system

Abstract

We provide the system. [Solution] Means for receiving biological information collected from observation devices, A means of analyzing received biometric information in real time and evaluating emotional state, A means of generating an optimal production plan based on emotional state, A means for controlling the equipment according to the generated performance 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 as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional live performances are based on a pre-determined performance plan, so they cannot immediately respond to the actual emotional state of the audience, and there is a problem that it is difficult to individualize the live experience and maximize the degree of emotion. Therefore, it is necessary to provide a more personalized experience for each audience and improve the degree of emotion of the entire live event.

Means for Solving the Problems

[0005] This invention provides a system that analyzes biometric information collected from audience members in real time and dynamically generates and optimizes performance plans based on the results. Specifically, it receives biometric information collected from observation devices, analyzes it, and evaluates the emotional state of the audience. Based on the results, it generates an optimal performance plan and controls equipment such as lighting and video to realize a live performance synchronized with the emotions of the audience. Furthermore, by integrating data obtained from multiple audience members, the system ensures a consistent atmosphere throughout the venue and learns from past data to improve the accuracy of future performance plans.

[0006] A "monitoring device" is a device used to acquire a user's biometric information, such as heart rate and facial expressions, which are collected in real time.

[0007] "Biometric information" refers to data that indicates the physical state of the audience, including information such as heart rate, facial expressions, and skin potential.

[0008] "Analysis" refers to the process of evaluating a user's emotional state based on collected biometric information, and includes statistical or machine learning-based processing of the data.

[0009] "Emotional state" refers to the user's current psychological and physiological state, including evaluations such as level of excitement and emotional response.

[0010] A "production plan" is a framework for the performance designed based on the emotional state of the audience, and specifically includes a plan that encompasses control patterns for lighting, sound, and video.

[0011] "Equipment" includes various devices and equipment used in live venues, such as lighting, video, and sound systems, and is subject to control based on the production plan.

[0012] "Control" refers to the process of instructing and operating equipment according to the performance plan, and making real-time adjustments based on that plan.

[0013] "Integration" is the process of combining biometric information collected from multiple users to create a state where overall trends and patterns can be analyzed.

[0014] "Learning" is the process of improving a system's analytical capabilities using past data, and it primarily involves the use of machine learning techniques. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This 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 a sentiment engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when a sentiment engine is combined.

Mode for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the language used in the following description will be explained.

[0018] In the following embodiments, the 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.

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the 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, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This section describes a form of implementing an emotion-driven live performance system. This system uses observation devices worn by audience members to collect and process user biometric information, dynamically optimizing the live performance. The following explains, in natural language, how this system's program works.

[0037] First, users wear a special monitoring device (such as a smart band or a smartphone with a camera) upon entering the live venue. This device detects biometric information such as the user's heart rate and facial expressions in real time. This data is then transmitted to a server via terminals within the venue.

[0038] The server uses generated AI to analyze the user's emotional state based on the received biometric information. For example, if the heart rate exceeds a certain threshold, the server determines that the user is excited. On the other hand, if facial expression analysis suggests that the user is moved, that information is also added to the emotional state evaluation.

[0039] Based on the server's analysis of the user's emotional state, an optimal performance plan is generated. This plan includes changes in lighting, selection of video content, and adjustment of sound effects. For example, if the user's emotional state has reached its climax, the server will instruct the system to enhance the lighting, increase the volume, and make the video more dynamic.

[0040] Furthermore, the server can integrate data from multiple users and evaluate the overall reaction of the venue, enabling it to provide a performance that takes into account the overall sense of unity. This allows for both personalized performances for individual users and a consistent overall performance simultaneously.

[0041] For example, if a user's heart rate is higher than normal at the start of a live performance, the server uses that information to provide an experience that maximizes excitement from the beginning (e.g., flashy lighting and high-energy music). Also, if facial analysis reveals that many users are moved during a ballad, the system will change the video to an emotional scene and adjust the lighting to a warmer tone.

[0042] In this way, the system according to the present invention can realize a more personalized and emotionally engaging live experience by responding to the user's emotions in real time.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] Users' biometric information is measured in real time through monitoring devices installed at the live venue. Heart rate sensors and cameras are used to acquire data on the user's physiological state, such as heart rate and facial expressions.

[0046] Step 2:

[0047] The terminal immediately processes the acquired biometric information and transmits it to the server using wireless communication. This data transfer is designed to minimize latency and requires real-time operation.

[0048] Step 3:

[0049] The server analyzes the received biometric information in real time. Generative AI is used, employing specific algorithms to evaluate the emotional state of individual users. For example, if a sudden increase in heart rate is detected, the server determines that the user is agitated.

[0050] Step 4:

[0051] The server generates a performance plan based on the analyzed emotional state. This plan includes changes in lighting, video transitions, and sound adjustments. For example, if the server determines that the emotional state is high, dynamic lighting effects will be incorporated into the performance plan.

[0052] Step 5:

[0053] The server transmits the generated performance plan to various control systems in real time. This allows for immediate control of the venue's lighting and sound equipment, resulting in live performances that are synchronized with the audience's emotions.

[0054] Step 6:

[0055] The server integrates data collected from multiple users to assess the overall emotional state of the venue. This allows it to adjust the presentation to deliver both a consistent group experience and individual emotional experiences.

[0056] Step 7:

[0057] After the live event ends, the server collects all the data and performs a detailed analysis of the effects of the performance. This allows the system to learn from past performances and improve the accuracy of future performance plans.

[0058] (Example 1)

[0059] 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."

[0060] Conventional live performance systems have difficulty optimizing the performance in real time according to the emotional state of the audience, and have been unable to provide a personalized experience that responds to the emotions of individual audience members. Furthermore, it has been difficult to adjust the performance while considering the sense of unity throughout the venue, and there is a need to maximize the overall emotional impact.

[0061] 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.

[0062] In this invention, the server includes means for receiving biometric information collected from an observation device, means for analyzing the received biometric information in real time and evaluating the emotional state, means for creating an optimal performance plan using a generated AI model based on the evaluated emotional state, means for controlling environmental devices according to the generated performance plan, and means for integrating data from multiple users to evaluate the overall emotions and provide a unified performance. This makes it possible to optimize the performance in real time to suit individual audience members and to create a unified performance for the entire venue.

[0063] A "monitoring device" is a device used to acquire a user's biometric information in real time, and specifically includes sensors that have the function of detecting heart rate and facial expressions.

[0064] "Biometric information" refers to physical data such as heart rate and facial expressions obtained from the user, and is used to analyze the user's emotional state in real time.

[0065] A "generative AI model" is an artificial intelligence model that evaluates the user's emotional state based on received biometric information and creates an optimal performance plan based on that evaluation.

[0066] A "production plan" is a set of specific environmental setting instructions that include the color and brightness of lighting, the type of video, and the volume of sound, all based on the user's emotional state.

[0067] "Environmental equipment" refers to equipment controlled according to the production plan, and is a broad term encompassing equipment for environmental adjustment, including lighting systems, video projectors, sound systems, and other such devices.

[0068] "Emotional state" refers to the emotional state a user is currently experiencing, analyzed based on biometric information such as heart rate and facial expressions.

[0069] "Sense of unity" is a concept that refers to the overall harmony and coherence of a venue, obtained as a result of comprehensively evaluating the emotional states of multiple users.

[0070] This invention is specifically implemented as an emotion-driven live performance system. When a user enters a live venue, they wear an observation device (e.g., a smart band or a smartphone with a camera) to acquire biometric information such as heart rate and facial expressions in real time. This observation device has the function of transmitting the biometric information detected by the user to a server via a terminal within the venue.

[0071] The server analyzes the user's emotional state using a generative AI model based on the received biometric information. By analyzing heart rate fluctuations and facial expression patterns, it evaluates the user's level of excitement and emotion. A multi-layer neural network is commonly used as the generative AI model.

[0072] Based on the analysis results, the server creates an optimal performance plan. This plan includes lighting color and brightness, video content selection, and sound volume adjustments. The generated performance plan provides instructions for controlling the venue's environmental equipment (lighting system, video projector, sound system, etc.).

[0073] For example, if a user's heart rate is high and facial analysis indicates they are in an excited state, the server will send instructions to the environmental device to make the lighting brighter and the sound more dynamic. Also, if facial analysis determines that many users are moved by a ballad, the server can adjust the lighting to a warmer tone and project emotional video content onto the projector.

[0074] This system enables real-time personalization of the live experience in response to the user's emotional state, resulting in a cohesive and immersive overall presentation.

[0075] (Example of a prompt message)

[0076] "Analyze the user's heart rate and facial expression data at the live venue to generate the optimal performance plan."

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] Users wear monitoring devices upon entering the live venue. These devices detect biometric information such as heart rate and facial expressions in real time. Inputs are the user's current heart rate and facial video data. Outputs are heart rate values ​​as biometric information and image data for facial expression analysis. Specifically, a sensor measures the user's pulse, and a camera captures their facial expressions.

[0080] Step 2:

[0081] The terminal receives biometric information acquired by the observation device and transmits that data to a server via the network within the venue. The input is biometric data from the observation device. The output is biometric information packets sent to the server. Specifically, the terminal packets the data using Bluetooth or Wi-Fi and transfers the data to the server according to the protocol.

[0082] Step 3:

[0083] The server analyzes the received biometric information and uses a generative AI model to evaluate the user's emotional state. The input consists of heart rate and image data as biometric information. The output is the analyzed emotional state information. Specifically, the server's AI model analyzes fluctuations in heart rate and uses image recognition technology to detect specific patterns in facial expressions and infer emotions.

[0084] Step 4:

[0085] The server uses a generative AI model to create an optimal performance plan based on the evaluated emotional state. The input is information about the user's emotional state. The output is instructions for the performance plan to be applied to the environmental equipment. Specifically, the AI ​​model determines the optimal combination of lighting, sound, and video based on data it has previously learned.

[0086] Step 5:

[0087] The server sends instructions to control the venue's environmental equipment based on the generated production plan. The input is the production plan instructions. The output is the specific control commands that each environmental device will execute. For example, the server instructs the lighting system to adjust color and brightness, and the sound system to change volume.

[0088] Step 6:

[0089] The server integrates data from multiple users to evaluate the overall emotional state of the venue. The input is emotional state data from multiple users. The output is a cohesive performance instruction based on the integrated emotional response. Specifically, the server analyzes the aggregated emotional data to determine and adjust the overall direction of the performance.

[0090] (Application Example 1)

[0091] 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."

[0092] In modern home environments, it is difficult to create living spaces that provide the optimal atmosphere tailored to the emotions and moods of each family member. In particular, there is a lack of means to alleviate stress within the home and create a comfortable living environment. Therefore, there is a need to dynamically adjust lighting and sound according to the emotions and state of mind of each family member.

[0093] 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.

[0094] In this invention, the server includes means for receiving biometric information collected from an observation device, means for analyzing the received biometric information in real time and evaluating the emotional state, and means for generating an optimal production plan based on the emotional state and adjusting the lighting and sound in the home environment. This makes it possible to provide each individual in the home with an optimal spatial production tailored to their emotions.

[0095] A "monitoring device" is a device that captures a user's biometric information and transmits it to a server or data processing system via a specific interface.

[0096] "Biometric information" refers to numerical values ​​or indicators that show the user's physical and emotional state, such as heart rate and facial expression data.

[0097] "Real-time analysis" refers to a technical process that performs analytical processing to infer emotional states almost simultaneously with the collection of data.

[0098] "Assessing emotional state" refers to a method of determining the type and degree of emotions a user is experiencing based on collected biometric information.

[0099] "Generating a production plan" is the process of setting up a scene by combining elements such as lighting, sound, and video based on the evaluated emotional state.

[0100] "Controlling the equipment" refers to the process of dynamically adjusting lighting and sound equipment according to the generated production plan to achieve the optimal environmental presentation.

[0101] "Home environment" refers to the physical and psychological conditions inside a house, including elements that influence the emotions and activities of the residents.

[0102] "Adjusting lighting and sound" refers to the act of creating a space that suits the residents' emotions by changing the color and intensity of light, and the type and volume of music according to the situation.

[0103] The system based on this application aims to analyze the biometric information of each individual in the home in real time and dynamically optimize the environmental settings based on the analysis results. The monitoring devices include wearable devices to measure family members' heart rates and cameras to capture facial expressions. These devices are connected to a central data processing unit (server) within the home via Bluetooth or Wi-Fi.

[0104] The server uses machine learning libraries such as TENSORFLOW® to analyze biometric information in real time. For example, a high heart rate is interpreted as a state of excitement, and emotions are inferred from facial expression data. This allows the server to assess whether the resident wants to relax or stay energetic.

[0105] Based on the analysis results, the server controls smart lights and sound systems. For example, it can adjust the color and intensity of Philips Hue lights and play appropriate music through music streaming services. It can even utilize services like Spotify to select classical music to create a relaxing environment.

[0106] For example, if a family member returns home after a long day and their heart rate is high, the server will play classical music and adjust the lighting to create a relaxing atmosphere. Alternatively, if they want to feel more energetic, it's possible to combine upbeat music with bright lighting.

[0107] This system uses a generative AI model to provide each resident with the optimal environmental design in real time. It can respond to questions such as: "What's the best lighting and music combination for when I'm feeling stressed?"

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The observation device collects biometric information. The user provides heart rate and facial expression data through a wearable device or camera. This data is transmitted to the terminal via Bluetooth or Wi-Fi.

[0111] Step 2:

[0112] The terminal receives data and sends it to the server. The terminal collects heart rate and facial expression data obtained from the observation device and transfers it to the server for data processing. The input is raw biometric data, and the output is the completion of data transfer.

[0113] Step 3:

[0114] The server analyzes the received biometric information. Using TensorFlow, the server analyzes excitement levels from heart rate and emotional states from facial expression data to evaluate the type and intensity of the user's emotions. The input is biometric information, and the output generates evaluation data that quantifies the emotional state.

[0115] Step 4:

[0116] The server generates a performance plan based on the user's emotional state. Using a generative AI model, it determines the appropriate lighting color and intensity, as well as the music genre, for the user's emotions. The input is analyzed emotional data, and the output is a specific lighting and sound setting plan.

[0117] Step 5:

[0118] The server controls the equipment according to the performance plan. It instructs smart lighting and sound systems to operate according to the set plan. Based on feedback, the performance is optimized for the user. The output is the modified lighting and sound 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] The system of the present invention incorporates a mechanism that dynamically optimizes live performances using an emotion engine for recognizing user emotions. The following describes a specific implementation of this system.

[0121] Upon arriving at the live venue, users wear monitoring devices designed to detect their heart rate and facial expressions. These devices collect the user's biometric information in real time and transmit this data to a device to enhance the fan experience. The device then transfers this data to a server via the venue's communication network.

[0122] The server activates an emotion engine upon receiving biometric information and analyzes the user's emotional state based on the obtained data. The emotion engine comprehensively analyzes changes in heart rate and facial expressions to recognize the user's state, such as whether they are excited or moved. This process utilizes machine learning algorithms, referencing past data to achieve more accurate emotion recognition.

[0123] Next, the server generates an optimal performance plan based on the analyzed emotional state. The generated plan includes specific control commands for lighting, sound, and video equipment, making it possible to provide a live experience that resonates with the audience's emotions. For example, if many users are showing excitement in common, the server, based on the emotion engine's judgment, can make the lighting brighter and speed up the tempo of the music playing from the speakers.

[0124] Furthermore, the server collects data from different users across multiple observation devices and comprehensively analyzes the emotional state of the group. This information serves as a guideline for determining the overall atmosphere of the live performance, and if there is a common emotional response throughout, it becomes possible to implement appropriate effects, such as projecting expansive images across the entire venue.

[0125] The system also includes a function that uses data accumulated from past live events to predict the effects of the performance. This allows the entire live performance to become more moving and personalized, providing new value that goes beyond the typical live experience.

[0126] In this way, the system of the present invention utilizes an emotion engine to provide advanced live entertainment tailored to each user.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] Users enter the live venue, and the monitoring device they wear measures biometric information such as heart rate and facial expressions in real time. This information is crucial data for understanding the user's emotional state.

[0130] Step 2:

[0131] The terminal transfers the user's biometric information, acquired from the observation device, to a server via the venue's network. This process is rapid, minimizing data latency.

[0132] Step 3:

[0133] The server analyzes the received biometric information using an emotion engine. The emotion engine uses machine learning algorithms to evaluate the user's emotional state based on their heart rate and facial expression changes.

[0134] Step 4:

[0135] Based on the analysis results, the server generates an optimal performance plan tailored to the user's emotions. This plan includes specific control commands, such as how to change the lighting and when to emphasize the music.

[0136] Step 5:

[0137] The server transmits the generated performance plan to the control systems for lighting, sound, and video equipment. This allows for the optimal performance to be delivered in real time, tailored to the audience's emotions.

[0138] Step 6:

[0139] The server integrates emotional data collected from multiple users to determine the overall emotional state. Based on this integrated data, the performance is further adjusted to enhance the sense of unity throughout the venue.

[0140] Step 7:

[0141] After a live performance ends, the server uses past live data to learn and predict effective staging for future performances. This process ensures that future live performances become even more refined and captivating.

[0142] (Example 2)

[0143] 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".

[0144] In recent years, there has been a growing demand for optimizing performances in response to audience emotions at live events. However, traditional methods have made it difficult to accurately reflect the emotions of individual audience members in real time. In particular, realizing integrated performances that consider the emotional trends of the group, and predictive performances using past data, have been major challenges.

[0145] 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.

[0146] In this invention, the server includes a component that receives biometric data collected from observation equipment, a component that analyzes the received biometric data in real time and estimates emotions, and a component that generates an optimal performance plan based on the emotions. This enables advanced performances based on real-time individual and collective emotion recognition.

[0147] "Observation equipment" refers to devices used to collect biological data in real time.

[0148] "Biometric data" refers to information that indicates the user's physical condition, such as their heart rate and facial expressions.

[0149] "Components for real-time analysis" refers to elements that include technologies for instantly processing received data and estimating emotions.

[0150] "Components for estimating emotions" refers to algorithms and technologies that analyze a user's biometric data to determine their emotional state.

[0151] "Components for generating an optimal production plan" refers to elements used to create a plan that dynamically adjusts production elements such as lighting and sound based on perceived emotions.

[0152] "Components for controlling the equipment" refers to the technology used to directly operate lighting, sound equipment, and other devices in a live venue according to the generated performance plan.

[0153] A "machine learning algorithm" refers to a learning method that uses historical data to improve the accuracy of emotion recognition, and is applied to the analysis of biometric data.

[0154] This invention is a system that enables dynamic performances based on audience emotions at live events. The following describes a specific form for implementing this system.

[0155] Upon arriving at the live venue, users put on monitoring equipment. This equipment collects biometric data such as heart rate and facial expression in real time. The terminal receives the collected biometric data and transmits it to a server via the venue's communication network.

[0156] The server activates an emotion recognition engine to process the received biometric data. This engine uses machine learning algorithms to analyze changes in heart rate and facial expression data to estimate the user's emotions. This includes, for example, a process to determine whether the user is excited or moved.

[0157] The server generates an optimal performance plan based on the analyzed emotional information. This plan includes lighting color and brightness, music tempo and volume, and video content. The server can also use a generative AI model to output the generated plan as a prompt message.

[0158] As a concrete example, the prompt might look like this: "Analyze the audience's heart rate and facial expression data to recognize their emotions. Then, propose a lighting and sound plan based on those emotions."

[0159] This system can utilize data accumulated from past events to improve the performance effects of future events. This data will provide valuable information for delivering a better emotional experience at the next live event. As a result, users will not simply be spectators, but will be able to enjoy personalized entertainment tailored to their emotions.

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

[0161] Step 1:

[0162] Upon entering the live venue, users wear monitoring devices that detect their heart rate and facial expressions. The devices collect real-time heart rate and facial expression data as input, and then transmit this biometric data to a terminal.

[0163] Step 2:

[0164] The terminal processes the biometric data received from the observation equipment. The inputs in this step are heart rate data and facial expression data. The terminal performs data processing by matching the collected biometric data, formatting it, and then transmitting it to the server via the communication network within the venue. The output is the formatted biometric data.

[0165] Step 3:

[0166] The server receives biometric data transmitted from the terminal. The input is formatted biometric data transmitted by the terminal. The server activates an emotion recognition engine and performs data calculations, such as analyzing changes in heart rate and facial features. The output is an estimated result of the user's emotion, determining the user's emotional state, such as whether they are excited or moved.

[0167] Step 4:

[0168] The server generates an optimal performance plan based on the emotion estimation results. The input is the user's emotion estimation result. The server uses a generative AI model to perform data calculations to formulate a control plan for elements such as lighting, music, and video. The output is a specific control plan and prompt messages.

[0169] Step 5:

[0170] The server transmits the generated performance plan to the various facilities at the venue, controlling the actual live performance. The input consists of the performance plan and prompt messages generated by the server. The server sends commands to the control systems of each piece of equipment, causing lighting, sound, and other elements to operate as instructed. The output is a live performance that responds to the audience's emotions.

[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 the "server," and the smart device 14 will be referred to as the "terminal."

[0173] In online content distribution, there is a challenge in that real-time optimization based on viewer emotions is not being achieved, resulting in a decline in the quality of the viewing experience. It is necessary to instantly understand the different emotions of each viewer and provide content that responds accordingly to improve the 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 means for receiving biometric information collected from an observation device, means for analyzing the received biometric information in real time and evaluating the emotional state, means for generating an optimal content plan based on the emotional state, and means for controlling the viewing device according to the generated content plan. This enables immediate content optimization that responds to the individual emotions of the viewer.

[0176] A "monitoring device" is a device used to collect a user's biometric information, such as heart rate and facial expressions.

[0177] "Biometric information" refers to data related to a user's body and behavior, including information such as heart rate and facial expressions.

[0178] "Real-time analysis" refers to the process of immediately analyzing collected data and deriving results.

[0179] "Emotional state" refers to the user's psychological state, and includes specific emotions such as excitement and emotion.

[0180] A "content plan" is a structure or plan designed to present the most suitable content according to the emotional state of the viewer.

[0181] "Viewing device" refers to a device used by viewers to consume content, and includes smartphones, tablets, and other similar devices.

[0182] To implement this invention, a system consisting of an observation device, a server, and a viewing device is required. The user first collects their own biometric information using the observation device. The observation device may include a smartwatch for measuring heart rate or a smartphone camera for recognizing facial expressions.

[0183] The terminal temporarily stores biometric information acquired from the observation device and transmits it to a server via a communication network connected within the venue or online. The server receives and analyzes the biometric information in real time. During this process, an emotion engine is activated to identify the user's emotional state from the received biometric information. This emotion engine uses machine learning algorithms based on the collected data, referencing past data to perform a highly accurate emotional assessment.

[0184] Based on the analysis results, the server generates a content plan tailored to the viewer's emotional state. This content plan includes elements that enhance the viewing experience in various ways; for example, it automatically provides behind-the-scenes footage to emotionally moved viewers. The viewing device controls the playback of video and audio according to the content plan sent from the server.

[0185] As a concrete example, when a user is watching an online live performance at home, if their heart rate increases and excitement is detected, the viewing device will instantly display powerful visuals or real-time behind-the-scenes footage to make the experience even more impressive. By utilizing a generative AI model, the system will generate a prompt message such as, "Estimate the user's emotions from their heart rate and facial expressions, and optimize the live stream content in real time," based on the user's state, thereby coordinating the entire system.

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

[0187] Step 1:

[0188] The user wears a monitoring device. The device acquires heart rate and facial expression data and transmits it to the terminal. The input is the user's biometric information, and the output is real-time acquired heart rate and facial expression data.

[0189] Step 2:

[0190] The terminal transmits biometric information received from the observation device to the server. The terminal transmits heart rate and facial expression data to the server in real time via the network. As a result of this data transfer, the server receives the user's latest biometric information.

[0191] Step 3:

[0192] The server analyzes the received biometric information. It activates an emotion engine and uses machine learning algorithms to comprehensively analyze changes in heart rate and facial expressions to evaluate the user's emotional state. Heart rate and facial expression data are used as input, and the output is the analyzed emotional state.

[0193] Step 4:

[0194] The server generates a content plan based on the analyzed emotional state. Utilizing a generative AI model, it selects the content best suited to the user's emotional state and generates instructions accordingly. The input is the emotional state evaluation result, and the output is the content plan. Specifically, if excitement is detected, it will instruct the user to watch a video with a strong emotional impact.

[0195] Step 5:

[0196] The server sends the generated content plan to the viewing device. The viewing device receives instructions from the server and plays the specified content. The input is the content plan from the server, and the output is the content the user views. Specifically, the viewing device changes the video and audio playback settings and provides content in real time.

[0197] 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.

[0198] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0199] 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.

[0200] [Second Embodiment]

[0201] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0202] 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.

[0203] 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).

[0204] 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.

[0205] 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.

[0206] 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).

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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".

[0213] This section describes a form of implementing an emotion-driven live performance system. This system uses observation devices worn by audience members to collect and process user biometric information, dynamically optimizing the live performance. The following explains, in natural language, how this system's program works.

[0214] First, users wear a special monitoring device (such as a smart band or a smartphone with a camera) upon entering the live venue. This device detects biometric information such as the user's heart rate and facial expressions in real time. This data is then transmitted to a server via terminals within the venue.

[0215] The server uses generated AI to analyze the user's emotional state based on the received biometric information. For example, if the heart rate exceeds a certain threshold, the server determines that the user is excited. On the other hand, if facial expression analysis suggests that the user is moved, that information is also added to the emotional state evaluation.

[0216] Based on the server's analysis of the user's emotional state, an optimal performance plan is generated. This plan includes changes in lighting, selection of video content, and adjustment of sound effects. For example, if the user's emotional state has reached its climax, the server will instruct the system to enhance the lighting, increase the volume, and make the video more dynamic.

[0217] Furthermore, the server can integrate data from multiple users and evaluate the overall reaction of the venue, enabling it to provide a performance that takes into account the overall sense of unity. This allows for both personalized performances for individual users and a consistent overall performance simultaneously.

[0218] For example, if a user's heart rate is higher than normal at the start of a live performance, the server uses that information to provide an experience that maximizes excitement from the beginning (e.g., flashy lighting and high-energy music). Also, if facial analysis reveals that many users are moved during a ballad, the system will change the video to an emotional scene and adjust the lighting to a warmer tone.

[0219] In this way, the system according to the present invention can realize a more personalized and emotionally engaging live experience by responding to the user's emotions in real time.

[0220] The following describes the processing flow.

[0221] Step 1:

[0222] Users' biometric information is measured in real time through monitoring devices installed at the live venue. Heart rate sensors and cameras are used to acquire data on the user's physiological state, such as heart rate and facial expressions.

[0223] Step 2:

[0224] The terminal immediately processes the acquired biometric information and transmits it to the server using wireless communication. This data transfer is designed to minimize latency and requires real-time operation.

[0225] Step 3:

[0226] The server analyzes the received biometric information in real time. Generative AI is used, employing specific algorithms to evaluate the emotional state of individual users. For example, if a sudden increase in heart rate is detected, the server determines that the user is agitated.

[0227] Step 4:

[0228] The server generates a performance plan based on the analyzed emotional state. This plan includes changes in lighting, video transitions, and sound adjustments. For example, if the server determines that the emotional state is high, dynamic lighting effects will be incorporated into the performance plan.

[0229] Step 5:

[0230] The server transmits the generated performance plan to various control systems in real time. This allows for immediate control of the venue's lighting and sound equipment, resulting in live performances that are synchronized with the audience's emotions.

[0231] Step 6:

[0232] The server integrates data collected from multiple users to assess the overall emotional state of the venue. This allows it to adjust the presentation to deliver both a consistent group experience and individual emotional experiences.

[0233] Step 7:

[0234] After the live event ends, the server collects all the data and performs a detailed analysis of the effects of the performance. This allows the system to learn from past performances and improve the accuracy of future performance plans.

[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] Conventional live performance systems have difficulty optimizing the performance in real time according to the emotional state of the audience, and have been unable to provide a personalized experience that responds to the emotions of individual audience members. Furthermore, it has been difficult to adjust the performance while considering the sense of unity throughout the venue, and there is a need to maximize the overall emotional impact.

[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 means for receiving biometric information collected from an observation device, means for analyzing the received biometric information in real time and evaluating the emotional state, means for creating an optimal performance plan using a generated AI model based on the evaluated emotional state, means for controlling environmental devices according to the generated performance plan, and means for integrating data from multiple users to evaluate the overall emotions and provide a unified performance. This makes it possible to optimize the performance in real time to suit individual audience members and to create a unified performance for the entire venue.

[0240] A "monitoring device" is a device used to acquire a user's biometric information in real time, and specifically includes sensors that have the function of detecting heart rate and facial expressions.

[0241] "Biometric information" refers to physical data such as heart rate and facial expressions obtained from the user, and is used to analyze the user's emotional state in real time.

[0242] A "generative AI model" is an artificial intelligence model that evaluates the user's emotional state based on received biometric information and creates an optimal performance plan based on that evaluation.

[0243] A "production plan" is a set of specific environmental setting instructions that include the color and brightness of lighting, the type of video, and the volume of sound, all based on the user's emotional state.

[0244] "Environmental equipment" refers to equipment controlled according to the production plan, and is a broad term encompassing equipment for environmental adjustment, including lighting systems, video projectors, sound systems, and other such devices.

[0245] "Emotional state" refers to the emotional state a user is currently experiencing, analyzed based on biometric information such as heart rate and facial expressions.

[0246] "Sense of unity" is a concept that refers to the overall harmony and coherence of a venue, obtained as a result of comprehensively evaluating the emotional states of multiple users.

[0247] This invention is specifically implemented as an emotion-driven live performance system. When a user enters a live venue, they wear an observation device (e.g., a smart band or a smartphone with a camera) to acquire biometric information such as heart rate and facial expressions in real time. This observation device has the function of transmitting the biometric information detected by the user to a server via a terminal within the venue.

[0248] The server analyzes the user's emotional state using a generative AI model based on the received biometric information. By analyzing heart rate fluctuations and facial expression patterns, it evaluates the user's level of excitement and emotion. A multi-layer neural network is commonly used as the generative AI model.

[0249] Based on the analysis results, the server creates an optimal performance plan. This plan includes lighting color and brightness, video content selection, and sound volume adjustments. The generated performance plan provides instructions for controlling the venue's environmental equipment (lighting system, video projector, sound system, etc.).

[0250] For example, if a user's heart rate is high and facial analysis indicates they are in an excited state, the server will send instructions to the environmental device to make the lighting brighter and the sound more dynamic. Also, if facial analysis determines that many users are moved by a ballad, the server can adjust the lighting to a warmer tone and project emotional video content onto the projector.

[0251] This system enables real-time personalization of the live experience in response to the user's emotional state, resulting in a cohesive and immersive overall presentation.

[0252] (Example of a prompt message)

[0253] "Analyze the user's heart rate and facial expression data at the live venue to generate the optimal performance plan."

[0254] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0255] Step 1:

[0256] Users wear monitoring devices upon entering the live venue. These devices detect biometric information such as heart rate and facial expressions in real time. Inputs are the user's current heart rate and facial video data. Outputs are heart rate values ​​as biometric information and image data for facial expression analysis. Specifically, a sensor measures the user's pulse, and a camera captures their facial expressions.

[0257] Step 2:

[0258] The terminal receives biometric information acquired by the observation device and transmits that data to a server via the network within the venue. The input is biometric data from the observation device. The output is biometric information packets sent to the server. Specifically, the terminal packets the data using Bluetooth or Wi-Fi and transfers the data to the server according to the protocol.

[0259] Step 3:

[0260] The server analyzes the received biometric information and uses a generative AI model to evaluate the user's emotional state. The input consists of heart rate and image data as biometric information. The output is the analyzed emotional state information. Specifically, the server's AI model analyzes fluctuations in heart rate and uses image recognition technology to detect specific patterns in facial expressions and infer emotions.

[0261] Step 4:

[0262] The server uses a generative AI model to create an optimal performance plan based on the evaluated emotional state. The input is information about the user's emotional state. The output is instructions for the performance plan to be applied to the environmental equipment. Specifically, the AI ​​model determines the optimal combination of lighting, sound, and video based on data it has previously learned.

[0263] Step 5:

[0264] The server sends instructions to control the venue's environmental equipment based on the generated production plan. The input is the production plan instructions. The output is the specific control commands that each environmental device will execute. For example, the server instructs the lighting system to adjust color and brightness, and the sound system to change volume.

[0265] Step 6:

[0266] The server integrates data from multiple users to evaluate the overall emotional state of the venue. The input is emotional state data from multiple users. The output is a cohesive performance instruction based on the integrated emotional response. Specifically, the server analyzes the aggregated emotional data to determine and adjust the overall direction of the performance.

[0267] (Application Example 1)

[0268] 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."

[0269] In modern home environments, it is difficult to create living spaces that provide the optimal atmosphere tailored to the emotions and moods of each family member. In particular, there is a lack of means to alleviate stress within the home and create a comfortable living environment. Therefore, there is a need to dynamically adjust lighting and sound according to the emotions and state of mind of each family member.

[0270] 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.

[0271] In this invention, the server includes means for receiving biometric information collected from an observation device, means for analyzing the received biometric information in real time and evaluating the emotional state, and means for generating an optimal production plan based on the emotional state and adjusting the lighting and sound in the home environment. This makes it possible to provide each individual in the home with an optimal spatial production tailored to their emotions.

[0272] A "monitoring device" is a device that captures a user's biometric information and transmits it to a server or data processing system via a specific interface.

[0273] "Biometric information" refers to numerical values ​​or indicators that show the user's physical and emotional state, such as heart rate and facial expression data.

[0274] "Real-time analysis" refers to a technical process that performs analytical processing to infer emotional states almost simultaneously with the collection of data.

[0275] "Assessing emotional state" refers to a method of determining the type and degree of emotions a user is experiencing based on collected biometric information.

[0276] "Generating a production plan" is the process of setting up a scene by combining elements such as lighting, sound, and video based on the evaluated emotional state.

[0277] "Controlling the equipment" refers to the process of dynamically adjusting lighting and sound equipment according to the generated production plan to achieve the optimal environmental presentation.

[0278] "Home environment" refers to the physical and psychological conditions inside a house, including elements that influence the emotions and activities of the residents.

[0279] "Adjusting lighting and sound" refers to the act of creating a space that suits the residents' emotions by changing the color and intensity of light, and the type and volume of music according to the situation.

[0280] The system based on this application aims to analyze the biometric information of each individual in the home in real time and dynamically optimize the environmental settings based on the analysis results. The monitoring devices include wearable devices to measure family members' heart rates and cameras to capture facial expressions. These devices are connected to a central data processing unit (server) within the home via Bluetooth or Wi-Fi.

[0281] The server uses machine learning libraries such as TensorFlow to analyze biometric information in real time. For example, a high heart rate is interpreted as a state of excitement, and emotions are inferred from facial expression data. This allows the server to assess whether the resident wants to relax or stay energetic.

[0282] According to the analysis results, the server controls smart lights and audio systems. For example, it adjusts the color and intensity of Philips Hue lights and plays appropriate music through a music streaming service. Services such as Spotify can be utilized to select classical music for providing a relaxing environment.

[0283] As a specific example, when the family returns home after a long day and the heart rate is high, the server plays classical music and adjusts the lighting to warm lighting to create a relaxation space. Also, when wanting to boost energy, it is possible to combine upbeat music and bright lighting.

[0284] This system uses a generative AI model to provide optimal environmental effects for each resident in real time. Such a query is possible: "Tell me the optimal combination of lighting and music when the user is stressed."

[0285] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0286] Step 1:

[0287] The observation device collects biometric information. The user provides heart rate and expression data through a wearable device or a camera. These data are transmitted to the terminal via Bluetooth or Wi-Fi.

[0288] Step 2:

[0289] The terminal receives the data and transmits it to the server. The terminal collects the heart rate and expression data obtained from the observation device and transfers them to the server for data processing. The input is the raw data of the biometric information, and the output is the completion of the data transfer.

[0290] Step 3:

[0291] The server analyzes the received biometric information. Using TensorFlow, the server analyzes excitement levels from heart rate and emotional states from facial expression data to evaluate the type and intensity of the user's emotions. The input is biometric information, and the output generates evaluation data that quantifies the emotional state.

[0292] Step 4:

[0293] The server generates a performance plan based on the user's emotional state. Using a generative AI model, it determines the appropriate lighting color and intensity, as well as the music genre, for the user's emotions. The input is analyzed emotional data, and the output is a specific lighting and sound setting plan.

[0294] Step 5:

[0295] The server controls the equipment according to the performance plan. It instructs smart lighting and sound systems to operate according to the set plan. Based on feedback, the performance is optimized for the user. The output is the modified lighting and sound environment.

[0296] 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.

[0297] The system of the present invention incorporates a mechanism that dynamically optimizes live performances using an emotion engine for recognizing user emotions. The following describes a specific implementation of this system.

[0298] Upon arriving at the live venue, users wear monitoring devices designed to detect their heart rate and facial expressions. These devices collect the user's biometric information in real time and transmit this data to a device to enhance the fan experience. The device then transfers this data to a server via the venue's communication network.

[0299] The server activates an emotion engine upon receiving biometric information and analyzes the user's emotional state based on the obtained data. The emotion engine comprehensively analyzes changes in heart rate and facial expressions to recognize the user's state, such as whether they are excited or moved. This process utilizes machine learning algorithms, referencing past data to achieve more accurate emotion recognition.

[0300] Next, the server generates an optimal performance plan based on the analyzed emotional state. The generated plan includes specific control commands for lighting, sound, and video equipment, making it possible to provide a live experience that resonates with the audience's emotions. For example, if many users are showing excitement in common, the server, based on the emotion engine's judgment, can make the lighting brighter and speed up the tempo of the music playing from the speakers.

[0301] Furthermore, the server collects data from different users across multiple observation devices and comprehensively analyzes the emotional state of the group. This information serves as a guideline for determining the overall atmosphere of the live performance, and if there is a common emotional response throughout, it becomes possible to implement appropriate effects, such as projecting expansive images across the entire venue.

[0302] The system also includes a function that uses data accumulated from past live events to predict the effects of the performance. This allows the entire live performance to become more moving and personalized, providing new value that goes beyond the typical live experience.

[0303] In this way, the system of the present invention utilizes an emotion engine to provide advanced live entertainment tailored to each user.

[0304] The following describes the processing flow.

[0305] Step 1:

[0306] The user enters the live venue, and the worn observation device measures biometric information such as heart rate and expression in real time. This information is important data for grasping the user's emotional state.

[0307] Step 2:

[0308] The terminal transfers the user's biometric information acquired from the observation device to the server via the network in the venue. This process is carried out quickly, minimizing data delay.

[0309] Step 3:

[0310] The server analyzes the received biometric information using an emotion engine. The emotion engine utilizes machine learning algorithms to evaluate the emotional state based on the user's heart rate and expression changes.

[0311] Step 4:

[0312] Based on the analysis results, the server generates an optimal production plan according to the user's emotions. This plan includes specific control commands such as how to change the lighting and when to emphasize the music.

[0313] Step 5:

[0314] The server transmits the generated production plan to the control systems of lighting, sound, and video equipment. As a result, an optimal production that matches the emotions of the audience in real time is performed.

[0315] Step 6:

[0316] The server integrates the emotion data collected from multiple users and determines the overall emotional state. Based on this integrated data, the production that enhances the sense of unity of the entire venue is further adjusted.

[0317] Step 7:

[0318] After a live performance ends, the server uses past live data to learn and predict effective staging for future performances. This process ensures that future live performances become even more refined and captivating.

[0319] (Example 2)

[0320] 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".

[0321] In recent years, there has been a growing demand for optimizing performances in response to audience emotions at live events. However, traditional methods have made it difficult to accurately reflect the emotions of individual audience members in real time. In particular, realizing integrated performances that consider the emotional trends of the group, and predictive performances using past data, have been major challenges.

[0322] 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.

[0323] In this invention, the server includes a component that receives biometric data collected from observation equipment, a component that analyzes the received biometric data in real time and estimates emotions, and a component that generates an optimal performance plan based on the emotions. This enables advanced performances based on real-time individual and collective emotion recognition.

[0324] "Observation equipment" refers to devices used to collect biological data in real time.

[0325] "Biometric data" refers to information that indicates the user's physical condition, such as their heart rate and facial expressions.

[0326] "Components for real-time analysis" refers to elements that include technologies for instantly processing received data and estimating emotions.

[0327] "Components for estimating emotions" refers to algorithms and technologies that analyze a user's biometric data to determine their emotional state.

[0328] "Components for generating an optimal production plan" refers to elements used to create a plan that dynamically adjusts production elements such as lighting and sound based on perceived emotions.

[0329] "Components for controlling the equipment" refers to the technology used to directly operate lighting, sound equipment, and other devices in a live venue according to the generated performance plan.

[0330] A "machine learning algorithm" refers to a learning method that uses historical data to improve the accuracy of emotion recognition, and is applied to the analysis of biometric data.

[0331] This invention is a system that enables dynamic performances based on audience emotions at live events. The following describes a specific form for implementing this system.

[0332] Upon arriving at the live venue, users put on monitoring equipment. This equipment collects biometric data such as heart rate and facial expression in real time. The terminal receives the collected biometric data and transmits it to a server via the venue's communication network.

[0333] The server activates an emotion recognition engine to process the received biometric data. This engine uses machine learning algorithms to analyze changes in heart rate and facial expression data to estimate the user's emotions. This includes, for example, a process to determine whether the user is excited or moved.

[0334] The server generates an optimal performance plan based on the analyzed emotional information. This plan includes lighting color and brightness, music tempo and volume, and video content. The server can also use a generative AI model to output the generated plan as a prompt message.

[0335] As a concrete example, the prompt might look like this: "Analyze the audience's heart rate and facial expression data to recognize their emotions. Then, propose a lighting and sound plan based on those emotions."

[0336] This system can utilize data accumulated from past events to improve the performance effects of future events. This data will provide valuable information for delivering a better emotional experience at the next live event. As a result, users will not simply be spectators, but will be able to enjoy personalized entertainment tailored to their emotions.

[0337] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0338] Step 1:

[0339] Upon entering the live venue, users wear monitoring devices that detect their heart rate and facial expressions. The devices collect real-time heart rate and facial expression data as input, and then transmit this biometric data to a terminal.

[0340] Step 2:

[0341] The terminal processes the biometric data received from the observation equipment. The inputs in this step are heart rate data and facial expression data. The terminal performs data processing by matching the collected biometric data, formatting it, and then transmitting it to the server via the communication network within the venue. The output is the formatted biometric data.

[0342] Step 3:

[0343] The server receives biometric data transmitted from the terminal. The input is formatted biometric data transmitted by the terminal. The server activates an emotion recognition engine and performs data calculations, such as analyzing changes in heart rate and facial features. The output is an estimated result of the user's emotion, determining the user's emotional state, such as whether they are excited or moved.

[0344] Step 4:

[0345] The server generates an optimal performance plan based on the emotion estimation results. The input is the user's emotion estimation result. The server uses a generative AI model to perform data calculations to formulate a control plan for elements such as lighting, music, and video. The output is a specific control plan and prompt messages.

[0346] Step 5:

[0347] The server transmits the generated performance plan to the various facilities at the venue, controlling the actual live performance. The input consists of the performance plan and prompt messages generated by the server. The server sends commands to the control systems of each piece of equipment, causing lighting, sound, and other elements to operate as instructed. The output is a live performance that responds to the audience's emotions.

[0348] (Application Example 2)

[0349] 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."

[0350] In online content distribution, there is a challenge in that real-time optimization based on viewer emotions is not being achieved, resulting in a decline in the quality of the viewing experience. It is necessary to instantly understand the different emotions of each viewer and provide content that responds accordingly to improve the viewing experience.

[0351] 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.

[0352] In this invention, the server includes means for receiving biometric information collected from an observation device, means for analyzing the received biometric information in real time and evaluating the emotional state, means for generating an optimal content plan based on the emotional state, and means for controlling the viewing device according to the generated content plan. This enables immediate content optimization that responds to the individual emotions of the viewer.

[0353] A "monitoring device" is a device used to collect a user's biometric information, such as heart rate and facial expressions.

[0354] "Biometric information" refers to data related to a user's body and behavior, including information such as heart rate and facial expressions.

[0355] "Real-time analysis" refers to the process of immediately analyzing collected data and deriving results.

[0356] "Emotional state" refers to the user's psychological state, and includes specific emotions such as excitement and emotion.

[0357] A "content plan" is a structure or plan designed to present the most suitable content according to the emotional state of the viewer.

[0358] "Viewing device" refers to a device used by viewers to consume content, and includes smartphones, tablets, and other similar devices.

[0359] To implement this invention, a system consisting of an observation device, a server, and a viewing device is required. The user first collects their own biometric information using the observation device. The observation device may include a smartwatch for measuring heart rate or a smartphone camera for recognizing facial expressions.

[0360] The terminal temporarily stores biometric information acquired from the observation device and transmits it to a server via a communication network connected within the venue or online. The server receives and analyzes the biometric information in real time. During this process, an emotion engine is activated to identify the user's emotional state from the received biometric information. This emotion engine uses machine learning algorithms based on the collected data, referencing past data to perform a highly accurate emotional assessment.

[0361] Based on the analysis results, the server generates a content plan tailored to the viewer's emotional state. This content plan includes elements that enhance the viewing experience in various ways; for example, it automatically provides behind-the-scenes footage to emotionally moved viewers. The viewing device controls the playback of video and audio according to the content plan sent from the server.

[0362] As a concrete example, when a user is watching an online live performance at home, if their heart rate increases and excitement is detected, the viewing device will instantly display powerful visuals or real-time behind-the-scenes footage to make the experience even more impressive. By utilizing a generative AI model, the system will generate a prompt message such as, "Estimate the user's emotions from their heart rate and facial expressions, and optimize the live stream content in real time," based on the user's state, thereby coordinating the entire system.

[0363] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0364] Step 1:

[0365] The user wears a monitoring device. The device acquires heart rate and facial expression data and transmits it to the terminal. The input is the user's biometric information, and the output is real-time acquired heart rate and facial expression data.

[0366] Step 2:

[0367] The terminal transmits biometric information received from the observation device to the server. The terminal transmits heart rate and facial expression data to the server in real time via the network. As a result of this data transfer, the server receives the user's latest biometric information.

[0368] Step 3:

[0369] The server analyzes the received biometric information. It activates an emotion engine and uses machine learning algorithms to comprehensively analyze changes in heart rate and facial expressions to evaluate the user's emotional state. Heart rate and facial expression data are used as input, and the output is the analyzed emotional state.

[0370] Step 4:

[0371] The server generates a content plan based on the analyzed emotional state. Utilizing a generative AI model, it selects the content best suited to the user's emotional state and generates instructions accordingly. The input is the emotional state evaluation result, and the output is the content plan. Specifically, if excitement is detected, it will instruct the user to watch a video with a strong emotional impact.

[0372] Step 5:

[0373] The server sends the generated content plan to the viewing device. The viewing device receives instructions from the server and plays the specified content. The input is the content plan from the server, and the output is the content the user views. Specifically, the viewing device changes the video and audio playback settings and provides content in real time.

[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). An 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 section describes a form of implementing an emotion-driven live performance system. This system uses observation devices worn by audience members to collect and process user biometric information, dynamically optimizing the live performance. The following explains, in natural language, how this system's program works.

[0391] First, users wear a special monitoring device (such as a smart band or a smartphone with a camera) upon entering the live venue. This device detects biometric information such as the user's heart rate and facial expressions in real time. This data is then transmitted to a server via terminals within the venue.

[0392] The server uses generated AI to analyze the user's emotional state based on the received biometric information. For example, if the heart rate exceeds a certain threshold, the server determines that the user is excited. On the other hand, if facial expression analysis suggests that the user is moved, that information is also added to the emotional state evaluation.

[0393] Based on the server's analysis of the user's emotional state, an optimal performance plan is generated. This plan includes changes in lighting, selection of video content, and adjustment of sound effects. For example, if the user's emotional state has reached its climax, the server will instruct the system to enhance the lighting, increase the volume, and make the video more dynamic.

[0394] Furthermore, the server can integrate data from multiple users and evaluate the overall reaction of the venue, enabling it to provide a performance that takes into account the overall sense of unity. This allows for both personalized performances for individual users and a consistent overall performance simultaneously.

[0395] For example, if a user's heart rate is higher than normal at the start of a live performance, the server uses that information to provide an experience that maximizes excitement from the beginning (e.g., flashy lighting and high-energy music). Also, if facial analysis reveals that many users are moved during a ballad, the system will change the video to an emotional scene and adjust the lighting to a warmer tone.

[0396] In this way, the system according to the present invention can realize a more personalized and emotionally engaging live experience by responding to the user's emotions in real time.

[0397] The following describes the processing flow.

[0398] Step 1:

[0399] Users' biometric information is measured in real time through monitoring devices installed at the live venue. Heart rate sensors and cameras are used to acquire data on the user's physiological state, such as heart rate and facial expressions.

[0400] Step 2:

[0401] The terminal immediately processes the acquired biometric information and transmits it to the server using wireless communication. This data transfer is designed to minimize latency and requires real-time operation.

[0402] Step 3:

[0403] The server analyzes the received biometric information in real time. Generative AI is used, employing specific algorithms to evaluate the emotional state of individual users. For example, if a sudden increase in heart rate is detected, the server determines that the user is agitated.

[0404] Step 4:

[0405] The server generates a performance plan based on the analyzed emotional state. This plan includes changes in lighting, video transitions, and sound adjustments. For example, if the server determines that the emotional state is high, dynamic lighting effects will be incorporated into the performance plan.

[0406] Step 5:

[0407] The server transmits the generated performance plan to various control systems in real time. This allows for immediate control of the venue's lighting and sound equipment, resulting in live performances that are synchronized with the audience's emotions.

[0408] Step 6:

[0409] The server integrates data collected from multiple users to assess the overall emotional state of the venue. This allows it to adjust the presentation to deliver both a consistent group experience and individual emotional experiences.

[0410] Step 7:

[0411] After the live event ends, the server collects all the data and performs a detailed analysis of the effects of the performance. This allows the system to learn from past performances and improve the accuracy of future performance plans.

[0412] (Example 1)

[0413] 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."

[0414] Conventional live performance systems have difficulty optimizing the performance in real time according to the emotional state of the audience, and have been unable to provide a personalized experience that responds to the emotions of individual audience members. Furthermore, it has been difficult to adjust the performance while considering the sense of unity throughout the venue, and there is a need to maximize the overall emotional impact.

[0415] 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.

[0416] In this invention, the server includes means for receiving biometric information collected from an observation device, means for analyzing the received biometric information in real time and evaluating the emotional state, means for creating an optimal performance plan using a generated AI model based on the evaluated emotional state, means for controlling environmental devices according to the generated performance plan, and means for integrating data from multiple users to evaluate the overall emotions and provide a unified performance. This makes it possible to optimize the performance in real time to suit individual audience members and to create a unified performance for the entire venue.

[0417] A "monitoring device" is a device used to acquire a user's biometric information in real time, and specifically includes sensors that have the function of detecting heart rate and facial expressions.

[0418] "Biometric information" refers to physical data such as heart rate and facial expressions obtained from the user, and is used to analyze the user's emotional state in real time.

[0419] A "generative AI model" is an artificial intelligence model that evaluates the user's emotional state based on received biometric information and creates an optimal performance plan based on that evaluation.

[0420] A "production plan" is a set of specific environmental setting instructions that include the color and brightness of lighting, the type of video, and the volume of sound, all based on the user's emotional state.

[0421] "Environmental equipment" refers to equipment controlled according to the production plan, and is a broad term encompassing equipment for environmental adjustment, including lighting systems, video projectors, sound systems, and other such devices.

[0422] "Emotional state" refers to the emotional state a user is currently experiencing, analyzed based on biometric information such as heart rate and facial expressions.

[0423] "Sense of unity" is a concept that refers to the overall harmony and coherence of a venue, obtained as a result of comprehensively evaluating the emotional states of multiple users.

[0424] This invention is specifically implemented as an emotion-driven live performance system. When a user enters a live venue, they wear an observation device (e.g., a smart band or a smartphone with a camera) to acquire biometric information such as heart rate and facial expressions in real time. This observation device has the function of transmitting the biometric information detected by the user to a server via a terminal within the venue.

[0425] The server analyzes the user's emotional state using a generative AI model based on the received biometric information. By analyzing heart rate fluctuations and facial expression patterns, it evaluates the user's level of excitement and emotion. A multi-layer neural network is commonly used as the generative AI model.

[0426] Based on the analysis results, the server creates an optimal performance plan. This plan includes lighting color and brightness, video content selection, and sound volume adjustments. The generated performance plan provides instructions for controlling the venue's environmental equipment (lighting system, video projector, sound system, etc.).

[0427] For example, if a user's heart rate is high and facial analysis indicates they are in an excited state, the server will send instructions to the environmental device to make the lighting brighter and the sound more dynamic. Also, if facial analysis determines that many users are moved by a ballad, the server can adjust the lighting to a warmer tone and project emotional video content onto the projector.

[0428] This system enables real-time personalization of the live experience in response to the user's emotional state, resulting in a cohesive and immersive overall presentation.

[0429] (Example of a prompt message)

[0430] "Analyze the user's heart rate and facial expression data at the live venue to generate the optimal performance plan."

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

[0432] Step 1:

[0433] Users wear monitoring devices upon entering the live venue. These devices detect biometric information such as heart rate and facial expressions in real time. Inputs are the user's current heart rate and facial video data. Outputs are heart rate values ​​as biometric information and image data for facial expression analysis. Specifically, a sensor measures the user's pulse, and a camera captures their facial expressions.

[0434] Step 2:

[0435] The terminal receives biometric information acquired by the observation device and transmits that data to a server via the network within the venue. The input is biometric data from the observation device. The output is biometric information packets sent to the server. Specifically, the terminal packets the data using Bluetooth or Wi-Fi and transfers the data to the server according to the protocol.

[0436] Step 3:

[0437] The server analyzes the received biometric information and uses a generative AI model to evaluate the user's emotional state. The input consists of heart rate and image data as biometric information. The output is the analyzed emotional state information. Specifically, the server's AI model analyzes fluctuations in heart rate and uses image recognition technology to detect specific patterns in facial expressions and infer emotions.

[0438] Step 4:

[0439] The server uses a generative AI model to create an optimal performance plan based on the evaluated emotional state. The input is information about the user's emotional state. The output is instructions for the performance plan to be applied to the environmental equipment. Specifically, the AI ​​model determines the optimal combination of lighting, sound, and video based on data it has previously learned.

[0440] Step 5:

[0441] The server sends instructions to control the venue's environmental equipment based on the generated production plan. The input is the production plan instructions. The output is the specific control commands that each environmental device will execute. For example, the server instructs the lighting system to adjust color and brightness, and the sound system to change volume.

[0442] Step 6:

[0443] The server integrates data from multiple users to evaluate the overall emotional state of the venue. The input is emotional state data from multiple users. The output is a cohesive performance instruction based on the integrated emotional response. Specifically, the server analyzes the aggregated emotional data to determine and adjust the overall direction of the performance.

[0444] (Application Example 1)

[0445] 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."

[0446] In modern home environments, it is difficult to create living spaces that provide the optimal atmosphere tailored to the emotions and moods of each family member. In particular, there is a lack of means to alleviate stress within the home and create a comfortable living environment. Therefore, there is a need to dynamically adjust lighting and sound according to the emotions and state of mind of each family member.

[0447] 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.

[0448] In this invention, the server includes means for receiving biometric information collected from an observation device, means for analyzing the received biometric information in real time and evaluating the emotional state, and means for generating an optimal production plan based on the emotional state and adjusting the lighting and sound in the home environment. This makes it possible to provide each individual in the home with an optimal spatial production tailored to their emotions.

[0449] A "monitoring device" is a device that captures a user's biometric information and transmits it to a server or data processing system via a specific interface.

[0450] "Biometric information" refers to numerical values ​​or indicators that show the user's physical and emotional state, such as heart rate and facial expression data.

[0451] "Real-time analysis" refers to a technical process that performs analytical processing to infer emotional states almost simultaneously with the collection of data.

[0452] "Assessing emotional state" refers to a method of determining the type and degree of emotions a user is experiencing based on collected biometric information.

[0453] "Generating a production plan" is the process of setting up a scene by combining elements such as lighting, sound, and video based on the evaluated emotional state.

[0454] "Controlling the equipment" refers to the process of dynamically adjusting lighting and sound equipment according to the generated production plan to achieve the optimal environmental presentation.

[0455] "Home environment" refers to the physical and psychological conditions inside a house, including elements that influence the emotions and activities of the residents.

[0456] "Adjusting lighting and sound" refers to the act of creating a space that suits the residents' emotions by changing the color and intensity of light, and the type and volume of music according to the situation.

[0457] The system based on this application aims to analyze the biometric information of each individual in the home in real time and dynamically optimize the environmental settings based on the analysis results. The monitoring devices include wearable devices to measure family members' heart rates and cameras to capture facial expressions. These devices are connected to a central data processing unit (server) within the home via Bluetooth or Wi-Fi.

[0458] The server uses machine learning libraries such as TensorFlow to analyze biometric information in real time. For example, a high heart rate is interpreted as a state of excitement, and emotions are inferred from facial expression data. This allows the server to assess whether the resident wants to relax or stay energetic.

[0459] Based on the analysis results, the server controls smart lights and sound systems. For example, it can adjust the color and intensity of Philips Hue lights and play appropriate music through music streaming services. It can even utilize services like Spotify to select classical music to create a relaxing environment.

[0460] For example, if a family member returns home after a long day and their heart rate is high, the server will play classical music and adjust the lighting to create a relaxing atmosphere. Alternatively, if they want to feel more energetic, it's possible to combine upbeat music with bright lighting.

[0461] This system uses a generative AI model to provide each resident with the optimal environmental design in real time. It can respond to questions such as: "What's the best lighting and music combination for when I'm feeling stressed?"

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

[0463] Step 1:

[0464] The observation device collects biometric information. The user provides heart rate and facial expression data through a wearable device or camera. This data is transmitted to the terminal via Bluetooth or Wi-Fi.

[0465] Step 2:

[0466] The terminal receives data and sends it to the server. The terminal collects heart rate and facial expression data obtained from the observation device and transfers it to the server for data processing. The input is raw biometric data, and the output is the completion of data transfer.

[0467] Step 3:

[0468] The server analyzes the received biometric information. Using TensorFlow, the server analyzes excitement levels from heart rate and emotional states from facial expression data to evaluate the type and intensity of the user's emotions. The input is biometric information, and the output generates evaluation data that quantifies the emotional state.

[0469] Step 4:

[0470] The server generates a performance plan based on the user's emotional state. Using a generative AI model, it determines the appropriate lighting color and intensity, as well as the music genre, for the user's emotions. The input is analyzed emotional data, and the output is a specific lighting and sound setting plan.

[0471] Step 5:

[0472] The server controls the equipment according to the performance plan. It instructs smart lighting and sound systems to operate according to the set plan. Based on feedback, the performance is optimized for the user. The output is the modified lighting and sound environment.

[0473] 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.

[0474] The system of the present invention incorporates a mechanism that dynamically optimizes live performances using an emotion engine for recognizing user emotions. The following describes a specific implementation of this system.

[0475] Upon arriving at the live venue, users wear monitoring devices designed to detect their heart rate and facial expressions. These devices collect the user's biometric information in real time and transmit this data to a device to enhance the fan experience. The device then transfers this data to a server via the venue's communication network.

[0476] The server activates an emotion engine upon receiving biometric information and analyzes the user's emotional state based on the obtained data. The emotion engine comprehensively analyzes changes in heart rate and facial expressions to recognize the user's state, such as whether they are excited or moved. This process utilizes machine learning algorithms, referencing past data to achieve more accurate emotion recognition.

[0477] Next, the server generates an optimal performance plan based on the analyzed emotional state. The generated plan includes specific control commands for lighting, sound, and video equipment, making it possible to provide a live experience that resonates with the audience's emotions. For example, if many users are showing excitement in common, the server, based on the emotion engine's judgment, can make the lighting brighter and speed up the tempo of the music playing from the speakers.

[0478] Furthermore, the server collects data from different users across multiple observation devices and comprehensively analyzes the emotional state of the group. This information serves as a guideline for determining the overall atmosphere of the live performance, and if there is a common emotional response throughout, it becomes possible to implement appropriate effects, such as projecting expansive images across the entire venue.

[0479] The system also includes a function that uses data accumulated from past live events to predict the effects of the performance. This allows the entire live performance to become more moving and personalized, providing new value that goes beyond the typical live experience.

[0480] In this way, the system of the present invention utilizes an emotion engine to provide advanced live entertainment tailored to each user.

[0481] The following describes the processing flow.

[0482] Step 1:

[0483] Users enter the live venue, and the monitoring device they wear measures biometric information such as heart rate and facial expressions in real time. This information is crucial data for understanding the user's emotional state.

[0484] Step 2:

[0485] The terminal transfers the user's biometric information, acquired from the observation device, to a server via the venue's network. This process is rapid, minimizing data latency.

[0486] Step 3:

[0487] The server analyzes the received biometric information using an emotion engine. The emotion engine uses machine learning algorithms to evaluate the user's emotional state based on their heart rate and facial expression changes.

[0488] Step 4:

[0489] Based on the analysis results, the server generates an optimal performance plan tailored to the user's emotions. This plan includes specific control commands, such as how to change the lighting and when to emphasize the music.

[0490] Step 5:

[0491] The server transmits the generated performance plan to the control systems for lighting, sound, and video equipment. This allows for the optimal performance to be delivered in real time, tailored to the audience's emotions.

[0492] Step 6:

[0493] The server integrates emotional data collected from multiple users to determine the overall emotional state. Based on this integrated data, the performance is further adjusted to enhance the sense of unity throughout the venue.

[0494] Step 7:

[0495] After a live performance ends, the server uses past live data to learn and predict effective staging for future performances. This process ensures that future live performances become even more refined and captivating.

[0496] (Example 2)

[0497] 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."

[0498] In recent years, there has been a growing demand for optimizing performances in response to audience emotions at live events. However, traditional methods have made it difficult to accurately reflect the emotions of individual audience members in real time. In particular, realizing integrated performances that consider the emotional trends of the group, and predictive performances using past data, have been major challenges.

[0499] 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.

[0500] In this invention, the server includes a component that receives biometric data collected from observation equipment, a component that analyzes the received biometric data in real time and estimates emotions, and a component that generates an optimal performance plan based on the emotions. This enables advanced performances based on real-time individual and collective emotion recognition.

[0501] "Observation equipment" refers to devices used to collect biological data in real time.

[0502] "Biometric data" refers to information that indicates the user's physical condition, such as their heart rate and facial expressions.

[0503] "Components for real-time analysis" refers to elements that include technologies for instantly processing received data and estimating emotions.

[0504] "Components for estimating emotions" refers to algorithms and technologies that analyze a user's biometric data to determine their emotional state.

[0505] "Components for generating an optimal production plan" refers to elements used to create a plan that dynamically adjusts production elements such as lighting and sound based on perceived emotions.

[0506] "Components for controlling the equipment" refers to the technology used to directly operate lighting, sound equipment, and other devices in a live venue according to the generated performance plan.

[0507] A "machine learning algorithm" refers to a learning method that uses historical data to improve the accuracy of emotion recognition, and is applied to the analysis of biometric data.

[0508] This invention is a system that enables dynamic performances based on audience emotions at live events. The following describes a specific form for implementing this system.

[0509] Upon arriving at the live venue, users put on monitoring equipment. This equipment collects biometric data such as heart rate and facial expression in real time. The terminal receives the collected biometric data and transmits it to a server via the venue's communication network.

[0510] The server activates an emotion recognition engine to process the received biometric data. This engine uses machine learning algorithms to analyze changes in heart rate and facial expression data to estimate the user's emotions. This includes, for example, a process to determine whether the user is excited or moved.

[0511] The server generates an optimal performance plan based on the analyzed emotional information. This plan includes lighting color and brightness, music tempo and volume, and video content. The server can also use a generative AI model to output the generated plan as a prompt message.

[0512] As a concrete example, the prompt might look like this: "Analyze the audience's heart rate and facial expression data to recognize their emotions. Then, propose a lighting and sound plan based on those emotions."

[0513] This system can utilize data accumulated from past events to improve the performance effects of future events. This data will provide valuable information for delivering a better emotional experience at the next live event. As a result, users will not simply be spectators, but will be able to enjoy personalized entertainment tailored to their emotions.

[0514] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0515] Step 1:

[0516] Upon entering the live venue, users wear monitoring devices that detect their heart rate and facial expressions. The devices collect real-time heart rate and facial expression data as input, and then transmit this biometric data to a terminal.

[0517] Step 2:

[0518] The terminal processes the biometric data received from the observation equipment. The inputs in this step are heart rate data and facial expression data. The terminal performs data processing by matching the collected biometric data, formatting it, and then transmitting it to the server via the communication network within the venue. The output is the formatted biometric data.

[0519] Step 3:

[0520] The server receives biometric data transmitted from the terminal. The input is formatted biometric data transmitted by the terminal. The server activates an emotion recognition engine and performs data calculations, such as analyzing changes in heart rate and facial features. The output is an estimated result of the user's emotion, determining the user's emotional state, such as whether they are excited or moved.

[0521] Step 4:

[0522] The server generates an optimal performance plan based on the emotion estimation results. The input is the user's emotion estimation result. The server uses a generative AI model to perform data calculations to formulate a control plan for elements such as lighting, music, and video. The output is a specific control plan and prompt messages.

[0523] Step 5:

[0524] The server transmits the generated performance plan to the various facilities at the venue, controlling the actual live performance. The input consists of the performance plan and prompt messages generated by the server. The server sends commands to the control systems of each piece of equipment, causing lighting, sound, and other elements to operate as instructed. The output is a live performance that responds to the audience's emotions.

[0525] (Application Example 2)

[0526] 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."

[0527] In online content distribution, there is a challenge in that real-time optimization based on viewer emotions is not being achieved, resulting in a decline in the quality of the viewing experience. It is necessary to instantly understand the different emotions of each viewer and provide content that responds accordingly to improve the viewing experience.

[0528] 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.

[0529] In this invention, the server includes means for receiving biometric information collected from an observation device, means for analyzing the received biometric information in real time and evaluating the emotional state, means for generating an optimal content plan based on the emotional state, and means for controlling the viewing device according to the generated content plan. This enables immediate content optimization that responds to the individual emotions of the viewer.

[0530] A "monitoring device" is a device used to collect a user's biometric information, such as heart rate and facial expressions.

[0531] "Biometric information" refers to data related to a user's body and behavior, including information such as heart rate and facial expressions.

[0532] "Real-time analysis" refers to the process of immediately analyzing collected data and deriving results.

[0533] "Emotional state" refers to the user's psychological state, and includes specific emotions such as excitement and emotion.

[0534] A "content plan" is a structure or plan designed to present the most suitable content according to the emotional state of the viewer.

[0535] "Viewing device" refers to a device used by viewers to consume content, and includes smartphones, tablets, and other similar devices.

[0536] To implement this invention, a system consisting of an observation device, a server, and a viewing device is required. The user first collects their own biometric information using the observation device. The observation device may include a smartwatch for measuring heart rate or a smartphone camera for recognizing facial expressions.

[0537] The terminal temporarily stores biometric information acquired from the observation device and transmits it to a server via a communication network connected within the venue or online. The server receives and analyzes the biometric information in real time. During this process, an emotion engine is activated to identify the user's emotional state from the received biometric information. This emotion engine uses machine learning algorithms based on the collected data, referencing past data to perform a highly accurate emotional assessment.

[0538] Based on the analysis results, the server generates a content plan tailored to the viewer's emotional state. This content plan includes elements that enhance the viewing experience in various ways; for example, it automatically provides behind-the-scenes footage to emotionally moved viewers. The viewing device controls the playback of video and audio according to the content plan sent from the server.

[0539] As a concrete example, when a user is watching an online live performance at home, if their heart rate increases and excitement is detected, the viewing device will instantly display powerful visuals or real-time behind-the-scenes footage to make the experience even more impressive. By utilizing a generative AI model, the system will generate a prompt message such as, "Estimate the user's emotions from their heart rate and facial expressions, and optimize the live stream content in real time," based on the user's state, thereby coordinating the entire system.

[0540] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0541] Step 1:

[0542] The user wears a monitoring device. The device acquires heart rate and facial expression data and transmits it to the terminal. The input is the user's biometric information, and the output is real-time acquired heart rate and facial expression data.

[0543] Step 2:

[0544] The terminal transmits biometric information received from the observation device to the server. The terminal transmits heart rate and facial expression data to the server in real time via the network. As a result of this data transfer, the server receives the user's latest biometric information.

[0545] Step 3:

[0546] The server analyzes the received biometric information. It activates an emotion engine and uses machine learning algorithms to comprehensively analyze changes in heart rate and facial expressions to evaluate the user's emotional state. Heart rate and facial expression data are used as input, and the output is the analyzed emotional state.

[0547] Step 4:

[0548] The server generates a content plan based on the analyzed emotional state. Utilizing a generative AI model, it selects the content best suited to the user's emotional state and generates instructions accordingly. The input is the emotional state evaluation result, and the output is the content plan. Specifically, if excitement is detected, it will instruct the user to watch a video with a strong emotional impact.

[0549] Step 5:

[0550] The server sends the generated content plan to the viewing device. The viewing device receives instructions from the server and plays the specified content. The input is the content plan from the server, and the output is the content the user views. Specifically, the viewing device changes the video and audio playback settings and provides content in real time.

[0551] 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.

[0552] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0553] 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.

[0554] [Fourth Embodiment]

[0555] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0556] 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.

[0557] 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).

[0558] 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.

[0559] 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.

[0560] 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).

[0561] 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.

[0562] 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.

[0563] 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.

[0564] 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.

[0565] 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.

[0566] 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.

[0567] 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".

[0568] This section describes a form of implementing an emotion-driven live performance system. This system uses observation devices worn by audience members to collect and process user biometric information, dynamically optimizing the live performance. The following explains, in natural language, how this system's program works.

[0569] First, users wear a special monitoring device (such as a smart band or a smartphone with a camera) upon entering the live venue. This device detects biometric information such as the user's heart rate and facial expressions in real time. This data is then transmitted to a server via terminals within the venue.

[0570] The server uses generated AI to analyze the user's emotional state based on the received biometric information. For example, if the heart rate exceeds a certain threshold, the server determines that the user is excited. On the other hand, if facial expression analysis suggests that the user is moved, that information is also added to the emotional state evaluation.

[0571] Based on the server's analysis of the user's emotional state, an optimal performance plan is generated. This plan includes changes in lighting, selection of video content, and adjustment of sound effects. For example, if the user's emotional state has reached its climax, the server will instruct the system to enhance the lighting, increase the volume, and make the video more dynamic.

[0572] Furthermore, the server can integrate data from multiple users and evaluate the overall reaction of the venue, enabling it to provide a performance that takes into account the overall sense of unity. This allows for both personalized performances for individual users and a consistent overall performance simultaneously.

[0573] For example, if a user's heart rate is higher than normal at the start of a live performance, the server uses that information to provide an experience that maximizes excitement from the beginning (e.g., flashy lighting and high-energy music). Also, if facial analysis reveals that many users are moved during a ballad, the system will change the video to an emotional scene and adjust the lighting to a warmer tone.

[0574] In this way, the system according to the present invention can realize a more personalized and emotionally engaging live experience by responding to the user's emotions in real time.

[0575] The following describes the processing flow.

[0576] Step 1:

[0577] Users' biometric information is measured in real time through monitoring devices installed at the live venue. Heart rate sensors and cameras are used to acquire data on the user's physiological state, such as heart rate and facial expressions.

[0578] Step 2:

[0579] The terminal immediately processes the acquired biometric information and transmits it to the server using wireless communication. This data transfer is designed to minimize latency and requires real-time operation.

[0580] Step 3:

[0581] The server analyzes the received biometric information in real time. Generative AI is used, employing specific algorithms to evaluate the emotional state of individual users. For example, if a sudden increase in heart rate is detected, the server determines that the user is agitated.

[0582] Step 4:

[0583] The server generates a performance plan based on the analyzed emotional state. This plan includes changes in lighting, video transitions, and sound adjustments. For example, if the server determines that the emotional state is high, dynamic lighting effects will be incorporated into the performance plan.

[0584] Step 5:

[0585] The server transmits the generated performance plan to various control systems in real time. This allows for immediate control of the venue's lighting and sound equipment, resulting in live performances that are synchronized with the audience's emotions.

[0586] Step 6:

[0587] The server integrates data collected from multiple users to assess the overall emotional state of the venue. This allows it to adjust the presentation to deliver both a consistent group experience and individual emotional experiences.

[0588] Step 7:

[0589] After the live event ends, the server collects all the data and performs a detailed analysis of the effects of the performance. This allows the system to learn from past performances and improve the accuracy of future performance plans.

[0590] (Example 1)

[0591] 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".

[0592] Conventional live performance systems have difficulty optimizing the performance in real time according to the emotional state of the audience, and have been unable to provide a personalized experience that responds to the emotions of individual audience members. Furthermore, it has been difficult to adjust the performance while considering the sense of unity throughout the venue, and there is a need to maximize the overall emotional impact.

[0593] 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.

[0594] In this invention, the server includes means for receiving biometric information collected from an observation device, means for analyzing the received biometric information in real time and evaluating the emotional state, means for creating an optimal performance plan using a generated AI model based on the evaluated emotional state, means for controlling environmental devices according to the generated performance plan, and means for integrating data from multiple users to evaluate the overall emotions and provide a unified performance. This makes it possible to optimize the performance in real time to suit individual audience members and to create a unified performance for the entire venue.

[0595] A "monitoring device" is a device used to acquire a user's biometric information in real time, and specifically includes sensors that have the function of detecting heart rate and facial expressions.

[0596] "Biometric information" refers to physical data such as heart rate and facial expressions obtained from the user, and is used to analyze the user's emotional state in real time.

[0597] A "generative AI model" is an artificial intelligence model that evaluates the user's emotional state based on received biometric information and creates an optimal performance plan based on that evaluation.

[0598] A "production plan" is a set of specific environmental setting instructions that include the color and brightness of lighting, the type of video, and the volume of sound, all based on the user's emotional state.

[0599] "Environmental equipment" refers to equipment controlled according to the production plan, and is a broad term encompassing equipment for environmental adjustment, including lighting systems, video projectors, sound systems, and other such devices.

[0600] "Emotional state" refers to the emotional state a user is currently experiencing, analyzed based on biometric information such as heart rate and facial expressions.

[0601] "Sense of unity" is a concept that refers to the overall harmony and coherence of a venue, obtained as a result of comprehensively evaluating the emotional states of multiple users.

[0602] This invention is specifically implemented as an emotion-driven live performance system. When a user enters a live venue, they wear an observation device (e.g., a smart band or a smartphone with a camera) to acquire biometric information such as heart rate and facial expressions in real time. This observation device has the function of transmitting the biometric information detected by the user to a server via a terminal within the venue.

[0603] The server analyzes the user's emotional state using a generative AI model based on the received biometric information. By analyzing heart rate fluctuations and facial expression patterns, it evaluates the user's level of excitement and emotion. A multi-layer neural network is commonly used as the generative AI model.

[0604] Based on the analysis results, the server creates an optimal performance plan. This plan includes lighting color and brightness, video content selection, and sound volume adjustments. The generated performance plan provides instructions for controlling the venue's environmental equipment (lighting system, video projector, sound system, etc.).

[0605] For example, if a user's heart rate is high and facial analysis indicates they are in an excited state, the server will send instructions to the environmental device to make the lighting brighter and the sound more dynamic. Also, if facial analysis determines that many users are moved by a ballad, the server can adjust the lighting to a warmer tone and project emotional video content onto the projector.

[0606] This system enables real-time personalization of the live experience in response to the user's emotional state, resulting in a cohesive and immersive overall presentation.

[0607] (Example of a prompt message)

[0608] "Analyze the user's heart rate and facial expression data at the live venue to generate the optimal performance plan."

[0609] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0610] Step 1:

[0611] Users wear monitoring devices upon entering the live venue. These devices detect biometric information such as heart rate and facial expressions in real time. Inputs are the user's current heart rate and facial video data. Outputs are heart rate values ​​as biometric information and image data for facial expression analysis. Specifically, a sensor measures the user's pulse, and a camera captures their facial expressions.

[0612] Step 2:

[0613] The terminal receives biometric information acquired by the observation device and transmits that data to a server via the network within the venue. The input is biometric data from the observation device. The output is biometric information packets sent to the server. Specifically, the terminal packets the data using Bluetooth or Wi-Fi and transfers the data to the server according to the protocol.

[0614] Step 3:

[0615] The server analyzes the received biometric information and uses a generative AI model to evaluate the user's emotional state. The input consists of heart rate and image data as biometric information. The output is the analyzed emotional state information. Specifically, the server's AI model analyzes fluctuations in heart rate and uses image recognition technology to detect specific patterns in facial expressions and infer emotions.

[0616] Step 4:

[0617] The server uses a generative AI model to create an optimal performance plan based on the evaluated emotional state. The input is information about the user's emotional state. The output is instructions for the performance plan to be applied to the environmental equipment. Specifically, the AI ​​model determines the optimal combination of lighting, sound, and video based on data it has previously learned.

[0618] Step 5:

[0619] The server sends instructions to control the venue's environmental equipment based on the generated production plan. The input is the production plan instructions. The output is the specific control commands that each environmental device will execute. For example, the server instructs the lighting system to adjust color and brightness, and the sound system to change volume.

[0620] Step 6:

[0621] The server integrates data from multiple users to evaluate the overall emotional state of the venue. The input is emotional state data from multiple users. The output is a cohesive performance instruction based on the integrated emotional response. Specifically, the server analyzes the aggregated emotional data to determine and adjust the overall direction of the performance.

[0622] (Application Example 1)

[0623] 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".

[0624] In modern home environments, it is difficult to create living spaces that provide the optimal atmosphere tailored to the emotions and moods of each family member. In particular, there is a lack of means to alleviate stress within the home and create a comfortable living environment. Therefore, there is a need to dynamically adjust lighting and sound according to the emotions and state of mind of each family member.

[0625] 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.

[0626] In this invention, the server includes means for receiving biometric information collected from an observation device, means for analyzing the received biometric information in real time and evaluating the emotional state, and means for generating an optimal production plan based on the emotional state and adjusting the lighting and sound in the home environment. This makes it possible to provide each individual in the home with an optimal spatial production tailored to their emotions.

[0627] A "monitoring device" is a device that captures a user's biometric information and transmits it to a server or data processing system via a specific interface.

[0628] "Biometric information" refers to numerical values ​​or indicators that show the user's physical and emotional state, such as heart rate and facial expression data.

[0629] "Real-time analysis" refers to a technical process that performs analytical processing to infer emotional states almost simultaneously with the collection of data.

[0630] "Assessing emotional state" refers to a method of determining the type and degree of emotions a user is experiencing based on collected biometric information.

[0631] "Generating a production plan" is the process of setting up a scene by combining elements such as lighting, sound, and video based on the evaluated emotional state.

[0632] "Controlling the equipment" refers to the process of dynamically adjusting lighting and sound equipment according to the generated production plan to achieve the optimal environmental presentation.

[0633] "Home environment" refers to the physical and psychological conditions inside a house, including elements that influence the emotions and activities of the residents.

[0634] "Adjusting lighting and sound" refers to the act of creating a space that suits the residents' emotions by changing the color and intensity of light, and the type and volume of music according to the situation.

[0635] The system based on this application aims to analyze the biometric information of each individual in the home in real time and dynamically optimize the environmental settings based on the analysis results. The monitoring devices include wearable devices to measure family members' heart rates and cameras to capture facial expressions. These devices are connected to a central data processing unit (server) within the home via Bluetooth or Wi-Fi.

[0636] The server uses machine learning libraries such as TensorFlow to analyze biometric information in real time. For example, a high heart rate is interpreted as a state of excitement, and emotions are inferred from facial expression data. This allows the server to assess whether the resident wants to relax or stay energetic.

[0637] Based on the analysis results, the server controls smart lights and sound systems. For example, it can adjust the color and intensity of Philips Hue lights and play appropriate music through music streaming services. It can even utilize services like Spotify to select classical music to create a relaxing environment.

[0638] For example, if a family member returns home after a long day and their heart rate is high, the server will play classical music and adjust the lighting to create a relaxing atmosphere. Alternatively, if they want to feel more energetic, it's possible to combine upbeat music with bright lighting.

[0639] This system uses a generative AI model to provide each resident with the optimal environmental design in real time. It can respond to questions such as: "What's the best lighting and music combination for when I'm feeling stressed?"

[0640] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0641] Step 1:

[0642] The observation device collects biometric information. The user provides heart rate and facial expression data through a wearable device or camera. This data is transmitted to the terminal via Bluetooth or Wi-Fi.

[0643] Step 2:

[0644] The terminal receives data and sends it to the server. The terminal collects heart rate and facial expression data obtained from the observation device and transfers it to the server for data processing. The input is raw biometric data, and the output is the completion of data transfer.

[0645] Step 3:

[0646] The server analyzes the received biometric information. Using TensorFlow, the server analyzes excitement levels from heart rate and emotional states from facial expression data to evaluate the type and intensity of the user's emotions. The input is biometric information, and the output generates evaluation data that quantifies the emotional state.

[0647] Step 4:

[0648] The server generates a performance plan based on the user's emotional state. Using a generative AI model, it determines the appropriate lighting color and intensity, as well as the music genre, for the user's emotions. The input is analyzed emotional data, and the output is a specific lighting and sound setting plan.

[0649] Step 5:

[0650] The server controls the equipment according to the performance plan. It instructs smart lighting and sound systems to operate according to the set plan. Based on feedback, the performance is optimized for the user. The output is the modified lighting and sound environment.

[0651] 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.

[0652] The system of the present invention incorporates a mechanism that dynamically optimizes live performances using an emotion engine for recognizing user emotions. The following describes a specific implementation of this system.

[0653] Upon arriving at the live venue, users wear monitoring devices designed to detect their heart rate and facial expressions. These devices collect the user's biometric information in real time and transmit this data to a device to enhance the fan experience. The device then transfers this data to a server via the venue's communication network.

[0654] The server activates an emotion engine upon receiving biometric information and analyzes the user's emotional state based on the obtained data. The emotion engine comprehensively analyzes changes in heart rate and facial expressions to recognize the user's state, such as whether they are excited or moved. This process utilizes machine learning algorithms, referencing past data to achieve more accurate emotion recognition.

[0655] Next, the server generates an optimal performance plan based on the analyzed emotional state. The generated plan includes specific control commands for lighting, sound, and video equipment, making it possible to provide a live experience that resonates with the audience's emotions. For example, if many users are showing excitement in common, the server, based on the emotion engine's judgment, can make the lighting brighter and speed up the tempo of the music playing from the speakers.

[0656] Furthermore, the server collects data from different users across multiple observation devices and comprehensively analyzes the emotional state of the group. This information serves as a guideline for determining the overall atmosphere of the live performance, and if there is a common emotional response throughout, it becomes possible to implement appropriate effects, such as projecting expansive images across the entire venue.

[0657] The system also includes a function that uses data accumulated from past live events to predict the effects of the performance. This allows the entire live performance to become more moving and personalized, providing new value that goes beyond the typical live experience.

[0658] In this way, the system of the present invention utilizes an emotion engine to provide advanced live entertainment tailored to each user.

[0659] The following describes the processing flow.

[0660] Step 1:

[0661] Users enter the live venue, and the monitoring device they wear measures biometric information such as heart rate and facial expressions in real time. This information is crucial data for understanding the user's emotional state.

[0662] Step 2:

[0663] The terminal transfers the user's biometric information, acquired from the observation device, to a server via the venue's network. This process is rapid, minimizing data latency.

[0664] Step 3:

[0665] The server analyzes the received biometric information using an emotion engine. The emotion engine uses machine learning algorithms to evaluate the user's emotional state based on their heart rate and facial expression changes.

[0666] Step 4:

[0667] Based on the analysis results, the server generates an optimal performance plan tailored to the user's emotions. This plan includes specific control commands, such as how to change the lighting and when to emphasize the music.

[0668] Step 5:

[0669] The server transmits the generated performance plan to the control systems for lighting, sound, and video equipment. This allows for the optimal performance to be delivered in real time, tailored to the audience's emotions.

[0670] Step 6:

[0671] The server integrates emotional data collected from multiple users to determine the overall emotional state. Based on this integrated data, the performance is further adjusted to enhance the sense of unity throughout the venue.

[0672] Step 7:

[0673] After a live performance ends, the server uses past live data to learn and predict effective staging for future performances. This process ensures that future live performances become even more refined and captivating.

[0674] (Example 2)

[0675] 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".

[0676] In recent years, there has been a growing demand for optimizing performances in response to audience emotions at live events. However, traditional methods have made it difficult to accurately reflect the emotions of individual audience members in real time. In particular, realizing integrated performances that consider the emotional trends of the group, and predictive performances using past data, have been major challenges.

[0677] 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.

[0678] In this invention, the server includes a component that receives biometric data collected from observation equipment, a component that analyzes the received biometric data in real time and estimates emotions, and a component that generates an optimal performance plan based on the emotions. This enables advanced performances based on real-time individual and collective emotion recognition.

[0679] "Observation equipment" refers to devices used to collect biological data in real time.

[0680] "Biometric data" refers to information that indicates the user's physical condition, such as their heart rate and facial expressions.

[0681] "Components for real-time analysis" refers to elements that include technologies for instantly processing received data and estimating emotions.

[0682] "Components for estimating emotions" refers to algorithms and technologies that analyze a user's biometric data to determine their emotional state.

[0683] "Components for generating an optimal production plan" refers to elements used to create a plan that dynamically adjusts production elements such as lighting and sound based on perceived emotions.

[0684] "Components for controlling the equipment" refers to the technology used to directly operate lighting, sound equipment, and other devices in a live venue according to the generated performance plan.

[0685] A "machine learning algorithm" refers to a learning method that uses historical data to improve the accuracy of emotion recognition, and is applied to the analysis of biometric data.

[0686] This invention is a system that enables dynamic performances based on audience emotions at live events. The following describes a specific form for implementing this system.

[0687] Upon arriving at the live venue, users put on monitoring equipment. This equipment collects biometric data such as heart rate and facial expression in real time. The terminal receives the collected biometric data and transmits it to a server via the venue's communication network.

[0688] The server activates an emotion recognition engine to process the received biometric data. This engine uses machine learning algorithms to analyze changes in heart rate and facial expression data to estimate the user's emotions. This includes, for example, a process to determine whether the user is excited or moved.

[0689] The server generates an optimal performance plan based on the analyzed emotional information. This plan includes lighting color and brightness, music tempo and volume, and video content. The server can also use a generative AI model to output the generated plan as a prompt message.

[0690] As a concrete example, the prompt might look like this: "Analyze the audience's heart rate and facial expression data to recognize their emotions. Then, propose a lighting and sound plan based on those emotions."

[0691] This system can utilize data accumulated from past events to improve the performance effects of future events. This data will provide valuable information for delivering a better emotional experience at the next live event. As a result, users will not simply be spectators, but will be able to enjoy personalized entertainment tailored to their emotions.

[0692] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0693] Step 1:

[0694] Upon entering the live venue, users wear monitoring devices that detect their heart rate and facial expressions. The devices collect real-time heart rate and facial expression data as input, and then transmit this biometric data to a terminal.

[0695] Step 2:

[0696] The terminal processes the biometric data received from the observation equipment. The inputs in this step are heart rate data and facial expression data. The terminal performs data processing by matching the collected biometric data, formatting it, and then transmitting it to the server via the communication network within the venue. The output is the formatted biometric data.

[0697] Step 3:

[0698] The server receives biometric data transmitted from the terminal. The input is formatted biometric data transmitted by the terminal. The server activates an emotion recognition engine and performs data calculations, such as analyzing changes in heart rate and facial features. The output is an estimated result of the user's emotion, determining the user's emotional state, such as whether they are excited or moved.

[0699] Step 4:

[0700] The server generates an optimal performance plan based on the emotion estimation results. The input is the user's emotion estimation result. The server uses a generative AI model to perform data calculations to formulate a control plan for elements such as lighting, music, and video. The output is a specific control plan and prompt messages.

[0701] Step 5:

[0702] The server transmits the generated performance plan to the various facilities at the venue, controlling the actual live performance. The input consists of the performance plan and prompt messages generated by the server. The server sends commands to the control systems of each piece of equipment, causing lighting, sound, and other elements to operate as instructed. The output is a live performance that responds to the audience's emotions.

[0703] (Application Example 2)

[0704] 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".

[0705] In online content distribution, there is a challenge in that real-time optimization based on viewer emotions is not being achieved, resulting in a decline in the quality of the viewing experience. It is necessary to instantly understand the different emotions of each viewer and provide content that responds accordingly to improve the viewing experience.

[0706] 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.

[0707] In this invention, the server includes means for receiving biometric information collected from an observation device, means for analyzing the received biometric information in real time and evaluating the emotional state, means for generating an optimal content plan based on the emotional state, and means for controlling the viewing device according to the generated content plan. This enables immediate content optimization that responds to the individual emotions of the viewer.

[0708] A "monitoring device" is a device used to collect a user's biometric information, such as heart rate and facial expressions.

[0709] "Biometric information" refers to data related to a user's body and behavior, including information such as heart rate and facial expressions.

[0710] "Real-time analysis" refers to the process of immediately analyzing collected data and deriving results.

[0711] "Emotional state" refers to the user's psychological state, and includes specific emotions such as excitement and emotion.

[0712] A "content plan" is a structure or plan designed to present the most suitable content according to the emotional state of the viewer.

[0713] "Viewing device" refers to a device used by viewers to consume content, and includes smartphones, tablets, and other similar devices.

[0714] To implement this invention, a system consisting of an observation device, a server, and a viewing device is required. The user first collects their own biometric information using the observation device. The observation device may include a smartwatch for measuring heart rate or a smartphone camera for recognizing facial expressions.

[0715] The terminal temporarily stores biometric information acquired from the observation device and transmits it to a server via a communication network connected within the venue or online. The server receives and analyzes the biometric information in real time. During this process, an emotion engine is activated to identify the user's emotional state from the received biometric information. This emotion engine uses machine learning algorithms based on the collected data, referencing past data to perform a highly accurate emotional assessment.

[0716] Based on the analysis results, the server generates a content plan tailored to the viewer's emotional state. This content plan includes elements that enhance the viewing experience in various ways; for example, it automatically provides behind-the-scenes footage to emotionally moved viewers. The viewing device controls the playback of video and audio according to the content plan sent from the server.

[0717] As a concrete example, when a user is watching an online live performance at home, if their heart rate increases and excitement is detected, the viewing device will instantly display powerful visuals or real-time behind-the-scenes footage to make the experience even more impressive. By utilizing a generative AI model, the system will generate a prompt message such as, "Estimate the user's emotions from their heart rate and facial expressions, and optimize the live stream content in real time," based on the user's state, thereby coordinating the entire system.

[0718] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0719] Step 1:

[0720] The user wears a monitoring device. The device acquires heart rate and facial expression data and transmits it to the terminal. The input is the user's biometric information, and the output is real-time acquired heart rate and facial expression data.

[0721] Step 2:

[0722] The terminal transmits biometric information received from the observation device to the server. The terminal transmits heart rate and facial expression data to the server in real time via the network. As a result of this data transfer, the server receives the user's latest biometric information.

[0723] Step 3:

[0724] The server analyzes the received biometric information. It activates an emotion engine and uses machine learning algorithms to comprehensively analyze changes in heart rate and facial expressions to evaluate the user's emotional state. Heart rate and facial expression data are used as input, and the output is the analyzed emotional state.

[0725] Step 4:

[0726] The server generates a content plan based on the analyzed emotional state. Utilizing a generative AI model, it selects the content best suited to the user's emotional state and generates instructions accordingly. The input is the emotional state evaluation result, and the output is the content plan. Specifically, if excitement is detected, it will instruct the user to watch a video with a strong emotional impact.

[0727] Step 5:

[0728] The server sends the generated content plan to the viewing device. The viewing device receives instructions from the server and plays the specified content. The input is the content plan from the server, and the output is the content the user views. Specifically, the viewing device changes the video and audio playback settings and provides content in real time.

[0729] 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.

[0730] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0731] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0732] 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.

[0733] 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.

[0734] 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.

[0735] 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.

[0736] 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.

[0737] 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."

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] The following is further disclosed regarding the embodiments described above.

[0751] (Claim 1)

[0752] Means for receiving biological information collected from observation devices,

[0753] A means of analyzing received biometric information in real time and evaluating emotional state,

[0754] A means of generating an optimal production plan based on emotional state,

[0755] A means for controlling the equipment according to the generated performance plan,

[0756] A system that includes this.

[0757] (Claim 2)

[0758] The system according to claim 1, comprising means for integrating and analyzing biological information collected from multiple observation devices.

[0759] (Claim 3)

[0760] The system according to claim 1, comprising means for learning past data and predicting the effect of the performance.

[0761] "Example 1"

[0762] (Claim 1)

[0763] A means equipped with the function of receiving biological information collected from an observation device,

[0764] A means of analyzing received biometric information in real time and evaluating emotional state,

[0765] A means of creating an optimal production plan using a generative AI model based on evaluated emotional states,

[0766] Means for controlling environmental devices according to the generated production plan,

[0767] A means of integrating data from multiple users to evaluate overall emotions and provide a sense of unity in the experience,

[0768] A system that includes this.

[0769] (Claim 2)

[0770] The system according to claim 1, comprising means for a function that learns past data and predicts the effect of the performance.

[0771] (Claim 3)

[0772] The system according to claim 1, comprising means for generating prompt sentences to be input to an AI model based on biometric information obtained from multiple users in real time.

[0773] "Application Example 1"

[0774] (Claim 1)

[0775] Means for receiving biological information collected from observation devices,

[0776] A means of analyzing received biometric information in real time and evaluating emotional state,

[0777] A means of generating an optimal production plan based on emotional state,

[0778] A means for controlling the equipment according to the generated performance plan,

[0779] A means of adjusting lighting and sound in the home environment to provide an optimal spatial setting according to emotions,

[0780] A system that includes this.

[0781] (Claim 2)

[0782] The system according to claim 1, comprising means for integrating and analyzing biological information collected from multiple observation devices and providing a home environment tailored to the emotional state of each individual.

[0783] (Claim 3)

[0784] The system according to claim 1, comprising means for learning past data and predicting and applying theatrical effects based on the emotions of family members.

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

[0786] (Claim 1)

[0787] The components receive biological data collected from observation instruments,

[0788] The system analyzes received biometric data in real time and includes components for estimating emotions,

[0789] Components for generating the optimal production plan based on emotions,

[0790] Components that control the device according to the generated performance plan,

[0791] A component that has a function to predict the effect of the performance using past data,

[0792] A system that includes this.

[0793] (Claim 2)

[0794] The system according to claim 1, comprising a component for integrating and analyzing biological data collected from multiple observation instruments.

[0795] (Claim 3)

[0796] The system according to claim 1, comprising a component that improves the accuracy of emotion recognition using a machine learning algorithm.

[0797] "Application example 2 when combining with an emotional engine"

[0798] (Claim 1)

[0799] Means for receiving biological information collected from observation devices,

[0800] A means of analyzing received biometric information in real time and evaluating emotional state,

[0801] A means of generating an optimal content plan based on emotional state,

[0802] Means for controlling the viewing device according to the generated content plan,

[0803] A system that includes this.

[0804] (Claim 2)

[0805] The system according to claim 1, comprising means for integrating and analyzing biological information collected from multiple observation devices.

[0806] (Claim 3)

[0807] The system according to claim 1, comprising means for learning past distribution data and predicting content effectiveness. [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. Means for receiving biological information collected from observation devices, A means of analyzing received biometric information in real time and evaluating emotional state, A means of generating an optimal production plan based on emotional state, A means for controlling the equipment according to the generated performance plan, A system that includes this.

2. The system according to claim 1, comprising means for integrating and analyzing biological information collected from multiple observation devices.

3. The system according to claim 1, comprising means for learning past data and predicting the effect of the performance.

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