249] Step 2:

[0250] The terminal combines captured video data and operation data into a single packet and sends it to the server in real time. If possible, compression technology is used to minimize the data size and improve communication efficiency.

[0251] Step 3:

[0252] The server receives the data sent from the terminal and decodes it. Next, it formats the data, removing noise and extracting important elements such as the player's location and actions.

[0253] Step 4:

[0254] The server inputs the formatted data into an AI generation model. This model recognizes the current state of the game and generates personalized commentary based on the user's past play data and profile.

[0255] Step 5:

[0256] The server sends the generated live report to the terminal. During the transmission process, the optimal protocol is selected to minimize communication delays.

[0257] Step 6:

[0258] The terminal uses a speech synthesis engine to convert the live commentary sent from the server into speech. This speech is then provided to the user in real time, synchronized with the gameplay video.

[0259] Step 7:

[0260] Users can enjoy a more immersive experience by listening to the generated commentary while playing the game. If user feedback is received, the system uses this feedback to improve the accuracy of the generative model.

[0261] (Example 1)

[0262] 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."

[0263] In modern digital entertainment, there is a demand for users to be able to have an immersive experience even when working independently. However, conventional technologies do not adequately utilize visual information and user interaction information, making it difficult to provide personalized audio commentary in real time. Furthermore, delays associated with information transmission can impair the quality of the experience, and there is a lack of means to resolve this.

[0264] 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.

[0265] In this invention, the server includes means for acquiring visual information and user operation information in digital entertainment, means for formatting the acquired information and extracting important information, and means including an artificial intelligence model that generates explanatory text based on the extracted information. This enables users to receive high-quality, personalized audio commentary in real time, even when working independently.

[0266] "Digital entertainment" refers to all forms of entertainment provided using computers and electronic devices, and specifically includes games and video content.

[0267] "Visual information" refers to the data of screens and images displayed within digital content, and includes information that users can visually recognize.

[0268] "User operation information" refers to input data generated when a user interacts with digital content, and includes instructions given via controllers, keyboards, mice, etc.

[0269] "Means of acquisition" refers to devices and methods for collecting necessary information, and includes functions that combine software and hardware.

[0270] "Methods for formatting and extracting important information" refers to techniques that process collected data to remove unnecessary information and select the data necessary for a specific purpose.

[0271] An "artificial intelligence model" refers to a computational model that uses machine learning techniques to perform specific tasks, possessing functions such as data analysis, prediction, and generation.

[0272] "Generating explanatory text" refers to the process of automatically creating explanatory text in natural language based on data.

[0273] "Means of converting to audio and outputting it" refers to technology that converts text data into audio data and plays it back through speakers or other devices.

[0274] This invention provides a system that allows users to enjoy digital entertainment with a sense of realism, even when alone. The system comprises a user terminal, a central server, and an audio output device.

[0275] The user's device acquires visual and user interaction information in real time during gameplay. For this purpose, the device can utilize in-game APIs or dedicated capture software. The acquired information is compressed and efficiently transmitted to the server. For example, in a shooting game, when the user is facing an enemy, their actions and the situation are captured.

[0276] The server formats the information received from the terminal, removes noise, and extracts important information. After this analysis, a generative AI model is used to generate explanatory text. In generating this explanatory text, the user's operating habits and knowledge level are taken into consideration, and novel content is generated. For example, for beginner users, specific instructions such as "An enemy is approaching, please be careful" are included.

[0277] The generated explanatory text is sent from the server to the user's terminal. On the user's terminal, the explanatory text is converted into speech by a speech synthesis engine and played back via an audio output device. This allows the user to listen to live game commentary in real time and enjoy an immersive experience.

[0278] For example, if a user is playing a tense shooting game in real time, the device instantly captures the situation and sends it to the server. The server generates commentary such as, "Only one left, aim carefully now!" and plays it back on the device as audio, allowing the user to become more deeply immersed in the game.

[0279] Example prompt sentence: "Please generate a real-time game commentary considering the user's play style and situation. For example, I would like to express the tense moments in a shooting game."

[0280] The flow of the specific process in Example 1 will be described using FIG. 11.

[0281] Step 1:

[0282] The user's terminal acquires visual information and user operation information in real time during game play. It receives in-game screen display data and user control operation data as input. Based on this, a capture program packages the data into a specific format and performs compression processing. Compressed data packets are generated as output.

[0283] Step 2:

[0284] The terminal sends the compressed data packets to the server. It receives compressed visual information and operation information as input and transfers the data to the server via a communication protocol. In order to minimize data loss and delay, a highly efficient transfer technology is used as much as possible. As output, the data reaches the server safely.

[0285] Step 3:

[0286] The server decompresses the data packets received from the terminal and performs shaping processing. It receives compressed data as input, decompresses it, and performs data shaping to remove noise. A filtering algorithm is used to extract important information. A clean and analyzable data set is obtained as output.

[0287] Step 4:

[0288] The server feeds the formatted data into a generating AI model to generate explanatory text. The input dataset includes player location information, score information, and enemy movements. The generating AI model analyzes this data and automatically generates grammatically correct and contextually appropriate explanations. The output provides explanatory text tailored to the user's play style and knowledge level.

[0289] Step 5:

[0290] The server sends the generated explanatory text to the user's terminal. It receives the generated explanatory text as input and sends it to the terminal using the appropriate communication protocol. The explanatory text arrives at the terminal as output.

[0291] Step 6:

[0292] The user's device converts the received explanatory text into audio and outputs it through the speaker. It receives explanatory text sent from the server as input and converts it into audio data using a speech synthesis engine. The audio is played in harmony with the game's background sounds. As output, the user can listen to the audio commentary in real time.

[0293] (Application Example 1)

[0294] 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."

[0295] There is a need to enhance immersion for both viewers and players by providing personalized audio commentary in real time alongside a visual game experience. However, conventional systems suffer from problems such as delays in generating commentary language and multi-media distribution, which detract from the user experience. In particular, there is a challenge in providing personalized commentary that is tailored to the user's operating style and knowledge level.

[0296] 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.

[0297] In this invention, the server includes means for acquiring visual data and user input data, means for processing and analyzing the acquired data, and means including a generative model for generating commentary language expressions based on the analyzed data. This makes it possible to generate personalized commentary in real time according to the user's play style and provide it with minimal latency.

[0298] "Visual data" refers to all visual information obtained from the game, including video footage of the user's gameplay.

[0299] "User input data" refers to data that includes control information and actions provided by the user during gameplay.

[0300] "Means of acquisition" refers to the processes and mechanisms for collecting visual data and user input data.

[0301] "Means of processing and analysis" refers to methods for converting acquired data into a usable format and extracting necessary information.

[0302] "Live commentary" refers to text-based descriptions of the game's progress that are later converted into audio.

[0303] A "generative model" is a machine learning algorithm that automatically creates commentary language expressions based on user gameplay data.

[0304] "Means of outputting as audio" refers to the technology and devices that convert the generated commentary language expression into an audio format.

[0305] "Methods for integrating and distributing across multiple media" refers to the process of delivering generated audio and video content to viewers as a single piece of content.

[0306] The system for implementing this invention is mainly composed of a user terminal, a central server, and a viewer device. When the user plays a game, the user terminal acquires visual data and user input data in real time. This data is transmitted to the server while reducing communication traffic using compression technology.

[0307] The server processes the received visual data and user input data, and performs analysis while removing noise. For this, advanced data processing algorithms and machine learning technologies are applied to extract the characteristics of the user's game play. As the necessary hardware, the server utilizes a cloud computing platform to achieve rapid data processing.

[0308] Based on the analyzed data, the generated AI model generates a live language expression. In this generation process, the user's play style and knowledge level are considered, and an individualized live situation is provided. The generated live language expression is converted into audio data using a speech synthesis engine. Google Cloud Text-to-Speech API is used for speech synthesis to generate natural and immersive audio.

[0309] The converted audio data is returned to the user terminal, integrated as multimedia together with the visual data of the game, and distributed to the viewer device. Through this process, the viewer can experience the game play with a live commentary in real time.

[0310] As a specific example, when the user is playing a soccer game, a real-time live situation such as "One-on-one with the goalkeeper! Great goal!" is generated as the live language expression. At this time, as an example of the prompt sentence for the generated AI model, "During the user's soccer game, shooting scene. Please include the player name and shooting result in the live commentary." is used.

[0311] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0312] Step 1:

[0313] The user terminal acquires visual data and user input data during gameplay. It takes in real-time visual information output from the game and user operation data as input, and preprocesses this data to compress it and reduce communication traffic. The output is compressed composite data.

[0314] Step 2:

[0315] The user terminal sends compressed visual data and user input data to the server. It receives compressed data as input, and the output is the proper transmission of data. The server stores the received data and prepares it for the next analysis.

[0316] Step 3:

[0317] The server processes and analyzes the received visual data and user input data. The input is data stored on the server, which undergoes data analysis to remove noise and structure it. The output is clean data that has been noise-free and analyzed.

[0318] Step 4:

[0319] The server generates commentary language expressions using a generative AI model based on the analyzed data. The input is clean data, which the AI ​​model interprets to generate appropriate text for commentary. The output is the generated commentary language expression.

[0320] Step 5:

[0321] The server sends the generated commentary language expression to the user terminal. The input is the commentary text generated on the server, and the output is the completion of transmission to the user terminal.

[0322] Step 6:

[0323] The user terminal converts the received commentary text into speech data using a speech synthesis engine. The input is commentary text, and the synthesis process generates natural-sounding speech data. The output is speech data.

[0324] Step 7:

[0325] The user terminal integrates audio and visual data and delivers it to the viewer's device. Input consists of audio data and game visual data, which are combined as multimedia content. Output is real-time game footage with commentary.

[0326] 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.

[0327] This invention is implemented as a system incorporating emotion recognition technology to highly personalize the user experience and enhance the sense of realism in a gaming environment. This system detects the user's emotional state and dynamically adjusts the content of the commentary based on that information.

[0328] System Configuration

[0329] This system primarily consists of the user's terminal, a central server, an emotion engine, and an audio output device. The emotion engine recognizes the user's emotions in real time and optimizes the user experience based on this. The server plays a central role in processing the received data and generating commentary that aligns with the emotion recognition.

[0330] The role of the emotional engine

[0331] The emotion engine infers the user's emotional state based on behavioral patterns derived from the speed and precision of user actions and interactions, as well as data obtained from biosensors (e.g., heart rate monitors and skin electrical activity sensors) as needed. For example, if a user suddenly speeds up their movements in a game, this behavior can be associated with stress or excitement.

[0332] Generating and adjusting commentary text

[0333] The server combines game data sent from the terminal with emotion data provided by the emotion engine and uses an AI generation model to generate commentary appropriate to the situation. This commentary is adjusted in real time according to the user's emotional state. For example, when the user is relaxed, the comments are delivered in a calm tone, and when the user is stressed, the comments are concise and provide only the necessary information.

[0334] Audio output

[0335] The terminal uses a speech synthesis engine to convert live commentary sent from the server into speech, and provides it to the user in real time, synchronized with the gameplay video. This allows for appropriate interventions tailored to the user's emotional state, improving immersion in the game.

[0336] Specific example

[0337] For example, imagine a scenario where a user is concentrating intensely on a challenging task. If the emotion engine detects the user's tension, the device will offer advice such as, "Calm down and use your remaining time wisely." This allows the user to receive emotionally-driven support while remaining deeply immersed in the game. By combining emotion recognition with AI-generated responses in this way, it is possible to provide an unprecedentedly personalized gaming experience.

[0338] The following describes the processing flow.

[0339] Step 1:

[0340] The device captures video data and user input data at the start of the game. Simultaneously, an emotion engine installed in the device receives data from sensors that capture the user's biometric data. This data is transmitted to the server in real time.

[0341] Step 2:

[0342] The server decodes the received video and operation data and performs the necessary formatting. This process removes noise while extracting important in-game information such as the player's position, score, and elapsed time. Simultaneously, emotional data provided by the emotion engine is also analyzed.

[0343] Step 3:

[0344] The server provides formatted game data and emotion data to an AI generation model, which then generates commentary tailored to specific game situations. At this stage, comments are added or emphasized based on the user's emotional state.

[0345] Step 4:

[0346] The server sends the generated commentary text to the terminal and prepares it for audio conversion. During transmission, the optimal protocol is used to minimize communication delays.

[0347] Step 5:

[0348] The terminal receives the commentary text sent from the server and converts it into speech using a speech synthesis engine. At this time, adjustments are made to synchronize the audio with the game video.

[0349] Step 6:

[0350] Users continue playing the game while listening to audio commentary output from their device. Because the commentary is based on emotion recognition, users can receive appropriate support in response to changes in their emotions during gameplay.

[0351] Step 7:

[0352] The server will collect user gameplay experience and emotional feedback, which will be used to optimize future commentary generation algorithms. This data will also be used to improve the user experience in the long term.

[0353] (Example 2)

[0354] 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".

[0355] There is a need for highly personalized gaming experiences and adaptive feedback that responds to user emotions. However, conventional technologies have struggled to detect user emotional states in real time and dynamically adjust the experience based on them. To solve this problem, optimal commentary generation and audio output that respond to changes in user emotions are necessary.

[0356] 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.

[0357] In this invention, the server includes means for detecting the user's emotional state, means for analyzing the data to dynamically adjust the user's experience based on the detected emotional state, and means for generating commentary text using a generative AI model based on the analyzed data. This enables the provision of adaptive feedback that responds to the user's emotions and a highly personalized gaming experience.

[0358] "User emotional state" refers to the emotional responses and psychological states that a user exhibits in specific situations, including stress, excitement, and relaxation.

[0359] "Dynamic adjustment" refers to a system changing its response in real time based on input information, adapting the user experience as needed.

[0360] A "generative AI model" refers to a technology or method that uses artificial intelligence to generate new content, such as commentary, from specific data.

[0361] "Commentary text" refers to explanatory text that describes the situation in the game in real time and is provided to the user as audio or text.

[0362] "Outputting as audio" refers to providing the generated text data to the user in audio format through speech synthesis technology.

[0363] This invention implements a system for providing a highly personalized gaming experience that responds to the user's emotions. The system comprises a user terminal, a central server, an emotion engine, and an audio output device.

[0364] The user's device collects operation information and biosensor data during gameplay. Specifically, it monitors heart rate and skin electrical activity using a variety of sensors. This makes it possible to detect the user's emotional state in real time.

[0365] The server receives data sent from the terminal and analyzes it in conjunction with the emotion engine. The emotion engine infers the emotional state based on the user's operation patterns and biometric information. This analysis result is sent to a generative AI model, which then generates appropriate commentary based on its output.

[0366] The generative AI model runs on a server and receives specific prompt messages as input to generate appropriate commentary. For example, a prompt message might be "Generate commentary appropriate for when the user's emotional state is excited."

[0367] The device uses a speech synthesis engine to convert live commentary received from the server into speech and provides it to the user in real time within the game. This allows the user to receive appropriate support and feedback based on their emotional state through voice. This method enables the user to become more deeply immersed in the game.

[0368] For example, if a user is playing a stage in a game where the difficulty gradually increases, the emotion engine might detect this tension. In that case, the device would provide specific voice advice such as, "Concentrate and calmly proceed to the next step." This enables a real-time experience that is tailored to the user's actions and emotions.

[0369] The above describes specific embodiments for carrying out this invention.

[0370] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0371] Step 1:

[0372] During gameplay, the user collects operational information and biosensor data through their device. Inputs include the user's controller input, heart rate, and skin electrical activity. The device transmits this data to the emotion engine in real time. The output generates raw data that reflects the user's actions.

[0373] Step 2:

[0374] The device sends the received data to the emotion engine, which analyzes the emotional state. The input to the emotion engine is the operation information and biosensor data collected in step 1. By analyzing the data, the system infers the user's emotional state. For example, a sudden change in operation speed may indicate stress or excitement. This result is output as an emotional state profile.

[0375] Step 3:

[0376] The server runs a generative AI model based on the emotional state profile received from the emotion engine. The input includes the current game state and the user's emotional state profile. The server sends prompts to the generative AI model to generate appropriate commentary. An example prompt might be, "Generate commentary suitable for when the user's emotional state is relaxed." The output of this step is the generated commentary.

[0377] Step 4:

[0378] The terminal uses a speech synthesis engine to convert the generated commentary text sent from the server into speech. The input is the commentary text generated in step 3. The speech synthesis engine converts this text into speech that is easy for the user to understand. The output is real-time audio feedback synchronized with the game video. Through this audio, the user can receive adaptive feedback that matches their emotional state.

[0379] Step 5:

[0380] Users can continue playing the game based on voice feedback. Adaptive voice support allows users to become more immersed in the game and concentrate more effectively. This step is crucial as the ultimate outcome of the entire system. In summary, a highly personalized experience is provided to the user.

[0381] (Application Example 2)

[0382] 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."

[0383] Traditional virtual stores have struggled to provide real-time purchasing support that reflects the emotional state of users, resulting in decreased purchasing efficiency. In particular, they have been unable to provide appropriate information when users show interest in a specific product or when they are hesitant about making a purchase, leading to decreased immersion and satisfaction.

[0384] 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.

[0385] In this invention, the server includes a device for acquiring video data and user input data, a device for detecting the user's emotional state and dynamically adjusting text based on that state, and a device for processing and evaluating the acquired data. This makes it possible to provide a personalized purchase assistant that responds to the user's emotions in real time.

[0386] "Visual data" refers to information obtained from visual media and is a visual representation stored in digital or analog format.

[0387] "User input data" refers to information provided by users through their devices, and is digital data that reflects their actions and choices.

[0388] A "device" is a set of interconnected hardware or software elements designed to perform a specific function.

[0389] "Processing" refers to the process of transforming, organizing, or modifying acquired data to suit a specific purpose.

[0390] "Evaluation" is the process of analyzing data and judging its content and value.

[0391] A "generative engine" is software or an algorithm used to create content based on data.

[0392] Sound is a vibration that travels through the air or other media, and is perceived through the sense of hearing.

[0393] An "information processing system" is a computer-based system designed to acquire, analyze, transform, and output data.

[0394] "Emotional state" refers to characteristics and data that indicate a user's mental and emotional state.

[0395] "Dynamic adjustment" refers to a process of flexibly changing or optimizing according to the situation and the user's condition.

[0396] "Personalization" refers to the act of customizing or personalizing something to suit the specific characteristics and needs of a particular user.

[0397] "Distribution" is the process of supplying content or data to multiple receiving devices.

[0398] A "transmission method" refers to the technical techniques or protocols used to transmit data from one point to another.

[0399] An "encoding method" is an algorithm or technique designed to compress or transform data to make it easier to transmit.

[0400] To realize this invention, it is necessary to build a system in which a virtual store provides a personalized assistant that responds to the user's emotions. The central elements of this system are an emotion recognition device, a data evaluation device, a generation engine, and a voice output device.

[0401] The server acquires video data and user input data from the user's smart glasses. This involves using hardware devices such as cameras and sensors. The acquired data is processed and analyzed by a data evaluation device to identify the user's emotional state. This evaluation utilizes open-source emotion recognition libraries (e.g., OpenCV and TensorFlow).

[0402] Once the user's emotional state is identified, the generation engine uses AI to generate information in real time. Specifically, a generation AI model (e.g., GPT-3) is used to generate appropriate sentences based on the prompt. These sentences are then spoken by a speech output device and provided to the user. Throughout this entire process, the user receives a personalized voice assistant tailored to their emotions, allowing them to receive appropriate support in selecting and purchasing products.

[0403] For example, if a user stands in front of a specific product shelf for an extended period, the emotion recognition device detects that the user is "interested." Based on this, the generation engine generates a message such as, "This product is currently on sale and offers excellent performance. Please consider it," and the voice output device delivers this message to the user as sound. Another example of a prompt for the generation AI model would be a question like, "Explain the features of the latest PC in an easy-to-understand way to an interested buyer."

[0404] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0405] Step 1:

[0406] The server acquires video data and user input data from the user's smart glasses. The input data includes the user's facial expressions and gestures indicating interest. This data is acquired in real time by cameras and other sensors. The server records this raw data.

[0407] Step 2:

[0408] The server analyzes the user's emotional state using the acquired video data. Open-source emotion recognition libraries (such as OpenCV or TensorFlow) are used for this purpose. These libraries analyze facial expressions and body movements to estimate emotions such as interest, confusion, relaxation, and stress. The output is the analyzed emotion data.

[0409] Step 3:

[0410] The server calls a generative AI model based on the analyzed sentiment data to create an appropriate prompt. For example, a prompt such as "Explain the features of the latest PC clearly to an interested buyer" might be generated. In this case, the input is sentiment data, and the output is the prompt.

[0411] Step 4:

[0412] The server uses a generative AI model (e.g., GPT-3) to generate natural language assistant messages based on the prompt. This AI, having learned from a large amount of text data, has the capability to produce clear and persuasive sentences. The input is the prompt, and the output is the generated assistant message.

[0413] Step 5:

[0414] The terminal sends the generated assistant message to the voice output device and provides it to the user as synthesized speech. The text is converted into speech using speech synthesis software, which the user can then hear. Here, the input is the generated text message, and the output is the voice message.

[0415] Step 6:

[0416] Users listen to voice messages and use that information to select products and make purchasing decisions. By modifying their actions based on the information they receive, the purchasing experience is improved. The user's responses are then re-entered into the system as data for the next processing cycle.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] [Third Embodiment]

[0421] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0422] 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.

[0423] 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).

[0424] 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.

[0425] 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.

[0426] 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).

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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".

[0433] This invention relates to a system that automatically generates commentary based on game video data and user input data in order to improve the user's gaming experience. This system analyzes game data captured in real time and enables the output of personalized commentary as audio, tailored to the user's play style and knowledge level.

[0434] Overall system configuration

[0435] The system primarily consists of the user's terminal, a central server, and an audio output device. The user's terminal captures data during gameplay and sends it to the central server. The server analyzes this data and uses an AI generative model to create commentary text. This commentary text is then sent from the server to the user's terminal, which outputs it as audible data using speech synthesis technology.

[0436] Specific operation of the device

[0437] The user's device has the capability to capture video data and user input data during gameplay. This captured data is periodically sent to the server. During capture, the data is compressed whenever possible to minimize communication traffic. This process is achieved by utilizing APIs built into the game or a separate capture program.

[0438] Specific server operation

[0439] The server organizes and formats the received data packets to remove noise and extract important information. This includes, for example, the player's location, score, enemy presence, and remaining time in the game. Then, based on the analyzed data, a generative AI model creates commentary. This commentary is customized according to the user's play style and knowledge level. For example, beginners will find more explanations of controls, while advanced players will find more strategic advice.

[0440] How to perform audio output

[0441] The commentary text sent from the server to the terminal is converted into audio data by a speech synthesis engine on the terminal. This audio is overlaid on the game's video as needed, allowing the user to listen to the commentary in real time. This process allows the user to enjoy an immersive gaming experience with commentary, even when playing alone.

[0442] Specific example

[0443] For example, consider a situation where a user is playing a shooting game. If they are in a close battle and facing the last enemy, the device captures the situation and sends the data to the server. The server generates commentary such as, "Only one left, aim carefully now!" and sends this as audio to the device, allowing the user to further experience the tension of the game through the commentary.

[0444] The following describes the processing flow.

[0445] Step 1:

[0446] When a user starts a game, the device begins capturing gameplay footage and user input data. This process is carried out using in-game APIs or a dedicated capture program, and data is collected at regular intervals.

[0447] Step 2:

[0448] The terminal combines captured video data and operation data into a single packet and sends it to the server in real time. If possible, compression technology is used to minimize the data size and improve communication efficiency.

[0449] Step 3:

[0450] The server receives the data sent from the terminal and decodes it. Next, it formats the data, removing noise and extracting important elements such as the player's location and actions.

[0451] Step 4:

[0452] The server inputs the formatted data into an AI generation model. This model recognizes the current state of the game and generates personalized commentary based on the user's past play data and profile.

[0453] Step 5:

[0454] The server sends the generated live report to the terminal. During the transmission process, the optimal protocol is selected to minimize communication delays.

[0455] Step 6:

[0456] The terminal uses a speech synthesis engine to convert the live commentary sent from the server into speech. This speech is then provided to the user in real time, synchronized with the gameplay video.

[0457] Step 7:

[0458] Users can enjoy a more immersive experience by listening to the generated commentary while playing the game. If user feedback is received, the system uses this feedback to improve the accuracy of the generative model.

[0459] (Example 1)

[0460] 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."

[0461] In modern digital entertainment, there is a demand for users to be able to have an immersive experience even when working independently. However, conventional technologies do not adequately utilize visual information and user interaction information, making it difficult to provide personalized audio commentary in real time. Furthermore, delays associated with information transmission can impair the quality of the experience, and there is a lack of means to resolve this.

[0462] 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.

[0463] In this invention, the server includes means for acquiring visual information and user operation information in digital entertainment, means for formatting the acquired information and extracting important information, and means including an artificial intelligence model that generates explanatory text based on the extracted information. This enables users to receive high-quality, personalized audio commentary in real time, even when working independently.

[0464] "Digital entertainment" refers to all forms of entertainment provided using computers and electronic devices, and specifically includes games and video content.

[0465] "Visual information" refers to the data of screens and images displayed within digital content, and includes information that users can visually recognize.

[0466] "User operation information" refers to input data generated when a user interacts with digital content, and includes instructions given via controllers, keyboards, mice, etc.

[0467] "Means of acquisition" refers to devices and methods for collecting necessary information, and includes functions that combine software and hardware.

[0468] "Methods for formatting and extracting important information" refers to techniques that process collected data to remove unnecessary information and select the data necessary for a specific purpose.

[0469] An "artificial intelligence model" refers to a computational model that uses machine learning techniques to perform specific tasks, possessing functions such as data analysis, prediction, and generation.

[0470] "Generating explanatory text" refers to the process of automatically creating explanatory text in natural language based on data.

[0471] "Means of converting to audio and outputting it" refers to technology that converts text data into audio data and plays it back through speakers or other devices.

[0472] This invention provides a system that allows users to enjoy digital entertainment with a sense of realism, even when alone. The system comprises a user terminal, a central server, and an audio output device.

[0473] The user's device acquires visual and user interaction information in real time during gameplay. For this purpose, the device can utilize in-game APIs or dedicated capture software. The acquired information is compressed and efficiently transmitted to the server. For example, in a shooting game, when the user is facing an enemy, their actions and the situation are captured.

[0474] The server formats the information received from the terminal, removes noise, and extracts important information. After this analysis, a generative AI model is used to generate explanatory text. In generating this explanatory text, the user's operating habits and knowledge level are taken into consideration, and novel content is generated. For example, for beginner users, specific instructions such as "An enemy is approaching, please be careful" are included.

[0475] The generated explanatory text is sent from the server to the user's terminal. On the user's terminal, the explanatory text is converted into speech by a speech synthesis engine and played back via an audio output device. This allows the user to listen to live game commentary in real time and enjoy an immersive experience.

[0476] For example, if a user is playing a tense shooting game in real time, the device instantly captures the situation and sends it to the server. The server generates commentary such as, "Only one left, aim carefully now!" and plays it back on the device as audio, allowing the user to become more deeply immersed in the game.

[0477] Example prompt: "Generate real-time game commentary that takes into account the user's play style and situation. For example, I want to portray a tense moment in a shooting game."

[0478] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0479] Step 1:

[0480] The user's device acquires visual and user operation information in real time during gameplay. It receives in-game screen display data and user control operation data as input. Based on this, the capture program packages the data into a specific format and performs compression. Compressed data packets are generated as output.

[0481] Step 2:

[0482] The terminal sends compressed data packets to the server. It receives compressed visual and operational information as input and transfers the data to the server via a communication protocol. To minimize data loss and delay, it uses the most efficient transfer technology possible. As output, the data safely reaches the server.

[0483] Step 3:

[0484] The server decompresses and formats the data packets received from the terminal. It receives compressed data as input, decompresses it, and performs data formatting to remove noise. A filtering algorithm is used to extract important information. The output is a clean, analyzable dataset.

[0485] Step 4:

[0486] The server feeds the formatted data into a generating AI model to generate explanatory text. The input dataset includes player location information, score information, and enemy movements. The generating AI model analyzes this data and automatically generates grammatically correct and contextually appropriate explanations. The output provides explanatory text tailored to the user's play style and knowledge level.

[0487] Step 5:

[0488] The server sends the generated explanatory text to the user's terminal. It receives the generated explanatory text as input and sends it to the terminal using the appropriate communication protocol. The explanatory text arrives at the terminal as output.

[0489] Step 6:

[0490] The user's device converts the received explanatory text into audio and outputs it through the speaker. It receives explanatory text sent from the server as input and converts it into audio data using a speech synthesis engine. The audio is played in harmony with the game's background sounds. As output, the user can listen to the audio commentary in real time.

[0491] (Application Example 1)

[0492] 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."

[0493] There is a need to enhance immersion for both viewers and players by providing personalized audio commentary in real time alongside a visual game experience. However, conventional systems suffer from problems such as delays in generating commentary language and multi-media distribution, which detract from the user experience. In particular, there is a challenge in providing personalized commentary that is tailored to the user's operating style and knowledge level.

[0494] 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.

[0495] In this invention, the server includes means for acquiring visual data and user input data, means for processing and analyzing the acquired data, and means including a generative model for generating commentary language expressions based on the analyzed data. This makes it possible to generate personalized commentary in real time according to the user's play style and provide it with minimal latency.

[0496] "Visual data" refers to all visual information obtained from the game, including video footage of the user's gameplay.

[0497] "User input data" refers to data that includes control information and actions provided by the user during gameplay.

[0498] "Means of acquisition" refers to the processes and mechanisms for collecting visual data and user input data.

[0499] "Means of processing and analysis" refers to methods for converting acquired data into a usable format and extracting necessary information.

[0500] "Live commentary" refers to text-based descriptions of the game's progress that are later converted into audio.

[0501] A "generative model" is a machine learning algorithm that automatically creates commentary language expressions based on user gameplay data.

[0502] "Means of outputting as audio" refers to the technology and devices that convert the generated commentary language expression into an audio format.

[0503] "Methods for integrating and distributing across multiple media" refers to the process of delivering generated audio and video content to viewers as a single piece of content.

[0504] The system implementing this invention consists mainly of a user terminal, a central server, and a viewer device. When a user plays a game, the user terminal acquires visual data and user input data in real time. This data is transmitted to the server while reducing communication traffic using compression technology.

[0505] The server processes received visual and user input data, performing analysis while removing noise. This involves applying advanced data processing algorithms and machine learning techniques to extract the user's gameplay characteristics. The server utilizes a cloud computing platform to enable rapid data processing.

[0506] Based on the analyzed data, a generative AI model generates commentary language expressions. This generation process takes into account the user's play style and knowledge level to provide personalized commentary. The generated commentary language expressions are converted into audio data using a speech synthesis engine. The Google Cloud Text-to-Speech API is used for speech synthesis, generating natural and immersive audio.

[0507] The converted audio data is sent back to the user's device, integrated with the game's visual data as a multimedia product, and then delivered to the viewer's device. This process allows viewers to experience gameplay with live commentary in real time.

[0508] As a concrete example, if a user is playing a soccer game, real-time commentary such as "It's a one-on-one with the goalkeeper! He's scored a magnificent goal!" will be generated. In this case, an example of a prompt to the generating AI model would be, "The user is playing a soccer game, and it's a shooting scene. Please include the player's name and the result of the shot in the commentary."

[0509] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0510] Step 1:

[0511] The user terminal acquires visual data and user input data during gameplay. It takes in real-time visual information output from the game and user operation data as input, and preprocesses this data to compress it and reduce communication traffic. The output is compressed composite data.

[0512] Step 2:

[0513] The user terminal sends compressed visual data and user input data to the server. It receives compressed data as input, and the output is the proper transmission of data. The server stores the received data and prepares it for the next analysis.

[0514] Step 3:

[0515] The server processes and analyzes the received visual data and user input data. The input is data stored on the server, which undergoes data analysis to remove noise and structure it. The output is clean data that has been noise-free and analyzed.

[0516] Step 4:

[0517] The server generates commentary language expressions using a generative AI model based on the analyzed data. The input is clean data, which the AI ​​model interprets to generate appropriate text for commentary. The output is the generated commentary language expression.

[0518] Step 5:

[0519] The server sends the generated commentary language expression to the user terminal. The input is the commentary text generated on the server, and the output is the completion of transmission to the user terminal.

[0520] Step 6:

[0521] The user terminal converts the received commentary text into speech data using a speech synthesis engine. The input is commentary text, and the synthesis process generates natural-sounding speech data. The output is speech data.

[0522] Step 7:

[0523] The user terminal integrates audio and visual data and delivers it to the viewer's device. Input consists of audio data and game visual data, which are combined as multimedia content. Output is real-time game footage with commentary.

[0524] 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.

[0525] This invention is implemented as a system incorporating emotion recognition technology to highly personalize the user experience and enhance the sense of realism in a gaming environment. This system detects the user's emotional state and dynamically adjusts the content of the commentary based on that information.

[0526] System Configuration

[0527] This system primarily consists of the user's terminal, a central server, an emotion engine, and an audio output device. The emotion engine recognizes the user's emotions in real time and optimizes the user experience based on this. The server plays a central role in processing the received data and generating commentary that aligns with the emotion recognition.

[0528] The role of the emotional engine

[0529] The emotion engine infers the user's emotional state based on behavioral patterns derived from the speed and precision of user actions and interactions, as well as data obtained from biosensors (e.g., heart rate monitors and skin electrical activity sensors) as needed. For example, if a user suddenly speeds up their movements in a game, this behavior can be associated with stress or excitement.

[0530] Generating and adjusting commentary text

[0531] The server combines game data sent from the terminal with emotion data provided by the emotion engine and uses an AI generation model to generate commentary appropriate to the situation. This commentary is adjusted in real time according to the user's emotional state. For example, when the user is relaxed, the comments are delivered in a calm tone, and when the user is stressed, the comments are concise and provide only the necessary information.

[0532] Audio output

[0533] The terminal uses a speech synthesis engine to convert live commentary sent from the server into speech, and provides it to the user in real time, synchronized with the gameplay video. This allows for appropriate interventions tailored to the user's emotional state, improving immersion in the game.

[0534] Specific example

[0535] For example, imagine a scenario where a user is concentrating intensely on a challenging task. If the emotion engine detects the user's tension, the device will offer advice such as, "Calm down and use your remaining time wisely." This allows the user to receive emotionally-driven support while remaining deeply immersed in the game. By combining emotion recognition with AI-generated responses in this way, it is possible to provide an unprecedentedly personalized gaming experience.

[0536] The following describes the processing flow.

[0537] Step 1:

[0538] The device captures video data and user input data at the start of the game. Simultaneously, an emotion engine installed in the device receives data from sensors that capture the user's biometric data. This data is transmitted to the server in real time.

[0539] Step 2:

[0540] The server decodes the received video and operation data and performs the necessary formatting. This process removes noise while extracting important in-game information such as the player's position, score, and elapsed time. Simultaneously, emotional data provided by the emotion engine is also analyzed.

[0541] Step 3:

[0542] The server provides formatted game data and emotion data to an AI generation model, which then generates commentary tailored to specific game situations. At this stage, comments are added or emphasized based on the user's emotional state.

[0543] Step 4:

[0544] The server sends the generated commentary text to the terminal and prepares it for audio conversion. During transmission, the optimal protocol is used to minimize communication delays.

[0545] Step 5:

[0546] The terminal receives the commentary text sent from the server and converts it into speech using a speech synthesis engine. At this time, adjustments are made to synchronize the audio with the game video.

[0547] Step 6:

[0548] Users continue playing the game while listening to audio commentary output from their device. Because the commentary is based on emotion recognition, users can receive appropriate support in response to changes in their emotions during gameplay.

[0549] Step 7:

[0550] The server will collect user gameplay experience and emotional feedback, which will be used to optimize future commentary generation algorithms. This data will also be used to improve the user experience in the long term.

[0551] (Example 2)

[0552] 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."

[0553] There is a need for highly personalized gaming experiences and adaptive feedback that responds to user emotions. However, conventional technologies have struggled to detect user emotional states in real time and dynamically adjust the experience based on them. To solve this problem, optimal commentary generation and audio output that respond to changes in user emotions are necessary.

[0554] 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.

[0555] In this invention, the server includes means for detecting the user's emotional state, means for analyzing the data to dynamically adjust the user's experience based on the detected emotional state, and means for generating commentary text using a generative AI model based on the analyzed data. This enables the provision of adaptive feedback that responds to the user's emotions and a highly personalized gaming experience.

[0556] "User emotional state" refers to the emotional responses and psychological states that a user exhibits in specific situations, including stress, excitement, and relaxation.

[0557] "Dynamic adjustment" refers to a system changing its response in real time based on input information, adapting the user experience as needed.

[0558] A "generative AI model" refers to a technology or method that uses artificial intelligence to generate new content, such as commentary, from specific data.

[0559] "Commentary text" refers to explanatory text that describes the situation in the game in real time and is provided to the user as audio or text.

[0560] "Outputting as audio" refers to providing the generated text data to the user in audio format through speech synthesis technology.

[0561] This invention implements a system for providing a highly personalized gaming experience that responds to the user's emotions. The system comprises a user terminal, a central server, an emotion engine, and an audio output device.

[0562] The user's device collects operation information and biosensor data during gameplay. Specifically, it monitors heart rate and skin electrical activity using a variety of sensors. This makes it possible to detect the user's emotional state in real time.

[0563] The server receives data sent from the terminal and analyzes it in conjunction with the emotion engine. The emotion engine infers the emotional state based on the user's operation patterns and biometric information. This analysis result is sent to a generative AI model, which then generates appropriate commentary based on its output.

[0564] The generative AI model runs on a server and receives specific prompt messages as input to generate appropriate commentary. For example, a prompt message might be "Generate commentary appropriate for when the user's emotional state is excited."

[0565] The device uses a speech synthesis engine to convert live commentary received from the server into speech and provides it to the user in real time within the game. This allows the user to receive appropriate support and feedback based on their emotional state through voice. This method enables the user to become more deeply immersed in the game.

[0566] For example, if a user is playing a stage in a game where the difficulty gradually increases, the emotion engine might detect this tension. In that case, the device would provide specific voice advice such as, "Concentrate and calmly proceed to the next step." This enables a real-time experience that is tailored to the user's actions and emotions.

[0567] The above describes specific embodiments for carrying out this invention.

[0568] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0569] Step 1:

[0570] During gameplay, the user collects operational information and biosensor data through their device. Inputs include the user's controller input, heart rate, and skin electrical activity. The device transmits this data to the emotion engine in real time. The output generates raw data that reflects the user's actions.

[0571] Step 2:

[0572] The device sends the received data to the emotion engine, which analyzes the emotional state. The input to the emotion engine is the operation information and biosensor data collected in step 1. By analyzing the data, the system infers the user's emotional state. For example, a sudden change in operation speed may indicate stress or excitement. This result is output as an emotional state profile.

[0573] Step 3:

[0574] The server runs a generative AI model based on the emotional state profile received from the emotion engine. The input includes the current game state and the user's emotional state profile. The server sends prompts to the generative AI model to generate appropriate commentary. An example prompt might be, "Generate commentary suitable for when the user's emotional state is relaxed." The output of this step is the generated commentary.

[0575] Step 4:

[0576] The terminal uses a speech synthesis engine to convert the generated commentary text sent from the server into speech. The input is the commentary text generated in step 3. The speech synthesis engine converts this text into speech that is easy for the user to understand. The output is real-time audio feedback synchronized with the game video. Through this audio, the user can receive adaptive feedback that matches their emotional state.

[0577] Step 5:

[0578] Users can continue playing the game based on voice feedback. Adaptive voice support allows users to become more immersed in the game and concentrate more effectively. This step is crucial as the ultimate outcome of the entire system. In summary, a highly personalized experience is provided to the user.

[0579] (Application Example 2)

[0580] 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."

[0581] Traditional virtual stores have struggled to provide real-time purchasing support that reflects the emotional state of users, resulting in decreased purchasing efficiency. In particular, they have been unable to provide appropriate information when users show interest in a specific product or when they are hesitant about making a purchase, leading to decreased immersion and satisfaction.

[0582] 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.

[0583] In this invention, the server includes a device for acquiring video data and user input data, a device for detecting the user's emotional state and dynamically adjusting text based on that state, and a device for processing and evaluating the acquired data. This makes it possible to provide a personalized purchase assistant that responds to the user's emotions in real time.

[0584] "Visual data" refers to information obtained from visual media and is a visual representation stored in digital or analog format.

[0585] "User input data" refers to information provided by users through their devices, and is digital data that reflects their actions and choices.

[0586] A "device" is a set of interconnected hardware or software elements designed to perform a specific function.

[0587] "Processing" refers to the process of transforming, organizing, or modifying acquired data to suit a specific purpose.

[0588] "Evaluation" is the process of analyzing data and judging its content and value.

[0589] A "generative engine" is software or an algorithm used to create content based on data.

[0590] Sound is a vibration that travels through the air or other media, and is perceived through the sense of hearing.

[0591] An "information processing system" is a computer-based system designed to acquire, analyze, transform, and output data.

[0592] "Emotional state" refers to characteristics and data that indicate a user's mental and emotional state.

[0593] "Dynamic adjustment" refers to a process of flexibly changing or optimizing according to the situation and the user's condition.

[0594] "Personalization" refers to the act of customizing or personalizing something to suit the specific characteristics and needs of a particular user.

[0595] "Distribution" is the process of supplying content or data to multiple receiving devices.

[0596] A "transmission method" refers to the technical techniques or protocols used to transmit data from one point to another.

[0597] An "encoding method" is an algorithm or technique designed to compress or transform data to make it easier to transmit.

[0598] To realize this invention, it is necessary to build a system in which a virtual store provides a personalized assistant that responds to the user's emotions. The central elements of this system are an emotion recognition device, a data evaluation device, a generation engine, and a voice output device.

[0599] The server acquires video data and user input data from the user's smart glasses. This involves using hardware devices such as cameras and sensors. The acquired data is processed and analyzed by a data evaluation device to identify the user's emotional state. This evaluation utilizes open-source emotion recognition libraries (e.g., OpenCV and TensorFlow).

[0600] Once the user's emotional state is identified, the generation engine uses AI to generate information in real time. Specifically, a generation AI model (e.g., GPT-3) is used to generate appropriate sentences based on the prompt. These sentences are then spoken by a speech output device and provided to the user. Throughout this entire process, the user receives a personalized voice assistant tailored to their emotions, allowing them to receive appropriate support in selecting and purchasing products.

[0601] For example, if a user stands in front of a specific product shelf for an extended period, the emotion recognition device detects that the user is "interested." Based on this, the generation engine generates a message such as, "This product is currently on sale and offers excellent performance. Please consider it," and the voice output device delivers this message to the user as sound. Another example of a prompt for the generation AI model would be a question like, "Explain the features of the latest PC in an easy-to-understand way to an interested buyer."

[0602] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0603] Step 1:

[0604] The server acquires video data and user input data from the user's smart glasses. The input data includes the user's facial expressions and gestures indicating interest. This data is acquired in real time by cameras and other sensors. The server records this raw data.

[0605] Step 2:

[0606] The server analyzes the user's emotional state using the acquired video data. Open-source emotion recognition libraries (such as OpenCV or TensorFlow) are used for this purpose. These libraries analyze facial expressions and body movements to estimate emotions such as interest, confusion, relaxation, and stress. The output is the analyzed emotion data.

[0607] Step 3:

[0608] The server calls a generative AI model based on the analyzed sentiment data to create an appropriate prompt. For example, a prompt such as "Explain the features of the latest PC clearly to an interested buyer" might be generated. In this case, the input is sentiment data, and the output is the prompt.

[0609] Step 4:

[0610] The server uses a generative AI model (e.g., GPT-3) to generate natural language assistant messages based on the prompt. This AI, having learned from a large amount of text data, has the capability to produce clear and persuasive sentences. The input is the prompt, and the output is the generated assistant message.

[0611] Step 5:

[0612] The terminal sends the generated assistant message to the voice output device and provides it to the user as synthesized speech. The text is converted into speech using speech synthesis software, which the user can then hear. Here, the input is the generated text message, and the output is the voice message.

[0613] Step 6:

[0614] Users listen to voice messages and use that information to select products and make purchasing decisions. By modifying their actions based on the information they receive, the purchasing experience is improved. The user's responses are then re-entered into the system as data for the next processing cycle.

[0615] 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.

[0616] 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.

[0617] 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.

[0618] [Fourth Embodiment]

[0619] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0620] 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.

[0621] 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).

[0622] 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.

[0623] 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.

[0624] 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).

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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".

[0632] This invention relates to a system that automatically generates commentary based on game video data and user input data in order to improve the user's gaming experience. This system analyzes game data captured in real time and enables the output of personalized commentary as audio, tailored to the user's play style and knowledge level.

[0633] Overall system configuration

[0634] The system primarily consists of the user's terminal, a central server, and an audio output device. The user's terminal captures data during gameplay and sends it to the central server. The server analyzes this data and uses an AI generative model to create commentary text. This commentary text is then sent from the server to the user's terminal, which outputs it as audible data using speech synthesis technology.

[0635] Specific operation of the device

[0636] The user's device has the capability to capture video data and user input data during gameplay. This captured data is periodically sent to the server. During capture, the data is compressed whenever possible to minimize communication traffic. This process is achieved by utilizing APIs built into the game or a separate capture program.

[0637] Specific server operation

[0638] The server organizes and formats the received data packets to remove noise and extract important information. This includes, for example, the player's location, score, enemy presence, and remaining time in the game. Then, based on the analyzed data, a generative AI model creates commentary. This commentary is customized according to the user's play style and knowledge level. For example, beginners will find more explanations of controls, while advanced players will find more strategic advice.

[0639] How to perform audio output

[0640] The commentary text sent from the server to the terminal is converted into audio data by a speech synthesis engine on the terminal. This audio is overlaid on the game's video as needed, allowing the user to listen to the commentary in real time. This process allows the user to enjoy an immersive gaming experience with commentary, even when playing alone.

[0641] Specific example

[0642] For example, consider a situation where a user is playing a shooting game. If they are in a close battle and facing the last enemy, the device captures the situation and sends the data to the server. The server generates commentary such as, "Only one left, aim carefully now!" and sends this as audio to the device, allowing the user to further experience the tension of the game through the commentary.

[0643] The following describes the processing flow.

[0644] Step 1:

[0645] When a user starts a game, the device begins capturing gameplay footage and user input data. This process is carried out using in-game APIs or a dedicated capture program, and data is collected at regular intervals.

[0646] Step 2:

[0647] The terminal combines captured video data and operation data into a single packet and sends it to the server in real time. If possible, compression technology is used to minimize the data size and improve communication efficiency.

[0648] Step 3:

[0649] The server receives the data sent from the terminal and decodes it. Next, it formats the data, removing noise and extracting important elements such as the player's location and actions.

[0650] Step 4:

[0651] The server inputs the formatted data into an AI generation model. This model recognizes the current state of the game and generates personalized commentary based on the user's past play data and profile.

[0652] Step 5:

[0653] The server sends the generated live report to the terminal. During the transmission process, the optimal protocol is selected to minimize communication delays.

[0654] Step 6:

[0655] The terminal uses a speech synthesis engine to convert the live commentary sent from the server into speech. This speech is then provided to the user in real time, synchronized with the gameplay video.

[0656] Step 7:

[0657] Users can enjoy a more immersive experience by listening to the generated commentary while playing the game. If user feedback is received, the system uses this feedback to improve the accuracy of the generative model.

[0658] (Example 1)

[0659] 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".

[0660] In modern digital entertainment, there is a demand for users to be able to have an immersive experience even when working independently. However, conventional technologies do not adequately utilize visual information and user interaction information, making it difficult to provide personalized audio commentary in real time. Furthermore, delays associated with information transmission can impair the quality of the experience, and there is a lack of means to resolve this.

[0661] 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.

[0662] In this invention, the server includes means for acquiring visual information and user operation information in digital entertainment, means for formatting the acquired information and extracting important information, and means including an artificial intelligence model that generates explanatory text based on the extracted information. This enables users to receive high-quality, personalized audio commentary in real time, even when working independently.

[0663] "Digital entertainment" refers to all forms of entertainment provided using computers and electronic devices, and specifically includes games and video content.

[0664] "Visual information" refers to the data of screens and images displayed within digital content, and includes information that users can visually recognize.

[0665] "User operation information" refers to input data generated when a user interacts with digital content, and includes instructions given via controllers, keyboards, mice, etc.

[0666] "Means of acquisition" refers to devices and methods for collecting necessary information, and includes functions that combine software and hardware.

[0667] "Methods for formatting and extracting important information" refers to techniques that process collected data to remove unnecessary information and select the data necessary for a specific purpose.

[0668] An "artificial intelligence model" refers to a computational model that uses machine learning techniques to perform specific tasks, possessing functions such as data analysis, prediction, and generation.

[0669] "Generating explanatory text" refers to the process of automatically creating explanatory text in natural language based on data.

[0670] "Means of converting to audio and outputting it" refers to technology that converts text data into audio data and plays it back through speakers or other devices.

[0671] This invention provides a system that allows users to enjoy digital entertainment with a sense of realism, even when alone. The system comprises a user terminal, a central server, and an audio output device.

[0672] The user's device acquires visual and user interaction information in real time during gameplay. For this purpose, the device can utilize in-game APIs or dedicated capture software. The acquired information is compressed and efficiently transmitted to the server. For example, in a shooting game, when the user is facing an enemy, their actions and the situation are captured.

[0673] The server formats the information received from the terminal, removes noise, and extracts important information. After this analysis, a generative AI model is used to generate explanatory text. In generating this explanatory text, the user's operating habits and knowledge level are taken into consideration, and novel content is generated. For example, for beginner users, specific instructions such as "An enemy is approaching, please be careful" are included.

[0674] The generated explanatory text is sent from the server to the user's terminal. On the user's terminal, the explanatory text is converted into speech by a speech synthesis engine and played back via an audio output device. This allows the user to listen to live game commentary in real time and enjoy an immersive experience.

[0675] For example, if a user is playing a tense shooting game in real time, the device instantly captures the situation and sends it to the server. The server generates commentary such as, "Only one left, aim carefully now!" and plays it back on the device as audio, allowing the user to become more deeply immersed in the game.

[0676] Example prompt: "Generate real-time game commentary that takes into account the user's play style and situation. For example, I want to portray a tense moment in a shooting game."

[0677] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0678] Step 1:

[0679] The user's device acquires visual and user operation information in real time during gameplay. It receives in-game screen display data and user control operation data as input. Based on this, the capture program packages the data into a specific format and performs compression. Compressed data packets are generated as output.

[0680] Step 2:

[0681] The terminal sends compressed data packets to the server. It receives compressed visual and operational information as input and transfers the data to the server via a communication protocol. To minimize data loss and delay, it uses the most efficient transfer technology possible. As output, the data safely reaches the server.

[0682] Step 3:

[0683] The server decompresses and formats the data packets received from the terminal. It receives compressed data as input, decompresses it, and performs data formatting to remove noise. A filtering algorithm is used to extract important information. The output is a clean, analyzable dataset.

[0684] Step 4:

[0685] The server feeds the formatted data into a generating AI model to generate explanatory text. The input dataset includes player location information, score information, and enemy movements. The generating AI model analyzes this data and automatically generates grammatically correct and contextually appropriate explanations. The output provides explanatory text tailored to the user's play style and knowledge level.

[0686] Step 5:

[0687] The server sends the generated explanatory text to the user's terminal. It receives the generated explanatory text as input and sends it to the terminal using the appropriate communication protocol. The explanatory text arrives at the terminal as output.

[0688] Step 6:

[0689] The user's device converts the received explanatory text into audio and outputs it through the speaker. It receives explanatory text sent from the server as input and converts it into audio data using a speech synthesis engine. The audio is played in harmony with the game's background sounds. As output, the user can listen to the audio commentary in real time.

[0690] (Application Example 1)

[0691] 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".

[0692] There is a need to enhance immersion for both viewers and players by providing personalized audio commentary in real time alongside a visual game experience. However, conventional systems suffer from problems such as delays in generating commentary language and multi-media distribution, which detract from the user experience. In particular, there is a challenge in providing personalized commentary that is tailored to the user's operating style and knowledge level.

[0693] 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.

[0694] In this invention, the server includes means for acquiring visual data and user input data, means for processing and analyzing the acquired data, and means including a generative model for generating commentary language expressions based on the analyzed data. This makes it possible to generate personalized commentary in real time according to the user's play style and provide it with minimal latency.

[0695] "Visual data" refers to all visual information obtained from the game, including video footage of the user's gameplay.

[0696] "User input data" refers to data that includes control information and actions provided by the user during gameplay.

[0697] "Means of acquisition" refers to the processes and mechanisms for collecting visual data and user input data.

[0698] "Means of processing and analysis" refers to methods for converting acquired data into a usable format and extracting necessary information.

[0699] "Live commentary" refers to text-based descriptions of the game's progress that are later converted into audio.

[0700] A "generative model" is a machine learning algorithm that automatically creates commentary language expressions based on user gameplay data.

[0701] "Means of outputting as audio" refers to the technology and devices that convert the generated commentary language expression into an audio format.

[0702] "Methods for integrating and distributing across multiple media" refers to the process of delivering generated audio and video content to viewers as a single piece of content.

[0703] The system implementing this invention consists mainly of a user terminal, a central server, and a viewer device. When a user plays a game, the user terminal acquires visual data and user input data in real time. This data is transmitted to the server while reducing communication traffic using compression technology.

[0704] The server processes received visual and user input data, performing analysis while removing noise. This involves applying advanced data processing algorithms and machine learning techniques to extract the user's gameplay characteristics. The server utilizes a cloud computing platform to enable rapid data processing.

[0705] Based on the analyzed data, a generative AI model generates commentary language expressions. This generation process takes into account the user's play style and knowledge level to provide personalized commentary. The generated commentary language expressions are converted into audio data using a speech synthesis engine. The Google Cloud Text-to-Speech API is used for speech synthesis, generating natural and immersive audio.

[0706] The converted audio data is sent back to the user's device, integrated with the game's visual data as a multimedia product, and then delivered to the viewer's device. This process allows viewers to experience gameplay with live commentary in real time.

[0707] As a concrete example, if a user is playing a soccer game, real-time commentary such as "It's a one-on-one with the goalkeeper! He's scored a magnificent goal!" will be generated. In this case, an example of a prompt to the generating AI model would be, "The user is playing a soccer game, and it's a shooting scene. Please include the player's name and the result of the shot in the commentary."

[0708] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0709] Step 1:

[0710] The user terminal acquires visual data and user input data during gameplay. It takes in real-time visual information output from the game and user operation data as input, and preprocesses this data to compress it and reduce communication traffic. The output is compressed composite data.

[0711] Step 2:

[0712] The user terminal sends compressed visual data and user input data to the server. It receives compressed data as input, and the output is the proper transmission of data. The server stores the received data and prepares it for the next analysis.

[0713] Step 3:

[0714] The server processes and analyzes the received visual data and user input data. The input is data stored on the server, which undergoes data analysis to remove noise and structure it. The output is clean data that has been noise-free and analyzed.

[0715] Step 4:

[0716] The server generates commentary language expressions using a generative AI model based on the analyzed data. The input is clean data, which the AI ​​model interprets to generate appropriate text for commentary. The output is the generated commentary language expression.

[0717] Step 5:

[0718] The server sends the generated commentary language expression to the user terminal. The input is the commentary text generated on the server, and the output is the completion of transmission to the user terminal.

[0719] Step 6:

[0720] The user terminal converts the received commentary text into speech data using a speech synthesis engine. The input is commentary text, and the synthesis process generates natural-sounding speech data. The output is speech data.

[0721] Step 7:

[0722] The user terminal integrates audio and visual data and delivers it to the viewer's device. Input consists of audio data and game visual data, which are combined as multimedia content. Output is real-time game footage with commentary.

[0723] 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.

[0724] This invention is implemented as a system incorporating emotion recognition technology to highly personalize the user experience and enhance the sense of realism in a gaming environment. This system detects the user's emotional state and dynamically adjusts the content of the commentary based on that information.

[0725] System Configuration

[0726] This system primarily consists of the user's terminal, a central server, an emotion engine, and an audio output device. The emotion engine recognizes the user's emotions in real time and optimizes the user experience based on this. The server plays a central role in processing the received data and generating commentary that aligns with the emotion recognition.

[0727] The role of the emotional engine

[0728] The emotion engine infers the user's emotional state based on behavioral patterns derived from the speed and precision of user actions and interactions, as well as data obtained from biosensors (e.g., heart rate monitors and skin electrical activity sensors) as needed. For example, if a user suddenly speeds up their movements in a game, this behavior can be associated with stress or excitement.

[0729] Generating and adjusting commentary text

[0730] The server combines game data sent from the terminal with emotion data provided by the emotion engine and uses an AI generation model to generate commentary appropriate to the situation. This commentary is adjusted in real time according to the user's emotional state. For example, when the user is relaxed, the comments are delivered in a calm tone, and when the user is stressed, the comments are concise and provide only the necessary information.

[0731] Audio output

[0732] The terminal uses a speech synthesis engine to convert live commentary sent from the server into speech, and provides it to the user in real time, synchronized with the gameplay video. This allows for appropriate interventions tailored to the user's emotional state, improving immersion in the game.

[0733] Specific example

[0734] For example, imagine a scenario where a user is concentrating intensely on a challenging task. If the emotion engine detects the user's tension, the device will offer advice such as, "Calm down and use your remaining time wisely." This allows the user to receive emotionally-driven support while remaining deeply immersed in the game. By combining emotion recognition with AI-generated responses in this way, it is possible to provide an unprecedentedly personalized gaming experience.

[0735] The following describes the processing flow.

[0736] Step 1:

[0737] The device captures video data and user input data at the start of the game. Simultaneously, an emotion engine installed in the device receives data from sensors that capture the user's biometric data. This data is transmitted to the server in real time.

[0738] Step 2:

[0739] The server decodes the received video and operation data and performs the necessary formatting. This process removes noise while extracting important in-game information such as the player's position, score, and elapsed time. Simultaneously, emotional data provided by the emotion engine is also analyzed.

[0740] Step 3:

[0741] The server provides formatted game data and emotion data to an AI generation model, which then generates commentary tailored to specific game situations. At this stage, comments are added or emphasized based on the user's emotional state.

[0742] Step 4:

[0743] The server sends the generated commentary text to the terminal and prepares it for audio conversion. During transmission, the optimal protocol is used to minimize communication delays.

[0744] Step 5:

[0745] The terminal receives the commentary text sent from the server and converts it into speech using a speech synthesis engine. At this time, adjustments are made to synchronize the audio with the game video.

[0746] Step 6:

[0747] Users continue playing the game while listening to audio commentary output from their device. Because the commentary is based on emotion recognition, users can receive appropriate support in response to changes in their emotions during gameplay.

[0748] Step 7:

[0749] The server will collect user gameplay experience and emotional feedback, which will be used to optimize future commentary generation algorithms. This data will also be used to improve the user experience in the long term.

[0750] (Example 2)

[0751] 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".

[0752] There is a need for highly personalized gaming experiences and adaptive feedback that responds to user emotions. However, conventional technologies have struggled to detect user emotional states in real time and dynamically adjust the experience based on them. To solve this problem, optimal commentary generation and audio output that respond to changes in user emotions are necessary.

[0753] 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.

[0754] In this invention, the server includes means for detecting the user's emotional state, means for analyzing the data to dynamically adjust the user's experience based on the detected emotional state, and means for generating commentary text using a generative AI model based on the analyzed data. This enables the provision of adaptive feedback that responds to the user's emotions and a highly personalized gaming experience.

[0755] "User emotional state" refers to the emotional responses and psychological states that a user exhibits in specific situations, including stress, excitement, and relaxation.

[0756] "Dynamic adjustment" refers to a system changing its response in real time based on input information, adapting the user experience as needed.

[0757] A "generative AI model" refers to a technology or method that uses artificial intelligence to generate new content, such as commentary, from specific data.

[0758] "Commentary text" refers to explanatory text that describes the situation in the game in real time and is provided to the user as audio or text.

[0759] "Outputting as audio" refers to providing the generated text data to the user in audio format through speech synthesis technology.

[0760] This invention implements a system for providing a highly personalized gaming experience that responds to the user's emotions. The system comprises a user terminal, a central server, an emotion engine, and an audio output device.

[0761] The user's device collects operation information and biosensor data during gameplay. Specifically, it monitors heart rate and skin electrical activity using a variety of sensors. This makes it possible to detect the user's emotional state in real time.

[0762] The server receives data sent from the terminal and analyzes it in conjunction with the emotion engine. The emotion engine infers the emotional state based on the user's operation patterns and biometric information. This analysis result is sent to a generative AI model, which then generates appropriate commentary based on its output.

[0763] The generative AI model runs on a server and receives specific prompt messages as input to generate appropriate commentary. For example, a prompt message might be "Generate commentary appropriate for when the user's emotional state is excited."

[0764] The device uses a speech synthesis engine to convert live commentary received from the server into speech and provides it to the user in real time within the game. This allows the user to receive appropriate support and feedback based on their emotional state through voice. This method enables the user to become more deeply immersed in the game.

[0765] For example, if a user is playing a stage in a game where the difficulty gradually increases, the emotion engine might detect this tension. In that case, the device would provide specific voice advice such as, "Concentrate and calmly proceed to the next step." This enables a real-time experience that is tailored to the user's actions and emotions.

[0766] The above describes specific embodiments for carrying out this invention.

[0767] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0768] Step 1:

[0769] During gameplay, the user collects operational information and biosensor data through their device. Inputs include the user's controller input, heart rate, and skin electrical activity. The device transmits this data to the emotion engine in real time. The output generates raw data that reflects the user's actions.

[0770] Step 2:

[0771] The device sends the received data to the emotion engine, which analyzes the emotional state. The input to the emotion engine is the operation information and biosensor data collected in step 1. By analyzing the data, the system infers the user's emotional state. For example, a sudden change in operation speed may indicate stress or excitement. This result is output as an emotional state profile.

[0772] Step 3:

[0773] The server runs a generative AI model based on the emotional state profile received from the emotion engine. The input includes the current game state and the user's emotional state profile. The server sends prompts to the generative AI model to generate appropriate commentary. An example prompt might be, "Generate commentary suitable for when the user's emotional state is relaxed." The output of this step is the generated commentary.

[0774] Step 4:

[0775] The terminal uses a speech synthesis engine to convert the generated commentary text sent from the server into speech. The input is the commentary text generated in step 3. The speech synthesis engine converts this text into speech that is easy for the user to understand. The output is real-time audio feedback synchronized with the game video. Through this audio, the user can receive adaptive feedback that matches their emotional state.

[0776] Step 5:

[0777] Users can continue playing the game based on voice feedback. Adaptive voice support allows users to become more immersed in the game and concentrate more effectively. This step is crucial as the ultimate outcome of the entire system. In summary, a highly personalized experience is provided to the user.

[0778] (Application Example 2)

[0779] 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".

[0780] Traditional virtual stores have struggled to provide real-time purchasing support that reflects the emotional state of users, resulting in decreased purchasing efficiency. In particular, they have been unable to provide appropriate information when users show interest in a specific product or when they are hesitant about making a purchase, leading to decreased immersion and satisfaction.

[0781] 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.

[0782] In this invention, the server includes a device for acquiring video data and user input data, a device for detecting the user's emotional state and dynamically adjusting text based on that state, and a device for processing and evaluating the acquired data. This makes it possible to provide a personalized purchase assistant that responds to the user's emotions in real time.

[0783] "Visual data" refers to information obtained from visual media and is a visual representation stored in digital or analog format.

[0784] "User input data" refers to information provided by users through their devices, and is digital data that reflects their actions and choices.

[0785] A "device" is a set of interconnected hardware or software elements designed to perform a specific function.

[0786] "Processing" refers to the process of transforming, organizing, or modifying acquired data to suit a specific purpose.

[0787] "Evaluation" is the process of analyzing data and judging its content and value.

[0788] A "generative engine" is software or an algorithm used to create content based on data.

[0789] Sound is a vibration that travels through the air or other media, and is perceived through the sense of hearing.

[0790] An "information processing system" is a computer-based system designed to acquire, analyze, transform, and output data.

[0791] "Emotional state" refers to characteristics and data that indicate a user's mental and emotional state.

[0792] "Dynamic adjustment" refers to a process of flexibly changing or optimizing according to the situation and the user's condition.

[0793] "Personalization" refers to the act of customizing or personalizing something to suit the specific characteristics and needs of a particular user.

[0794] "Distribution" is the process of supplying content or data to multiple receiving devices.

[0795] A "transmission method" refers to the technical techniques or protocols used to transmit data from one point to another.

[0796] An "encoding method" is an algorithm or technique designed to compress or transform data to make it easier to transmit.

[0797] To realize this invention, it is necessary to build a system in which a virtual store provides a personalized assistant that responds to the user's emotions. The central elements of this system are an emotion recognition device, a data evaluation device, a generation engine, and a voice output device.

[0798] The server acquires video data and user input data from the user's smart glasses. This involves using hardware devices such as cameras and sensors. The acquired data is processed and analyzed by a data evaluation device to identify the user's emotional state. This evaluation utilizes open-source emotion recognition libraries (e.g., OpenCV and TensorFlow).

[0799] Once the user's emotional state is identified, the generation engine uses AI to generate information in real time. Specifically, a generation AI model (e.g., GPT-3) is used to generate appropriate sentences based on the prompt. These sentences are then spoken by a speech output device and provided to the user. Throughout this entire process, the user receives a personalized voice assistant tailored to their emotions, allowing them to receive appropriate support in selecting and purchasing products.

[0800] For example, if a user stands in front of a specific product shelf for an extended period, the emotion recognition device detects that the user is "interested." Based on this, the generation engine generates a message such as, "This product is currently on sale and offers excellent performance. Please consider it," and the voice output device delivers this message to the user as sound. Another example of a prompt for the generation AI model would be a question like, "Explain the features of the latest PC in an easy-to-understand way to an interested buyer."

[0801] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0802] Step 1:

[0803] The server acquires video data and user input data from the user's smart glasses. The input data includes the user's facial expressions and gestures indicating interest. This data is acquired in real time by cameras and other sensors. The server records this raw data.

[0804] Step 2:

[0805] The server analyzes the user's emotional state using the acquired video data. Open-source emotion recognition libraries (such as OpenCV or TensorFlow) are used for this purpose. These libraries analyze facial expressions and body movements to estimate emotions such as interest, confusion, relaxation, and stress. The output is the analyzed emotion data.

[0806] Step 3:

[0807] The server calls a generative AI model based on the analyzed sentiment data to create an appropriate prompt. For example, a prompt such as "Explain the features of the latest PC clearly to an interested buyer" might be generated. In this case, the input is sentiment data, and the output is the prompt.

[0808] Step 4:

[0809] The server uses a generative AI model (e.g., GPT-3) to generate natural language assistant messages based on the prompt. This AI, having learned from a large amount of text data, has the capability to produce clear and persuasive sentences. The input is the prompt, and the output is the generated assistant message.

[0810] Step 5:

[0811] The terminal sends the generated assistant message to the voice output device and provides it to the user as synthesized speech. The text is converted into speech using speech synthesis software, which the user can then hear. Here, the input is the generated text message, and the output is the voice message.

[0812] Step 6:

[0813] Users listen to voice messages and use that information to select products and make purchasing decisions. By modifying their actions based on the information they receive, the purchasing experience is improved. The user's responses are then re-entered into the system as data for the next processing cycle.

[0814] 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.

[0815] 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.

[0816] In the above embodiment, an example was given in which the 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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."

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] The following is further disclosed regarding the embodiments described above.

[0836] (Claim 1)

[0837] A means for capturing video data and user input data in a game,

[0838] A means for formatting and analyzing the captured data,

[0839] Means including a generation model that generates commentary text based on the analyzed data,

[0840] A means for outputting the generated commentary text as audio,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, characterized in that it includes means for personalizing the commentary text based on the user's play style and knowledge level.

[0844] (Claim 3)

[0845] The system according to claim 1, characterized in that it includes means for selecting a data transfer protocol and encoding method in order to minimize latency during streaming.

[0846] "Example 1"

[0847] (Claim 1)

[0848] Means for acquiring visual information and user operation information in digital entertainment,

[0849] Means for compressing the acquired information and transmitting it to a control device,

[0850] A means for formatting the acquired information and extracting important information,

[0851] Means including an artificial intelligence model that generates explanatory text based on the extracted information,

[0852] A means for converting the generated explanatory text into audio and outputting it,

[0853] A system that includes this.

[0854] (Claim 2)

[0855] The system according to claim 1, characterized in that it includes means for personalizing the explanatory text based on the user's operating tendencies and knowledge level.

[0856] (Claim 3)

[0857] The system according to claim 1, characterized in that it includes means for selecting a protocol and encoding scheme for data communication in order to minimize delays during information transmission.

[0858] "Application Example 1"

[0859] (Claim 1)

[0860] A means of acquiring visual data and user input data in a game,

[0861] Means for processing and analyzing the acquired data,

[0862] Means including a generative model that generates commentary language expressions based on the analyzed data,

[0863] A means for outputting the generated commentary language expression as audio,

[0864] A means for integrating and distributing the aforementioned live commentary language expression across multiple media,

[0865] A system that includes this.

[0866] (Claim 2)

[0867] The system according to claim 1, comprising means for personalizing the commentary language expression based on the user's operating style and knowledge level.

[0868] (Claim 3)

[0869] The system according to claim 1, comprising means for selecting a data transfer agreement and encoding method to minimize delay during media distribution.

[0870] "Example 2 of combining an emotion engine"

[0871] (Claim 1)

[0872] A means of detecting the user's emotional state,

[0873] A means for analyzing the data in order to dynamically adjust the user experience based on the detected emotional state,

[0874] A means for generating commentary text using a generative AI model based on the analyzed data,

[0875] A means for outputting the generated commentary text as audio,

[0876] A system that includes this.

[0877] (Claim 2)

[0878] The system according to claim 1, characterized in that it includes means for adaptively personalizing the commentary text based on the user's emotional state.

[0879] (Claim 3)

[0880] The system according to claim 1, characterized in that it includes means for providing real-time feedback in accordance with the user's emotional state.

[0881] "Application example 2 when combining with an emotional engine"

[0882] (Claim 1)

[0883] A device for acquiring video data and user input data,

[0884] A device for processing and evaluating the acquired data,

[0885] A device including a generation engine that generates text based on the evaluated data,

[0886] A device that outputs the generated text as sound,

[0887] A device that detects the emotional state of the user and dynamically adjusts the text based on that state,

[0888] An information processing system that includes this.

[0889] (Claim 2)

[0890] The information processing system according to claim 1, characterized in that it includes a device for individualizing the text based on the user's behavioral characteristics and knowledge level.

[0891] (Claim 3)

[0892] The information processing system according to claim 1, characterized in that it includes a device for selecting a data transmission method and encoding method in order to minimize delay during distribution. [Explanation of Symbols]

[0893] 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. A means for capturing video data and user input data in a game, A means for formatting and analyzing the captured data, Means including a generation model that generates commentary text based on the analyzed data, A means for outputting the generated commentary text as audio, A system that includes this.

2. The system according to claim 1, characterized in that it includes means for personalizing the commentary text based on the user's play style and knowledge level.

3. The system according to claim 1, characterized in that it includes means for selecting a data transfer protocol and encoding method in order to minimize latency during streaming.

Images