system

Abstract

We provide the system. [Solution] A method for receiving brainwave data from the user in real time, analyzing that data, and identifying the stage of the user's dream. A means for retrieving data from storage to generate dream scenarios based on the user's prior selections and creating individually customized dreams, A means of sending the generated dream scenario to the user's terminal and allowing the user to experience it through sight and sound, A means to securely collect user feedback on dreams, analyze that data, and update the learning model to reflect it in future dream generation, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] It is a problem that the sleeping time, which accounts for about one-third of a human's life, is spent as inactive time. If it becomes possible to select and control dreams during sleep, it is expected to effectively utilize this time and improve productivity and the richness of life. Furthermore, in the current technology, there is no method for freely selecting and experiencing dreams, and it is an issue to realize the dream experience desired by the user and provide new experiences during sleep.

Means for Solving the Problems

[0005] This invention relates to a system that analyzes a user's brainwave data in real time and generates customized dreams based on that analysis. The system generates an optimal scenario based on dream category information pre-selected by the user and guides the dream experience through the user's device. Furthermore, it continuously improves the system's learning model by collecting and analyzing feedback information from the user and incorporating it into subsequent dream generation processes. In this way, the system provides a means for users to experience their desired dreams and effectively utilize their sleep time.

[0006] "User" refers to the individual who uses this system to select and experience their dreams.

[0007] "Electroencephalogram (EEG) data" refers to data representing the electrical activity of a user's brain, and is signal data used to analyze the state of dreams.

[0008] "Real-time" means that data is collected and processed immediately, and a response is obtained without any time lag.

[0009] "Dream stages" refer to the state in which a user dreams during different phases of sleep, including REM and non-REM sleep.

[0010] A "scenario" refers to a set of settings that make up the content of the dream that the user experiences, and this determines the content of the dream.

[0011] "Customization" refers to the process of adjusting the dream scenario based on the individual user's wishes and attributes.

[0012] A "terminal" refers to a device used by a user to input and output information, and often includes sensors.

[0013] "Through sight and hearing" refers to methods of stimulating the user's senses during a dream experience.

[0014] "Feedback" refers to the evaluation and impressions regarding the dreams experienced by the user, which are used for improving the system.

[0015] "Learning model" refers to a computational model that analyzes patterns based on the received feedback data and improves the output accuracy of the system.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of the data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of the data processing device and the smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of the data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of the data processing device and the smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of the data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of the data processing device and the headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of the data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of the data processing device and the robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Example 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

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

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

[0019] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be 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.

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

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

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

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

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention relates to a system that allows a user to select a dream during sleep and experience that dream. The system is configured as follows: First, the user uses a dedicated terminal to pre-select the category of dream and specific scenario they wish to experience. At this stage, the terminal records the user's selection as digital data and prepares to transmit it to a server.

[0038] Next, the device is equipped with sensors to collect brainwave data while the user is sleeping. These sensors non-invasively measure the user's brainwaves and transmit the data to a server in real time. The server analyzes this brainwave data to identify the stage in the user's dreaming. Depending on the identified stage, the server obtains the information necessary to generate a dream scenario and, combined with the user's past experience data, generates a personalized dream.

[0039] The generated dream scenario is sent from the server to the terminal as digital video data. The terminal uses this data to induce a dream experience in the user through sight and sound. The user can experience the dream they have chosen while sleeping, and after waking up, they input feedback about the dream via the terminal.

[0040] Feedback information is sent to the server, which then updates the machine learning model based on it. This continuous learning improves the quality of dreams delivered in subsequent sessions, making it easier to realize the user's expected experience.

[0041] As a concrete example, consider a scenario where a user selects a dream category called "diving." In this case, the user can set their desired ocean environment and diving depth via their device. After the scenario is generated, this dream is visualized while the user sleeps, allowing them to experience observing fish and coral up close, as if they were actually in the ocean. Upon waking, the user provides feedback on the experience, contributing to improving the accuracy of future dream experiences.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user uses their device to select the dream category and specific scenario they want to see from the interface. Once the user confirms their selection, that information is saved on the device.

[0045] Step 2:

[0046] The terminal structures the user's selections as digital data and prepares to send them to the server using security protocols. During this process, the terminal verifies the integrity of the data.

[0047] Step 3:

[0048] The server receives the user's selection data sent from the terminal and begins analysis. This analysis identifies the content of the dream the user desires. The server then refers to the user's past data and prepares to generate the optimal dream scenario.

[0049] Step 4:

[0050] The device uses an electroencephalogram (EEG) sensor to acquire real-time brainwave data while the user is sleeping. The EEG data recorded by the sensor is important for analyzing the state of dreams.

[0051] Step 5:

[0052] The device transmits acquired brainwave data to the server at regular intervals. The server analyzes this data in real time to identify the stage in the user's dreaming process. Based on this identified information, the server generates a dream scenario.

[0053] Step 6:

[0054] The server generates optimal dream imagery based on the user's brainwave data and selected scenario. This generated data is then encoded and ready to be sent to the terminal.

[0055] Step 7:

[0056] The device receives dream image data transmitted from the server and presents it to the user through sight and sound. Through this presentation, the user experiences the scenario they selected within the dream.

[0057] Step 8:

[0058] After the dream experience ends and the user wakes up, they use a device to enter feedback about the dream. This feedback includes an evaluation of the quality of the experience and suggestions for improvement.

[0059] Step 9:

[0060] The device sends feedback data received from the user to the server. The server analyzes this feedback and updates its machine learning model to optimize the dream generation process for subsequent attempts.

[0061] (Example 1)

[0062] 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."

[0063] In modern society, where stress and anxiety are increasing, there is a need for means to provide high-quality rest and pleasant experiences during sleep. However, conventional dream experience systems fail to meet user expectations because they cannot adequately address individual user needs and do not allow for sufficient customization of the dream content. Therefore, there is a need to develop a system that provides individually customized dream experiences in real time based on the user's specific wishes.

[0064] 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.

[0065] This invention includes a server that includes means for receiving biosignal data from a user in real time and analyzing that information to identify a process for visualizing the user's dreams; means for acquiring information for generating dream content from a data storage device based on the user's prior selections and designing an individually customized dream; and means for transmitting the generated dream content to the user's information terminal to allow the user to experience it through sight and hearing. This makes it possible to have a specific and highly personalized dream experience that the user desires.

[0066] "Biosignal data" refers to electrical or physical signals obtained from the user's body, and generally includes information such as brain waves and heart rate.

[0067] A "user" refers to an individual who uses this system to experience their dreams, selects their dreams based on their own desires, and provides feedback.

[0068] A "terminal" is a device used by users to select dreams, collects biometric data, transmits it to a server, and provides dream experiences.

[0069] A "server" is a central processing unit that analyzes information sent by users, generates and manages dream scenarios, and has the function of receiving and processing data in real time.

[0070] A "machine learning model" is an algorithm used to improve the quality of dreams based on feedback information obtained from users, and is a technology for continuously improving the performance of the system.

[0071] "Generation" refers to the process of designing new dream scenarios based on user selections and existing data, with the aim of creating individually customized content.

[0072] A "data storage device" is a medium that holds the information necessary for a server to generate dream scenarios, and is a device for efficiently accumulating and managing large amounts of digital data.

[0073] The embodiment of this invention is supported by three main components: a user, a terminal, and a server. The system is initiated when the user uses a dedicated terminal to select a dream category and a specific scenario. The terminal is responsible for recording the user's selection as digital data, which the server later uses to generate a customized dream scenario.

[0074] Non-invasive sensors embedded in the device collect biosignal data while the user sleeps. These sensors are used to measure information such as the user's brainwaves in real time, and the measurement results are immediately transmitted to a server. The server uses a generating AI model to analyze the received biosignal data and identify the stage in the user's dreaming. Based on this analysis, the server retrieves the necessary information from the data storage device and designs a customized dream scenario according to the user's wishes.

[0075] The server transmits the generated dream scenario as digital video data to the terminal. The terminal uses this data to provide the user with a dream experience through sight and sound. In this process, the terminal uses its display and speakers to play the dream so that the user can realistically experience the content of the dream they have chosen.

[0076] As a concrete example, consider a case where a user wants to dream about "diving." In this case, the user inputs a wish through their device, such as "I want to dive to a depth of 20 meters in a tropical sea." Based on this wish sent from the device to the server, the server generates a customized diving dream scenario tailored to the user and provides that experience.

[0077] After experiencing a dream, the user enters feedback about the dream via their device upon waking. This information is securely transmitted to a server, where a machine learning model is updated. This improves the accuracy and satisfaction of future dreams, enabling the system to deliver experiences that meet user expectations.

[0078] An example of a prompt message would be, "I want to dream about diving. The ocean environment is tropical, and the diving depth is 20 meters. I want to see a variety of fish and coral." In this way, users can efficiently enjoy a highly customized dream experience.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The user operates a dedicated terminal to select their desired dream category and specific scenario. For example, they might select the "diving" category and enter specific details such as, "I want to dive to a depth of 20 meters in a tropical sea." This information is recorded as digital data by the terminal and prepared for later transmission to a server. The input is the information selected by the user, and the output is the digital data prepared by the terminal.

[0082] Step 2:

[0083] The device uses non-invasive sensors to collect biosignal data during the user's sleep. These sensors record the user's brainwaves in real time and transmit this data to a server. The output data received by the server includes the user's brainwave measurements. The device performs real-time data collection and transmission to ensure that brainwave information is reliably transmitted to the server.

[0084] Step 3:

[0085] The server analyzes the received biosignal data using a generating AI model to identify when the user is dreaming. The input is biosignal data, and the output generates information about the user's dreaming stage. Through this data analysis process, the server determines the appropriate timing to begin generating dreams.

[0086] Step 4:

[0087] The server generates a dream scenario by retrieving relevant information from storage based on the dream category selected by the user and the analysis results. The server receives the user's selection information and analysis results as input and generates a customized dream scenario as output. Based on this information, the server designs dream content that suits the user's wishes and prepares the prepared scenario as digital video data.

[0088] Step 5:

[0089] The server transmits generated digital video data to the terminal, which then uses this data to provide the user with a dream experience. The terminal's input is the digital video data received from the server, and its output provides the user with visual and auditory stimuli. The terminal displays the video on its screen and plays the sound through its speakers, allowing the user to experience the dream they have chosen.

[0090] Step 6:

[0091] After waking up, the user enters feedback about the dream they experienced into the device. This feedback includes details and impressions of the experience. The input is the user's feedback, which the device records and sends to the server as output.

[0092] Step 7:

[0093] The server analyzes user feedback and updates its machine learning model. The input is user feedback information, and the output is an improved dream generation algorithm. This feedback loop allows the server to improve the quality of future dream experiences.

[0094] (Application Example 1)

[0095] 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."

[0096] In modern society, reducing stress and achieving effective relaxation in daily life is a crucial challenge. However, securing sufficient relaxation time amidst a busy daily routine is not easy. This invention aims to provide a novel method for enhancing relaxation by allowing users to experience specific dreams of their choice during sleep. Furthermore, it aims to apply this technology in real-world facilities to provide users with valuable experiences.

[0097] 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.

[0098] This invention includes a server that receives brainwave data from a user in real time, analyzes the data to identify the user's dream stage, retrieves information from a storage device to generate a dream scenario based on the user's prior selections, and creates an individually customized experience, and provides a dedicated environment within a real-world facility for customers to experience their selected dream along with relaxation effects. This makes it possible to have personalized dream experiences that enhance relaxation effects in daily life, even in real-world environments.

[0099] "Electroencephalogram (EEG) data" is digital information representing electrical signals that indicate a user's brain activity, and is collected to monitor the state of the brain during sleep.

[0100] "Dream stages" refer to the state of brain activity when a user experiences a particular dream during sleep, and are an important indicator for maximizing the relaxation experience.

[0101] "Scenario generation" is the process of creating individually customized dream experiences as visual and auditory content based on the user's prior selections.

[0102] A "memory device" is a device for storing necessary data and programs, and is used to efficiently retrieve the user's desired dream scenario.

[0103] "Feedback" refers to the act of users providing information about their impressions and areas for improvement after a dream experience, and this is important data for improving the dream generation process in the future.

[0104] A "machine learning model" is an artificial intelligence model that improves itself based on accumulated feedback data, providing users with a more optimized dream experience.

[0105] A "dedicated environment" is a space within a real-world facility that provides physical and technological equipment optimized for users to experience their chosen dreams.

[0106] This invention is a system that allows users to experience relaxation effects by having them experience specific dreams during sleep. This system is implemented using a dedicated terminal, a server in the cloud, and a dedicated environment in the real world.

[0107] Before starting a dream experience, the user selects their desired dream category and specific scenario through the device. This information is securely stored and sent to a cloud server. The device collects the user's brainwave data in real time using a non-invasive EEG sensor (e.g., Muse 2). This data is transmitted to the server via Bluetooth and analyzed using an EEG processing library (e.g., MNE-Python).

[0108] The server identifies the user's dream stage and, based on the user's past experience data and choices, generates a personalized dream scenario using a generative AI model (e.g., GPT-3®).5. The generated dream scenario is filtered as visual and auditory data and sent to the terminal.

[0109] In a dedicated real-world environment, users can experience dreams in a comfortable and relaxing space. This environment is equipped with appropriately tuned sound and video equipment to enhance the user's dream experience.

[0110] After the dream experience ends, users enter feedback, which is sent to the server. This feedback is used to update the machine learning model and improve the quality of future dream experiences.

[0111] As a concrete example, consider a scenario where a user chooses a dream involving a "relaxing seaside scene." Through the device, the gentle sound of waves and the scenery of the sandy beach are visualized, allowing the user to experience a relaxing time as if they were actually there.

[0112] User: I want to have a relaxing dream about the beach the next time I sleep. Please describe the experience and atmosphere of your dream.

[0113] AI Model: You are sitting on a soft sandy beach under a gentle sun and soothing waves. The blue ocean stretches out before you. A gentle breeze caresses your cheek, and the rhythmic sound of the waves calms your mind. In this dream, you will spend a leisurely time.

[0114] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0115] Step 1:

[0116] The user uses a dedicated terminal to select their desired dream category and specific scenario. The selection results are entered as digital data and saved on the terminal.

[0117] Specifically, the user selects their desired scenario from a menu via the terminal's interface. The saved data is then prepared to be sent to the server later.

[0118] Step 2:

[0119] The device uses a non-invasive electroencephalogram (EEG) sensor to collect the user's brainwave data in real time. This EEG data serves as input for the program.

[0120] Specifically, the sensor records the user's brain's electrical activity and transmits the data to a server via Bluetooth. This data serves as the basis for analyzing the user's sleep patterns.

[0121] Step 3:

[0122] The server analyzes the received brainwave data to identify the stage of the dream. The analysis results are output and become input for the next processing step.

[0123] Specifically, an EEG processing library (e.g., MNE-Python) is used on the server to identify when the user has entered REM sleep. After detecting this state, the process then proceeds to generate a dream scenario.

[0124] Step 4:

[0125] Based on the user's selections and analysis results, the server generates a dream scenario using a generative AI model. The scenario data is output and sent to the terminal.

[0126] Specifically, a generative AI model (e.g., GPT-3.5) is used to generate text and video data based on the selected scenario. This creates a customized dream scenario.

[0127] Step 5:

[0128] The device allows the user to experience the received dream scenario as visual and auditory content. This content playback is the output.

[0129] Specifically, the device uses a display and speakers to provide the user with generated images and sounds. Because the user experiences it in a dedicated environment, they can immerse themselves in a relaxed dreamlike state.

[0130] Step 6:

[0131] After the dream experience ends, the user enters feedback. This feedback data becomes input for the program.

[0132] Specifically, users input their satisfaction level and requests regarding the experience using a form on their device. The collected feedback is sent to the server and used to update the learning model.

[0133] Step 7:

[0134] The server updates its machine learning model based on the feedback it receives. This update process is the output, and it improves the accuracy of dream generation for the next generation.

[0135] Specifically, the server analyzes feedback data and optimizes the dream scenario generation method. This makes it possible to improve the quality of future experiences.

[0136] 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.

[0137] This invention provides a system that takes into account the user's emotional state when selecting and allowing the user to experience a specific dream during sleep. This system incorporates an emotion engine and identifies the user's emotional state by analyzing the user's brainwave data and other biometric data.

[0138] First, the user operates the device to select the dream category and scenario they wish to experience. After selection, this information is sent to the server as digital data by the device. The device is equipped with non-invasive sensors that collect biometric data such as the user's brainwaves, heart rate, and breathing rhythm in real time.

[0139] After receiving this biometric data, the server uses an emotion engine to analyze the user's current emotional state. Based on the analysis, the server generates a dream scenario adapted to the user's emotions and optimizes it. Depending on the emotional state, specific elements may be added to or adjusted in the dream scenario.

[0140] The generated dream scenario is sent to the device as encoded video data. The device uses this data to provide the user with a dream experience through sight and sound. This dream experience becomes more natural and richer by adapting to the user's emotional state.

[0141] After waking up, users input feedback about their dreams into a device. This feedback includes their thoughts and changes in emotions regarding the dream's content. The device sends this information to a server, which uses it as training data for the emotion engine. This improves the dream generation process for subsequent dreams, further enhancing the user's emotional experience.

[0142] For example, if a user chooses a dream with the theme of "adventure," and the emotion engine identifies the user's current state as "excited," the server will dynamically modify the dream scenario to match that emotional state. For instance, more adventurous elements might be emphasized in the dream, or challenging situations might be added. In this way, the user can have a more engaging dream experience that aligns with their emotions.

[0143] The following describes the processing flow.

[0144] Step 1:

[0145] The user uses their device to select their desired dream category and specific scenario from the interface. This selection information is saved on the device.

[0146] Step 2:

[0147] The terminal organizes the user's selection data and sends it to the server via a security protocol. Here, the category information of the selected dream becomes important.

[0148] Step 3:

[0149] Non-invasive sensors embedded in the device collect biometric data such as brainwaves and heart rate in real time while the user sleeps. This data is necessary for interpreting the user's emotions.

[0150] Step 4:

[0151] The device sends the collected biometric data to the server. The server analyzes the received data and uses an emotion engine to identify the user's current emotional state.

[0152] Step 5:

[0153] The server optimizes the selected dream scenario based on the identified emotional state. In doing so, it modifies or emphasizes elements within the scenario to adapt to the user's emotions.

[0154] Step 6:

[0155] The generated dream scenario is transmitted from the server to the terminal as digital video data. The terminal processes this data and delivers the dream to the user through sight and sound.

[0156] Step 7:

[0157] Users enter feedback about the content of their dreams via their device. This feedback includes details about their satisfaction with the dream and how their emotions changed.

[0158] Step 8:

[0159] The device sends feedback data to the server, which analyzes it as training data for the emotion engine. This feedback is then used in the next dream generation process.

[0160] This allows the system to recognize the user's emotions and adaptively adjust the content of the dream, providing the user with a richer dream experience.

[0161] (Example 2)

[0162] 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".

[0163] Traditional technologies have not adequately achieved providing individualized dream experiences that adapt to each user's emotional state. Therefore, it is necessary to improve the quality of the dream experience by generating dreams based on the user's emotions and using the resulting feedback for learning.

[0164] 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.

[0165] In this invention, the server includes means for receiving and analyzing biometric data in real time, means for acquiring and generating dream scenarios from a storage medium based on prior selections and emotional states, and means for adjusting and generating video data using a generation AI model. This enables a dream experience optimized to the emotions of each individual user.

[0166] "Biometric data" refers to information related to the user's physical condition, such as the user's brain waves, heart rate, and respiratory rhythm.

[0167] "Emotional state" refers to the user's current psychological or emotional condition, identified from analyzed biometric data.

[0168] A "dream scenario" refers to a description or plan that includes the content of a dream experience, generated based on the user's choices and emotional state.

[0169] A "storage medium" refers to a device or apparatus used to retain digital information over a long period of time.

[0170] A "generative AI model" refers to a model that utilizes artificial intelligence technology to automatically generate content based on input data.

[0171] "Feedback" refers to the comments, opinions, and evaluations that users provide after experiencing a dream.

[0172] This system is designed to allow users to experience specific dreams tailored to their emotional state. Users first use a device to select the dream category and scenario they wish to experience. The device incorporates non-invasive sensors that collect biometric data in real time, including brainwaves, heart rate, and respiratory rhythm. This data is then analyzed as digital signals.

[0173] The device transmits information about the dreams selected by the user, along with biometric data collected in real time, to the server. After receiving this biometric data, the server uses an emotion engine to analyze the user's emotional state. The emotion engine utilizes a generative AI model and, by referring to a vast database, classifies the user's current emotions into states such as "excited" or "relaxed."

[0174] Based on the analyzed emotional state, the server retrieves a scenario optimized for the user's choices and emotions from the storage medium and dynamically adjusts the dream scenario using a generative AI model. The generated scenario is encoded as video data and sent to the terminal. The terminal plays this video data through visual and auditory devices to provide the user with a dream experience.

[0175] After waking up, the user enters feedback about their dream into the device. This feedback includes their thoughts on the dream's content and any emotional changes they experienced. The device sends this feedback to a server, which uses it as training data for the emotion engine. This cyclical feedback process refines the dream generation process for subsequent dreams, further personalizing the user's dream experience.

[0176] For example, if a user selects a dream themed around "adventure" and their emotional state is analyzed as "excited," the server will generate a scenario that enhances the adventurous elements in line with that emotional state. An example of a prompt message would be, "Generate an adventure scenario that is optimal for the user's current emotional state."

[0177] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0178] Step 1:

[0179] The user operates the device to select a dream category and scenario. This input is made through the device's interface, and the selected data is recorded in digital format. The device stores this information in its temporary storage.

[0180] Step 2:

[0181] The device's non-invasive sensors operate to collect biometric data such as the user's brainwaves, heart rate, and respiratory rhythm in real time. This data is converted from analog to digital and recorded as a digital signal within the device via a signal processing unit. The collected biometric data is then transmitted to a server for processing.

[0182] Step 3:

[0183] The server receives dream selection information and biometric data transmitted from the terminal. The input biometric data is fed into the emotion engine, where an emotion analysis model analyzes the user's current emotional state. This analysis utilizes a generative AI model to classify the emotional state into categories such as "excited" or "relaxed." The results of the analysis are output as digital data.

[0184] Step 4:

[0185] The server retrieves appropriate scenario data from the storage medium based on the analyzed emotional state and selected dream category. Then, using a generative AI model, it optimizes this scenario and generates a dream scenario adapted to the user's emotional state. This process uses pre-defined prompts, such as "Generate an adventure scenario best suited to the user's current emotional state." The generated scenario is encoded as video data.

[0186] Step 5:

[0187] The server sends encoded video data to the terminal. The data received by the terminal is played back to the user through visual and auditory devices. At this time, the data is decoded, and the user begins a dream experience in a virtual space. The video and audio are combined in a way that is appropriate to the user's emotions.

[0188] Step 6:

[0189] After a dream experience, users enter feedback via a device. This feedback includes the content of the dream, their satisfaction level, and changes in their emotions after the experience. The feedback data is reformatted digitally and sent from the device to a server.

[0190] Step 7:

[0191] The server integrates the received feedback data and processes it as training data for the emotion engine. Data analysis lays the foundation for further improving the next dream generation process and making the user experience more personalized. This learning result updates the algorithm of the generative AI model, improving the accuracy of the next dream scenario generated.

[0192] (Application Example 2)

[0193] 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."

[0194] There is a growing demand to provide richer, more emotionally resonant dream experiences by individually optimizing each user's dream experience. However, conventional systems have struggled to generate dream scenarios that precisely reflect the user's emotional state, and there has been a lack of appropriate methods to improve the quality of personalized dream experiences. Furthermore, it has been difficult to effectively incorporate user feedback and apply it to future dream generation.

[0195] 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.

[0196] In this invention, the server includes means for receiving biometric information from the user in real time and analyzing that information to identify the user's emotional state; means for acquiring a dream experience theme based on the user's prior selection at the terminal and generating a dream scenario adapted to the emotional analysis results; and means for transmitting the generated visual and auditory information to the user's device to provide the dream experience. This enables the provision of a personalized dream experience that corresponds to the user's emotional state, and makes it possible to generate high-quality dreams tailored to individual users, which was previously difficult.

[0197] "Biometric information" refers to data obtained from the user's body, such as brain waves, heart rate, and breathing patterns.

[0198] "Emotional state" is an indicator that shows the user's current psychological or emotional state.

[0199] "Terminal" refers to a device used by a user, specifically a device used for collecting biometric information and providing dream experiences.

[0200] A "dream experience theme" is a concept that indicates the broad categories or scenarios of dreams that a user wishes to experience.

[0201] "Emotional analysis results" refer to data that indicates the user's emotional state, identified based on collected biometric information.

[0202] A "dream scenario" is a plan that defines the specific content and development of the dream offered to the user.

[0203] "Visual and auditory information" refers to information about images and sounds provided to users to enhance their dream experience.

[0204] "Feedback" refers to opinions and information such as user impressions and suggestions for improvement regarding their dream experiences.

[0205] The system implementing this invention operates by coordinating smart devices and a server to provide a dream experience tailored to the user's emotional state. The terminal uses non-invasive sensors to collect the user's biometric information, specifically brain waves, heart rate, and breathing patterns, in real time. This data is transmitted to the server via wireless communication. The server utilizes an advanced emotion engine and applies a generative AI model to analyze the collected biometric information and identify the user's emotional state.

[0206] Based on the identified emotional state, the server uses the user's prior dream theme selection information to generate an optimal, adaptive dream scenario. The generated scenario is encoded as visual and auditory information and sent to the user's device. The device uses this information to play the scenario through a display and sound system to provide the user with an engaging dream experience.

[0207] After a user experiences a dream, the device receives feedback from the user. This feedback includes information about their feelings and changes in emotions regarding the dream's content. By sending this information to the server, the server updates the generating AI model, improving future dream experiences to be even more personalized.

[0208] For example, if a user selects a dream with the theme of "adventure," the server, sensing "excitement" through the user's emotion engine, generates a dream scenario that emphasizes adventurous elements and challenging situations. In this way, the system enables users to experience dreams that are best suited to their emotions, providing more engaging dreams that match their feelings.

[0209] Examples of prompts for generative AI models:

[0210] "Generate an adventure dream scenario that matches the user's current emotional state and chosen theme. Highlight elements that will excite the user and include challenging situations."

[0211] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0212] Step 1:

[0213] The user selects a theme for their dream experience via their device and enters the selection information into the device. The entered theme information is saved on the device and used in subsequent processing steps.

[0214] Step 2:

[0215] The device uses non-invasive sensors to collect biometric information such as the user's brainwaves, heart rate, and breathing patterns in real time. This biometric information is converted into a digital format and transmitted as input data to the server.

[0216] Step 3:

[0217] The server receives biometric information transmitted from the terminal and analyzes it using an emotion engine. The analysis applies a generative AI model based on the user's brainwave data to identify the user's emotional state. This results in an output representing the emotional state, which is used in the next step.

[0218] Step 4:

[0219] The server generates a dream scenario using the theme information selected by the user and the analyzed emotional state. By inputting prompt sentences into the AI ​​generation model, it generates a scenario adapted to the emotion and encodes it as visual and auditory information. The scenario generated in this way becomes the output.

[0220] Step 5:

[0221] The server transmits the generated visual and auditory information to the terminal. Based on the received data, the terminal uses its display and sound equipment to provide the user with a dream experience. This allows the user to receive a dream experience that matches their emotions.

[0222] Step 6:

[0223] After a dream experience, the user inputs feedback information, such as their impressions and changes in emotions, into the device. This feedback is saved on the device as input data to help improve the dream generation process for the next time.

[0224] Step 7:

[0225] The device sends user feedback information to the server. The server analyzes this feedback information and uses it to update the generated AI model. This prepares the system to make the next dream experience even more personalized.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] [Second Embodiment]

[0230] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0231] 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.

[0232] 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).

[0233] 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.

[0234] 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.

[0235] 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).

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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".

[0242] This invention relates to a system that allows a user to select a dream during sleep and experience that dream. The system is configured as follows: First, the user uses a dedicated terminal to pre-select the category of dream and specific scenario they wish to experience. At this stage, the terminal records the user's selection as digital data and prepares to transmit it to a server.

[0243] Next, the device is equipped with sensors to collect brainwave data while the user is sleeping. These sensors non-invasively measure the user's brainwaves and transmit the data to a server in real time. The server analyzes this brainwave data to identify the stage in the user's dreaming. Depending on the identified stage, the server obtains the information necessary to generate a dream scenario and, combined with the user's past experience data, generates a personalized dream.

[0244] The generated dream scenario is sent from the server to the terminal as digital video data. The terminal uses this data to induce a dream experience in the user through sight and sound. The user can experience the dream they have chosen while sleeping, and after waking up, they input feedback about the dream via the terminal.

[0245] Feedback information is sent to the server, which then updates the machine learning model based on it. This continuous learning improves the quality of dreams delivered in subsequent sessions, making it easier to realize the user's expected experience.

[0246] As a concrete example, consider a scenario where a user selects a dream category called "diving." In this case, the user can set their desired ocean environment and diving depth via their device. After the scenario is generated, this dream is visualized while the user sleeps, allowing them to experience observing fish and coral up close, as if they were actually in the ocean. Upon waking, the user provides feedback on the experience, contributing to improving the accuracy of future dream experiences.

[0247] The following describes the processing flow.

[0248] Step 1:

[0249] The user uses their device to select the dream category and specific scenario they want to see from the interface. Once the user confirms their selection, that information is saved on the device.

[0250] Step 2:

[0251] The terminal structures the user's selections as digital data and prepares to send them to the server using security protocols. During this process, the terminal verifies the integrity of the data.

[0252] Step 3:

[0253] The server receives the user's selection data sent from the terminal and begins analysis. This analysis identifies the content of the dream the user desires. The server then refers to the user's past data and prepares to generate the optimal dream scenario.

[0254] Step 4:

[0255] The device uses an electroencephalogram (EEG) sensor to acquire real-time brainwave data while the user is sleeping. The EEG data recorded by the sensor is important for analyzing the state of dreams.

[0256] Step 5:

[0257] The device transmits acquired brainwave data to the server at regular intervals. The server analyzes this data in real time to identify the stage in the user's dreaming process. Based on this identified information, the server generates a dream scenario.

[0258] Step 6:

[0259] The server generates optimal dream imagery based on the user's brainwave data and selected scenario. This generated data is then encoded and ready to be sent to the terminal.

[0260] Step 7:

[0261] The device receives dream image data transmitted from the server and presents it to the user through sight and sound. Through this presentation, the user experiences the scenario they selected within the dream.

[0262] Step 8:

[0263] After the dream experience ends and the user wakes up, they use a device to enter feedback about the dream. This feedback includes an evaluation of the quality of the experience and suggestions for improvement.

[0264] Step 9:

[0265] The device sends feedback data received from the user to the server. The server analyzes this feedback and updates its machine learning model to optimize the dream generation process for subsequent attempts.

[0266] (Example 1)

[0267] 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."

[0268] In modern society, where stress and anxiety are increasing, there is a need for means to provide high-quality rest and pleasant experiences during sleep. However, conventional dream experience systems fail to meet user expectations because they cannot adequately address individual user needs and do not allow for sufficient customization of the dream content. Therefore, there is a need to develop a system that provides individually customized dream experiences in real time based on the user's specific wishes.

[0269] 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.

[0270] This invention includes a server that includes means for receiving biosignal data from a user in real time and analyzing that information to identify a process for visualizing the user's dreams; means for acquiring information for generating dream content from a data storage device based on the user's prior selections and designing an individually customized dream; and means for transmitting the generated dream content to the user's information terminal to allow the user to experience it through sight and hearing. This makes it possible to have a specific and highly personalized dream experience that the user desires.

[0271] "Biosignal data" refers to electrical or physical signals obtained from the user's body, and generally includes information such as brain waves and heart rate.

[0272] A "user" refers to an individual who uses this system to experience their dreams, selects their dreams based on their own desires, and provides feedback.

[0273] A "terminal" is a device used by users to select dreams, collects biometric data, transmits it to a server, and provides dream experiences.

[0274] A "server" is a central processing unit that analyzes information sent by users, generates and manages dream scenarios, and has the function of receiving and processing data in real time.

[0275] A "machine learning model" is an algorithm used to improve the quality of dreams based on feedback information obtained from users, and is a technology for continuously improving the performance of the system.

[0276] "Generation" refers to the process of designing new dream scenarios based on user selections and existing data, with the aim of creating individually customized content.

[0277] A "data storage device" is a medium that holds the information necessary for a server to generate dream scenarios, and is a device for efficiently accumulating and managing large amounts of digital data.

[0278] The embodiment of this invention is supported by three main components: a user, a terminal, and a server. The system is initiated when the user uses a dedicated terminal to select a dream category and a specific scenario. The terminal is responsible for recording the user's selection as digital data, which the server later uses to generate a customized dream scenario.

[0279] Non-invasive sensors embedded in the device collect biosignal data while the user sleeps. These sensors are used to measure information such as the user's brainwaves in real time, and the measurement results are immediately transmitted to a server. The server uses a generating AI model to analyze the received biosignal data and identify the stage in the user's dreaming. Based on this analysis, the server retrieves the necessary information from the data storage device and designs a customized dream scenario according to the user's wishes.

[0280] The server transmits the generated dream scenario as digital video data to the terminal. The terminal uses this data to provide the user with a dream experience through sight and sound. In this process, the terminal uses its display and speakers to play the dream so that the user can realistically experience the content of the dream they have chosen.

[0281] As a concrete example, consider a case where a user wants to dream about "diving." In this case, the user inputs a wish through their device, such as "I want to dive to a depth of 20 meters in a tropical sea." Based on this wish sent from the device to the server, the server generates a customized diving dream scenario tailored to the user and provides that experience.

[0282] After the user experiences a dream, upon waking up, the user inputs feedback regarding the dream via the terminal. This information is securely transmitted to the server, and the machine learning model on the server side is updated. As a result, the accuracy and satisfaction of the dreams provided after the next time onwards are improved, enabling the realization of an experience that meets the user's expectations.

[0283] As an example of the prompt text, it would be something like "I want to have a dream of diving. The ocean environment is tropical and the diving depth is 20 meters. I want to see various fish and corals." In this way, the user can efficiently enjoy a highly customized dream experience.

[0284] The flow of the specific process in Example 1 will be described using FIG. 11.

[0285] Step 1:

[0286] The user operates a dedicated terminal and selects the category of the desired dream and specific scenarios. For example, the user selects the category of "diving" and inputs the specific details of "wanting to dive to a depth of 20 meters in a tropical sea". This information is recorded as digital data by the terminal and is prepared to be transmitted to the server later. There is the information selected by the user as the input, and there is the digital data prepared by the terminal as the output.

[0287] Step 2:

[0288] The terminal uses the equipped non-invasive sensors to collect biometric signal data during the user's sleep. This sensor records the user's brain waves in real time and transmits the data to the server as an input. The output data received by the server includes the measurement results of the user's brain waves. The terminal performs real-time data collection and transmission to ensure that the brain wave information is transmitted to the server.

[0289] Step 3:

[0290] The server analyzes the received biosignal data using a generating AI model to identify when the user is dreaming. The input is biosignal data, and the output generates information about the user's dreaming stage. Through this data analysis process, the server determines the appropriate timing to begin generating dreams.

[0291] Step 4:

[0292] The server generates a dream scenario by retrieving relevant information from storage based on the dream category selected by the user and the analysis results. The server receives the user's selection information and analysis results as input and generates a customized dream scenario as output. Based on this information, the server designs dream content that suits the user's wishes and prepares the prepared scenario as digital video data.

[0293] Step 5:

[0294] The server transmits generated digital video data to the terminal, which then uses this data to provide the user with a dream experience. The terminal's input is the digital video data received from the server, and its output provides the user with visual and auditory stimuli. The terminal displays the video on its screen and plays the sound through its speakers, allowing the user to experience the dream they have chosen.

[0295] Step 6:

[0296] After waking up, the user enters feedback about the dream they experienced into the device. This feedback includes details and impressions of the experience. The input is the user's feedback, which the device records and sends to the server as output.

[0297] Step 7:

[0298] The server analyzes user feedback and updates its machine learning model. The input is user feedback information, and the output is an improved dream generation algorithm. This feedback loop allows the server to improve the quality of future dream experiences.

[0299] (Application Example 1)

[0300] 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."

[0301] In modern society, reducing stress and achieving effective relaxation in daily life is a crucial challenge. However, securing sufficient relaxation time amidst a busy daily routine is not easy. This invention aims to provide a novel method for enhancing relaxation by allowing users to experience specific dreams of their choice during sleep. Furthermore, it aims to apply this technology in real-world facilities to provide users with valuable experiences.

[0302] 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.

[0303] This invention includes a server that receives brainwave data from a user in real time, analyzes the data to identify the user's dream stage, retrieves information from a storage device to generate a dream scenario based on the user's prior selections, and creates an individually customized experience, and provides a dedicated environment within a real-world facility for customers to experience their selected dream along with relaxation effects. This makes it possible to have personalized dream experiences that enhance relaxation effects in daily life, even in real-world environments.

[0304] "Electroencephalogram (EEG) data" is digital information representing electrical signals that indicate a user's brain activity, and is collected to monitor the state of the brain during sleep.

[0305] The "dream stage" refers to the brain's activity state when the user sees a specific dream during sleep and is an important indicator for maximizing the relaxation experience.

[0306] "Scenario generation" is a process of constructing an individually customized dream experience as visual and auditory content based on the user's prior selection.

[0307] The "memory device" is a device for storing necessary data and programs and is used to efficiently retrieve the dream scenarios desired by the user.

[0308] "Feedback" is an act of providing information on the user's feelings and improvement points obtained after the dream experience and is important data for improving the next dream generation process.

[0309] The "machine learning model" is an artificial intelligence model that self-improves based on the accumulated feedback data and provides a dream experience optimized for the user.

[0310] The "dedicated environment" is a space within a real-world facility that provides physical and technical facilities optimized for the user to experience the selected dream.

[0311] The present invention is a system that obtains a relaxation effect by the user experiencing a specific dream during sleep. This system is realized using a dedicated terminal, a server on the cloud, and a dedicated environment in the real world.

[0312] Before starting a dream experience, the user selects their desired dream category and specific scenario through the device. This information is securely stored and sent to a cloud server. The device collects the user's brainwave data in real time using a non-invasive EEG sensor (e.g., Muse 2). This data is transmitted to the server via Bluetooth and analyzed using an EEG processing library (e.g., MNE-Python).

[0313] The server identifies the user's dream stage and, based on the user's past experience data and choices, generates a personalized dream scenario using a generative AI model (e.g., GPT-3.5). The generated dream scenario is filtered as visual and auditory data and sent to the terminal.

[0314] In a dedicated real-world environment, users can experience dreams in a comfortable and relaxing space. This environment is equipped with appropriately tuned sound and video equipment to enhance the user's dream experience.

[0315] After the dream experience ends, users enter feedback, which is sent to the server. This feedback is used to update the machine learning model and improve the quality of future dream experiences.

[0316] As a concrete example, consider a scenario where a user chooses a dream involving a "relaxing seaside scene." Through the device, the gentle sound of waves and the scenery of the sandy beach are visualized, allowing the user to experience a relaxing time as if they were actually there.

[0317] User: I want to have a relaxing dream about the beach the next time I sleep. Please describe the experience and atmosphere of your dream.

[0318] AI Model: You are sitting on a soft sandy beach under a gentle sun and soothing waves. The blue ocean stretches out before you. A gentle breeze caresses your cheek, and the rhythmic sound of the waves calms your mind. In this dream, you will spend a leisurely time.

[0319] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0320] Step 1:

[0321] The user uses a dedicated terminal to select their desired dream category and specific scenario. The selection results are entered as digital data and saved on the terminal.

[0322] Specifically, the user selects their desired scenario from a menu via the terminal's interface. The saved data is then prepared to be sent to the server later.

[0323] Step 2:

[0324] The device uses a non-invasive electroencephalogram (EEG) sensor to collect the user's brainwave data in real time. This EEG data serves as input for the program.

[0325] Specifically, the sensor records the user's brain's electrical activity and transmits the data to a server via Bluetooth. This data serves as the basis for analyzing the user's sleep patterns.

[0326] Step 3:

[0327] The server analyzes the received brainwave data to identify the stage of the dream. The analysis results are output and become input for the next processing step.

[0328] Specifically, an EEG processing library (e.g., MNE-Python) is used on the server to identify when the user has entered REM sleep. After detecting this state, the process then proceeds to generate a dream scenario.

[0329] Step 4:

[0330] Based on the user's selections and analysis results, the server generates a dream scenario using a generative AI model. The scenario data is output and sent to the terminal.

[0331] Specifically, a generative AI model (e.g., GPT-3.5) is used to generate text and video data based on the selected scenario. This creates a customized dream scenario.

[0332] Step 5:

[0333] The device allows the user to experience the received dream scenario as visual and auditory content. This content playback is the output.

[0334] Specifically, the device uses a display and speakers to provide the user with generated images and sounds. Because the user experiences it in a dedicated environment, they can immerse themselves in a relaxed dreamlike state.

[0335] Step 6:

[0336] After the dream experience ends, the user enters feedback. This feedback data becomes input for the program.

[0337] Specifically, users input their satisfaction level and requests regarding the experience using a form on their device. The collected feedback is sent to the server and used to update the learning model.

[0338] Step 7:

[0339] The server updates its machine learning model based on the feedback it receives. This update process is the output, and it improves the accuracy of dream generation for the next generation.

[0340] Specifically, the server analyzes feedback data and optimizes the dream scenario generation method. This makes it possible to improve the quality of future experiences.

[0341] 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.

[0342] This invention provides a system that takes into account the user's emotional state when selecting and allowing the user to experience a specific dream during sleep. This system incorporates an emotion engine and identifies the user's emotional state by analyzing the user's brainwave data and other biometric data.

[0343] First, the user operates the device to select the dream category and scenario they wish to experience. After selection, this information is sent to the server as digital data by the device. The device is equipped with non-invasive sensors that collect biometric data such as the user's brainwaves, heart rate, and breathing rhythm in real time.

[0344] After receiving this biometric data, the server uses an emotion engine to analyze the user's current emotional state. Based on the analysis, the server generates a dream scenario adapted to the user's emotions and optimizes it. Depending on the emotional state, specific elements may be added to or adjusted in the dream scenario.

[0345] The generated dream scenario is sent to the device as encoded video data. The device uses this data to provide the user with a dream experience through sight and sound. This dream experience becomes more natural and richer by adapting to the user's emotional state.

[0346] After waking up, users input feedback about their dreams into a device. This feedback includes their thoughts and changes in emotions regarding the dream's content. The device sends this information to a server, which uses it as training data for the emotion engine. This improves the dream generation process for subsequent dreams, further enhancing the user's emotional experience.

[0347] For example, if a user chooses a dream with the theme of "adventure," and the emotion engine identifies the user's current state as "excited," the server will dynamically modify the dream scenario to match that emotional state. For instance, more adventurous elements might be emphasized in the dream, or challenging situations might be added. In this way, the user can have a more engaging dream experience that aligns with their emotions.

[0348] The following describes the processing flow.

[0349] Step 1:

[0350] The user uses their device to select their desired dream category and specific scenario from the interface. This selection information is saved on the device.

[0351] Step 2:

[0352] The terminal organizes the user's selection data and sends it to the server via a security protocol. Here, the category information of the selected dream becomes important.

[0353] Step 3:

[0354] Non-invasive sensors embedded in the device collect biometric data such as brainwaves and heart rate in real time while the user sleeps. This data is necessary for interpreting the user's emotions.

[0355] Step 4:

[0356] The device sends the collected biometric data to the server. The server analyzes the received data and uses an emotion engine to identify the user's current emotional state.

[0357] Step 5:

[0358] The server optimizes the selected dream scenario based on the identified emotional state. In doing so, it modifies or emphasizes elements within the scenario to adapt to the user's emotions.

[0359] Step 6:

[0360] The generated dream scenario is transmitted from the server to the terminal as digital video data. The terminal processes this data and delivers the dream to the user through sight and sound.

[0361] Step 7:

[0362] Users enter feedback about the content of their dreams via their device. This feedback includes details about their satisfaction with the dream and how their emotions changed.

[0363] Step 8:

[0364] The device sends feedback data to the server, which analyzes it as training data for the emotion engine. This feedback is then used in the next dream generation process.

[0365] This allows the system to recognize the user's emotions and adaptively adjust the content of the dream, providing the user with a richer dream experience.

[0366] (Example 2)

[0367] 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".

[0368] Traditional technologies have not adequately achieved providing individualized dream experiences that adapt to each user's emotional state. Therefore, it is necessary to improve the quality of the dream experience by generating dreams based on the user's emotions and using the resulting feedback for learning.

[0369] 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.

[0370] In this invention, the server includes means for receiving and analyzing biometric data in real time, means for acquiring and generating dream scenarios from a storage medium based on prior selections and emotional states, and means for adjusting and generating video data using a generation AI model. This enables a dream experience optimized to the emotions of each individual user.

[0371] "Biometric data" refers to information related to the user's physical condition, such as the user's brain waves, heart rate, and respiratory rhythm.

[0372] "Emotional state" refers to the user's current psychological or emotional condition, identified from analyzed biometric data.

[0373] A "dream scenario" refers to a description or plan that includes the content of a dream experience, generated based on the user's choices and emotional state.

[0374] A "storage medium" refers to a device or apparatus used to retain digital information over a long period of time.

[0375] A "generative AI model" refers to a model that utilizes artificial intelligence technology to automatically generate content based on input data.

[0376] "Feedback" refers to the comments, opinions, and evaluations that users provide after experiencing a dream.

[0377] This system is designed to allow users to experience specific dreams tailored to their emotional state. Users first use a device to select the dream category and scenario they wish to experience. The device incorporates non-invasive sensors that collect biometric data in real time, including brainwaves, heart rate, and respiratory rhythm. This data is then analyzed as digital signals.

[0378] The device transmits information about the dreams selected by the user, along with biometric data collected in real time, to the server. After receiving this biometric data, the server uses an emotion engine to analyze the user's emotional state. The emotion engine utilizes a generative AI model and, by referring to a vast database, classifies the user's current emotions into states such as "excited" or "relaxed."

[0379] Based on the analyzed emotional state, the server retrieves a scenario optimized for the user's choices and emotions from the storage medium and dynamically adjusts the dream scenario using a generative AI model. The generated scenario is encoded as video data and sent to the terminal. The terminal plays this video data through visual and auditory devices to provide the user with a dream experience.

[0380] After waking up, the user enters feedback about their dream into the device. This feedback includes their thoughts on the dream's content and any emotional changes they experienced. The device sends this feedback to a server, which uses it as training data for the emotion engine. This cyclical feedback process refines the dream generation process for subsequent dreams, further personalizing the user's dream experience.

[0381] For example, if a user selects a dream themed around "adventure" and their emotional state is analyzed as "excited," the server will generate a scenario that enhances the adventurous elements in line with that emotional state. An example of a prompt message would be, "Generate an adventure scenario that is optimal for the user's current emotional state."

[0382] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0383] Step 1:

[0384] The user operates the device to select a dream category and scenario. This input is made through the device's interface, and the selected data is recorded in digital format. The device stores this information in its temporary storage.

[0385] Step 2:

[0386] The device's non-invasive sensors operate to collect biometric data such as the user's brainwaves, heart rate, and respiratory rhythm in real time. This data is converted from analog to digital and recorded as a digital signal within the device via a signal processing unit. The collected biometric data is then transmitted to a server for processing.

[0387] Step 3:

[0388] The server receives dream selection information and biometric data transmitted from the terminal. The input biometric data is fed into the emotion engine, where an emotion analysis model analyzes the user's current emotional state. This analysis utilizes a generative AI model to classify the emotional state into categories such as "excited" or "relaxed." The results of the analysis are output as digital data.

[0389] Step 4:

[0390] The server retrieves appropriate scenario data from the storage medium based on the analyzed emotional state and selected dream category. Then, using a generative AI model, it optimizes this scenario and generates a dream scenario adapted to the user's emotional state. This process uses pre-defined prompts, such as "Generate an adventure scenario best suited to the user's current emotional state." The generated scenario is encoded as video data.

[0391] Step 5:

[0392] The server sends encoded video data to the terminal. The data received by the terminal is played back to the user through visual and auditory devices. At this time, the data is decoded, and the user begins a dream experience in a virtual space. The video and audio are combined in a way that is appropriate to the user's emotions.

[0393] Step 6:

[0394] After a dream experience, users enter feedback via a device. This feedback includes the content of the dream, their satisfaction level, and changes in their emotions after the experience. The feedback data is reformatted digitally and sent from the device to a server.

[0395] Step 7:

[0396] The server integrates the received feedback data and processes it as training data for the emotion engine. Data analysis lays the foundation for further improving the next dream generation process and making the user experience more personalized. This learning result updates the algorithm of the generative AI model, improving the accuracy of the next dream scenario generated.

[0397] (Application Example 2)

[0398] 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."

[0399] There is a growing demand to provide richer, more emotionally resonant dream experiences by individually optimizing each user's dream experience. However, conventional systems have struggled to generate dream scenarios that precisely reflect the user's emotional state, and there has been a lack of appropriate methods to improve the quality of personalized dream experiences. Furthermore, it has been difficult to effectively incorporate user feedback and apply it to future dream generation.

[0400] 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.

[0401] In this invention, the server includes means for receiving biometric information from the user in real time and analyzing that information to identify the user's emotional state; means for acquiring a dream experience theme based on the user's prior selection at the terminal and generating a dream scenario adapted to the emotional analysis results; and means for transmitting the generated visual and auditory information to the user's device to provide the dream experience. This enables the provision of a personalized dream experience that corresponds to the user's emotional state, and makes it possible to generate high-quality dreams tailored to individual users, which was previously difficult.

[0402] "Biometric information" refers to data obtained from the user's body, such as brain waves, heart rate, and breathing patterns.

[0403] "Emotional state" is an indicator that shows the user's current psychological or emotional state.

[0404] "Terminal" refers to a device used by a user, specifically a device used for collecting biometric information and providing dream experiences.

[0405] A "dream experience theme" is a concept that indicates the broad categories or scenarios of dreams that a user wishes to experience.

[0406] "Emotional analysis results" refer to data that indicates the user's emotional state, identified based on collected biometric information.

[0407] A "dream scenario" is a plan that defines the specific content and development of the dream offered to the user.

[0408] "Visual and auditory information" refers to information about images and sounds provided to users to enhance their dream experience.

[0409] "Feedback" refers to opinions and information such as user impressions and suggestions for improvement regarding their dream experiences.

[0410] The system implementing this invention operates by coordinating smart devices and a server to provide a dream experience tailored to the user's emotional state. The terminal uses non-invasive sensors to collect the user's biometric information, specifically brain waves, heart rate, and breathing patterns, in real time. This data is transmitted to the server via wireless communication. The server utilizes an advanced emotion engine and applies a generative AI model to analyze the collected biometric information and identify the user's emotional state.

[0411] Based on the identified emotional state, the server uses the user's prior dream theme selection information to generate an optimal, adaptive dream scenario. The generated scenario is encoded as visual and auditory information and sent to the user's device. The device uses this information to play the scenario through a display and sound system to provide the user with an engaging dream experience.

[0412] After a user experiences a dream, the device receives feedback from the user. This feedback includes information about their feelings and changes in emotions regarding the dream's content. By sending this information to the server, the server updates the generating AI model, improving future dream experiences to be even more personalized.

[0413] For example, if a user selects a dream with the theme of "adventure," the server, sensing "excitement" through the user's emotion engine, generates a dream scenario that emphasizes adventurous elements and challenging situations. In this way, the system enables users to experience dreams that are best suited to their emotions, providing more engaging dreams that match their feelings.

[0414] Examples of prompts for generative AI models:

[0415] "Generate an adventure dream scenario that matches the user's current emotional state and chosen theme. Highlight elements that will excite the user and include challenging situations."

[0416] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0417] Step 1:

[0418] The user selects a theme for their dream experience via their device and enters the selection information into the device. The entered theme information is saved on the device and used in subsequent processing steps.

[0419] Step 2:

[0420] The device uses non-invasive sensors to collect biometric information such as the user's brainwaves, heart rate, and breathing patterns in real time. This biometric information is converted into a digital format and transmitted as input data to the server.

[0421] Step 3:

[0422] The server receives biometric information transmitted from the terminal and analyzes it using an emotion engine. The analysis applies a generative AI model based on the user's brainwave data to identify the user's emotional state. This results in an output representing the emotional state, which is used in the next step.

[0423] Step 4:

[0424] The server generates a dream scenario using the theme information selected by the user and the analyzed emotional state. By inputting prompt sentences into the AI ​​generation model, it generates a scenario adapted to the emotion and encodes it as visual and auditory information. The scenario generated in this way becomes the output.

[0425] Step 5:

[0426] The server transmits the generated visual and auditory information to the terminal. Based on the received data, the terminal uses its display and sound equipment to provide the user with a dream experience. This allows the user to receive a dream experience that matches their emotions.

[0427] Step 6:

[0428] After a dream experience, the user inputs feedback information, such as their impressions and changes in emotions, into the device. This feedback is saved on the device as input data to help improve the dream generation process for the next time.

[0429] Step 7:

[0430] The device sends user feedback information to the server. The server analyzes this feedback information and uses it to update the generated AI model. This prepares the system to make the next dream experience even more personalized.

[0431] 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.

[0432] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0433] 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.

[0434] [Third Embodiment]

[0435] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0436] 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.

[0437] 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).

[0438] 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.

[0439] 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.

[0440] 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).

[0441] 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.

[0442] 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.

[0443] 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.

[0444] 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.

[0445] 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.

[0446] 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".

[0447] This invention relates to a system that allows a user to select a dream during sleep and experience that dream. The system is configured as follows: First, the user uses a dedicated terminal to pre-select the category of dream and specific scenario they wish to experience. At this stage, the terminal records the user's selection as digital data and prepares to transmit it to a server.

[0448] Next, the device is equipped with sensors to collect brainwave data while the user is sleeping. These sensors non-invasively measure the user's brainwaves and transmit the data to a server in real time. The server analyzes this brainwave data to identify the stage in the user's dreaming. Depending on the identified stage, the server obtains the information necessary to generate a dream scenario and, combined with the user's past experience data, generates a personalized dream.

[0449] The generated dream scenario is sent from the server to the terminal as digital video data. The terminal uses this data to induce a dream experience in the user through sight and sound. The user can experience the dream they have chosen while sleeping, and after waking up, they input feedback about the dream via the terminal.

[0450] Feedback information is sent to the server, which then updates the machine learning model based on it. This continuous learning improves the quality of dreams delivered in subsequent sessions, making it easier to realize the user's expected experience.

[0451] As a concrete example, consider a scenario where a user selects a dream category called "diving." In this case, the user can set their desired ocean environment and diving depth via their device. After the scenario is generated, this dream is visualized while the user sleeps, allowing them to experience observing fish and coral up close, as if they were actually in the ocean. Upon waking, the user provides feedback on the experience, contributing to improving the accuracy of future dream experiences.

[0452] The following describes the processing flow.

[0453] Step 1:

[0454] The user uses their device to select the dream category and specific scenario they want to see from the interface. Once the user confirms their selection, that information is saved on the device.

[0455] Step 2:

[0456] The terminal structures the user's selections as digital data and prepares to send them to the server using security protocols. During this process, the terminal verifies the integrity of the data.

[0457] Step 3:

[0458] The server receives the user's selection data sent from the terminal and begins analysis. This analysis identifies the content of the dream the user desires. The server then refers to the user's past data and prepares to generate the optimal dream scenario.

[0459] Step 4:

[0460] The device uses an electroencephalogram (EEG) sensor to acquire real-time brainwave data while the user is sleeping. The EEG data recorded by the sensor is important for analyzing the state of dreams.

[0461] Step 5:

[0462] The device transmits acquired brainwave data to the server at regular intervals. The server analyzes this data in real time to identify the stage in the user's dreaming process. Based on this identified information, the server generates a dream scenario.

[0463] Step 6:

[0464] The server generates optimal dream imagery based on the user's brainwave data and selected scenario. This generated data is then encoded and ready to be sent to the terminal.

[0465] Step 7:

[0466] The device receives dream image data transmitted from the server and presents it to the user through sight and sound. Through this presentation, the user experiences the scenario they selected within the dream.

[0467] Step 8:

[0468] After the dream experience ends and the user wakes up, they use a device to enter feedback about the dream. This feedback includes an evaluation of the quality of the experience and suggestions for improvement.

[0469] Step 9:

[0470] The device sends feedback data received from the user to the server. The server analyzes this feedback and updates its machine learning model to optimize the dream generation process for subsequent attempts.

[0471] (Example 1)

[0472] 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."

[0473] In modern society, where stress and anxiety are increasing, there is a need for means to provide high-quality rest and pleasant experiences during sleep. However, conventional dream experience systems fail to meet user expectations because they cannot adequately address individual user needs and do not allow for sufficient customization of the dream content. Therefore, there is a need to develop a system that provides individually customized dream experiences in real time based on the user's specific wishes.

[0474] 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.

[0475] This invention includes a server that includes means for receiving biosignal data from a user in real time and analyzing that information to identify a process for visualizing the user's dreams; means for acquiring information for generating dream content from a data storage device based on the user's prior selections and designing an individually customized dream; and means for transmitting the generated dream content to the user's information terminal to allow the user to experience it through sight and hearing. This makes it possible to have a specific and highly personalized dream experience that the user desires.

[0476] "Biosignal data" refers to electrical or physical signals obtained from the user's body, and generally includes information such as brain waves and heart rate.

[0477] A "user" refers to an individual who uses this system to experience their dreams, selects their dreams based on their own desires, and provides feedback.

[0478] A "terminal" is a device used by users to select dreams, collects biometric data, transmits it to a server, and provides dream experiences.

[0479] A "server" is a central processing unit that analyzes information sent by users, generates and manages dream scenarios, and has the function of receiving and processing data in real time.

[0480] A "machine learning model" is an algorithm used to improve the quality of dreams based on feedback information obtained from users, and is a technology for continuously improving the performance of the system.

[0481] "Generation" refers to the process of designing new dream scenarios based on user selections and existing data, with the aim of creating individually customized content.

[0482] A "data storage device" is a medium that holds the information necessary for a server to generate dream scenarios, and is a device for efficiently accumulating and managing large amounts of digital data.

[0483] The embodiment of this invention is supported by three main components: a user, a terminal, and a server. The system is initiated when the user uses a dedicated terminal to select a dream category and a specific scenario. The terminal is responsible for recording the user's selection as digital data, which the server later uses to generate a customized dream scenario.

[0484] Non-invasive sensors embedded in the device collect biosignal data while the user sleeps. These sensors are used to measure information such as the user's brainwaves in real time, and the measurement results are immediately transmitted to a server. The server uses a generating AI model to analyze the received biosignal data and identify the stage in the user's dreaming. Based on this analysis, the server retrieves the necessary information from the data storage device and designs a customized dream scenario according to the user's wishes.

[0485] The server transmits the generated dream scenario as digital video data to the terminal. The terminal uses this data to provide the user with a dream experience through sight and sound. In this process, the terminal uses its display and speakers to play the dream so that the user can realistically experience the content of the dream they have chosen.

[0486] As a concrete example, consider a case where a user wants to dream about "diving." In this case, the user inputs a wish through their device, such as "I want to dive to a depth of 20 meters in a tropical sea." Based on this wish sent from the device to the server, the server generates a customized diving dream scenario tailored to the user and provides that experience.

[0487] After experiencing a dream, the user enters feedback about the dream via their device upon waking. This information is securely transmitted to a server, where a machine learning model is updated. This improves the accuracy and satisfaction of future dreams, enabling the system to deliver experiences that meet user expectations.

[0488] An example of a prompt message would be, "I want to dream about diving. The ocean environment is tropical, and the diving depth is 20 meters. I want to see a variety of fish and coral." In this way, users can efficiently enjoy a highly customized dream experience.

[0489] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0490] Step 1:

[0491] The user operates a dedicated terminal to select their desired dream category and specific scenario. For example, they might select the "diving" category and enter specific details such as, "I want to dive to a depth of 20 meters in a tropical sea." This information is recorded as digital data by the terminal and prepared for later transmission to a server. The input is the information selected by the user, and the output is the digital data prepared by the terminal.

[0492] Step 2:

[0493] The device uses non-invasive sensors to collect biosignal data during the user's sleep. These sensors record the user's brainwaves in real time and transmit this data to a server. The output data received by the server includes the user's brainwave measurements. The device performs real-time data collection and transmission to ensure that brainwave information is reliably transmitted to the server.

[0494] Step 3:

[0495] The server analyzes the received biosignal data using a generating AI model to identify when the user is dreaming. The input is biosignal data, and the output generates information about the user's dreaming stage. Through this data analysis process, the server determines the appropriate timing to begin generating dreams.

[0496] Step 4:

[0497] The server generates a dream scenario by retrieving relevant information from storage based on the dream category selected by the user and the analysis results. The server receives the user's selection information and analysis results as input and generates a customized dream scenario as output. Based on this information, the server designs dream content that suits the user's wishes and prepares the prepared scenario as digital video data.

[0498] Step 5:

[0499] The server transmits generated digital video data to the terminal, which then uses this data to provide the user with a dream experience. The terminal's input is the digital video data received from the server, and its output provides the user with visual and auditory stimuli. The terminal displays the video on its screen and plays the sound through its speakers, allowing the user to experience the dream they have chosen.

[0500] Step 6:

[0501] After waking up, the user enters feedback about the dream they experienced into the device. This feedback includes details and impressions of the experience. The input is the user's feedback, which the device records and sends to the server as output.

[0502] Step 7:

[0503] The server analyzes user feedback and updates its machine learning model. The input is user feedback information, and the output is an improved dream generation algorithm. This feedback loop allows the server to improve the quality of future dream experiences.

[0504] (Application Example 1)

[0505] 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."

[0506] In modern society, reducing stress and achieving effective relaxation in daily life is a crucial challenge. However, securing sufficient relaxation time amidst a busy daily routine is not easy. This invention aims to provide a novel method for enhancing relaxation by allowing users to experience specific dreams of their choice during sleep. Furthermore, it aims to apply this technology in real-world facilities to provide users with valuable experiences.

[0507] 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.

[0508] This invention includes a server that receives brainwave data from a user in real time, analyzes the data to identify the user's dream stage, retrieves information from a storage device to generate a dream scenario based on the user's prior selections, and creates an individually customized experience, and provides a dedicated environment within a real-world facility for customers to experience their selected dream along with relaxation effects. This makes it possible to have personalized dream experiences that enhance relaxation effects in daily life, even in real-world environments.

[0509] "Electroencephalogram (EEG) data" is digital information representing electrical signals that indicate a user's brain activity, and is collected to monitor the state of the brain during sleep.

[0510] "Dream stages" refer to the state of brain activity when a user experiences a particular dream during sleep, and are an important indicator for maximizing the relaxation experience.

[0511] "Scenario generation" is the process of creating individually customized dream experiences as visual and auditory content based on the user's prior selections.

[0512] A "memory device" is a device for storing necessary data and programs, and is used to efficiently retrieve the user's desired dream scenario.

[0513] "Feedback" refers to the act of users providing information about their impressions and areas for improvement after a dream experience, and this is important data for improving the dream generation process in the future.

[0514] A "machine learning model" is an artificial intelligence model that improves itself based on accumulated feedback data, providing users with a more optimized dream experience.

[0515] A "dedicated environment" is a space within a real-world facility that provides physical and technological equipment optimized for users to experience their chosen dreams.

[0516] This invention is a system that allows users to experience relaxation effects by having them experience specific dreams during sleep. This system is implemented using a dedicated terminal, a server in the cloud, and a dedicated environment in the real world.

[0517] Before starting a dream experience, the user selects their desired dream category and specific scenario through the device. This information is securely stored and sent to a cloud server. The device collects the user's brainwave data in real time using a non-invasive EEG sensor (e.g., Muse 2). This data is transmitted to the server via Bluetooth and analyzed using an EEG processing library (e.g., MNE-Python).

[0518] The server identifies the user's dream stage and, based on the user's past experience data and choices, generates a personalized dream scenario using a generative AI model (e.g., GPT-3.5). The generated dream scenario is filtered as visual and auditory data and sent to the terminal.

[0519] In a dedicated real-world environment, users can experience dreams in a comfortable and relaxing space. This environment is equipped with appropriately tuned sound and video equipment to enhance the user's dream experience.

[0520] After the dream experience ends, users enter feedback, which is sent to the server. This feedback is used to update the machine learning model and improve the quality of future dream experiences.

[0521] As a concrete example, consider a scenario where a user chooses a dream involving a "relaxing seaside scene." Through the device, the gentle sound of waves and the scenery of the sandy beach are visualized, allowing the user to experience a relaxing time as if they were actually there.

[0522] User: I want to have a relaxing dream about the beach the next time I sleep. Please describe the experience and atmosphere of your dream.

[0523] AI Model: You are sitting on a soft sandy beach under a gentle sun and soothing waves. The blue ocean stretches out before you. A gentle breeze caresses your cheek, and the rhythmic sound of the waves calms your mind. In this dream, you will spend a leisurely time.

[0524] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0525] Step 1:

[0526] The user uses a dedicated terminal to select their desired dream category and specific scenario. The selection results are entered as digital data and saved on the terminal.

[0527] Specifically, the user selects their desired scenario from a menu via the terminal's interface. The saved data is then prepared to be sent to the server later.

[0528] Step 2:

[0529] The device uses a non-invasive electroencephalogram (EEG) sensor to collect the user's brainwave data in real time. This EEG data serves as input for the program.

[0530] Specifically, the sensor records the user's brain's electrical activity and transmits the data to a server via Bluetooth. This data serves as the basis for analyzing the user's sleep patterns.

[0531] Step 3:

[0532] The server analyzes the received brainwave data to identify the stage of the dream. The analysis results are output and become input for the next processing step.

[0533] Specifically, an EEG processing library (e.g., MNE-Python) is used on the server to identify when the user has entered REM sleep. After detecting this state, the process then proceeds to generate a dream scenario.

[0534] Step 4:

[0535] Based on the user's selections and analysis results, the server generates a dream scenario using a generative AI model. The scenario data is output and sent to the terminal.

[0536] Specifically, a generative AI model (e.g., GPT-3.5) is used to generate text and video data based on the selected scenario. This creates a customized dream scenario.

[0537] Step 5:

[0538] The device allows the user to experience the received dream scenario as visual and auditory content. This content playback is the output.

[0539] Specifically, the device uses a display and speakers to provide the user with generated images and sounds. Because the user experiences it in a dedicated environment, they can immerse themselves in a relaxed dreamlike state.

[0540] Step 6:

[0541] After the dream experience ends, the user enters feedback. This feedback data becomes input for the program.

[0542] Specifically, users input their satisfaction level and requests regarding the experience using a form on their device. The collected feedback is sent to the server and used to update the learning model.

[0543] Step 7:

[0544] The server updates its machine learning model based on the feedback it receives. This update process is the output, and it improves the accuracy of dream generation for the next generation.

[0545] Specifically, the server analyzes feedback data and optimizes the dream scenario generation method. This makes it possible to improve the quality of future experiences.

[0546] 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.

[0547] This invention provides a system that takes into account the user's emotional state when selecting and allowing the user to experience a specific dream during sleep. This system incorporates an emotion engine and identifies the user's emotional state by analyzing the user's brainwave data and other biometric data.

[0548] First, the user operates the device to select the dream category and scenario they wish to experience. After selection, this information is sent to the server as digital data by the device. The device is equipped with non-invasive sensors that collect biometric data such as the user's brainwaves, heart rate, and breathing rhythm in real time.

[0549] After receiving this biometric data, the server uses an emotion engine to analyze the user's current emotional state. Based on the analysis, the server generates a dream scenario adapted to the user's emotions and optimizes it. Depending on the emotional state, specific elements may be added to or adjusted in the dream scenario.

[0550] The generated dream scenario is sent to the device as encoded video data. The device uses this data to provide the user with a dream experience through sight and sound. This dream experience becomes more natural and richer by adapting to the user's emotional state.

[0551] After waking up, users input feedback about their dreams into a device. This feedback includes their thoughts and changes in emotions regarding the dream's content. The device sends this information to a server, which uses it as training data for the emotion engine. This improves the dream generation process for subsequent dreams, further enhancing the user's emotional experience.

[0552] For example, if a user chooses a dream with the theme of "adventure," and the emotion engine identifies the user's current state as "excited," the server will dynamically modify the dream scenario to match that emotional state. For instance, more adventurous elements might be emphasized in the dream, or challenging situations might be added. In this way, the user can have a more engaging dream experience that aligns with their emotions.

[0553] The following describes the processing flow.

[0554] Step 1:

[0555] The user uses their device to select their desired dream category and specific scenario from the interface. This selection information is saved on the device.

[0556] Step 2:

[0557] The terminal organizes the user's selection data and sends it to the server via a security protocol. Here, the category information of the selected dream becomes important.

[0558] Step 3:

[0559] Non-invasive sensors embedded in the device collect biometric data such as brainwaves and heart rate in real time while the user sleeps. This data is necessary for interpreting the user's emotions.

[0560] Step 4:

[0561] The device sends the collected biometric data to the server. The server analyzes the received data and uses an emotion engine to identify the user's current emotional state.

[0562] Step 5:

[0563] The server optimizes the selected dream scenario based on the identified emotional state. In doing so, it modifies or emphasizes elements within the scenario to adapt to the user's emotions.

[0564] Step 6:

[0565] The generated dream scenario is transmitted from the server to the terminal as digital video data. The terminal processes this data and delivers the dream to the user through sight and sound.

[0566] Step 7:

[0567] Users enter feedback about the content of their dreams via their device. This feedback includes details about their satisfaction with the dream and how their emotions changed.

[0568] Step 8:

[0569] The device sends feedback data to the server, which analyzes it as training data for the emotion engine. This feedback is then used in the next dream generation process.

[0570] This allows the system to recognize the user's emotions and adaptively adjust the content of the dream, providing the user with a richer dream experience.

[0571] (Example 2)

[0572] 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."

[0573] Traditional technologies have not adequately achieved providing individualized dream experiences that adapt to each user's emotional state. Therefore, it is necessary to improve the quality of the dream experience by generating dreams based on the user's emotions and using the resulting feedback for learning.

[0574] 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.

[0575] In this invention, the server includes means for receiving and analyzing biometric data in real time, means for acquiring and generating dream scenarios from a storage medium based on prior selections and emotional states, and means for adjusting and generating video data using a generation AI model. This enables a dream experience optimized to the emotions of each individual user.

[0576] "Biometric data" refers to information related to the user's physical condition, such as the user's brain waves, heart rate, and respiratory rhythm.

[0577] "Emotional state" refers to the user's current psychological or emotional condition, identified from analyzed biometric data.

[0578] A "dream scenario" refers to a description or plan that includes the content of a dream experience, generated based on the user's choices and emotional state.

[0579] A "storage medium" refers to a device or apparatus used to retain digital information over a long period of time.

[0580] A "generative AI model" refers to a model that utilizes artificial intelligence technology to automatically generate content based on input data.

[0581] "Feedback" refers to the comments, opinions, and evaluations that users provide after experiencing a dream.

[0582] This system is designed to allow users to experience specific dreams tailored to their emotional state. Users first use a device to select the dream category and scenario they wish to experience. The device incorporates non-invasive sensors that collect biometric data in real time, including brainwaves, heart rate, and respiratory rhythm. This data is then analyzed as digital signals.

[0583] The device transmits information about the dreams selected by the user, along with biometric data collected in real time, to the server. After receiving this biometric data, the server uses an emotion engine to analyze the user's emotional state. The emotion engine utilizes a generative AI model and, by referring to a vast database, classifies the user's current emotions into states such as "excited" or "relaxed."

[0584] Based on the analyzed emotional state, the server retrieves a scenario optimized for the user's choices and emotions from the storage medium and dynamically adjusts the dream scenario using a generative AI model. The generated scenario is encoded as video data and sent to the terminal. The terminal plays this video data through visual and auditory devices to provide the user with a dream experience.

[0585] After waking up, the user enters feedback about their dream into the device. This feedback includes their thoughts on the dream's content and any emotional changes they experienced. The device sends this feedback to a server, which uses it as training data for the emotion engine. This cyclical feedback process refines the dream generation process for subsequent dreams, further personalizing the user's dream experience.

[0586] For example, if a user selects a dream themed around "adventure" and their emotional state is analyzed as "excited," the server will generate a scenario that enhances the adventurous elements in line with that emotional state. An example of a prompt message would be, "Generate an adventure scenario that is optimal for the user's current emotional state."

[0587] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0588] Step 1:

[0589] The user operates the device to select a dream category and scenario. This input is made through the device's interface, and the selected data is recorded in digital format. The device stores this information in its temporary storage.

[0590] Step 2:

[0591] The device's non-invasive sensors operate to collect biometric data such as the user's brainwaves, heart rate, and respiratory rhythm in real time. This data is converted from analog to digital and recorded as a digital signal within the device via a signal processing unit. The collected biometric data is then transmitted to a server for processing.

[0592] Step 3:

[0593] The server receives dream selection information and biometric data transmitted from the terminal. The input biometric data is fed into the emotion engine, where an emotion analysis model analyzes the user's current emotional state. This analysis utilizes a generative AI model to classify the emotional state into categories such as "excited" or "relaxed." The results of the analysis are output as digital data.

[0594] Step 4:

[0595] The server retrieves appropriate scenario data from the storage medium based on the analyzed emotional state and selected dream category. Then, using a generative AI model, it optimizes this scenario and generates a dream scenario adapted to the user's emotional state. This process uses pre-defined prompts, such as "Generate an adventure scenario best suited to the user's current emotional state." The generated scenario is encoded as video data.

[0596] Step 5:

[0597] The server sends encoded video data to the terminal. The data received by the terminal is played back to the user through visual and auditory devices. At this time, the data is decoded, and the user begins a dream experience in a virtual space. The video and audio are combined in a way that is appropriate to the user's emotions.

[0598] Step 6:

[0599] After a dream experience, users enter feedback via a device. This feedback includes the content of the dream, their satisfaction level, and changes in their emotions after the experience. The feedback data is reformatted digitally and sent from the device to a server.

[0600] Step 7:

[0601] The server integrates the received feedback data and processes it as training data for the emotion engine. Data analysis lays the foundation for further improving the next dream generation process and making the user experience more personalized. This learning result updates the algorithm of the generative AI model, improving the accuracy of the next dream scenario generated.

[0602] (Application Example 2)

[0603] 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."

[0604] There is a growing demand to provide richer, more emotionally resonant dream experiences by individually optimizing each user's dream experience. However, conventional systems have struggled to generate dream scenarios that precisely reflect the user's emotional state, and there has been a lack of appropriate methods to improve the quality of personalized dream experiences. Furthermore, it has been difficult to effectively incorporate user feedback and apply it to future dream generation.

[0605] 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.

[0606] In this invention, the server includes means for receiving biometric information from the user in real time and analyzing that information to identify the user's emotional state; means for acquiring a dream experience theme based on the user's prior selection at the terminal and generating a dream scenario adapted to the emotional analysis results; and means for transmitting the generated visual and auditory information to the user's device to provide the dream experience. This enables the provision of a personalized dream experience that corresponds to the user's emotional state, and makes it possible to generate high-quality dreams tailored to individual users, which was previously difficult.

[0607] "Biometric information" refers to data obtained from the user's body, such as brain waves, heart rate, and breathing patterns.

[0608] "Emotional state" is an indicator that shows the user's current psychological or emotional state.

[0609] "Terminal" refers to a device used by a user, specifically a device used for collecting biometric information and providing dream experiences.

[0610] A "dream experience theme" is a concept that indicates the broad categories or scenarios of dreams that a user wishes to experience.

[0611] "Emotional analysis results" refer to data that indicates the user's emotional state, identified based on collected biometric information.

[0612] A "dream scenario" is a plan that defines the specific content and development of the dream offered to the user.

[0613] "Visual and auditory information" refers to information about images and sounds provided to users to enhance their dream experience.

[0614] "Feedback" refers to opinions and information such as user impressions and suggestions for improvement regarding their dream experiences.

[0615] The system implementing this invention operates by coordinating smart devices and a server to provide a dream experience tailored to the user's emotional state. The terminal uses non-invasive sensors to collect the user's biometric information, specifically brain waves, heart rate, and breathing patterns, in real time. This data is transmitted to the server via wireless communication. The server utilizes an advanced emotion engine and applies a generative AI model to analyze the collected biometric information and identify the user's emotional state.

[0616] Based on the identified emotional state, the server uses the user's prior dream theme selection information to generate an optimal, adaptive dream scenario. The generated scenario is encoded as visual and auditory information and sent to the user's device. The device uses this information to play the scenario through a display and sound system to provide the user with an engaging dream experience.

[0617] After a user experiences a dream, the device receives feedback from the user. This feedback includes information about their feelings and changes in emotions regarding the dream's content. By sending this information to the server, the server updates the generating AI model, improving future dream experiences to be even more personalized.

[0618] For example, if a user selects a dream with the theme of "adventure," the server, sensing "excitement" through the user's emotion engine, generates a dream scenario that emphasizes adventurous elements and challenging situations. In this way, the system enables users to experience dreams that are best suited to their emotions, providing more engaging dreams that match their feelings.

[0619] Examples of prompts for generative AI models:

[0620] "Generate an adventure dream scenario that matches the user's current emotional state and chosen theme. Highlight elements that will excite the user and include challenging situations."

[0621] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0622] Step 1:

[0623] The user selects a theme for their dream experience via their device and enters the selection information into the device. The entered theme information is saved on the device and used in subsequent processing steps.

[0624] Step 2:

[0625] The device uses non-invasive sensors to collect biometric information such as the user's brainwaves, heart rate, and breathing patterns in real time. This biometric information is converted into a digital format and transmitted as input data to the server.

[0626] Step 3:

[0627] The server receives biometric information transmitted from the terminal and analyzes it using an emotion engine. The analysis applies a generative AI model based on the user's brainwave data to identify the user's emotional state. This results in an output representing the emotional state, which is used in the next step.

[0628] Step 4:

[0629] The server generates a dream scenario using the theme information selected by the user and the analyzed emotional state. By inputting prompt sentences into the AI ​​generation model, it generates a scenario adapted to the emotion and encodes it as visual and auditory information. The scenario generated in this way becomes the output.

[0630] Step 5:

[0631] The server transmits the generated visual and auditory information to the terminal. Based on the received data, the terminal uses its display and sound equipment to provide the user with a dream experience. This allows the user to receive a dream experience that matches their emotions.

[0632] Step 6:

[0633] After a dream experience, the user inputs feedback information, such as their impressions and changes in emotions, into the device. This feedback is saved on the device as input data to help improve the dream generation process for the next time.

[0634] Step 7:

[0635] The device sends user feedback information to the server. The server analyzes this feedback information and uses it to update the generated AI model. This prepares the system to make the next dream experience even more personalized.

[0636] 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.

[0637] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0638] 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.

[0639] [Fourth Embodiment]

[0640] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0641] 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.

[0642] 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).

[0643] 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.

[0644] 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.

[0645] 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).

[0646] 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.

[0647] 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.

[0648] 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.

[0649] 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.

[0650] 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.

[0651] 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.

[0652] 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".

[0653] This invention relates to a system that allows a user to select a dream during sleep and experience that dream. The system is configured as follows: First, the user uses a dedicated terminal to pre-select the category of dream and specific scenario they wish to experience. At this stage, the terminal records the user's selection as digital data and prepares to transmit it to a server.

[0654] Next, the device is equipped with sensors to collect brainwave data while the user is sleeping. These sensors non-invasively measure the user's brainwaves and transmit the data to a server in real time. The server analyzes this brainwave data to identify the stage in the user's dreaming. Depending on the identified stage, the server obtains the information necessary to generate a dream scenario and, combined with the user's past experience data, generates a personalized dream.

[0655] The generated dream scenario is sent from the server to the terminal as digital video data. The terminal uses this data to induce a dream experience in the user through sight and sound. The user can experience the dream they have chosen while sleeping, and after waking up, they input feedback about the dream via the terminal.

[0656] Feedback information is sent to the server, which then updates the machine learning model based on it. This continuous learning improves the quality of dreams delivered in subsequent sessions, making it easier to realize the user's expected experience.

[0657] As a concrete example, consider a scenario where a user selects a dream category called "diving." In this case, the user can set their desired ocean environment and diving depth via their device. After the scenario is generated, this dream is visualized while the user sleeps, allowing them to experience observing fish and coral up close, as if they were actually in the ocean. Upon waking, the user provides feedback on the experience, contributing to improving the accuracy of future dream experiences.

[0658] The following describes the processing flow.

[0659] Step 1:

[0660] The user uses their device to select the dream category and specific scenario they want to see from the interface. Once the user confirms their selection, that information is saved on the device.

[0661] Step 2:

[0662] The terminal structures the user's selections as digital data and prepares to send them to the server using security protocols. During this process, the terminal verifies the integrity of the data.

[0663] Step 3:

[0664] The server receives the user's selection data sent from the terminal and begins analysis. This analysis identifies the content of the dream the user desires. The server then refers to the user's past data and prepares to generate the optimal dream scenario.

[0665] Step 4:

[0666] The device uses an electroencephalogram (EEG) sensor to acquire real-time brainwave data while the user is sleeping. The EEG data recorded by the sensor is important for analyzing the state of dreams.

[0667] Step 5:

[0668] The device transmits acquired brainwave data to the server at regular intervals. The server analyzes this data in real time to identify the stage in the user's dreaming process. Based on this identified information, the server generates a dream scenario.

[0669] Step 6:

[0670] The server generates optimal dream imagery based on the user's brainwave data and selected scenario. This generated data is then encoded and ready to be sent to the terminal.

[0671] Step 7:

[0672] The device receives dream image data transmitted from the server and presents it to the user through sight and sound. Through this presentation, the user experiences the scenario they selected within the dream.

[0673] Step 8:

[0674] After the dream experience ends and the user wakes up, they use a device to enter feedback about the dream. This feedback includes an evaluation of the quality of the experience and suggestions for improvement.

[0675] Step 9:

[0676] The device sends feedback data received from the user to the server. The server analyzes this feedback and updates its machine learning model to optimize the dream generation process for subsequent attempts.

[0677] (Example 1)

[0678] 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".

[0679] In modern society, where stress and anxiety are increasing, there is a need for means to provide high-quality rest and pleasant experiences during sleep. However, conventional dream experience systems fail to meet user expectations because they cannot adequately address individual user needs and do not allow for sufficient customization of the dream content. Therefore, there is a need to develop a system that provides individually customized dream experiences in real time based on the user's specific wishes.

[0680] 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.

[0681] This invention includes a server that includes means for receiving biosignal data from a user in real time and analyzing that information to identify a process for visualizing the user's dreams; means for acquiring information for generating dream content from a data storage device based on the user's prior selections and designing an individually customized dream; and means for transmitting the generated dream content to the user's information terminal to allow the user to experience it through sight and hearing. This makes it possible to have a specific and highly personalized dream experience that the user desires.

[0682] "Biosignal data" refers to electrical or physical signals obtained from the user's body, and generally includes information such as brain waves and heart rate.

[0683] A "user" refers to an individual who uses this system to experience their dreams, selects their dreams based on their own desires, and provides feedback.

[0684] A "terminal" is a device used by users to select dreams, collects biometric data, transmits it to a server, and provides dream experiences.

[0685] A "server" is a central processing unit that analyzes information sent by users, generates and manages dream scenarios, and has the function of receiving and processing data in real time.

[0686] A "machine learning model" is an algorithm used to improve the quality of dreams based on feedback information obtained from users, and is a technology for continuously improving the performance of the system.

[0687] "Generation" refers to the process of designing new dream scenarios based on user selections and existing data, with the aim of creating individually customized content.

[0688] A "data storage device" is a medium that holds the information necessary for a server to generate dream scenarios, and is a device for efficiently accumulating and managing large amounts of digital data.

[0689] The embodiment of this invention is supported by three main components: a user, a terminal, and a server. The system is initiated when the user uses a dedicated terminal to select a dream category and a specific scenario. The terminal is responsible for recording the user's selection as digital data, which the server later uses to generate a customized dream scenario.

[0690] Non-invasive sensors embedded in the device collect biosignal data while the user sleeps. These sensors are used to measure information such as the user's brainwaves in real time, and the measurement results are immediately transmitted to a server. The server uses a generating AI model to analyze the received biosignal data and identify the stage in the user's dreaming. Based on this analysis, the server retrieves the necessary information from the data storage device and designs a customized dream scenario according to the user's wishes.

[0691] The server transmits the generated dream scenario as digital video data to the terminal. The terminal uses this data to provide the user with a dream experience through sight and sound. In this process, the terminal uses its display and speakers to play the dream so that the user can realistically experience the content of the dream they have chosen.

[0692] As a concrete example, consider a case where a user wants to dream about "diving." In this case, the user inputs a wish through their device, such as "I want to dive to a depth of 20 meters in a tropical sea." Based on this wish sent from the device to the server, the server generates a customized diving dream scenario tailored to the user and provides that experience.

[0693] After experiencing a dream, the user enters feedback about the dream via their device upon waking. This information is securely transmitted to a server, where a machine learning model is updated. This improves the accuracy and satisfaction of future dreams, enabling the system to deliver experiences that meet user expectations.

[0694] An example of a prompt message would be, "I want to dream about diving. The ocean environment is tropical, and the diving depth is 20 meters. I want to see a variety of fish and coral." In this way, users can efficiently enjoy a highly customized dream experience.

[0695] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0696] Step 1:

[0697] The user operates a dedicated terminal to select their desired dream category and specific scenario. For example, they might select the "diving" category and enter specific details such as, "I want to dive to a depth of 20 meters in a tropical sea." This information is recorded as digital data by the terminal and prepared for later transmission to a server. The input is the information selected by the user, and the output is the digital data prepared by the terminal.

[0698] Step 2:

[0699] The device uses non-invasive sensors to collect biosignal data during the user's sleep. These sensors record the user's brainwaves in real time and transmit this data to a server. The output data received by the server includes the user's brainwave measurements. The device performs real-time data collection and transmission to ensure that brainwave information is reliably transmitted to the server.

[0700] Step 3:

[0701] The server analyzes the received biosignal data using a generating AI model to identify when the user is dreaming. The input is biosignal data, and the output generates information about the user's dreaming stage. Through this data analysis process, the server determines the appropriate timing to begin generating dreams.

[0702] Step 4:

[0703] The server generates a dream scenario by retrieving relevant information from storage based on the dream category selected by the user and the analysis results. The server receives the user's selection information and analysis results as input and generates a customized dream scenario as output. Based on this information, the server designs dream content that suits the user's wishes and prepares the prepared scenario as digital video data.

[0704] Step 5:

[0705] The server transmits generated digital video data to the terminal, which then uses this data to provide the user with a dream experience. The terminal's input is the digital video data received from the server, and its output provides the user with visual and auditory stimuli. The terminal displays the video on its screen and plays the sound through its speakers, allowing the user to experience the dream they have chosen.

[0706] Step 6:

[0707] After waking up, the user enters feedback about the dream they experienced into the device. This feedback includes details and impressions of the experience. The input is the user's feedback, which the device records and sends to the server as output.

[0708] Step 7:

[0709] The server analyzes user feedback and updates its machine learning model. The input is user feedback information, and the output is an improved dream generation algorithm. This feedback loop allows the server to improve the quality of future dream experiences.

[0710] (Application Example 1)

[0711] 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".

[0712] In modern society, reducing stress and achieving effective relaxation in daily life is a crucial challenge. However, securing sufficient relaxation time amidst a busy daily routine is not easy. This invention aims to provide a novel method for enhancing relaxation by allowing users to experience specific dreams of their choice during sleep. Furthermore, it aims to apply this technology in real-world facilities to provide users with valuable experiences.

[0713] 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.

[0714] This invention includes a server that receives brainwave data from a user in real time, analyzes the data to identify the user's dream stage, retrieves information from a storage device to generate a dream scenario based on the user's prior selections, and creates an individually customized experience, and provides a dedicated environment within a real-world facility for customers to experience their selected dream along with relaxation effects. This makes it possible to have personalized dream experiences that enhance relaxation effects in daily life, even in real-world environments.

[0715] "Electroencephalogram (EEG) data" is digital information representing electrical signals that indicate a user's brain activity, and is collected to monitor the state of the brain during sleep.

[0716] "Dream stages" refer to the state of brain activity when a user experiences a particular dream during sleep, and are an important indicator for maximizing the relaxation experience.

[0717] "Scenario generation" is the process of creating individually customized dream experiences as visual and auditory content based on the user's prior selections.

[0718] A "memory device" is a device for storing necessary data and programs, and is used to efficiently retrieve the user's desired dream scenario.

[0719] "Feedback" refers to the act of users providing information about their impressions and areas for improvement after a dream experience, and this is important data for improving the dream generation process in the future.

[0720] A "machine learning model" is an artificial intelligence model that improves itself based on accumulated feedback data, providing users with a more optimized dream experience.

[0721] A "dedicated environment" is a space within a real-world facility that provides physical and technological equipment optimized for users to experience their chosen dreams.

[0722] This invention is a system that allows users to experience relaxation effects by having them experience specific dreams during sleep. This system is implemented using a dedicated terminal, a server in the cloud, and a dedicated environment in the real world.

[0723] Before starting a dream experience, the user selects their desired dream category and specific scenario through the device. This information is securely stored and sent to a cloud server. The device collects the user's brainwave data in real time using a non-invasive EEG sensor (e.g., Muse 2). This data is transmitted to the server via Bluetooth and analyzed using an EEG processing library (e.g., MNE-Python).

[0724] The server identifies the user's dream stage and, based on the user's past experience data and choices, generates a personalized dream scenario using a generative AI model (e.g., GPT-3.5). The generated dream scenario is filtered as visual and auditory data and sent to the terminal.

[0725] In a dedicated real-world environment, users can experience dreams in a comfortable and relaxing space. This environment is equipped with appropriately tuned sound and video equipment to enhance the user's dream experience.

[0726] After the dream experience ends, users enter feedback, which is sent to the server. This feedback is used to update the machine learning model and improve the quality of future dream experiences.

[0727] As a concrete example, consider a scenario where a user chooses a dream involving a "relaxing seaside scene." Through the device, the gentle sound of waves and the scenery of the sandy beach are visualized, allowing the user to experience a relaxing time as if they were actually there.

[0728] User: I want to have a relaxing dream about the beach the next time I sleep. Please describe the experience and atmosphere of your dream.

[0729] AI Model: You are sitting on a soft sandy beach under a gentle sun and soothing waves. The blue ocean stretches out before you. A gentle breeze caresses your cheek, and the rhythmic sound of the waves calms your mind. In this dream, you will spend a leisurely time.

[0730] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0731] Step 1:

[0732] The user uses a dedicated terminal to select their desired dream category and specific scenario. The selection results are entered as digital data and saved on the terminal.

[0733] Specifically, the user selects their desired scenario from a menu via the terminal's interface. The saved data is then prepared to be sent to the server later.

[0734] Step 2:

[0735] The device uses a non-invasive electroencephalogram (EEG) sensor to collect the user's brainwave data in real time. This EEG data serves as input for the program.

[0736] Specifically, the sensor records the user's brain's electrical activity and transmits the data to a server via Bluetooth. This data serves as the basis for analyzing the user's sleep patterns.

[0737] Step 3:

[0738] The server analyzes the received brainwave data to identify the stage of the dream. The analysis results are output and become input for the next processing step.

[0739] Specifically, an EEG processing library (e.g., MNE-Python) is used on the server to identify when the user has entered REM sleep. After detecting this state, the process then proceeds to generate a dream scenario.

[0740] Step 4:

[0741] Based on the user's selections and analysis results, the server generates a dream scenario using a generative AI model. The scenario data is output and sent to the terminal.

[0742] Specifically, a generative AI model (e.g., GPT-3.5) is used to generate text and video data based on the selected scenario. This creates a customized dream scenario.

[0743] Step 5:

[0744] The device allows the user to experience the received dream scenario as visual and auditory content. This content playback is the output.

[0745] Specifically, the device uses a display and speakers to provide the user with generated images and sounds. Because the user experiences it in a dedicated environment, they can immerse themselves in a relaxed dreamlike state.

[0746] Step 6:

[0747] After the dream experience ends, the user enters feedback. This feedback data becomes input for the program.

[0748] Specifically, users input their satisfaction level and requests regarding the experience using a form on their device. The collected feedback is sent to the server and used to update the learning model.

[0749] Step 7:

[0750] The server updates its machine learning model based on the feedback it receives. This update process is the output, and it improves the accuracy of dream generation for the next generation.

[0751] Specifically, the server analyzes feedback data and optimizes the dream scenario generation method. This makes it possible to improve the quality of future experiences.

[0752] 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.

[0753] This invention provides a system that takes into account the user's emotional state when selecting and allowing the user to experience a specific dream during sleep. This system incorporates an emotion engine and identifies the user's emotional state by analyzing the user's brainwave data and other biometric data.

[0754] First, the user operates the device to select the dream category and scenario they wish to experience. After selection, this information is sent to the server as digital data by the device. The device is equipped with non-invasive sensors that collect biometric data such as the user's brainwaves, heart rate, and breathing rhythm in real time.

[0755] After receiving this biometric data, the server uses an emotion engine to analyze the user's current emotional state. Based on the analysis, the server generates a dream scenario adapted to the user's emotions and optimizes it. Depending on the emotional state, specific elements may be added to or adjusted in the dream scenario.

[0756] The generated dream scenario is sent to the device as encoded video data. The device uses this data to provide the user with a dream experience through sight and sound. This dream experience becomes more natural and richer by adapting to the user's emotional state.

[0757] After waking up, users input feedback about their dreams into a device. This feedback includes their thoughts and changes in emotions regarding the dream's content. The device sends this information to a server, which uses it as training data for the emotion engine. This improves the dream generation process for subsequent dreams, further enhancing the user's emotional experience.

[0758] For example, if a user chooses a dream with the theme of "adventure," and the emotion engine identifies the user's current state as "excited," the server will dynamically modify the dream scenario to match that emotional state. For instance, more adventurous elements might be emphasized in the dream, or challenging situations might be added. In this way, the user can have a more engaging dream experience that aligns with their emotions.

[0759] The following describes the processing flow.

[0760] Step 1:

[0761] The user uses their device to select their desired dream category and specific scenario from the interface. This selection information is saved on the device.

[0762] Step 2:

[0763] The terminal organizes the user's selection data and sends it to the server via a security protocol. Here, the category information of the selected dream becomes important.

[0764] Step 3:

[0765] Non-invasive sensors embedded in the device collect biometric data such as brainwaves and heart rate in real time while the user sleeps. This data is necessary for interpreting the user's emotions.

[0766] Step 4:

[0767] The device sends the collected biometric data to the server. The server analyzes the received data and uses an emotion engine to identify the user's current emotional state.

[0768] Step 5:

[0769] The server optimizes the selected dream scenario based on the identified emotional state. In doing so, it modifies or emphasizes elements within the scenario to adapt to the user's emotions.

[0770] Step 6:

[0771] The generated dream scenario is transmitted from the server to the terminal as digital video data. The terminal processes this data and delivers the dream to the user through sight and sound.

[0772] Step 7:

[0773] Users enter feedback about the content of their dreams via their device. This feedback includes details about their satisfaction with the dream and how their emotions changed.

[0774] Step 8:

[0775] The device sends feedback data to the server, which analyzes it as training data for the emotion engine. This feedback is then used in the next dream generation process.

[0776] This allows the system to recognize the user's emotions and adaptively adjust the content of the dream, providing the user with a richer dream experience.

[0777] (Example 2)

[0778] 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".

[0779] Traditional technologies have not adequately achieved providing individualized dream experiences that adapt to each user's emotional state. Therefore, it is necessary to improve the quality of the dream experience by generating dreams based on the user's emotions and using the resulting feedback for learning.

[0780] 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.

[0781] In this invention, the server includes means for receiving and analyzing biometric data in real time, means for acquiring and generating dream scenarios from a storage medium based on prior selections and emotional states, and means for adjusting and generating video data using a generation AI model. This enables a dream experience optimized to the emotions of each individual user.

[0782] "Biometric data" refers to information related to the user's physical condition, such as the user's brain waves, heart rate, and respiratory rhythm.

[0783] "Emotional state" refers to the user's current psychological or emotional condition, identified from analyzed biometric data.

[0784] A "dream scenario" refers to a description or plan that includes the content of a dream experience, generated based on the user's choices and emotional state.

[0785] A "storage medium" refers to a device or apparatus used to retain digital information over a long period of time.

[0786] A "generative AI model" refers to a model that utilizes artificial intelligence technology to automatically generate content based on input data.

[0787] "Feedback" refers to the comments, opinions, and evaluations that users provide after experiencing a dream.

[0788] This system is designed to allow users to experience specific dreams tailored to their emotional state. Users first use a device to select the dream category and scenario they wish to experience. The device incorporates non-invasive sensors that collect biometric data in real time, including brainwaves, heart rate, and respiratory rhythm. This data is then analyzed as digital signals.

[0789] The device transmits information about the dreams selected by the user, along with biometric data collected in real time, to the server. After receiving this biometric data, the server uses an emotion engine to analyze the user's emotional state. The emotion engine utilizes a generative AI model and, by referring to a vast database, classifies the user's current emotions into states such as "excited" or "relaxed."

[0790] Based on the analyzed emotional state, the server retrieves a scenario optimized for the user's choices and emotions from the storage medium and dynamically adjusts the dream scenario using a generative AI model. The generated scenario is encoded as video data and sent to the terminal. The terminal plays this video data through visual and auditory devices to provide the user with a dream experience.

[0791] After waking up, the user enters feedback about their dream into the device. This feedback includes their thoughts on the dream's content and any emotional changes they experienced. The device sends this feedback to a server, which uses it as training data for the emotion engine. This cyclical feedback process refines the dream generation process for subsequent dreams, further personalizing the user's dream experience.

[0792] For example, if a user selects a dream themed around "adventure" and their emotional state is analyzed as "excited," the server will generate a scenario that enhances the adventurous elements in line with that emotional state. An example of a prompt message would be, "Generate an adventure scenario that is optimal for the user's current emotional state."

[0793] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0794] Step 1:

[0795] The user operates the device to select a dream category and scenario. This input is made through the device's interface, and the selected data is recorded in digital format. The device stores this information in its temporary storage.

[0796] Step 2:

[0797] The device's non-invasive sensors operate to collect biometric data such as the user's brainwaves, heart rate, and respiratory rhythm in real time. This data is converted from analog to digital and recorded as a digital signal within the device via a signal processing unit. The collected biometric data is then transmitted to a server for processing.

[0798] Step 3:

[0799] The server receives dream selection information and biometric data transmitted from the terminal. The input biometric data is fed into the emotion engine, where an emotion analysis model analyzes the user's current emotional state. This analysis utilizes a generative AI model to classify the emotional state into categories such as "excited" or "relaxed." The results of the analysis are output as digital data.

[0800] Step 4:

[0801] The server retrieves appropriate scenario data from the storage medium based on the analyzed emotional state and selected dream category. Then, using a generative AI model, it optimizes this scenario and generates a dream scenario adapted to the user's emotional state. This process uses pre-defined prompts, such as "Generate an adventure scenario best suited to the user's current emotional state." The generated scenario is encoded as video data.

[0802] Step 5:

[0803] The server sends encoded video data to the terminal. The data received by the terminal is played back to the user through visual and auditory devices. At this time, the data is decoded, and the user begins a dream experience in a virtual space. The video and audio are combined in a way that is appropriate to the user's emotions.

[0804] Step 6:

[0805] After a dream experience, users enter feedback via a device. This feedback includes the content of the dream, their satisfaction level, and changes in their emotions after the experience. The feedback data is reformatted digitally and sent from the device to a server.

[0806] Step 7:

[0807] The server integrates the received feedback data and processes it as training data for the emotion engine. Data analysis lays the foundation for further improving the next dream generation process and making the user experience more personalized. This learning result updates the algorithm of the generative AI model, improving the accuracy of the next dream scenario generated.

[0808] (Application Example 2)

[0809] 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".

[0810] There is a growing demand to provide richer, more emotionally resonant dream experiences by individually optimizing each user's dream experience. However, conventional systems have struggled to generate dream scenarios that precisely reflect the user's emotional state, and there has been a lack of appropriate methods to improve the quality of personalized dream experiences. Furthermore, it has been difficult to effectively incorporate user feedback and apply it to future dream generation.

[0811] 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.

[0812] In this invention, the server includes means for receiving biometric information from the user in real time and analyzing that information to identify the user's emotional state; means for acquiring a dream experience theme based on the user's prior selection at the terminal and generating a dream scenario adapted to the emotional analysis results; and means for transmitting the generated visual and auditory information to the user's device to provide the dream experience. This enables the provision of a personalized dream experience that corresponds to the user's emotional state, and makes it possible to generate high-quality dreams tailored to individual users, which was previously difficult.

[0813] "Biometric information" refers to data obtained from the user's body, such as brain waves, heart rate, and breathing patterns.

[0814] "Emotional state" is an indicator that shows the user's current psychological or emotional state.

[0815] "Terminal" refers to a device used by a user, specifically a device used for collecting biometric information and providing dream experiences.

[0816] A "dream experience theme" is a concept that indicates the broad categories or scenarios of dreams that a user wishes to experience.

[0817] "Emotional analysis results" refer to data that indicates the user's emotional state, identified based on collected biometric information.

[0818] A "dream scenario" is a plan that defines the specific content and development of the dream offered to the user.

[0819] "Visual and auditory information" refers to information about images and sounds provided to users to enhance their dream experience.

[0820] "Feedback" refers to opinions and information such as user impressions and suggestions for improvement regarding their dream experiences.

[0821] The system implementing this invention operates by coordinating smart devices and a server to provide a dream experience tailored to the user's emotional state. The terminal uses non-invasive sensors to collect the user's biometric information, specifically brain waves, heart rate, and breathing patterns, in real time. This data is transmitted to the server via wireless communication. The server utilizes an advanced emotion engine and applies a generative AI model to analyze the collected biometric information and identify the user's emotional state.

[0822] Based on the identified emotional state, the server uses the user's prior dream theme selection information to generate an optimal, adaptive dream scenario. The generated scenario is encoded as visual and auditory information and sent to the user's device. The device uses this information to play the scenario through a display and sound system to provide the user with an engaging dream experience.

[0823] After a user experiences a dream, the device receives feedback from the user. This feedback includes information about their feelings and changes in emotions regarding the dream's content. By sending this information to the server, the server updates the generating AI model, improving future dream experiences to be even more personalized.

[0824] For example, if a user selects a dream with the theme of "adventure," the server, sensing "excitement" through the user's emotion engine, generates a dream scenario that emphasizes adventurous elements and challenging situations. In this way, the system enables users to experience dreams that are best suited to their emotions, providing more engaging dreams that match their feelings.

[0825] Examples of prompts for generative AI models:

[0826] "Generate an adventure dream scenario that matches the user's current emotional state and chosen theme. Highlight elements that will excite the user and include challenging situations."

[0827] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0828] Step 1:

[0829] The user selects a theme for their dream experience via their device and enters the selection information into the device. The entered theme information is saved on the device and used in subsequent processing steps.

[0830] Step 2:

[0831] The device uses non-invasive sensors to collect biometric information such as the user's brainwaves, heart rate, and breathing patterns in real time. This biometric information is converted into a digital format and transmitted as input data to the server.

[0832] Step 3:

[0833] The server receives biometric information transmitted from the terminal and analyzes it using an emotion engine. The analysis applies a generative AI model based on the user's brainwave data to identify the user's emotional state. This results in an output representing the emotional state, which is used in the next step.

[0834] Step 4:

[0835] The server generates a dream scenario using the theme information selected by the user and the analyzed emotional state. By inputting prompt sentences into the AI ​​generation model, it generates a scenario adapted to the emotion and encodes it as visual and auditory information. The scenario generated in this way becomes the output.

[0836] Step 5:

[0837] The server transmits the generated visual and auditory information to the terminal. Based on the received data, the terminal uses its display and sound equipment to provide the user with a dream experience. This allows the user to receive a dream experience that matches their emotions.

[0838] Step 6:

[0839] After a dream experience, the user inputs feedback information, such as their impressions and changes in emotions, into the device. This feedback is saved on the device as input data to help improve the dream generation process for the next time.

[0840] Step 7:

[0841] The device sends user feedback information to the server. The server analyzes this feedback information and uses it to update the generated AI model. This prepares the system to make the next dream experience even more personalized.

[0842] 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.

[0843] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0844] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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."

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] The following is further disclosed regarding the embodiments described above.

[0864] (Claim 1)

[0865] A method for receiving brainwave data from the user in real time, analyzing that data, and identifying the stage of the user's dream.

[0866] A means for retrieving data from storage to generate dream scenarios based on the user's prior selections and creating individually customized dreams,

[0867] A means of sending the generated dream scenario to the user's terminal and allowing the user to experience it through sight and sound,

[0868] A means to securely collect user feedback on dreams, analyze that data, and update the learning model to reflect it in future dream generation,

[0869] A system that includes this.

[0870] (Claim 2)

[0871] The system according to claim 1, which applies an algorithm to select the optimal scenario based on the dream category selected by the user.

[0872] (Claim 3)

[0873] The system according to claim 1, wherein the terminal is equipped with a function to provide a non-invasive sensor for collecting the user's brainwave data and to transmit the data to a server in real time.

[0874] "Example 1"

[0875] (Claim 1)

[0876] A means to receive biosignal data from the user in real time, analyze that information, and identify the process of visualizing the user's dreams,

[0877] A means for designing individually customized dreams by obtaining information from a data storage device to generate dream content based on the user's prior selections,

[0878] A means of transmitting the generated dream content to the user's information terminal and allowing the user to experience it through sight and sound,

[0879] A means to securely collect user feedback on dreams, analyze that information, and update machine learning models to reflect the findings in future dream generation,

[0880] A system that includes this.

[0881] (Claim 2)

[0882] The system according to claim 1, which applies a calculation method to select the optimal content based on the type of dream selected by the user.

[0883] (Claim 3)

[0884] The system according to claim 1, comprising a non-invasive detection device for acquiring a user's biosignal data and an information terminal equipped with a function for transmitting the information to an information processing device in real time.

[0885] "Application Example 1"

[0886] (Claim 1)

[0887] A method for receiving brainwave data from the user in real time, analyzing that data, and identifying the stage of the user's dream.

[0888] A means for retrieving information from storage to generate a dream scenario based on the user's prior selections and creating an individually customized experience,

[0889] A means of transmitting the generated dream scenario to the user's device and allowing the user to experience it through sight and hearing,

[0890] A means to securely collect user feedback on dreams, analyze that information, and update machine learning models to reflect it in future dream generation,

[0891] A means of providing a dedicated environment within a real-world facility for customers to experience their chosen dreams along with relaxation effects,

[0892] A system that includes this.

[0893] (Claim 2)

[0894] The system according to claim 1, which applies a processing method for selecting the optimal scenario based on the dream category selected by the user.

[0895] (Claim 3)

[0896] The system according to claim 1, comprising a terminal equipped with a non-invasive measurement device for collecting the user's electroencephalogram data and a function for transmitting the data to an information processing device in real time.

[0897] "Example 2 of combining an emotion engine"

[0898] (Claim 1)

[0899] A means of receiving biometric data from users in real time and analyzing that data to identify the user's emotional state,

[0900] A means for creating individually optimized dreams by obtaining data from a storage medium to generate dream scenarios based on the user's prior selections and emotional state,

[0901] A means of dynamically adjusting dream scenarios using a generative AI model and generating video data adapted to the user's emotions,

[0902] A means of transmitting generated dream video data to the user's terminal and allowing the user to experience it through sight and sound,

[0903] A means to securely collect user feedback on dreams, analyze that data, and update the learning model to reflect it in future dream generation,

[0904] A system that includes this.

[0905] (Claim 2)

[0906] The system according to claim 1, which applies an algorithm to select the optimal scenario based on the dream category and emotional state selected by the user.

[0907] (Claim 3)

[0908] The system according to claim 1, wherein the terminal is equipped with a function to provide a non-invasive sensor for collecting the user's biometric data and to transmit the data to a server in real time.

[0909] "Application example 2 when combining with an emotional engine"

[0910] (Claim 1)

[0911] A means of receiving biometric information from users in real time, analyzing that information, and identifying the user's emotional state.

[0912] A means for obtaining the theme of a dream experience based on the user's prior selection on the terminal, and for generating a dream scenario adapted to the results of emotion analysis,

[0913] A means of providing a dream experience by transmitting generated visual and auditory information to the user's device,

[0914] A means for securely processing input information from users regarding their dream experiences, analyzing that information, and updating the learning process to apply it to future dream generation,

[0915] A system that includes this.

[0916] (Claim 2)

[0917] The system according to claim 1, which applies an algorithm that dynamically selects a scenario optimized for the user's emotions based on the theme of the dream experience selected by the user.

[0918] (Claim 3)

[0919] The system according to claim 1, wherein the device is equipped with a non-invasive sensor for collecting user biometric information and has a function to transmit said information to a core device in real time. [Explanation of symbols]

[0920] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A method for receiving brainwave data from the user in real time, analyzing that data, and identifying the stage of the user's dream. A means for retrieving data from storage to generate dream scenarios based on the user's prior selections and creating individually customized dreams, A means of sending the generated dream scenario to the user's terminal and allowing the user to experience it through sight and sound, A means to securely collect user feedback on dreams, analyze that data, and update the learning model to reflect it in future dream generation, A system that includes this.

2. The system according to claim 1, which applies an algorithm to select the optimal scenario based on the dream category selected by the user.

3. The system according to claim 1, wherein the terminal is equipped with a function to provide a non-invasive sensor for collecting the user's brainwave data and to transmit the data to a server in real time.

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