system

Abstract

We provide the system. [Solution] A data acquisition method for collecting biometric information from users, A data analysis means that analyzes the aforementioned biometric information to quantify the user's emotional state, A plan generation means that generates an interaction plan based on the aforementioned emotional state, Interaction execution means for executing the aforementioned interaction plan, 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 persona chatbot control method 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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional live performances, it is difficult to perform performances that immediately respond to the emotional states and reactions of the audience, and there is a problem that it is difficult to provide an individual experience that suits the emotions of each audience with a fixed performance plan. For this reason, in many live events, it has been difficult for the audience to have an experience of participating and feeling a more emotional connection.

Means for Solving the Problems

[0005] This invention collects biometric information from the audience, analyzes it to quantify their emotional state, and generates a performance plan based on the audience's reactions in real time. By providing a means for executing that performance plan, the system can provide a live experience tailored to the individual emotional state of each audience member. This allows the audience to have a deeper emotional participation experience in live performances.

[0006] "Information gathering means" refers to a device or method for acquiring biometric information from the audience.

[0007] "Analysis means" refers to a process or device that uses collected biometric information to evaluate and quantify the emotional state of the audience.

[0008] "Plan generation means" refers to a device or method for designing or determining an optimal performance plan based on the results of the analyzed emotional state.

[0009] "Performance execution means" refers to the process or apparatus for concretely executing the generated performance plan and controlling lighting, sound, and video effects.

[0010] "Biometric information" refers to data that indicates the physical condition of the audience, including information such as heart rate and facial expressions.

[0011] "Emotional state" refers to a quantitative representation of the emotions an audience feels during a live performance, expressed as a level of excitement, emotional impact, or other similar indicators.

[0012] A "production plan" is a plan or design that includes the combination of lighting, sound, and visual effects to be implemented during a live performance. [Brief explanation of the drawing]

[0013] [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]It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] 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 a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] 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.

[0018] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, a numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention is implemented as a system that collects biometric information from the audience in real time, analyzes it to generate a performance plan based on the audience's emotional state, and then executes the live performance. The following describes a specific embodiment of the system and its processes.

[0035] The system consists mainly of terminals for collecting biometric information from the audience, servers for receiving and processing the information, and performance equipment for executing the show.

[0036] First, the terminal periodically acquires biometric information, specifically heart rate and facial expression data, using wearable devices distributed to the audience and cameras installed within the venue. This information is then transmitted from the terminal to a server.

[0037] Next, the server analyzes the received biometric information using a generating AI model. As a result of the analysis, the audience's emotional state, i.e., their level of excitement and emotional response, is quantified. This analysis result is used as basic data to control lighting, sound, and visual effects.

[0038] Based on the audience's emotional state, as determined by the analysis, the server dynamically generates a performance plan. For example, if the audience's excitement level is high, a performance plan with brighter lighting and more dramatic sound is designed. On the other hand, if the audience's emotional response is high, a gentler, more emotionally resonant performance is selected.

[0039] The performance plan generated in this way is sent from the server to the performance equipment and actually executed at the live venue. The performance equipment controls the lighting system, sound system, and video display device, and delivers performances in real time that respond to the audience's emotions.

[0040] As a concrete example, suppose that during the climax of a song, a device receives excited heart rate data from many audience members. The server analyzes this data and sets up a performance plan, including flashing lights and loud sound effects, which are then executed by the performance equipment. As a result, the audience can feel the heightened emotions through both sight and sound, enhancing the live experience.

[0041] In this embodiment, live performances can be instantly optimized in response to the emotional reactions of individual audience members, enabling the provision of a deeper, more personal experience.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The device collects biometric information from the audience. Specifically, it uses wearable devices distributed to the audience and cameras within the venue to acquire heart rate and facial expression data. This process is carried out periodically, enabling real-time data collection.

[0045] Step 2:

[0046] The device transmits the collected biometric information to a server. This information serves as foundational data for evaluating the emotional state of the audience.

[0047] Step 3:

[0048] The server receives biometric information transmitted from the terminal. The received data is processed using a generative AI model for analysis.

[0049] Step 4:

[0050] The server analyzes the audience's emotional state using a generative AI model. Specifically, it quantifies levels of excitement and emotion from heart rate and facial expression data. This quantified data becomes the information necessary for subsequent processing.

[0051] Step 5:

[0052] The server generates a performance plan based on the analysis results. The combination of lighting, sound, and visual effects is determined according to the audience's emotional state as indicated by the analysis. This plan allows for flashy performances during states of excitement and more subdued performances during states of emotion.

[0053] Step 6:

[0054] The server transmits the generated performance plan to the performance equipment. This enables real-time control of the entire venue's performance.

[0055] Step 7:

[0056] The performance system executes the live show based on the performance plan received from the server. Specifically, it controls the color and brightness of the lighting, the volume of the sound, and the content of the video, providing a dynamic live experience that responds to the emotional reactions of the audience.

[0057] (Example 1)

[0058] 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."

[0059] In traditional live performances, the staging is often based on pre-set scripts, making it difficult to respond flexibly to the audience's real-time emotional changes. This has limited the ability to fully utilize the emotional responses of each individual audience member. Therefore, there is a need to develop a system that dynamically optimizes the staging based on the audience's emotional state, providing a more personalized and immersive experience.

[0060] 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.

[0061] In this invention, the server includes acquisition means for acquiring biometric information from the audience, analysis means for quantifying the audience's emotional state using an artificial intelligence model generated with the biometric information, plan generation means for dynamically generating a performance plan based on the emotional state, and execution device control means for immediately executing the performance plan. This makes it possible to optimize performance direction in accordance with the audience's real-time emotional state.

[0062] "Means of acquisition" refers to functions or devices for collecting biometric information from audience members.

[0063] "Analysis means" refers to functions and methods that quantify the emotional state of the audience using an artificial intelligence model generated based on collected biometric information.

[0064] A "plan generation means" is a function or system for generating dynamic performance plans that are appropriate to the situation at the time, based on the analyzed emotional state.

[0065] The "execution device control means" refers to the function of controlling the device in order to execute the generated performance plan at the live venue.

[0066] "Biometric information" refers to data about the audience's physical condition, including heart rate and facial expression.

[0067] A "generative artificial intelligence model" is a model that uses artificial intelligence technology to analyze biometric information and quantify the emotional state of the audience.

[0068] A "performance plan" is a plan that includes specific lighting, sound, and video effects to be implemented during a performance, designed based on the analyzed emotional state.

[0069] This invention is specifically implemented as a system that optimizes performances in real time based on the audience's biometric information. The system consists of a terminal that collects biometric information from the audience, a server that processes the received data, and a device that executes the performance.

[0070] The terminal acquires biometric information using wearable devices distributed to audience members and cameras installed at the venue. Specifically, the wearable devices measure audience members' heart rates using heart rate sensors, and the cameras collect facial expression data using facial recognition technology. This data is transmitted from the terminal to a server.

[0071] The server uses a generative AI model to analyze the received biometric data. The AI ​​model analyzes heart rate and changes in facial expression, outputting the audience's emotional state as specific numerical values. This analysis result is used as basic data for generating the performance plan.

[0072] Based on the analysis results, the server dynamically formulates a performance plan. For example, if it determines that the audience's excitement level is high, the server generates a plan to brighten the lighting and enhance the sound. On the other hand, if it is confirmed that the audience is moved, a quieter, more emotionally evocative performance is selected. This performance plan is sent from the server to the performance equipment and executed immediately at the venue.

[0073] Users can use prompts to give instructions to the generating AI model. An example of a prompt would be: "We have received audience heart rate and facial expression data. Analyze their current level of excitement and emotion, and generate the optimal performance plan based on that."

[0074] This allows the system to provide an optimal performance experience that responds to the audience's emotions in real time. Unlike traditional, fixed staging, it enables a more personalized and immersive experience by immediately responding to audience reactions.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The terminal acquires biometric information from wearable devices worn by audience members and cameras installed within the venue. Specifically, the wearable devices measure heart rate, and the cameras analyze facial expression data using facial recognition software. The input data consists of heart rate and facial expression information, and the output data is a combination of this information.

[0078] Step 2:

[0079] The device transmits acquired biometric information to the server. The data is transferred in real time at regular intervals. Protocols such as HTTP and WebSocket are used to securely transmit the data and enable rapid reception on the server side. The input data consists of heart rate and facial expression information, and the output is the transmission of data to the server.

[0080] Step 3:

[0081] The server inputs the received biometric information into a generating AI model to quantify the audience's emotional state. Specifically, it analyzes heart rate fluctuations and facial features to output levels of excitement and emotion. The AI ​​model analyzes this data to generate scoring data that represents the audience's emotional state. The input data consists of heart rate and facial expression information, and the output is numerical data representing the emotional state.

[0082] Step 4:

[0083] The server dynamically generates a performance plan based on quantified emotional states. This plan uses an algorithm to translate the analysis results into specific performance settings for lighting and sound. For example, if the level of excitement is high, the lighting colors will be made more vibrant and the sound will be amplified. The input data is quantified data of emotional states, and the output is a specific performance plan.

[0084] Step 5:

[0085] The server transmits the generated performance plan to the performance equipment, which then executes it immediately. The performance equipment uses the DMX protocol to control each component of lighting and sound. This allows for a performance that responds to the audience's real-time emotional state. The input data is the specific performance plan, and the output is the actual performance effect.

[0086] (Application Example 1)

[0087] 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."

[0088] Traditional home entertainment devices have struggled to provide flexible interactions that respond to the individual emotional states of participants, failing to maximize the emotional experience at parties and events. There is a growing need for automated interactions that reflect participants' emotional responses in real time.

[0089] 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.

[0090] In this invention, the server includes data acquisition means for collecting biometric information from the user, data analysis means for analyzing the biometric information and quantifying the user's emotional state, and plan generation means for generating an interaction plan based on the emotional state. This makes it possible to automate interactions that respond immediately to the emotions of participants in events within the home.

[0091] "User" refers to a participant in a home entertainment environment who possesses heart rate and facial expression data.

[0092] "Biometric information" refers to physical data such as heart rate and facial expressions obtained from the user.

[0093] "Data acquisition means" refers to a system or device for efficiently collecting biometric information from users.

[0094] "Data analysis means" refers to methods and devices for quantifying a user's emotional state using acquired biometric information.

[0095] "Emotional state" refers to a user's psychological response expressed numerically or categorically, and is the subject of analysis by the system.

[0096] "Plan generation means" refers to methods or devices that create interaction plans, such as lighting, sound, and video, based on analyzed emotional states.

[0097] "Interaction execution means" refers to devices or methods for executing a generated interaction plan in a real-world environment.

[0098] The system implementing this invention is designed for home entertainment and enables interaction that responds immediately to the user's emotions. The system mainly consists of data acquisition means, data analysis means, plan generation means, and interaction execution means.

[0099] Users will participate in events and parties wearing wearable devices that can measure their heart rate and cameras that recognize their facial expressions. These devices will collect the user's biometric information in real time and transmit it to a server via data acquisition methods.

[0100] The server uses Python and software such as TENSORFLOW® to analyze the acquired biometric information. Specifically, it utilizes a generative AI model to analyze heart rate and facial expression data, quantifying the user's emotional state. For example, if the heart rate is high and the user is smiling continuously, it is judged to be in an excited state.

[0101] The plan generation mechanism generates an interaction plan based on the analysis results. For example, if the user is relaxed, it creates a plan that includes setting calming music and gentle lighting effects.

[0102] The interaction execution mechanism controls lighting control devices (e.g., a typical smart lighting system), sound devices, and video projectors to execute the generated plan. This allows the user to experience emotionally resonant interactions.

[0103] As a concrete example, in a family home party, the living room lighting could be automatically adjusted and cheerful music selected based on the heart rate and facial expressions of the children sitting next to each other. This would create the perfect atmosphere for everyone gathered.

[0104] An example of a prompt statement is shown below:

[0105] "Analyze the biometric data from the participant list and quantify their emotional state. The heart rate and facial expression data are as follows: Participant 1: Heart rate 85, Facial expression: Smiling; Participant 2: Heart rate 78, Facial expression: Surprised. Based on the results, suggest the optimal music and lighting settings."

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The terminal collects heart rate and facial expression data from wearable devices worn by the user and installed cameras. These devices acquire biometric information in real time, and the terminal compiles this as initial data. The input is the user's biometric information, and the output is biometric information packets for transmission by the terminal.

[0109] Step 2:

[0110] The terminal transmits the collected biometric information to the server. The input is the biometric information packet collected by the terminal, and the output is the biometric information transmitted to the server. The terminal uses its data communication function to transfer data to the server via a wireless or wired network.

[0111] Step 3:

[0112] The server analyzes the received biometric information using data analysis tools. Specifically, it uses a generative AI model to perform data calculations that infer the user's emotional state from heart rate and facial expression data. The input is the biometric information sent to the server, and the output is numerical data of the analyzed emotional state.

[0113] Step 4:

[0114] The server generates an interaction plan based on the analysis results using a plan generation mechanism. The server constructs a plan, including lighting and music settings, based on the emotional states obtained from the analysis. The input is numerical data of the emotional states, and the output is the generated interaction plan.

[0115] Step 5:

[0116] The server sends the interaction plan to the interaction execution device. The input is the generated interaction plan, and the output is the control command sent to the execution device. The server transmits commands to control lighting and sound equipment over the network.

[0117] Step 6:

[0118] The interaction execution mechanism controls lighting, sound, and video equipment based on the received interaction plan. Specifically, it performs actions such as adjusting the brightness and color of lighting, and changing music selection and volume. The input is a control command, and the output is the application of the interaction in the actual environment.

[0119] 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.

[0120] This invention relates to a system that collects biometric information from audience members, analyzes it to recognize their emotional state in real time, and generates and executes a performance plan based on the results. Furthermore, it incorporates an emotion engine for detailed recognition of user emotions.

[0121] The system consists of the following main components: information gathering means for collecting audience biometric information, analysis means for analyzing the collected information and quantifying emotional states, an emotion engine, a plan generation means for generating performance plans, and a performance execution means for executing the generated plans.

[0122] First, the terminal collects heart rate and facial expression data through wearable devices distributed to the audience and cameras installed throughout the venue. This data reflects the audience's physical and mental state in real time. The terminal immediately transmits this data to a server.

[0123] Next, the server uses the received biometric information to activate the emotion engine. The emotion engine analyzes the information more deeply to recognize the audience's emotions. Specifically, it analyzes a combination of heart rate fluctuations and changes in facial expression to determine emotions such as excitement, emotion, joy, and surprise.

[0124] Based on the emotional state of the audience obtained, the plan generation system generates an appropriate performance plan on the spot. Through detailed emotion recognition by the emotion engine, the plan becomes more tailored to the individual emotional state of each audience member. For example, if a user experiences surprise, the server instantly changes the lighting and sound to provide a performance that further heightens the audience's emotions.

[0125] The generated performance plan is executed through the performance execution system, adjusting the lighting, sound, and video display devices throughout the venue to create a live experience that resonates with the audience's emotions.

[0126] As a concrete example, suppose a scene occurs during a live performance where a particular song is played and many audience members simultaneously feel excitement and joy. In this case, the emotion engine recognizes these emotions, and the server, based on that data, formulates a performance plan that includes changes in color and amplification effects through sound, which is then immediately executed by the execution device. This performance allows the audience to feel a greater sense of unity and makes the live experience even more fulfilling.

[0127] Thus, the present invention provides more moving and personalized live entertainment by recognizing the audience's emotions in detail in real time and implementing performances that respond accordingly.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The terminals collect biometric information using sensors and cameras installed at the venue, as well as wearable devices distributed to the audience. Specifically, they acquire heart rate and facial expression data, and immediately save this information as digital data.

[0131] Step 2:

[0132] The device transmits collected biometric information to the server in real time. This information includes the individual heart rate and facial expression changes of each audience member.

[0133] Step 3:

[0134] The server activates an emotion engine based on the received biometric information. The emotion engine analyzes heart rate fluctuations and facial features to identify the emotional state the audience is exhibiting. This analysis quantifies emotions such as excitement, joy, emotion, and surprise.

[0135] Step 4:

[0136] The server generates a performance plan based on the audience's emotional state analyzed by the emotion engine. The plan generation mechanism creates an optimal performance plan by combining lighting, sound, and visual effects according to the audience's specific emotions.

[0137] Step 5:

[0138] The server transmits the generated performance plan to the performance execution system. The performance execution system controls the venue's lighting system, sound system, and video display device to adjust the performance to further enhance the audience's emotions.

[0139] Step 6:

[0140] Users (audience members) experience the performances implemented by the server. For example, if an audience member expresses surprise, the lighting changes color and the sound intensifies, amplifying that emotion through both sight and sound, resulting in an even greater emotional impact.

[0141] Step 7:

[0142] The server continuously receives new biometric information during the live performance, dynamically updating the performance plan while re-evaluating the emotional state using the emotion engine. This enables flexible live performances that respond to the audience's real-time emotional changes.

[0143] (Example 2)

[0144] 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".

[0145] There is a growing demand for more personalized entertainment experiences by accurately recognizing audiences' emotional states in real time and providing performances that respond accordingly. However, existing technologies lack systems that can grasp and instantly reflect audience emotions in detail, making it difficult to provide timely performances that are in line with those emotions.

[0146] 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.

[0147] In this invention, the server includes means for collecting data from the audience, means for analyzing the data to quantify the audience's emotional state, and means for generating a performance plan using information processing technology based on the quantified emotional state. This makes it possible to recognize the audience's emotions in real time and immediately provide appropriate performances that correspond to those emotions.

[0148] "Audience" refers to people who watch an event or performance.

[0149] "Data" refers to a collection of information, including physiological variables and facial expression data, gathered from the audience.

[0150] "Means of collection" refers to devices or methods for obtaining data from the audience.

[0151] "Means of analysis" refers to methods or devices for processing acquired data and quantifying the emotional state of the audience.

[0152] "Emotional state" refers to a numerical representation of the psychological or physiological response that an audience member exhibits to a particular situation or stimulus.

[0153] "Information processing technology" refers to the techniques and methods for processing and analyzing data.

[0154] A "performance plan" refers to a plan that combines visual, auditory, and visual effects based on the emotional state of the audience.

[0155] This invention is a system that realizes a more personalized entertainment experience by analyzing audience emotions in real time and providing performances that respond accordingly. This system collects biometric data from the audience, analyzes that data to determine their emotional state, and generates and executes an appropriate performance plan.

[0156] The terminal collects biometric data using wearable devices distributed to audience members and cameras installed at the venue. The wearable devices are equipped with heart rate sensors, and the cameras use facial recognition technology to capture changes in facial expressions. The terminal transmits this data to a server in real time.

[0157] The server uses collected biometric data to analyze the audience's emotions through an emotion engine. The emotion engine analyzes heart rate fluctuations and facial expression data, quantifying various emotions such as excitement, happiness, and surprise. Based on these quantified emotional states, the server uses information processing technology to generate a performance plan. The performance plan includes instructions for controlling the lighting and sound systems.

[0158] As a concrete example, if the system analyzes that many audience members are excited when a particular song is played during a live event, the server will generate and immediately execute a performance plan that increases the brightness of the lights and enhances the sound effects. In this way, immediate responses to the audience's emotions become possible.

[0159] Furthermore, by integrating with a generative AI model, it is possible to propose more detailed performance plans based on prompt messages. An example of a prompt message would be, "Based on the current audience emotion data, please suggest the next performance step." This system can accurately capture audience emotions and improve the live entertainment experience at the venue.

[0160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0161] Step 1:

[0162] The terminal collects biometric data from the audience. Specifically, wearable devices distributed to the audience acquire heart rate data using heart rate sensors, and cameras installed in the venue acquire facial expression data using facial recognition technology. The inputs include heart rate and image data. The terminal aggregates this data and prepares it for transmission to the server.

[0163] Step 2:

[0164] The device transmits collected heart rate and facial expression data to the server. Input includes real-time data acquired from the wearable device and camera. Output is encrypted data securely transferred to the server. This transmission prepares the data for calculation.

[0165] Step 3:

[0166] The server analyzes the received biometric data. The emotion engine uses heart rate fluctuation patterns and facial expression data to quantify the audience's emotional state, such as excitement or surprise. The input is biometric data transmitted from the terminal. The output is numerical data of the analyzed emotion. This analysis makes it possible to understand the emotional state of the audience.

[0167] Step 4:

[0168] The server generates a performance plan based on the analysis results. Using information processing technology, it constructs an optimal performance plan based on quantified emotional states. The input includes quantified emotional data. The output is a performance plan that aggregates instructions for lighting and sound systems.

[0169] Step 5:

[0170] The server sends instructions to the venue's control system to execute the generated performance plan. The venue's lighting and sound equipment operates according to the performance plan. The input is the performance plan, and the output is the performance with actual lighting and sound effects. This execution can provide the audience with a real-time, emotionally engaging experience.

[0171] (Application Example 2)

[0172] 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".

[0173] In today's world, online content distribution is increasing, and there is a growing demand for personalized experiences that resonate with viewers' emotions. However, traditional systems have struggled to recognize viewers' emotions in real time and dynamically adjust the presentation, making it difficult to provide a deeper online viewing experience.

[0174] 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.

[0175] In this invention, the server includes information gathering means for collecting biometric information from the audience, analysis means for analyzing the biometric information and quantifying the audience's emotional state, and plan generation means for generating a performance plan based on the emotional state. This enables dynamic adjustment of the streamed video based on the viewer's emotions.

[0176] "Information gathering means" refers to a device or function for acquiring biometric information from audience members or viewers.

[0177] "Analysis means" refers to a device or function that quantifies the emotional state of audience members or viewers based on biological information and performs a process of identifying emotions.

[0178] "Plan generation means" refers to a device or function for constructing an optimal performance plan based on the analyzed emotional state.

[0179] "Performance execution means" refers to a device or function for concretely executing the generated performance plan and providing lighting, sound, and video effects.

[0180] "Distribution adjustment means" refers to a device or function for dynamically adjusting the video and other content being distributed based on biometric information obtained from the viewer's mobile device.

[0181] The system for implementing the present invention mainly consists of a user's personal information terminal, a server, and various hardware and software that support their respective functions. The user's personal information terminal incorporates a wearable device capable of measuring heart rate and a camera capable of capturing facial expressions, and biometric information is collected using these devices.

[0182] The device transmits collected heart rate and facial expression data to the server in real time. An encrypted communication protocol is used for data security. The server uses an emotion analysis engine to process the received biometric information. This engine analyzes the audience's emotions and generates a quantified emotional state, for example, by using a generative AI model such as AWS®'s emotion recognition API.

[0183] Based on the analyzed emotional state, the server activates a production plan generation algorithm to construct the optimal production plan. This plan is output in a format that allows for real-time adjustment of lighting, sound, and the video being streamed. As a result, dynamic content that aligns with the viewer's emotions is provided, improving the quality of the viewing experience.

[0184] As a concrete example, in a live streaming event, if the server analyzes the audience's level of excitement, it can immediately apply a performance plan based on that data, such as increasing the music volume or enhancing the video's saturation. This allows viewers to have a more emotionally engaging experience.

[0185] An example of a prompt to provide to a generative AI model is: "Consider a concept for a smart app that analyzes viewer sentiment in real time during a live broadcast and adjusts the video and audio based on that analysis."

[0186] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0187] Step 1:

[0188] The user collects biometric information using a heart rate sensor and camera built into their mobile device. The user's heart rate data and facial expression data are entered. This data serves as foundational data to indicate the user's emotional state. The collected data is encrypted for data security and immediately transmitted to the server.

[0189] Step 2:

[0190] The server passes the received biometric information to the emotion analysis engine. Based on the input heart rate and facial expression data, a generative AI model is used to quantify the emotional state. This data processing uses algorithms that analyze heart rate fluctuations and facial expression patterns. The resulting output is a numerical value indicating a specific emotional state, such as excitement or emotion.

[0191] Step 3:

[0192] The server runs a performance plan generation algorithm based on the analysis. The input is numerical values ​​of emotional states obtained from the emotion analysis engine. Through data calculation, an optimal performance plan is generated, including adjustments to lighting, sound, and video. The performance plan includes specific actions to enhance a particular emotion.

[0193] Step 4:

[0194] The server sends commands to the distribution system to execute the generated production plan. The output commands adjust the lighting, sound, and visual effects of live streaming and recorded content in real time. This ensures that content is delivered to viewers that instantly responds to the user's emotions.

[0195] This entire process allows users to have a more deeply emotionally engaging viewing experience.

[0196] 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.

[0197] Data generation model 58 is a type of 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.

[0198] 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.

[0199] [Second Embodiment]

[0200] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0201] 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.

[0202] 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).

[0203] 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.

[0204] 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.

[0205] 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).

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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".

[0212] This invention is implemented as a system that collects biometric information from the audience in real time, analyzes it to generate a performance plan based on the audience's emotional state, and then executes the live performance. The following describes a specific embodiment of the system and its processes.

[0213] The system consists mainly of terminals for collecting biometric information from the audience, servers for receiving and processing the information, and performance equipment for executing the show.

[0214] First, the terminal periodically acquires biometric information, specifically heart rate and facial expression data, using wearable devices distributed to the audience and cameras installed within the venue. This information is then transmitted from the terminal to a server.

[0215] Next, the server analyzes the received biometric information using a generating AI model. As a result of the analysis, the audience's emotional state, i.e., their level of excitement and emotional response, is quantified. This analysis result is used as basic data to control lighting, sound, and visual effects.

[0216] Based on the audience's emotional state, as determined by the analysis, the server dynamically generates a performance plan. For example, if the audience's excitement level is high, a performance plan with brighter lighting and more dramatic sound is designed. On the other hand, if the audience's emotional response is high, a gentler, more emotionally resonant performance is selected.

[0217] The performance plan generated in this way is sent from the server to the performance equipment and actually executed at the live venue. The performance equipment controls the lighting system, sound system, and video display device, and delivers performances in real time that respond to the audience's emotions.

[0218] As a concrete example, suppose that during the climax of a song, a device receives excited heart rate data from many audience members. The server analyzes this data and sets up a performance plan, including flashing lights and loud sound effects, which are then executed by the performance equipment. As a result, the audience can feel the heightened emotions through both sight and sound, enhancing the live experience.

[0219] In this embodiment, live performances can be instantly optimized in response to the emotional reactions of individual audience members, enabling the provision of a deeper, more personal experience.

[0220] The following describes the processing flow.

[0221] Step 1:

[0222] The device collects biometric information from the audience. Specifically, it uses wearable devices distributed to the audience and cameras within the venue to acquire heart rate and facial expression data. This process is carried out periodically, enabling real-time data collection.

[0223] Step 2:

[0224] The device transmits the collected biometric information to a server. This information serves as foundational data for evaluating the emotional state of the audience.

[0225] Step 3:

[0226] The server receives biometric information transmitted from the terminal. The received data is processed using a generative AI model for analysis.

[0227] Step 4:

[0228] The server analyzes the audience's emotional state using a generative AI model. Specifically, it quantifies levels of excitement and emotion from heart rate and facial expression data. This quantified data becomes the information necessary for subsequent processing.

[0229] Step 5:

[0230] The server generates a performance plan based on the analysis results. The combination of lighting, sound, and visual effects is determined according to the audience's emotional state as indicated by the analysis. This plan allows for flashy performances during states of excitement and more subdued performances during states of emotion.

[0231] Step 6:

[0232] The server transmits the generated performance plan to the performance equipment. This enables real-time control of the entire venue's performance.

[0233] Step 7:

[0234] The performance system executes the live show based on the performance plan received from the server. Specifically, it controls the color and brightness of the lighting, the volume of the sound, and the content of the video, providing a dynamic live experience that responds to the emotional reactions of the audience.

[0235] (Example 1)

[0236] 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."

[0237] In traditional live performances, the staging is often based on pre-set scripts, making it difficult to respond flexibly to the audience's real-time emotional changes. This has limited the ability to fully utilize the emotional responses of each individual audience member. Therefore, there is a need to develop a system that dynamically optimizes the staging based on the audience's emotional state, providing a more personalized and immersive experience.

[0238] 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.

[0239] In this invention, the server includes acquisition means for acquiring biometric information from the audience, analysis means for quantifying the audience's emotional state using an artificial intelligence model generated with the biometric information, plan generation means for dynamically generating a performance plan based on the emotional state, and execution device control means for immediately executing the performance plan. This makes it possible to optimize performance direction in accordance with the audience's real-time emotional state.

[0240] "Means of acquisition" refers to functions or devices for collecting biometric information from audience members.

[0241] "Analysis means" refers to functions and methods that quantify the emotional state of the audience using an artificial intelligence model generated based on collected biometric information.

[0242] A "plan generation means" is a function or system for generating dynamic performance plans that are appropriate to the situation at the time, based on the analyzed emotional state.

[0243] The "execution device control means" refers to the function of controlling the device in order to execute the generated performance plan at the live venue.

[0244] "Biometric information" refers to data about the audience's physical condition, including heart rate and facial expression.

[0245] A "generative artificial intelligence model" is a model that uses artificial intelligence technology to analyze biometric information and quantify the emotional state of the audience.

[0246] A "performance plan" is a plan that includes specific lighting, sound, and video effects to be implemented during a performance, designed based on the analyzed emotional state.

[0247] This invention is specifically implemented as a system that optimizes performances in real time based on the audience's biometric information. The system consists of a terminal that collects biometric information from the audience, a server that processes the received data, and a device that executes the performance.

[0248] The terminal acquires biometric information using wearable devices distributed to audience members and cameras installed at the venue. Specifically, the wearable devices measure audience members' heart rates using heart rate sensors, and the cameras collect facial expression data using facial recognition technology. This data is transmitted from the terminal to a server.

[0249] The server uses a generative AI model to analyze the received biometric data. The AI ​​model analyzes heart rate and changes in facial expression, outputting the audience's emotional state as specific numerical values. This analysis result is used as basic data for generating the performance plan.

[0250] Based on the analysis results, the server dynamically formulates a performance plan. For example, if it determines that the audience's excitement level is high, the server generates a plan to brighten the lighting and enhance the sound. On the other hand, if it is confirmed that the audience is moved, a quieter, more emotionally evocative performance is selected. This performance plan is sent from the server to the performance equipment and executed immediately at the venue.

[0251] Users can use prompts to give instructions to the generating AI model. An example of a prompt would be: "We have received audience heart rate and facial expression data. Analyze their current level of excitement and emotion, and generate the optimal performance plan based on that."

[0252] This allows the system to provide an optimal performance experience that responds to the audience's emotions in real time. Unlike traditional, fixed staging, it enables a more personalized and immersive experience by immediately responding to audience reactions.

[0253] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0254] Step 1:

[0255] The terminal acquires biometric information from wearable devices worn by audience members and cameras installed within the venue. Specifically, the wearable devices measure heart rate, and the cameras analyze facial expression data using facial recognition software. The input data consists of heart rate and facial expression information, and the output data is a combination of this information.

[0256] Step 2:

[0257] The device transmits acquired biometric information to the server. The data is transferred in real time at regular intervals. Protocols such as HTTP and WebSocket are used to securely transmit the data and enable rapid reception on the server side. The input data consists of heart rate and facial expression information, and the output is the transmission of data to the server.

[0258] Step 3:

[0259] The server inputs the received biometric information into a generating AI model to quantify the audience's emotional state. Specifically, it analyzes heart rate fluctuations and facial features to output levels of excitement and emotion. The AI ​​model analyzes this data to generate scoring data that represents the audience's emotional state. The input data consists of heart rate and facial expression information, and the output is numerical data representing the emotional state.

[0260] Step 4:

[0261] The server dynamically generates a performance plan based on quantified emotional states. This plan uses an algorithm to translate the analysis results into specific performance settings for lighting and sound. For example, if the level of excitement is high, the lighting colors will be made more vibrant and the sound will be amplified. The input data is quantified data of emotional states, and the output is a specific performance plan.

[0262] Step 5:

[0263] The server transmits the generated performance plan to the performance equipment, which then executes it immediately. The performance equipment uses the DMX protocol to control each component of lighting and sound. This allows for a performance that responds to the audience's real-time emotional state. The input data is the specific performance plan, and the output is the actual performance effect.

[0264] (Application Example 1)

[0265] 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."

[0266] Traditional home entertainment devices have struggled to provide flexible interactions that respond to the individual emotional states of participants, failing to maximize the emotional experience at parties and events. There is a growing need for automated interactions that reflect participants' emotional responses in real time.

[0267] 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.

[0268] In this invention, the server includes data acquisition means for collecting biometric information from the user, data analysis means for analyzing the biometric information and quantifying the user's emotional state, and plan generation means for generating an interaction plan based on the emotional state. This makes it possible to automate interactions that respond immediately to the emotions of participants in events within the home.

[0269] "User" refers to a participant in a home entertainment environment who possesses heart rate and facial expression data.

[0270] "Biometric information" refers to physical data such as heart rate and facial expressions obtained from the user.

[0271] "Data acquisition means" refers to a system or device for efficiently collecting biometric information from users.

[0272] "Data analysis means" refers to methods and devices for quantifying a user's emotional state using acquired biometric information.

[0273] "Emotional state" refers to a user's psychological response expressed numerically or categorically, and is the subject of analysis by the system.

[0274] "Plan generation means" refers to methods or devices that create interaction plans, such as lighting, sound, and video, based on analyzed emotional states.

[0275] "Interaction execution means" refers to devices or methods for executing a generated interaction plan in a real-world environment.

[0276] The system implementing this invention is designed for home entertainment and enables interaction that responds immediately to the user's emotions. The system mainly consists of data acquisition means, data analysis means, plan generation means, and interaction execution means.

[0277] Users will participate in events and parties wearing wearable devices that can measure their heart rate and cameras that recognize their facial expressions. These devices will collect the user's biometric information in real time and transmit it to a server via data acquisition methods.

[0278] The server uses software such as Python and TensorFlow to analyze the acquired biometric information. Specifically, it utilizes a generative AI model to analyze heart rate and facial expression data, quantifying the user's emotional state. For example, if the heart rate is high and the user is smiling continuously, it is judged to be in an excited state.

[0279] The plan generation mechanism generates an interaction plan based on the analysis results. For example, if the user is relaxed, it creates a plan that includes setting calming music and gentle lighting effects.

[0280] The interaction execution mechanism controls lighting control devices (e.g., a typical smart lighting system), sound devices, and video projectors to execute the generated plan. This allows the user to experience emotionally resonant interactions.

[0281] As a concrete example, in a family home party, the living room lighting could be automatically adjusted and cheerful music selected based on the heart rate and facial expressions of the children sitting next to each other. This would create the perfect atmosphere for everyone gathered.

[0282] An example of a prompt statement is shown below:

[0283] "Please analyze the biometric data of the participant list and quantify the emotional state. The heart rate and expression data are as follows: Participant 1: Heart rate 85, Expression: Smile; Participant 2: Heart rate 78, Expression: Surprise. Based on the results, please propose the optimal settings for music and lighting."

[0284] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0285] Step 1:

[0286] The terminal collects heart rate and expression data from the wearable device worn by the user or the installed camera. These devices acquire biometric information in real time, and the terminal aggregates this as initial data. The input is the user's biometric information, and the output is the biometric information packet for transmission by the terminal.

[0287] Step 2:

[0288] The terminal transmits the collected biometric information to the server. The input is the biometric information packet collected by the terminal, and the output is the biometric information transmitted to the server. The terminal uses the data communication function to transfer data to the server through a wireless or wired network.

[0289] Step 3:

[0290] The server analyzes the received biometric information using data analysis means. Specifically, data calculations are performed to infer the user's emotional state from the heart rate and expression data using the generated AI model. The input is the biometric information transmitted to the server, and the output is the numerical data of the analyzed emotional state.

[0291] Step 4:

[0292] The server generates an interaction plan based on the analysis results using a plan generation mechanism. The server constructs a plan, including lighting and music settings, based on the emotional states obtained from the analysis. The input is numerical data of the emotional states, and the output is the generated interaction plan.

[0293] Step 5:

[0294] The server sends the interaction plan to the interaction execution device. The input is the generated interaction plan, and the output is the control command sent to the execution device. The server transmits commands to control lighting and sound equipment over the network.

[0295] Step 6:

[0296] The interaction execution mechanism controls lighting, sound, and video equipment based on the received interaction plan. Specifically, it performs actions such as adjusting the brightness and color of lighting, and changing music selection and volume. The input is a control command, and the output is the application of the interaction in the actual environment.

[0297] 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.

[0298] This invention relates to a system that collects biometric information from audience members, analyzes it to recognize their emotional state in real time, and generates and executes a performance plan based on the results. Furthermore, it incorporates an emotion engine for detailed recognition of user emotions.

[0299] The system consists of the following main components: information gathering means for collecting audience biometric information, analysis means for analyzing the collected information and quantifying emotional states, an emotion engine, a plan generation means for generating performance plans, and a performance execution means for executing the generated plans.

[0300] First, the terminal collects heart rate and facial expression data through wearable devices distributed to the audience and cameras installed throughout the venue. This data reflects the audience's physical and mental state in real time. The terminal immediately transmits this data to a server.

[0301] Next, the server uses the received biometric information to activate the emotion engine. The emotion engine analyzes the information more deeply to recognize the audience's emotions. Specifically, it analyzes a combination of heart rate fluctuations and changes in facial expression to determine emotions such as excitement, emotion, joy, and surprise.

[0302] Based on the emotional state of the audience obtained, the plan generation system generates an appropriate performance plan on the spot. Through detailed emotion recognition by the emotion engine, the plan becomes more tailored to the individual emotional state of each audience member. For example, if a user experiences surprise, the server instantly changes the lighting and sound to provide a performance that further heightens the audience's emotions.

[0303] The generated performance plan is executed through the performance execution system, adjusting the lighting, sound, and video display devices throughout the venue to create a live experience that resonates with the audience's emotions.

[0304] As a concrete example, suppose a scene occurs during a live performance where a particular song is played and many audience members simultaneously feel excitement and joy. In this case, the emotion engine recognizes these emotions, and the server, based on that data, formulates a performance plan that includes changes in color and amplification effects through sound, which is then immediately executed by the execution device. This performance allows the audience to feel a greater sense of unity and makes the live experience even more fulfilling.

[0305] Thus, the present invention provides more moving and personalized live entertainment by recognizing the audience's emotions in detail in real time and implementing performances that respond accordingly.

[0306] The following describes the processing flow.

[0307] Step 1:

[0308] The terminal collects biometric information using sensors, cameras installed in the venue, and wearable devices distributed to the audience. Specifically, it acquires heart rate and expression data and immediately stores them as digital data.

[0309] Step 2:

[0310] The terminal transmits the collected biometric information to the server in real time. This information includes the individual heart rates and changes in expressions of the audience.

[0311] Step 3:

[0312] Based on the received biometric information, the server operates an emotion engine. The emotion engine analyzes fluctuations in heart rate and features of expressions to identify the emotional state shown by the audience. In this analysis, emotions such as excitement, joy,感动 (moved), and surprise are quantified.

[0313] Step 4:

[0314] Based on the emotional state of the audience analyzed by the emotion engine, the server generates a performance plan. The plan generation means creates an optimal performance plan that combines lighting, sound, and video effects according to the specific emotions of the audience.

[0315] Step 5:

[0316] The server transmits the generated performance plan to the performance execution means. The performance execution means controls the lighting system, sound system, and video display device in the venue to adjust the performance so as to further enhance the emotions of the audience.

[0317] Step 6:

[0318] Users (audience members) experience the performances implemented by the server. For example, if an audience member expresses surprise, the lighting changes color and the sound intensifies, amplifying that emotion through both sight and sound, resulting in an even greater emotional impact.

[0319] Step 7:

[0320] The server continuously receives new biometric information during the live performance, dynamically updating the performance plan while re-evaluating the emotional state using the emotion engine. This enables flexible live performances that respond to the audience's real-time emotional changes.

[0321] (Example 2)

[0322] 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".

[0323] There is a growing demand for more personalized entertainment experiences by accurately recognizing audiences' emotional states in real time and providing performances that respond accordingly. However, existing technologies lack systems that can grasp and instantly reflect audience emotions in detail, making it difficult to provide timely performances that are in line with those emotions.

[0324] 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.

[0325] In this invention, the server includes means for collecting data from the audience, means for analyzing the data to quantify the audience's emotional state, and means for generating a performance plan using information processing technology based on the quantified emotional state. This makes it possible to recognize the audience's emotions in real time and immediately provide appropriate performances that correspond to those emotions.

[0326] "Audience" refers to people who watch an event or performance.

[0327] "Data" refers to a collection of information, including physiological variables and facial expression data, gathered from the audience.

[0328] "Means of collection" refers to devices or methods for obtaining data from the audience.

[0329] "Means of analysis" refers to methods or devices for processing acquired data and quantifying the emotional state of the audience.

[0330] "Emotional state" refers to a numerical representation of the psychological or physiological response that an audience member exhibits to a particular situation or stimulus.

[0331] "Information processing technology" refers to the techniques and methods for processing and analyzing data.

[0332] A "performance plan" refers to a plan that combines visual, auditory, and visual effects based on the emotional state of the audience.

[0333] This invention is a system that realizes a more personalized entertainment experience by analyzing audience emotions in real time and providing performances that respond accordingly. This system collects biometric data from the audience, analyzes that data to determine their emotional state, and generates and executes an appropriate performance plan.

[0334] The terminal collects biometric data using wearable devices distributed to audience members and cameras installed at the venue. The wearable devices are equipped with heart rate sensors, and the cameras use facial recognition technology to capture changes in facial expressions. The terminal transmits this data to a server in real time.

[0335] The server uses collected biometric data to analyze the audience's emotions through an emotion engine. The emotion engine analyzes heart rate fluctuations and facial expression data, quantifying various emotions such as excitement, happiness, and surprise. Based on these quantified emotional states, the server uses information processing technology to generate a performance plan. The performance plan includes instructions for controlling the lighting and sound systems.

[0336] As a concrete example, if the system analyzes that many audience members are excited when a particular song is played during a live event, the server will generate and immediately execute a performance plan that increases the brightness of the lights and enhances the sound effects. In this way, immediate responses to the audience's emotions become possible.

[0337] Furthermore, by integrating with a generative AI model, it is possible to propose more detailed performance plans based on prompt messages. An example of a prompt message would be, "Based on the current audience emotion data, please suggest the next performance step." This system can accurately capture audience emotions and improve the live entertainment experience at the venue.

[0338] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0339] Step 1:

[0340] The terminal collects biometric data from the audience. Specifically, wearable devices distributed to the audience acquire heart rate data using heart rate sensors, and cameras installed in the venue acquire facial expression data using facial recognition technology. The inputs include heart rate and image data. The terminal aggregates this data and prepares it for transmission to the server.

[0341] Step 2:

[0342] The device transmits collected heart rate and facial expression data to the server. Input includes real-time data acquired from the wearable device and camera. Output is encrypted data securely transferred to the server. This transmission prepares the data for calculation.

[0343] Step 3:

[0344] The server analyzes the received biometric data. The emotion engine uses heart rate fluctuation patterns and facial expression data to quantify the audience's emotional state, such as excitement or surprise. The input is biometric data transmitted from the terminal. The output is numerical data of the analyzed emotion. This analysis makes it possible to understand the emotional state of the audience.

[0345] Step 4:

[0346] The server generates a performance plan based on the analysis results. Using information processing technology, it constructs an optimal performance plan based on quantified emotional states. The input includes quantified emotional data. The output is a performance plan that aggregates instructions for lighting and sound systems.

[0347] Step 5:

[0348] The server sends instructions to the venue's control system to execute the generated performance plan. The venue's lighting and sound equipment operates according to the performance plan. The input is the performance plan, and the output is the performance with actual lighting and sound effects. This execution can provide the audience with a real-time, emotionally engaging experience.

[0349] (Application Example 2)

[0350] 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."

[0351] In today's world, online content distribution is increasing, and there is a growing demand for personalized experiences that resonate with viewers' emotions. However, traditional systems have struggled to recognize viewers' emotions in real time and dynamically adjust the presentation, making it difficult to provide a deeper online viewing experience.

[0352] 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.

[0353] In this invention, the server includes information gathering means for collecting biometric information from the audience, analysis means for analyzing the biometric information and quantifying the audience's emotional state, and plan generation means for generating a performance plan based on the emotional state. This enables dynamic adjustment of the streamed video based on the viewer's emotions.

[0354] "Information gathering means" refers to a device or function for acquiring biometric information from audience members or viewers.

[0355] "Analysis means" refers to a device or function that quantifies the emotional state of audience members or viewers based on biological information and performs a process of identifying emotions.

[0356] "Plan generation means" refers to a device or function for constructing an optimal performance plan based on the analyzed emotional state.

[0357] "Performance execution means" refers to a device or function for concretely executing the generated performance plan and providing lighting, sound, and video effects.

[0358] "Distribution adjustment means" refers to a device or function for dynamically adjusting the video and other content being distributed based on biometric information obtained from the viewer's mobile device.

[0359] The system for implementing the present invention mainly consists of a user's personal information terminal, a server, and various hardware and software that support their respective functions. The user's personal information terminal incorporates a wearable device capable of measuring heart rate and a camera capable of capturing facial expressions, and biometric information is collected using these devices.

[0360] The device transmits collected heart rate and facial expression data to the server in real time. An encrypted communication protocol is used for data security. The server uses an emotion analysis engine to process the received biometric information. This engine analyzes the audience's emotions and generates a quantified emotional state, for example, by using a generative AI model such as AWS's emotion recognition API.

[0361] Based on the analyzed emotional state, the server activates a production plan generation algorithm to construct the optimal production plan. This plan is output in a format that allows for real-time adjustment of lighting, sound, and the video being streamed. As a result, dynamic content that aligns with the viewer's emotions is provided, improving the quality of the viewing experience.

[0362] As a concrete example, in a live streaming event, if the server analyzes the audience's level of excitement, it can immediately apply a performance plan based on that data, such as increasing the music volume or enhancing the video's saturation. This allows viewers to have a more emotionally engaging experience.

[0363] An example of a prompt to provide to a generative AI model is: "Consider a concept for a smart app that analyzes viewer sentiment in real time during a live broadcast and adjusts the video and audio based on that analysis."

[0364] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0365] Step 1:

[0366] The user collects biometric information using a heart rate sensor and camera built into their mobile device. The user's heart rate data and facial expression data are entered. This data serves as foundational data to indicate the user's emotional state. The collected data is encrypted for data security and immediately transmitted to the server.

[0367] Step 2:

[0368] The server passes the received biometric information to the emotion analysis engine. Based on the input heart rate and facial expression data, a generative AI model is used to quantify the emotional state. This data processing uses algorithms that analyze heart rate fluctuations and facial expression patterns. The resulting output is a numerical value indicating a specific emotional state, such as excitement or emotion.

[0369] Step 3:

[0370] The server runs a performance plan generation algorithm based on the analysis. The input is numerical values ​​of emotional states obtained from the emotion analysis engine. Through data calculation, an optimal performance plan is generated, including adjustments to lighting, sound, and video. The performance plan includes specific actions to enhance a particular emotion.

[0371] Step 4:

[0372] The server sends commands to the distribution system to execute the generated production plan. The output commands adjust the lighting, sound, and visual effects of live streaming and recorded content in real time. This ensures that content is delivered to viewers that instantly responds to the user's emotions.

[0373] This entire process allows users to have a more deeply emotionally engaging viewing experience.

[0374] 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.

[0375] 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.

[0376] 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.

[0377] [Third Embodiment]

[0378] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0379] 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.

[0380] 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).

[0381] 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.

[0382] 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.

[0383] 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).

[0384] 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.

[0385] 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.

[0386] 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.

[0387] 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.

[0388] 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.

[0389] 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".

[0390] This invention is implemented as a system that collects biometric information from the audience in real time, analyzes it to generate a performance plan based on the audience's emotional state, and then executes the live performance. The following describes a specific embodiment of the system and its processes.

[0391] The system consists mainly of terminals for collecting biometric information from the audience, servers for receiving and processing the information, and performance equipment for executing the show.

[0392] First, the terminal periodically acquires biometric information, specifically heart rate and facial expression data, using wearable devices distributed to the audience and cameras installed within the venue. This information is then transmitted from the terminal to a server.

[0393] Next, the server analyzes the received biometric information using a generating AI model. As a result of the analysis, the audience's emotional state, i.e., their level of excitement and emotional response, is quantified. This analysis result is used as basic data to control lighting, sound, and visual effects.

[0394] Based on the audience's emotional state, as determined by the analysis, the server dynamically generates a performance plan. For example, if the audience's excitement level is high, a performance plan with brighter lighting and more dramatic sound is designed. On the other hand, if the audience's emotional response is high, a gentler, more emotionally resonant performance is selected.

[0395] The performance plan generated in this way is sent from the server to the performance equipment and actually executed at the live venue. The performance equipment controls the lighting system, sound system, and video display device, and delivers performances in real time that respond to the audience's emotions.

[0396] As a concrete example, suppose that during the climax of a song, a device receives excited heart rate data from many audience members. The server analyzes this data and sets up a performance plan, including flashing lights and loud sound effects, which are then executed by the performance equipment. As a result, the audience can feel the heightened emotions through both sight and sound, enhancing the live experience.

[0397] In this embodiment, live performances can be instantly optimized in response to the emotional reactions of individual audience members, enabling the provision of a deeper, more personal experience.

[0398] The following describes the processing flow.

[0399] Step 1:

[0400] The device collects biometric information from the audience. Specifically, it uses wearable devices distributed to the audience and cameras within the venue to acquire heart rate and facial expression data. This process is carried out periodically, enabling real-time data collection.

[0401] Step 2:

[0402] The device transmits the collected biometric information to a server. This information serves as foundational data for evaluating the emotional state of the audience.

[0403] Step 3:

[0404] The server receives biometric information transmitted from the terminal. The received data is processed using a generative AI model for analysis.

[0405] Step 4:

[0406] The server analyzes the audience's emotional state using a generative AI model. Specifically, it quantifies levels of excitement and emotion from heart rate and facial expression data. This quantified data becomes the information necessary for subsequent processing.

[0407] Step 5:

[0408] The server generates a performance plan based on the analysis results. The combination of lighting, sound, and visual effects is determined according to the audience's emotional state as indicated by the analysis. This plan allows for flashy performances during states of excitement and more subdued performances during states of emotion.

[0409] Step 6:

[0410] The server transmits the generated performance plan to the performance equipment. This enables real-time control of the entire venue's performance.

[0411] Step 7:

[0412] The performance system executes the live show based on the performance plan received from the server. Specifically, it controls the color and brightness of the lighting, the volume of the sound, and the content of the video, providing a dynamic live experience that responds to the emotional reactions of the audience.

[0413] (Example 1)

[0414] 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."

[0415] In traditional live performances, the staging is often based on pre-set scripts, making it difficult to respond flexibly to the audience's real-time emotional changes. This has limited the ability to fully utilize the emotional responses of each individual audience member. Therefore, there is a need to develop a system that dynamically optimizes the staging based on the audience's emotional state, providing a more personalized and immersive experience.

[0416] 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.

[0417] In this invention, the server includes acquisition means for acquiring biometric information from the audience, analysis means for quantifying the audience's emotional state using an artificial intelligence model generated with the biometric information, plan generation means for dynamically generating a performance plan based on the emotional state, and execution device control means for immediately executing the performance plan. This makes it possible to optimize performance direction in accordance with the audience's real-time emotional state.

[0418] "Means of acquisition" refers to functions or devices for collecting biometric information from audience members.

[0419] "Analysis means" refers to functions and methods that quantify the emotional state of the audience using an artificial intelligence model generated based on collected biometric information.

[0420] A "plan generation means" is a function or system for generating dynamic performance plans that are appropriate to the situation at the time, based on the analyzed emotional state.

[0421] The "execution device control means" refers to the function of controlling the device in order to execute the generated performance plan at the live venue.

[0422] "Biometric information" refers to data about the audience's physical condition, including heart rate and facial expression.

[0423] A "generative artificial intelligence model" is a model that uses artificial intelligence technology to analyze biometric information and quantify the emotional state of the audience.

[0424] A "performance plan" is a plan that includes specific lighting, sound, and video effects to be implemented during a performance, designed based on the analyzed emotional state.

[0425] This invention is specifically implemented as a system that optimizes performances in real time based on the audience's biometric information. The system consists of a terminal that collects biometric information from the audience, a server that processes the received data, and a device that executes the performance.

[0426] The terminal acquires biometric information using wearable devices distributed to audience members and cameras installed at the venue. Specifically, the wearable devices measure audience members' heart rates using heart rate sensors, and the cameras collect facial expression data using facial recognition technology. This data is transmitted from the terminal to a server.

[0427] The server uses a generative AI model to analyze the received biometric data. The AI ​​model analyzes heart rate and changes in facial expression, outputting the audience's emotional state as specific numerical values. This analysis result is used as basic data for generating the performance plan.

[0428] Based on the analysis results, the server dynamically formulates a performance plan. For example, if it determines that the audience's excitement level is high, the server generates a plan to brighten the lighting and enhance the sound. On the other hand, if it is confirmed that the audience is moved, a quieter, more emotionally evocative performance is selected. This performance plan is sent from the server to the performance equipment and executed immediately at the venue.

[0429] Users can use prompts to give instructions to the generating AI model. An example of a prompt would be: "We have received audience heart rate and facial expression data. Analyze their current level of excitement and emotion, and generate the optimal performance plan based on that."

[0430] This allows the system to provide an optimal performance experience that responds to the audience's emotions in real time. Unlike traditional, fixed staging, it enables a more personalized and immersive experience by immediately responding to audience reactions.

[0431] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0432] Step 1:

[0433] The terminal acquires biometric information from wearable devices worn by audience members and cameras installed within the venue. Specifically, the wearable devices measure heart rate, and the cameras analyze facial expression data using facial recognition software. The input data consists of heart rate and facial expression information, and the output data is a combination of this information.

[0434] Step 2:

[0435] The device transmits acquired biometric information to the server. The data is transferred in real time at regular intervals. Protocols such as HTTP and WebSocket are used to securely transmit the data and enable rapid reception on the server side. The input data consists of heart rate and facial expression information, and the output is the transmission of data to the server.

[0436] Step 3:

[0437] The server inputs the received biometric information into a generating AI model to quantify the audience's emotional state. Specifically, it analyzes heart rate fluctuations and facial features to output levels of excitement and emotion. The AI ​​model analyzes this data to generate scoring data that represents the audience's emotional state. The input data consists of heart rate and facial expression information, and the output is numerical data representing the emotional state.

[0438] Step 4:

[0439] The server dynamically generates a performance plan based on quantified emotional states. This plan uses an algorithm to translate the analysis results into specific performance settings for lighting and sound. For example, if the level of excitement is high, the lighting colors will be made more vibrant and the sound will be amplified. The input data is quantified data of emotional states, and the output is a specific performance plan.

[0440] Step 5:

[0441] The server transmits the generated performance plan to the performance equipment, which then executes it immediately. The performance equipment uses the DMX protocol to control each component of lighting and sound. This allows for a performance that responds to the audience's real-time emotional state. The input data is the specific performance plan, and the output is the actual performance effect.

[0442] (Application Example 1)

[0443] 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."

[0444] Traditional home entertainment devices have struggled to provide flexible interactions that respond to the individual emotional states of participants, failing to maximize the emotional experience at parties and events. There is a growing need for automated interactions that reflect participants' emotional responses in real time.

[0445] 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.

[0446] In this invention, the server includes data acquisition means for collecting biometric information from the user, data analysis means for analyzing the biometric information and quantifying the user's emotional state, and plan generation means for generating an interaction plan based on the emotional state. This makes it possible to automate interactions that respond immediately to the emotions of participants in events within the home.

[0447] "User" refers to a participant in a home entertainment environment who possesses heart rate and facial expression data.

[0448] "Biometric information" refers to physical data such as heart rate and facial expressions obtained from the user.

[0449] "Data acquisition means" refers to a system or device for efficiently collecting biometric information from users.

[0450] "Data analysis means" refers to methods and devices for quantifying a user's emotional state using acquired biometric information.

[0451] "Emotional state" refers to a user's psychological response expressed numerically or categorically, and is the subject of analysis by the system.

[0452] "Plan generation means" refers to methods or devices that create interaction plans, such as lighting, sound, and video, based on analyzed emotional states.

[0453] "Interaction execution means" refers to devices or methods for executing a generated interaction plan in a real-world environment.

[0454] The system implementing this invention is designed for home entertainment and enables interaction that responds immediately to the user's emotions. The system mainly consists of data acquisition means, data analysis means, plan generation means, and interaction execution means.

[0455] Users will participate in events and parties wearing wearable devices that can measure their heart rate and cameras that recognize their facial expressions. These devices will collect the user's biometric information in real time and transmit it to a server via data acquisition methods.

[0456] The server uses software such as Python and TensorFlow to analyze the acquired biometric information. Specifically, it utilizes a generative AI model to analyze heart rate and facial expression data, quantifying the user's emotional state. For example, if the heart rate is high and the user is smiling continuously, it is judged to be in an excited state.

[0457] The plan generation mechanism generates an interaction plan based on the analysis results. For example, if the user is relaxed, it creates a plan that includes setting calming music and gentle lighting effects.

[0458] The interaction execution mechanism controls lighting control devices (e.g., a typical smart lighting system), sound devices, and video projectors to execute the generated plan. This allows the user to experience emotionally resonant interactions.

[0459] As a concrete example, in a family home party, the living room lighting could be automatically adjusted and cheerful music selected based on the heart rate and facial expressions of the children sitting next to each other. This would create the perfect atmosphere for everyone gathered.

[0460] An example of a prompt statement is shown below:

[0461] "Analyze the biometric data from the participant list and quantify their emotional state. The heart rate and facial expression data are as follows: Participant 1: Heart rate 85, Facial expression: Smiling; Participant 2: Heart rate 78, Facial expression: Surprised. Based on the results, suggest the optimal music and lighting settings."

[0462] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0463] Step 1:

[0464] The terminal collects heart rate and facial expression data from wearable devices worn by the user and installed cameras. These devices acquire biometric information in real time, and the terminal compiles this as initial data. The input is the user's biometric information, and the output is biometric information packets for transmission by the terminal.

[0465] Step 2:

[0466] The terminal transmits the collected biometric information to the server. The input is the biometric information packet collected by the terminal, and the output is the biometric information transmitted to the server. The terminal uses its data communication function to transfer data to the server via a wireless or wired network.

[0467] Step 3:

[0468] The server analyzes the received biometric information using data analysis tools. Specifically, it uses a generative AI model to perform data calculations that infer the user's emotional state from heart rate and facial expression data. The input is the biometric information sent to the server, and the output is numerical data of the analyzed emotional state.

[0469] Step 4:

[0470] The server generates an interaction plan based on the analysis results using a plan generation mechanism. The server constructs a plan, including lighting and music settings, based on the emotional states obtained from the analysis. The input is numerical data of the emotional states, and the output is the generated interaction plan.

[0471] Step 5:

[0472] The server sends the interaction plan to the interaction execution device. The input is the generated interaction plan, and the output is the control command sent to the execution device. The server transmits commands to control lighting and sound equipment over the network.

[0473] Step 6:

[0474] The interaction execution mechanism controls lighting, sound, and video equipment based on the received interaction plan. Specifically, it performs actions such as adjusting the brightness and color of lighting, and changing music selection and volume. The input is a control command, and the output is the application of the interaction in the actual environment.

[0475] 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.

[0476] This invention relates to a system that collects biometric information from audience members, analyzes it to recognize their emotional state in real time, and generates and executes a performance plan based on the results. Furthermore, it incorporates an emotion engine for detailed recognition of user emotions.

[0477] The system consists of the following main components: information gathering means for collecting audience biometric information, analysis means for analyzing the collected information and quantifying emotional states, an emotion engine, a plan generation means for generating performance plans, and a performance execution means for executing the generated plans.

[0478] First, the terminal collects heart rate and facial expression data through wearable devices distributed to the audience and cameras installed throughout the venue. This data reflects the audience's physical and mental state in real time. The terminal immediately transmits this data to a server.

[0479] Next, the server uses the received biometric information to activate the emotion engine. The emotion engine analyzes the information more deeply to recognize the audience's emotions. Specifically, it analyzes a combination of heart rate fluctuations and changes in facial expression to determine emotions such as excitement, emotion, joy, and surprise.

[0480] Based on the emotional state of the audience obtained, the plan generation system generates an appropriate performance plan on the spot. Through detailed emotion recognition by the emotion engine, the plan becomes more tailored to the individual emotional state of each audience member. For example, if a user experiences surprise, the server instantly changes the lighting and sound to provide a performance that further heightens the audience's emotions.

[0481] The generated performance plan is executed through the performance execution system, adjusting the lighting, sound, and video display devices throughout the venue to create a live experience that resonates with the audience's emotions.

[0482] As a concrete example, suppose a scene occurs during a live performance where a particular song is played and many audience members simultaneously feel excitement and joy. In this case, the emotion engine recognizes these emotions, and the server, based on that data, formulates a performance plan that includes changes in color and amplification effects through sound, which is then immediately executed by the execution device. This performance allows the audience to feel a greater sense of unity and makes the live experience even more fulfilling.

[0483] Thus, the present invention provides more moving and personalized live entertainment by recognizing the audience's emotions in detail in real time and implementing performances that respond accordingly.

[0484] The following describes the processing flow.

[0485] Step 1:

[0486] The terminals collect biometric information using sensors and cameras installed at the venue, as well as wearable devices distributed to the audience. Specifically, they acquire heart rate and facial expression data, and immediately save this information as digital data.

[0487] Step 2:

[0488] The device transmits collected biometric information to the server in real time. This information includes the individual heart rate and facial expression changes of each audience member.

[0489] Step 3:

[0490] The server activates an emotion engine based on the received biometric information. The emotion engine analyzes heart rate fluctuations and facial features to identify the emotional state the audience is exhibiting. This analysis quantifies emotions such as excitement, joy, emotion, and surprise.

[0491] Step 4:

[0492] The server generates a performance plan based on the audience's emotional state analyzed by the emotion engine. The plan generation mechanism creates an optimal performance plan by combining lighting, sound, and visual effects according to the audience's specific emotions.

[0493] Step 5:

[0494] The server transmits the generated performance plan to the performance execution system. The performance execution system controls the venue's lighting system, sound system, and video display device to adjust the performance to further enhance the audience's emotions.

[0495] Step 6:

[0496] Users (audience members) experience the performances implemented by the server. For example, if an audience member expresses surprise, the lighting changes color and the sound intensifies, amplifying that emotion through both sight and sound, resulting in an even greater emotional impact.

[0497] Step 7:

[0498] The server continuously receives new biometric information during the live performance, dynamically updating the performance plan while re-evaluating the emotional state using the emotion engine. This enables flexible live performances that respond to the audience's real-time emotional changes.

[0499] (Example 2)

[0500] 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."

[0501] There is a growing demand for more personalized entertainment experiences by accurately recognizing audiences' emotional states in real time and providing performances that respond accordingly. However, existing technologies lack systems that can grasp and instantly reflect audience emotions in detail, making it difficult to provide timely performances that are in line with those emotions.

[0502] 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.

[0503] In this invention, the server includes means for collecting data from the audience, means for analyzing the data to quantify the audience's emotional state, and means for generating a performance plan using information processing technology based on the quantified emotional state. This makes it possible to recognize the audience's emotions in real time and immediately provide appropriate performances that correspond to those emotions.

[0504] "Audience" refers to people who watch an event or performance.

[0505] "Data" refers to a collection of information, including physiological variables and facial expression data, gathered from the audience.

[0506] "Means of collection" refers to devices or methods for obtaining data from the audience.

[0507] "Means of analysis" refers to methods or devices for processing acquired data and quantifying the emotional state of the audience.

[0508] "Emotional state" refers to a numerical representation of the psychological or physiological response that an audience member exhibits to a particular situation or stimulus.

[0509] "Information processing technology" refers to the techniques and methods for processing and analyzing data.

[0510] A "performance plan" refers to a plan that combines visual, auditory, and visual effects based on the emotional state of the audience.

[0511] This invention is a system that realizes a more personalized entertainment experience by analyzing audience emotions in real time and providing performances that respond accordingly. This system collects biometric data from the audience, analyzes that data to determine their emotional state, and generates and executes an appropriate performance plan.

[0512] The terminal collects biometric data using wearable devices distributed to audience members and cameras installed at the venue. The wearable devices are equipped with heart rate sensors, and the cameras use facial recognition technology to capture changes in facial expressions. The terminal transmits this data to a server in real time.

[0513] The server uses collected biometric data to analyze the audience's emotions through an emotion engine. The emotion engine analyzes heart rate fluctuations and facial expression data, quantifying various emotions such as excitement, happiness, and surprise. Based on these quantified emotional states, the server uses information processing technology to generate a performance plan. The performance plan includes instructions for controlling the lighting and sound systems.

[0514] As a concrete example, if the system analyzes that many audience members are excited when a particular song is played during a live event, the server will generate and immediately execute a performance plan that increases the brightness of the lights and enhances the sound effects. In this way, immediate responses to the audience's emotions become possible.

[0515] Furthermore, by integrating with a generative AI model, it is possible to propose more detailed performance plans based on prompt messages. An example of a prompt message would be, "Based on the current audience emotion data, please suggest the next performance step." This system can accurately capture audience emotions and improve the live entertainment experience at the venue.

[0516] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0517] Step 1:

[0518] The terminal collects biometric data from the audience. Specifically, wearable devices distributed to the audience acquire heart rate data using heart rate sensors, and cameras installed in the venue acquire facial expression data using facial recognition technology. The inputs include heart rate and image data. The terminal aggregates this data and prepares it for transmission to the server.

[0519] Step 2:

[0520] The device transmits collected heart rate and facial expression data to the server. Input includes real-time data acquired from the wearable device and camera. Output is encrypted data securely transferred to the server. This transmission prepares the data for calculation.

[0521] Step 3:

[0522] The server analyzes the received biometric data. The emotion engine uses heart rate fluctuation patterns and facial expression data to quantify the audience's emotional state, such as excitement or surprise. The input is biometric data transmitted from the terminal. The output is numerical data of the analyzed emotion. This analysis makes it possible to understand the emotional state of the audience.

[0523] Step 4:

[0524] The server generates a performance plan based on the analysis results. Using information processing technology, it constructs an optimal performance plan based on quantified emotional states. The input includes quantified emotional data. The output is a performance plan that aggregates instructions for lighting and sound systems.

[0525] Step 5:

[0526] The server sends instructions to the venue's control system to execute the generated performance plan. The venue's lighting and sound equipment operates according to the performance plan. The input is the performance plan, and the output is the performance with actual lighting and sound effects. This execution can provide the audience with a real-time, emotionally engaging experience.

[0527] (Application Example 2)

[0528] 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."

[0529] In today's world, online content distribution is increasing, and there is a growing demand for personalized experiences that resonate with viewers' emotions. However, traditional systems have struggled to recognize viewers' emotions in real time and dynamically adjust the presentation, making it difficult to provide a deeper online viewing experience.

[0530] 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.

[0531] In this invention, the server includes information gathering means for collecting biometric information from the audience, analysis means for analyzing the biometric information and quantifying the audience's emotional state, and plan generation means for generating a performance plan based on the emotional state. This enables dynamic adjustment of the streamed video based on the viewer's emotions.

[0532] "Information gathering means" refers to a device or function for acquiring biometric information from audience members or viewers.

[0533] "Analysis means" refers to a device or function that quantifies the emotional state of audience members or viewers based on biological information and performs a process of identifying emotions.

[0534] "Plan generation means" refers to a device or function for constructing an optimal performance plan based on the analyzed emotional state.

[0535] "Performance execution means" refers to a device or function for concretely executing the generated performance plan and providing lighting, sound, and video effects.

[0536] "Distribution adjustment means" refers to a device or function for dynamically adjusting the video and other content being distributed based on biometric information obtained from the viewer's mobile device.

[0537] The system for implementing the present invention mainly consists of a user's personal information terminal, a server, and various hardware and software that support their respective functions. The user's personal information terminal incorporates a wearable device capable of measuring heart rate and a camera capable of capturing facial expressions, and biometric information is collected using these devices.

[0538] The device transmits collected heart rate and facial expression data to the server in real time. An encrypted communication protocol is used for data security. The server uses an emotion analysis engine to process the received biometric information. This engine analyzes the audience's emotions and generates a quantified emotional state, for example, by using a generative AI model such as AWS's emotion recognition API.

[0539] Based on the analyzed emotional state, the server activates a production plan generation algorithm to construct the optimal production plan. This plan is output in a format that allows for real-time adjustment of lighting, sound, and the video being streamed. As a result, dynamic content that aligns with the viewer's emotions is provided, improving the quality of the viewing experience.

[0540] As a concrete example, in a live streaming event, if the server analyzes the audience's level of excitement, it can immediately apply a performance plan based on that data, such as increasing the music volume or enhancing the video's saturation. This allows viewers to have a more emotionally engaging experience.

[0541] An example of a prompt to provide to a generative AI model is: "Consider a concept for a smart app that analyzes viewer sentiment in real time during a live broadcast and adjusts the video and audio based on that analysis."

[0542] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0543] Step 1:

[0544] The user collects biometric information using a heart rate sensor and camera built into their mobile device. The user's heart rate data and facial expression data are entered. This data serves as foundational data to indicate the user's emotional state. The collected data is encrypted for data security and immediately transmitted to the server.

[0545] Step 2:

[0546] The server passes the received biometric information to the emotion analysis engine. Based on the input heart rate and facial expression data, a generative AI model is used to quantify the emotional state. This data processing uses algorithms that analyze heart rate fluctuations and facial expression patterns. The resulting output is a numerical value indicating a specific emotional state, such as excitement or emotion.

[0547] Step 3:

[0548] The server runs a performance plan generation algorithm based on the analysis. The input is numerical values ​​of emotional states obtained from the emotion analysis engine. Through data calculation, an optimal performance plan is generated, including adjustments to lighting, sound, and video. The performance plan includes specific actions to enhance a particular emotion.

[0549] Step 4:

[0550] The server sends commands to the distribution system to execute the generated production plan. The output commands adjust the lighting, sound, and visual effects of live streaming and recorded content in real time. This ensures that content is delivered to viewers that instantly responds to the user's emotions.

[0551] This entire process allows users to have a more deeply emotionally engaging viewing experience.

[0552] 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.

[0553] 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.

[0554] 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.

[0555] [Fourth Embodiment]

[0556] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0557] 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.

[0558] 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).

[0559] 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.

[0560] 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.

[0561] 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).

[0562] 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.

[0563] 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.

[0564] 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.

[0565] 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.

[0566] 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.

[0567] 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.

[0568] 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".

[0569] This invention is implemented as a system that collects biometric information from the audience in real time, analyzes it to generate a performance plan based on the audience's emotional state, and then executes the live performance. The following describes a specific embodiment of the system and its processes.

[0570] The system consists mainly of terminals for collecting biometric information from the audience, servers for receiving and processing the information, and performance equipment for executing the show.

[0571] First, the terminal periodically acquires biometric information, specifically heart rate and facial expression data, using wearable devices distributed to the audience and cameras installed within the venue. This information is then transmitted from the terminal to a server.

[0572] Next, the server analyzes the received biometric information using a generating AI model. As a result of the analysis, the audience's emotional state, i.e., their level of excitement and emotional response, is quantified. This analysis result is used as basic data to control lighting, sound, and visual effects.

[0573] Based on the audience's emotional state, as determined by the analysis, the server dynamically generates a performance plan. For example, if the audience's excitement level is high, a performance plan with brighter lighting and more dramatic sound is designed. On the other hand, if the audience's emotional response is high, a gentler, more emotionally resonant performance is selected.

[0574] The performance plan generated in this way is sent from the server to the performance equipment and actually executed at the live venue. The performance equipment controls the lighting system, sound system, and video display device, and delivers performances in real time that respond to the audience's emotions.

[0575] As a concrete example, suppose that during the climax of a song, a device receives excited heart rate data from many audience members. The server analyzes this data and sets up a performance plan, including flashing lights and loud sound effects, which are then executed by the performance equipment. As a result, the audience can feel the heightened emotions through both sight and sound, enhancing the live experience.

[0576] In this embodiment, live performances can be instantly optimized in response to the emotional reactions of individual audience members, enabling the provision of a deeper, more personal experience.

[0577] The following describes the processing flow.

[0578] Step 1:

[0579] The device collects biometric information from the audience. Specifically, it uses wearable devices distributed to the audience and cameras within the venue to acquire heart rate and facial expression data. This process is carried out periodically, enabling real-time data collection.

[0580] Step 2:

[0581] The device transmits the collected biometric information to a server. This information serves as foundational data for evaluating the emotional state of the audience.

[0582] Step 3:

[0583] The server receives biometric information transmitted from the terminal. The received data is processed using a generative AI model for analysis.

[0584] Step 4:

[0585] The server analyzes the audience's emotional state using a generative AI model. Specifically, it quantifies levels of excitement and emotion from heart rate and facial expression data. This quantified data becomes the information necessary for subsequent processing.

[0586] Step 5:

[0587] The server generates a performance plan based on the analysis results. The combination of lighting, sound, and visual effects is determined according to the audience's emotional state as indicated by the analysis. This plan allows for flashy performances during states of excitement and more subdued performances during states of emotion.

[0588] Step 6:

[0589] The server transmits the generated performance plan to the performance equipment. This enables real-time control of the entire venue's performance.

[0590] Step 7:

[0591] The performance system executes the live show based on the performance plan received from the server. Specifically, it controls the color and brightness of the lighting, the volume of the sound, and the content of the video, providing a dynamic live experience that responds to the emotional reactions of the audience.

[0592] (Example 1)

[0593] 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".

[0594] In traditional live performances, the staging is often based on pre-set scripts, making it difficult to respond flexibly to the audience's real-time emotional changes. This has limited the ability to fully utilize the emotional responses of each individual audience member. Therefore, there is a need to develop a system that dynamically optimizes the staging based on the audience's emotional state, providing a more personalized and immersive experience.

[0595] 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.

[0596] In this invention, the server includes acquisition means for acquiring biometric information from the audience, analysis means for quantifying the audience's emotional state using an artificial intelligence model generated with the biometric information, plan generation means for dynamically generating a performance plan based on the emotional state, and execution device control means for immediately executing the performance plan. This makes it possible to optimize performance direction in accordance with the audience's real-time emotional state.

[0597] "Means of acquisition" refers to functions or devices for collecting biometric information from audience members.

[0598] "Analysis means" refers to functions and methods that quantify the emotional state of the audience using an artificial intelligence model generated based on collected biometric information.

[0599] A "plan generation means" is a function or system for generating dynamic performance plans that are appropriate to the situation at the time, based on the analyzed emotional state.

[0600] The "execution device control means" refers to the function of controlling the device in order to execute the generated performance plan at the live venue.

[0601] "Biometric information" refers to data about the audience's physical condition, including heart rate and facial expression.

[0602] A "generative artificial intelligence model" is a model that uses artificial intelligence technology to analyze biometric information and quantify the emotional state of the audience.

[0603] A "performance plan" is a plan that includes specific lighting, sound, and video effects to be implemented during a performance, designed based on the analyzed emotional state.

[0604] This invention is specifically implemented as a system that optimizes performances in real time based on the audience's biometric information. The system consists of a terminal that collects biometric information from the audience, a server that processes the received data, and a device that executes the performance.

[0605] The terminal acquires biometric information using wearable devices distributed to audience members and cameras installed at the venue. Specifically, the wearable devices measure audience members' heart rates using heart rate sensors, and the cameras collect facial expression data using facial recognition technology. This data is transmitted from the terminal to a server.

[0606] The server uses a generative AI model to analyze the received biometric data. The AI ​​model analyzes heart rate and changes in facial expression, outputting the audience's emotional state as specific numerical values. This analysis result is used as basic data for generating the performance plan.

[0607] Based on the analysis results, the server dynamically formulates a performance plan. For example, if it determines that the audience's excitement level is high, the server generates a plan to brighten the lighting and enhance the sound. On the other hand, if it is confirmed that the audience is moved, a quieter, more emotionally evocative performance is selected. This performance plan is sent from the server to the performance equipment and executed immediately at the venue.

[0608] Users can use prompts to give instructions to the generating AI model. An example of a prompt would be: "We have received audience heart rate and facial expression data. Analyze their current level of excitement and emotion, and generate the optimal performance plan based on that."

[0609] This allows the system to provide an optimal performance experience that responds to the audience's emotions in real time. Unlike traditional, fixed staging, it enables a more personalized and immersive experience by immediately responding to audience reactions.

[0610] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0611] Step 1:

[0612] The terminal acquires biometric information from wearable devices worn by audience members and cameras installed within the venue. Specifically, the wearable devices measure heart rate, and the cameras analyze facial expression data using facial recognition software. The input data consists of heart rate and facial expression information, and the output data is a combination of this information.

[0613] Step 2:

[0614] The device transmits acquired biometric information to the server. The data is transferred in real time at regular intervals. Protocols such as HTTP and WebSocket are used to securely transmit the data and enable rapid reception on the server side. The input data consists of heart rate and facial expression information, and the output is the transmission of data to the server.

[0615] Step 3:

[0616] The server inputs the received biometric information into a generating AI model to quantify the audience's emotional state. Specifically, it analyzes heart rate fluctuations and facial features to output levels of excitement and emotion. The AI ​​model analyzes this data to generate scoring data that represents the audience's emotional state. The input data consists of heart rate and facial expression information, and the output is numerical data representing the emotional state.

[0617] Step 4:

[0618] The server dynamically generates a performance plan based on quantified emotional states. This plan uses an algorithm to translate the analysis results into specific performance settings for lighting and sound. For example, if the level of excitement is high, the lighting colors will be made more vibrant and the sound will be amplified. The input data is quantified data of emotional states, and the output is a specific performance plan.

[0619] Step 5:

[0620] The server transmits the generated performance plan to the performance equipment, which then executes it immediately. The performance equipment uses the DMX protocol to control each component of lighting and sound. This allows for a performance that responds to the audience's real-time emotional state. The input data is the specific performance plan, and the output is the actual performance effect.

[0621] (Application Example 1)

[0622] 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".

[0623] Traditional home entertainment devices have struggled to provide flexible interactions that respond to the individual emotional states of participants, failing to maximize the emotional experience at parties and events. There is a growing need for automated interactions that reflect participants' emotional responses in real time.

[0624] 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.

[0625] In this invention, the server includes data acquisition means for collecting biometric information from the user, data analysis means for analyzing the biometric information and quantifying the user's emotional state, and plan generation means for generating an interaction plan based on the emotional state. This makes it possible to automate interactions that respond immediately to the emotions of participants in events within the home.

[0626] "User" refers to a participant in a home entertainment environment who possesses heart rate and facial expression data.

[0627] "Biometric information" refers to physical data such as heart rate and facial expressions obtained from the user.

[0628] "Data acquisition means" refers to a system or device for efficiently collecting biometric information from users.

[0629] "Data analysis means" refers to methods and devices for quantifying a user's emotional state using acquired biometric information.

[0630] "Emotional state" refers to a user's psychological response expressed numerically or categorically, and is the subject of analysis by the system.

[0631] "Plan generation means" refers to methods or devices that create interaction plans, such as lighting, sound, and video, based on analyzed emotional states.

[0632] "Interaction execution means" refers to devices or methods for executing a generated interaction plan in a real-world environment.

[0633] The system implementing this invention is designed for home entertainment and enables interaction that responds immediately to the user's emotions. The system mainly consists of data acquisition means, data analysis means, plan generation means, and interaction execution means.

[0634] Users will participate in events and parties wearing wearable devices that can measure their heart rate and cameras that recognize their facial expressions. These devices will collect the user's biometric information in real time and transmit it to a server via data acquisition methods.

[0635] The server uses software such as Python and TensorFlow to analyze the acquired biometric information. Specifically, it utilizes a generative AI model to analyze heart rate and facial expression data, quantifying the user's emotional state. For example, if the heart rate is high and the user is smiling continuously, it is judged to be in an excited state.

[0636] The plan generation mechanism generates an interaction plan based on the analysis results. For example, if the user is relaxed, it creates a plan that includes setting calming music and gentle lighting effects.

[0637] The interaction execution mechanism controls lighting control devices (e.g., a typical smart lighting system), sound devices, and video projectors to execute the generated plan. This allows the user to experience emotionally resonant interactions.

[0638] As a concrete example, in a family home party, the living room lighting could be automatically adjusted and cheerful music selected based on the heart rate and facial expressions of the children sitting next to each other. This would create the perfect atmosphere for everyone gathered.

[0639] An example of a prompt statement is shown below:

[0640] "Analyze the biometric data from the participant list and quantify their emotional state. The heart rate and facial expression data are as follows: Participant 1: Heart rate 85, Facial expression: Smiling; Participant 2: Heart rate 78, Facial expression: Surprised. Based on the results, suggest the optimal music and lighting settings."

[0641] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0642] Step 1:

[0643] The terminal collects heart rate and facial expression data from wearable devices worn by the user and installed cameras. These devices acquire biometric information in real time, and the terminal compiles this as initial data. The input is the user's biometric information, and the output is biometric information packets for transmission by the terminal.

[0644] Step 2:

[0645] The terminal transmits the collected biometric information to the server. The input is the biometric information packet collected by the terminal, and the output is the biometric information transmitted to the server. The terminal uses its data communication function to transfer data to the server via a wireless or wired network.

[0646] Step 3:

[0647] The server analyzes the received biometric information using data analysis tools. Specifically, it uses a generative AI model to perform data calculations that infer the user's emotional state from heart rate and facial expression data. The input is the biometric information sent to the server, and the output is numerical data of the analyzed emotional state.

[0648] Step 4:

[0649] The server generates an interaction plan based on the analysis results using a plan generation mechanism. The server constructs a plan, including lighting and music settings, based on the emotional states obtained from the analysis. The input is numerical data of the emotional states, and the output is the generated interaction plan.

[0650] Step 5:

[0651] The server sends the interaction plan to the interaction execution device. The input is the generated interaction plan, and the output is the control command sent to the execution device. The server transmits commands to control lighting and sound equipment over the network.

[0652] Step 6:

[0653] The interaction execution mechanism controls lighting, sound, and video equipment based on the received interaction plan. Specifically, it performs actions such as adjusting the brightness and color of lighting, and changing music selection and volume. The input is a control command, and the output is the application of the interaction in the actual environment.

[0654] 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.

[0655] This invention relates to a system that collects biometric information from audience members, analyzes it to recognize their emotional state in real time, and generates and executes a performance plan based on the results. Furthermore, it incorporates an emotion engine for detailed recognition of user emotions.

[0656] The system consists of the following main components: information gathering means for collecting audience biometric information, analysis means for analyzing the collected information and quantifying emotional states, an emotion engine, a plan generation means for generating performance plans, and a performance execution means for executing the generated plans.

[0657] First, the terminal collects heart rate and facial expression data through wearable devices distributed to the audience and cameras installed throughout the venue. This data reflects the audience's physical and mental state in real time. The terminal immediately transmits this data to a server.

[0658] Next, the server uses the received biometric information to activate the emotion engine. The emotion engine analyzes the information more deeply to recognize the audience's emotions. Specifically, it analyzes a combination of heart rate fluctuations and changes in facial expression to determine emotions such as excitement, emotion, joy, and surprise.

[0659] Based on the emotional state of the audience obtained, the plan generation system generates an appropriate performance plan on the spot. Through detailed emotion recognition by the emotion engine, the plan becomes more tailored to the individual emotional state of each audience member. For example, if a user experiences surprise, the server instantly changes the lighting and sound to provide a performance that further heightens the audience's emotions.

[0660] The generated performance plan is executed through the performance execution system, adjusting the lighting, sound, and video display devices throughout the venue to create a live experience that resonates with the audience's emotions.

[0661] As a concrete example, suppose a scene occurs during a live performance where a particular song is played and many audience members simultaneously feel excitement and joy. In this case, the emotion engine recognizes these emotions, and the server, based on that data, formulates a performance plan that includes changes in color and amplification effects through sound, which is then immediately executed by the execution device. This performance allows the audience to feel a greater sense of unity and makes the live experience even more fulfilling.

[0662] Thus, the present invention provides more moving and personalized live entertainment by recognizing the audience's emotions in detail in real time and implementing performances that respond accordingly.

[0663] The following describes the processing flow.

[0664] Step 1:

[0665] The terminals collect biometric information using sensors and cameras installed at the venue, as well as wearable devices distributed to the audience. Specifically, they acquire heart rate and facial expression data, and immediately save this information as digital data.

[0666] Step 2:

[0667] The device transmits collected biometric information to the server in real time. This information includes the individual heart rate and facial expression changes of each audience member.

[0668] Step 3:

[0669] The server activates an emotion engine based on the received biometric information. The emotion engine analyzes heart rate fluctuations and facial features to identify the emotional state the audience is exhibiting. This analysis quantifies emotions such as excitement, joy, emotion, and surprise.

[0670] Step 4:

[0671] The server generates a performance plan based on the audience's emotional state analyzed by the emotion engine. The plan generation mechanism creates an optimal performance plan by combining lighting, sound, and visual effects according to the audience's specific emotions.

[0672] Step 5:

[0673] The server transmits the generated performance plan to the performance execution system. The performance execution system controls the venue's lighting system, sound system, and video display device to adjust the performance to further enhance the audience's emotions.

[0674] Step 6:

[0675] Users (audience members) experience the performances implemented by the server. For example, if an audience member expresses surprise, the lighting changes color and the sound intensifies, amplifying that emotion through both sight and sound, resulting in an even greater emotional impact.

[0676] Step 7:

[0677] The server continuously receives new biometric information during the live performance, dynamically updating the performance plan while re-evaluating the emotional state using the emotion engine. This enables flexible live performances that respond to the audience's real-time emotional changes.

[0678] (Example 2)

[0679] 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".

[0680] There is a growing demand for more personalized entertainment experiences by accurately recognizing audiences' emotional states in real time and providing performances that respond accordingly. However, existing technologies lack systems that can grasp and instantly reflect audience emotions in detail, making it difficult to provide timely performances that are in line with those emotions.

[0681] 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.

[0682] In this invention, the server includes means for collecting data from the audience, means for analyzing the data to quantify the audience's emotional state, and means for generating a performance plan using information processing technology based on the quantified emotional state. This makes it possible to recognize the audience's emotions in real time and immediately provide appropriate performances that correspond to those emotions.

[0683] "Audience" refers to people who watch an event or performance.

[0684] "Data" refers to a collection of information, including physiological variables and facial expression data, gathered from the audience.

[0685] "Means of collection" refers to devices or methods for obtaining data from the audience.

[0686] "Means of analysis" refers to methods or devices for processing acquired data and quantifying the emotional state of the audience.

[0687] "Emotional state" refers to a numerical representation of the psychological or physiological response that an audience member exhibits to a particular situation or stimulus.

[0688] "Information processing technology" refers to the techniques and methods for processing and analyzing data.

[0689] A "performance plan" refers to a plan that combines visual, auditory, and visual effects based on the emotional state of the audience.

[0690] This invention is a system that realizes a more personalized entertainment experience by analyzing audience emotions in real time and providing performances that respond accordingly. This system collects biometric data from the audience, analyzes that data to determine their emotional state, and generates and executes an appropriate performance plan.

[0691] The terminal collects biometric data using wearable devices distributed to audience members and cameras installed at the venue. The wearable devices are equipped with heart rate sensors, and the cameras use facial recognition technology to capture changes in facial expressions. The terminal transmits this data to a server in real time.

[0692] The server uses collected biometric data to analyze the audience's emotions through an emotion engine. The emotion engine analyzes heart rate fluctuations and facial expression data, quantifying various emotions such as excitement, happiness, and surprise. Based on these quantified emotional states, the server uses information processing technology to generate a performance plan. The performance plan includes instructions for controlling the lighting and sound systems.

[0693] As a concrete example, if the system analyzes that many audience members are excited when a particular song is played during a live event, the server will generate and immediately execute a performance plan that increases the brightness of the lights and enhances the sound effects. In this way, immediate responses to the audience's emotions become possible.

[0694] Furthermore, by integrating with a generative AI model, it is possible to propose more detailed performance plans based on prompt messages. An example of a prompt message would be, "Based on the current audience emotion data, please suggest the next performance step." This system can accurately capture audience emotions and improve the live entertainment experience at the venue.

[0695] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0696] Step 1:

[0697] The terminal collects biometric data from the audience. Specifically, wearable devices distributed to the audience acquire heart rate data using heart rate sensors, and cameras installed in the venue acquire facial expression data using facial recognition technology. The inputs include heart rate and image data. The terminal aggregates this data and prepares it for transmission to the server.

[0698] Step 2:

[0699] The device transmits collected heart rate and facial expression data to the server. Input includes real-time data acquired from the wearable device and camera. Output is encrypted data securely transferred to the server. This transmission prepares the data for calculation.

[0700] Step 3:

[0701] The server analyzes the received biometric data. The emotion engine uses heart rate fluctuation patterns and facial expression data to quantify the audience's emotional state, such as excitement or surprise. The input is biometric data transmitted from the terminal. The output is numerical data of the analyzed emotion. This analysis makes it possible to understand the emotional state of the audience.

[0702] Step 4:

[0703] The server generates a performance plan based on the analysis results. Using information processing technology, it constructs an optimal performance plan based on quantified emotional states. The input includes quantified emotional data. The output is a performance plan that aggregates instructions for lighting and sound systems.

[0704] Step 5:

[0705] The server sends instructions to the venue's control system to execute the generated performance plan. The venue's lighting and sound equipment operates according to the performance plan. The input is the performance plan, and the output is the performance with actual lighting and sound effects. This execution can provide the audience with a real-time, emotionally engaging experience.

[0706] (Application Example 2)

[0707] 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".

[0708] In today's world, online content distribution is increasing, and there is a growing demand for personalized experiences that resonate with viewers' emotions. However, traditional systems have struggled to recognize viewers' emotions in real time and dynamically adjust the presentation, making it difficult to provide a deeper online viewing experience.

[0709] 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.

[0710] In this invention, the server includes information gathering means for collecting biometric information from the audience, analysis means for analyzing the biometric information and quantifying the audience's emotional state, and plan generation means for generating a performance plan based on the emotional state. This enables dynamic adjustment of the streamed video based on the viewer's emotions.

[0711] "Information gathering means" refers to a device or function for acquiring biometric information from audience members or viewers.

[0712] "Analysis means" refers to a device or function that quantifies the emotional state of audience members or viewers based on biological information and performs a process of identifying emotions.

[0713] "Plan generation means" refers to a device or function for constructing an optimal performance plan based on the analyzed emotional state.

[0714] "Performance execution means" refers to a device or function for concretely executing the generated performance plan and providing lighting, sound, and video effects.

[0715] "Distribution adjustment means" refers to a device or function for dynamically adjusting the video and other content being distributed based on biometric information obtained from the viewer's mobile device.

[0716] The system for implementing the present invention mainly consists of a user's personal information terminal, a server, and various hardware and software that support their respective functions. The user's personal information terminal incorporates a wearable device capable of measuring heart rate and a camera capable of capturing facial expressions, and biometric information is collected using these devices.

[0717] The device transmits collected heart rate and facial expression data to the server in real time. An encrypted communication protocol is used for data security. The server uses an emotion analysis engine to process the received biometric information. This engine analyzes the audience's emotions and generates a quantified emotional state, for example, by using a generative AI model such as AWS's emotion recognition API.

[0718] Based on the analyzed emotional state, the server activates a production plan generation algorithm to construct the optimal production plan. This plan is output in a format that allows for real-time adjustment of lighting, sound, and the video being streamed. As a result, dynamic content that aligns with the viewer's emotions is provided, improving the quality of the viewing experience.

[0719] As a concrete example, in a live streaming event, if the server analyzes the audience's level of excitement, it can immediately apply a performance plan based on that data, such as increasing the music volume or enhancing the video's saturation. This allows viewers to have a more emotionally engaging experience.

[0720] An example of a prompt to provide to a generative AI model is: "Consider a concept for a smart app that analyzes viewer sentiment in real time during a live broadcast and adjusts the video and audio based on that analysis."

[0721] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0722] Step 1:

[0723] The user collects biometric information using a heart rate sensor and camera built into their mobile device. The user's heart rate data and facial expression data are entered. This data serves as foundational data to indicate the user's emotional state. The collected data is encrypted for data security and immediately transmitted to the server.

[0724] Step 2:

[0725] The server passes the received biometric information to the emotion analysis engine. Based on the input heart rate and facial expression data, a generative AI model is used to quantify the emotional state. This data processing uses algorithms that analyze heart rate fluctuations and facial expression patterns. The resulting output is a numerical value indicating a specific emotional state, such as excitement or emotion.

[0726] Step 3:

[0727] The server runs a performance plan generation algorithm based on the analysis. The input is numerical values ​​of emotional states obtained from the emotion analysis engine. Through data calculation, an optimal performance plan is generated, including adjustments to lighting, sound, and video. The performance plan includes specific actions to enhance a particular emotion.

[0728] Step 4:

[0729] The server sends commands to the distribution system to execute the generated production plan. The output commands adjust the lighting, sound, and visual effects of live streaming and recorded content in real time. This ensures that content is delivered to viewers that instantly responds to the user's emotions.

[0730] This entire process allows users to have a more deeply emotionally engaging viewing experience.

[0731] 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.

[0732] 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.

[0733] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0734] 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.

[0735] 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.

[0736] 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.

[0737] 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.

[0738] 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.

[0739] 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."

[0740] 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.

[0741] 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.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] The following is further disclosed regarding the embodiments described above.

[0753] (Claim 1)

[0754] Information gathering means for collecting biometric information from the audience,

[0755] An analysis means that analyzes the aforementioned biological information to quantify the emotional state of the audience,

[0756] A plan generation means for generating a performance plan based on the aforementioned emotional state,

[0757] A means for executing the aforementioned production plan,

[0758] A system that includes this.

[0759] (Claim 2)

[0760] The system according to claim 1, wherein the biometric information includes heart rate and facial expression data.

[0761] (Claim 3)

[0762] The system according to claim 1, wherein the performance plan generated by the plan generation means includes lighting, sound, and video effects.

[0763] "Example 1"

[0764] (Claim 1)

[0765] A means of acquiring biometric information from the audience,

[0766] An analysis means that quantifies the emotional state of the audience using an artificial intelligence model generated using the aforementioned biometric information,

[0767] A plan generation means that dynamically generates a performance plan based on the aforementioned emotional state,

[0768] An execution device control means for immediately executing the aforementioned production plan,

[0769] A system that includes this.

[0770] (Claim 2)

[0771] The system according to claim 1, wherein the biological information includes biological indicators and facial expression information.

[0772] (Claim 3)

[0773] The system according to claim 1, wherein the performance plan generated by the plan generation means includes the effects of light, sound, and video.

[0774] "Application Example 1"

[0775] (Claim 1)

[0776] A data acquisition method for collecting biometric information from users,

[0777] A data analysis means that analyzes the aforementioned biometric information to quantify the user's emotional state,

[0778] A plan generation means that generates an interaction plan based on the aforementioned emotional state,

[0779] Interaction execution means for executing the aforementioned interaction plan,

[0780] A system that includes this.

[0781] (Claim 2)

[0782] The system according to claim 1, wherein the biometric information includes heart rate and facial expression data.

[0783] (Claim 3)

[0784] The system according to claim 1, wherein the interaction plan generated by the plan generation means includes lighting, sound, and video effects.

[0785] "Example 2 of combining an emotion engine"

[0786] (Claim 1)

[0787] Means of collecting data from the audience,

[0788] A means for analyzing the aforementioned data and quantifying the emotional state of the audience,

[0789] A means for generating a performance plan using information processing technology based on the aforementioned quantified emotional state,

[0790] Means for carrying out the aforementioned production plan,

[0791] A system that includes this.

[0792] (Claim 2)

[0793] The system according to claim 1, wherein the data includes physiological variables and facial expression information.

[0794] (Claim 3)

[0795] The system according to claim 1, wherein the performance plan includes visual effects, sound effects, and image display.

[0796] "Application example 2 when combining with an emotional engine"

[0797] (Claim 1)

[0798] Information gathering means for collecting biometric information from the audience,

[0799] An analysis means that analyzes the aforementioned biological information to quantify the emotional state of the audience,

[0800] A plan generation means for generating a performance plan based on the aforementioned emotional state,

[0801] A means for executing the aforementioned production plan,

[0802] A distribution adjustment means that dynamically adjusts the distributed video based on biometric information collected from the viewer's mobile device,

[0803] A system that includes this.

[0804] (Claim 2)

[0805] The system according to claim 1, wherein the biometric information includes heart rate and facial expression data.

[0806] (Claim 3)

[0807] The system according to claim 1, wherein the performance plan generated by the plan generation means includes dynamically adjusting the effects of lighting, sound, and video. [Explanation of Symbols]

[0808] 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 data acquisition method for collecting biometric information from users, A data analysis means that analyzes the aforementioned biometric information to quantify the user's emotional state, A plan generation means that generates an interaction plan based on the aforementioned emotional state, Interaction execution means for executing the aforementioned interaction plan, A system that includes this.

2. The system according to claim 1, wherein the biometric information includes heart rate and facial expression data.

3. The system according to claim 1, wherein the interaction plan generated by the plan generation means includes lighting, sound, and video effects.

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