System

Abstract

An object of the system according to the embodiment is to allow a user to freely select a dream that the user wants to see and to sleep while maintaining a mental and physical health condition.SOLUTION: A system includes a reception unit, a generation unit, a conversion unit, and a transmission unit. The receiving part receives a prompt for inputting the content of the dream that the user wants to see. The generation part analyzes the prompt received by the reception part and generates the content of the dream. The conversion unit converts the generated dream content into an electrical signal. The transmitter transmits the converted electrical signal to the brain of the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method 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 a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not allow users to freely choose the dream they want to have, and there is a risk that they may be plagued by nightmares.

[0005] The system according to the embodiment aims to enable a user to freely select the dream they want to have and sleep while maintaining a healthy state of mind and body. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, a converting unit, and a transmitting unit. The receiving unit receives a prompt from the user to input the content of the dream they wish to see. The generating unit analyzes the prompt received by the receiving unit and generates the content of the dream. The converting unit converts the generated dream content into an electrical signal. The transmitting unit sends the converted electrical signal to the user's brain. [Effects of the Invention]

[0007] The system according to the embodiment allows the user to freely select the dream they want to have and sleep while maintaining a healthy state of mind and body. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices 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), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A dream generation system according to an embodiment of the present invention prevents nightmares by allowing a user to input the dream they want to have into a generation AI, which then converts the dream into electrical signals and sends them to the brain. In the dream generation system, the user inputs the content of the dream they want to have as a prompt, and the generation AI analyzes the input prompt and generates the dream content. The generated dream content is converted into electrical signals and sent to the user's brain. This allows the user to dream the dream they want and sleep in a healthy state of mind and body without being troubled by nightmares. For example, the dream generation system can monitor the user's brain waves while they sleep and adjust the dream content in real time. For example, if the user encounters an unpleasant situation in a dream, the generation AI can detect this situation and change the dream content to maintain a comfortable dream. In this way, the user can dream the dream they want and sleep in a healthy state of mind and body without being troubled by nightmares. This allows the dream generation system to generate the dream the user wants to have and prevent nightmares. For example, by allowing the user to input the dream they want to have and convert the dream into electrical signals and send them to the brain, mental and physical health can be maintained. In addition, the device monitors the user's brain waves while they sleep and adjusts the content of their dreams in real time, helping to maintain a comfortable dream.

[0029] The dream creation system according to the embodiment includes a reception unit, a generation unit, a conversion unit, and a transmission unit. The reception unit receives a prompt for inputting the content of the dream the user wants to have. Examples of the prompt include, but are not limited to, a scenario, a theme, and characters. The reception unit can receive the prompt via, for example, text input, voice input, multiple-choice format, or other methods. The generation unit uses a generation AI to analyze the prompt received by the reception unit and generate the content of the dream. The generation AI analyzes the prompt using, for example, natural language processing technology or keyword extraction technology, and generates the content of the dream using a scenario generation algorithm or storytelling technology. The conversion unit converts the content of the dream generated by the generation unit into an electrical signal. The conversion unit converts the content of the dream into an electrical signal using, for example, signal processing technology or electroencephalogram conversion technology. The transmission unit sends the electrical signal converted by the conversion unit to the user's brain. The transmission unit transmits the electrical signal to the brain using, for example, wireless communication technology or electrode placement technology. This allows the dream creation system according to the embodiment to prevent nightmares and maintain physical and mental health by enabling the user to have the dream they want to have.

[0030] The dream generation system includes a monitoring unit that monitors the user's brain waves while sleeping. The monitoring unit monitors the user's brain waves while sleeping. For example, the monitoring unit measures brain waves using an EEG sensor and analyzes the brain wave data using data analysis technology. By monitoring the user's brain waves, the content of the dream can be adjusted in real time. For example, the monitoring unit can analyze the user's brain wave data in real time and provide feedback to adjust the content of the dream. The monitoring unit can also accumulate the user's brain wave data and analyze long-term changes in brain waves. This allows the user's brain waves to be understood in detail and the content of the dream to be optimized.

[0031] The dream generation system includes an adjustment unit that adjusts the content of a dream in real time based on the brain waves monitored by the monitoring unit. The adjustment unit adjusts the content of a dream in real time based on the brain waves monitored by the monitoring unit. The adjustment unit, for example, analyzes brain wave data using a feedback loop and executes an algorithm to adjust the content of the dream. For example, if the user encounters an unpleasant situation in a dream, the adjustment unit can detect the situation and change the content of the dream to maintain a comfortable dream. The adjustment unit can also dynamically change the content of the dream based on the user's brain wave data. This allows the user to maintain a comfortable state in the dream. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input brain wave data acquired by the monitoring unit into a generation AI and have the generation AI adjust the content of the dream.

[0032] The generation unit can create a detailed dream scenario based on the prompt. The generation unit uses a generation AI to create the detailed dream scenario based on the prompt. The generation AI creates the detailed dream scenario based on the prompt, for example, using a scenario generation algorithm or storytelling technology. For example, the generation AI analyzes keywords and phrases included in the prompt and builds a scenario based on them. The generation AI can also adjust the components of the scenario and the development of the story according to the content of the prompt. This allows the user to have a more specific dream by creating a detailed dream scenario. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input a prompt into the generation AI and cause the generation AI to create a detailed dream scenario.

[0033] The conversion unit can convert the generated dream scenario into an electrical signal. The conversion unit converts the dream scenario generated by the generation unit into an electrical signal. The conversion unit converts the dream scenario into an electrical signal using, for example, signal processing technology or electroencephalogram (EEG) conversion technology. For example, the conversion unit converts each element of the scenario into a corresponding electrical signal and then converts it into a signal to be transmitted to the brain. The conversion unit can also adjust the strength and pattern of the electrical signal depending on the content of the scenario. In this way, by converting the generated dream scenario into an electrical signal, the content of the dream can be transmitted to the user's brain. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input the generated dream scenario into a generation AI and have the generation AI convert it into an electrical signal.

[0034] The transmitting unit can send the converted electrical signal to the user's brain. The transmitting unit sends the electrical signal converted by the converting unit to the user's brain. The transmitting unit transmits the electrical signal to the brain using, for example, wireless communication technology or electrode placement technology. For example, the transmitting unit transmits the electrical signal wirelessly and transmits the signal through electrodes placed on the brain. The transmitting unit can also adjust the transmission timing and strength of the electrical signal. In this way, by sending the converted electrical signal to the user's brain, the user can have the dream they want to see. Some or all of the above-mentioned processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input the converted electrical signal to a generating AI and have the generating AI adjust the transmission method.

[0035] The reception unit can analyze the user's past dream history and suggest an optimal prompt input method. The reception unit can analyze the user's past dream history and suggest an optimal prompt input method. The reception unit can analyze the past dream history using, for example, data mining technology. For example, the reception unit can automatically suggest similar prompts based on the content of dreams previously input by the user. The reception unit can also preferentially suggest specific themes or scenarios from the user's past dream history. The reception unit can also analyze the user's preferred dream patterns in the past and customize an optimal input method. In this way, the analysis of the past dream history can suggest an optimal prompt input method for the user. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input past dream history data into a generation AI and have the generation AI suggest an optimal prompt input method.

[0036] The reception unit can select the optimal input means depending on the user's input method when inputting a prompt. The reception unit selects the optimal input means depending on the user's input method when inputting a prompt. The reception unit accepts prompts using methods such as voice input, text input, and image input. For example, when the user inputs a prompt by voice, the reception unit analyzes the input content using voice recognition technology. When the user inputs a prompt by text, the reception unit can analyze the input content using text analysis technology. When the user inputs a prompt by image, the reception unit can analyze the input content using image recognition technology. This allows the prompt to be input smoothly by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may input the user's input data to a generation AI and have the generation AI select the optimal input means.

[0037] The reception unit, when inputting a prompt, can prioritize receiving highly relevant prompts by taking into account the user's geographical location information. The reception unit, when inputting a prompt, prioritizes receiving highly relevant prompts by taking into account the user's geographical location information. The reception unit, for example, acquires the user's geographical location information using GPS data and filters prompts based on the acquired information. For example, when the user is traveling, the reception unit can prioritize receiving dream prompts related to the user's travel destination. Furthermore, when the user is at home, the reception unit can prioritize receiving dream prompts related to the user's home. Furthermore, when the user is in a specific location, the reception unit can prioritize receiving dream prompts related to that location. In this way, by taking the user's geographical location information into account, more relevant dream content can be generated. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize receiving highly relevant prompts.

[0038] The reception unit can analyze the user's social media activity and suggest related prompts when the user inputs a prompt. The reception unit can analyze the user's social media activity and suggest related prompts when the user inputs a prompt. The reception unit, for example, analyzes the content of social media posts and suggests prompts based on the content. For example, the reception unit can suggest related dream prompts based on content shared by the user on social media. The reception unit can also analyze the user's social media activity history and suggest related dream prompts. The reception unit can also suggest related dream prompts based on the activity of the user's friends on social media. In this way, more relevant dream content can be generated by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input social media data into a generation AI and have the generation AI suggest related prompts.

[0039] The reception unit can customize the input method by reflecting the user's past feedback when entering a prompt. The reception unit customizes the input method by reflecting the user's past feedback when entering a prompt. The reception unit, for example, analyzes feedback data and customizes the input method based on the feedback data. For example, the reception unit suggests an optimal input method based on the user's previously preferred input methods. The reception unit can also analyze the user's past feedback and improve the input method. The reception unit can also avoid input methods that the user has previously been dissatisfied with and suggest an optimal input method. In this way, a more appropriate input method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past feedback data to a generation AI and cause the generation AI to customize the input method.

[0040] The generation unit can adjust the level of detail of the scenario based on the importance of the prompt when generating a dream. The generation unit uses the generation AI to adjust the level of detail of the scenario based on the importance of the prompt when generating a dream. The generation AI, for example, evaluates the importance of the prompt and adjusts the level of detail of the scenario based on the evaluation. For example, the generation unit generates a detailed scenario for a prompt with high importance. The generation unit can also generate a simplified scenario for a prompt with low importance. The generation unit can also adjust the details of the scenario according to the importance of the prompt. In this way, by adjusting the level of detail of the scenario according to the importance of the prompt, more appropriate dream content can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input prompt importance data to the generation AI and cause the generation AI to adjust the level of detail of the scenario.

[0041] The generation unit can apply different generation algorithms depending on the category of the prompt when generating a dream. The generation unit uses a generation AI to apply different generation algorithms depending on the category of the prompt when generating a dream. The generation AI, for example, classifies the category of the prompt and applies an appropriate generation algorithm accordingly. For example, the generation unit applies a generation algorithm specialized for fantasy to a fantasy prompt. The generation unit can also apply a generation algorithm based on reality to a realistic prompt. The generation unit can also apply a generation algorithm specialized for horror to a horror prompt. In this way, by applying a generation algorithm depending on the category of the prompt, more appropriate dream content can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input prompt category data to the generation AI and cause the generation AI to apply the generation algorithm.

[0042] The generation unit can improve the accuracy of dream generation by referring to the results of the user's past dreams when generating dreams. The generation unit uses a generation AI to improve the accuracy of dream generation by referring to the results of the user's past dreams when generating dreams. The generation AI, for example, uses data mining technology to analyze the results of past dreams and optimizes the generation algorithm based on the results. For example, the generation unit generates similar scenarios based on the content of dreams the user has had in the past. The generation unit can also analyze the results of the user's past dreams and optimize the generation algorithm. The generation unit can also improve the accuracy of generation by referring to dream patterns that the user has preferred in the past. In this way, the accuracy of generation can be improved by referring to the results of the user's past dreams. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the results of past dreams into the generation AI and cause the generation AI to improve the accuracy of generation.

[0043] The generation unit can determine the priority of scenarios based on the timing of prompt submission when generating a dream. The generation unit uses a generation AI to determine the priority of scenarios based on the timing of prompt submission when generating a dream. The generation AI evaluates, for example, the submission date and time and the frequency of submission, and determines the priority of scenarios based on the evaluation. For example, the generation unit prioritizes generating a scenario if a prompt is submitted early. The generation unit can also generate a scenario later if a prompt is submitted late. The generation unit can also adjust the order in which scenarios are generated based on the timing of prompt submission. In this way, by determining the priority of scenarios based on the timing of prompt submission, more appropriate dream content can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input prompt submission time data into the generation AI and have the generation AI determine the priority of scenarios.

[0044] The generation unit can adjust the order of scenarios based on the relevance of prompts when generating a dream. The generation unit uses a generation AI to adjust the order of scenarios based on the relevance of prompts when generating a dream. The generation AI evaluates, for example, the degree of theme agreement or the relevance of content, and adjusts the order of scenarios based on the evaluation. For example, the generation unit prioritizes generating scenarios for highly relevant prompts. The generation unit can also postpone generating scenarios for less relevant prompts. The generation unit can also adjust the order of scenario generation based on the relevance of prompts. In this way, by adjusting the order of scenarios based on the relevance of prompts, more appropriate dream content can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input prompt relevance data into the generation AI and cause the generation AI to adjust the order of scenarios.

[0045] The generation unit can adjust the use of technical terms in the scenario according to the user's level of expertise when generating a dream. The generation unit uses a generation AI to adjust the use of technical terms in the scenario according to the user's level of expertise when generating a dream. The generation AI evaluates the user's level of expertise, for example, using a questionnaire survey or past historical data, and adjusts the use of technical terms in the scenario based on that evaluation. For example, if the user has technical expertise, the generation unit generates a scenario that makes heavy use of technical terms. Alternatively, if the user does not have technical expertise, the generation unit can generate a scenario that avoids technical terms. The generation unit can also adjust the use of technical terms in the scenario according to the user's level of expertise. This allows for the generation of more appropriate dream content by adjusting the use of technical terms in the scenario according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms in the scenario.

[0046] When converting a dream scenario, the conversion unit can adjust the level of detail of the conversion based on the importance of the scenario. When converting a dream scenario, the conversion unit adjusts the level of detail of the conversion based on the importance of the scenario. The conversion unit, for example, evaluates the importance of the scenario and adjusts the level of detail of the conversion based on the evaluation. For example, the conversion unit generates a detailed electrical signal for a scenario with high importance. The conversion unit can also generate a simplified electrical signal for a scenario with low importance. The conversion unit can also adjust the details of the electrical signal according to the importance of the scenario. In this way, by adjusting the level of detail of the conversion according to the importance of the scenario, a more appropriate electrical signal can be generated. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input scenario importance data to a generation AI and cause the generation AI to adjust the level of detail of the conversion.

[0047] When converting a dream scenario, the conversion unit can apply different conversion algorithms depending on the category of the scenario. When converting a dream scenario, the conversion unit can apply different conversion algorithms depending on the category of the scenario. For example, the conversion unit classifies the scenario category and applies an appropriate conversion algorithm accordingly. For example, the conversion unit applies a conversion algorithm specialized for fantasy to a fantasy scenario. The conversion unit can also apply a conversion algorithm based on reality to a realistic scenario. The conversion unit can also apply a conversion algorithm specialized for horror to a horror scenario. In this way, by applying a conversion algorithm depending on the scenario category, a more appropriate electrical signal can be generated. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input scenario category data to a generation AI and cause the generation AI to apply the conversion algorithm.

[0048] When converting a dream scenario, the conversion unit can improve the accuracy of the conversion by referring to the user's past conversion results. When converting a dream scenario, the conversion unit can improve the accuracy of the conversion by referring to the user's past conversion results. The conversion unit, for example, uses data mining technology to analyze past conversion results and optimize the conversion algorithm based on the results. For example, the conversion unit generates similar electrical signals based on the content of dreams the user has had in the past. The conversion unit can also analyze the user's past conversion results and optimize the conversion algorithm. The conversion unit can also improve the accuracy of the conversion by referring to the user's favorite dream patterns in the past. In this way, the accuracy of the conversion can be improved by referring to the user's past conversion results. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without AI. For example, the conversion unit can input past conversion result data into a generation AI and cause the generation AI to improve the accuracy of the conversion.

[0049] When converting a dream scenario, the conversion unit can determine the conversion priority based on the time of submission of the scenario. When converting a dream scenario, the conversion unit determines the conversion priority based on the time of submission of the scenario. The conversion unit evaluates, for example, the submission date and time or the frequency of submission, and determines the conversion priority based on the evaluation. For example, if a scenario is submitted early, the conversion unit can convert it into an electrical signal preferentially. Also, if a scenario is submitted late, the conversion unit can convert it into an electrical signal later. Also, the conversion unit can adjust the conversion priority based on the time of submission of the scenario. In this way, by determining the conversion priority based on the time of submission of the scenario, a more appropriate electrical signal can be generated. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input scenario submission time data into a generation AI and have the generation AI determine the conversion priority.

[0050] When converting a dream scenario, the conversion unit can adjust the order of conversion based on the relevance of the scenarios. When converting a dream scenario, the conversion unit adjusts the order of conversion based on the relevance of the scenarios. The conversion unit, for example, evaluates the degree of theme consistency or the relevance of the content and adjusts the order of conversion based on the evaluation. For example, the conversion unit prioritizes conversion into electrical signals for highly relevant scenarios. The conversion unit can also convert into electrical signals less relevant scenarios at a later date. The conversion unit can also adjust the order of conversion based on the relevance of the scenarios. In this way, by adjusting the order of conversion based on the relevance of the scenarios, a more appropriate electrical signal can be generated. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input scenario relevance data to a generation AI and cause the generation AI to adjust the order of conversion.

[0051] When converting a dream scenario, the conversion unit can adjust the level of detail of the conversion according to the user's level of expertise. When converting a dream scenario, the conversion unit adjusts the level of detail of the conversion according to the user's level of expertise. The conversion unit evaluates the user's level of expertise using, for example, a questionnaire survey or past history data and adjusts the level of detail of the conversion based on the evaluation. For example, the conversion unit generates a detailed electrical signal if the user has expertise. The conversion unit can also generate a simplified electrical signal if the user does not have expertise. The conversion unit can also adjust the level of detail of the electrical signal according to the user's level of expertise. In this way, by adjusting the level of detail of the conversion according to the user's level of expertise, a more appropriate electrical signal can be generated. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without AI. For example, the conversion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the level of detail of the conversion.

[0052] When transmitting an electrical signal, the transmitting unit can adjust the level of detail of the transmission based on the importance of the signal. When transmitting an electrical signal, the transmitting unit adjusts the level of detail of the transmission based on the importance of the signal. The transmitting unit, for example, evaluates the importance of the signal and adjusts the level of detail of the transmission based on the evaluation. For example, the transmitting unit may employ a detailed transmission method for a signal with high importance. The transmitting unit may also employ a simplified transmission method for a signal with low importance. The transmitting unit may also adjust the level of detail of the transmission according to the importance of the signal. In this way, by adjusting the level of detail of the transmission according to the importance of the signal, a more appropriate electrical signal can be transmitted. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit may input signal importance data to a generating AI and cause the generating AI to adjust the level of detail of the transmission.

[0053] When transmitting an electrical signal, the transmitting unit can apply different transmission algorithms depending on the signal category. When transmitting an electrical signal, the transmitting unit applies different transmission algorithms depending on the signal category. For example, the transmitting unit classifies the signal category and applies an appropriate transmission algorithm accordingly. For example, the transmitting unit applies a fantasy-specific transmission algorithm to a fantasy signal. The transmitting unit can also apply a reality-based transmission algorithm to a realistic signal. The transmitting unit can also apply a horror-specific transmission algorithm to a horror signal. In this way, by applying a transmission algorithm depending on the signal category, a more appropriate electrical signal can be transmitted. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input signal category data to a generation AI and cause the generation AI to apply the transmission algorithm.

[0054] When transmitting an electrical signal, the transmitting unit can improve the accuracy of the transmission by referring to the user's past transmission results. When transmitting an electrical signal, the transmitting unit can improve the accuracy of the transmission by referring to the user's past transmission results. The transmitting unit, for example, uses data mining technology to analyze past transmission results and optimize the transmission algorithm based on the results. For example, the transmitting unit may adopt a similar transmission method based on the content of signals the user has received in the past. The transmitting unit can also analyze the user's past transmission results and optimize the transmission algorithm. The transmitting unit can also improve the accuracy of the transmission by referring to signal patterns that the user has preferred in the past. In this way, the accuracy of the transmission can be improved by referring to the user's past transmission results. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without AI. For example, the transmitting unit may input past transmission result data into a generating AI and have the generating AI improve the accuracy of the transmission.

[0055] When transmitting an electrical signal, the transmitting unit can determine the transmission priority based on the time of signal submission. When transmitting an electrical signal, the transmitting unit determines the transmission priority based on the time of signal submission. The transmitting unit evaluates, for example, the submission date and time or the frequency of submission, and determines the transmission priority based on the evaluation. For example, the transmitting unit prioritizes transmission of a signal submitted early. The transmitting unit can also transmit a signal submitted late at a later date and time. The transmitting unit can also adjust the transmission priority based on the time of signal submission. In this way, by determining the transmission priority based on the time of signal submission, more appropriate electrical signals can be transmitted. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input signal submission time data to a generation AI and have the generation AI determine the transmission priority.

[0056] When transmitting electrical signals, the transmitting unit can adjust the order of transmission based on the relevance of the signals. When transmitting electrical signals, the transmitting unit adjusts the order of transmission based on the relevance of the signals. The transmitting unit evaluates, for example, the degree of theme agreement or the relevance of the content, and adjusts the order of transmission based on the evaluation. For example, the transmitting unit prioritizes transmission of highly relevant signals. The transmitting unit can also postpone transmission of less relevant signals. The transmitting unit can also adjust the order of transmission based on the relevance of the signals. In this way, by adjusting the order of transmission based on the relevance of the signals, more appropriate electrical signals can be transmitted. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input signal relevance data to a generating AI and cause the generating AI to adjust the order of transmission.

[0057] When transmitting an electrical signal, the transmitting unit can adjust the level of detail of the transmission according to the user's level of expertise. When transmitting an electrical signal, the transmitting unit adjusts the level of detail of the transmission according to the user's level of expertise. The transmitting unit evaluates the user's level of expertise using, for example, a questionnaire survey or past history data and adjusts the level of detail of the transmission based on the evaluation. For example, the transmitting unit transmits a detailed signal if the user has expertise. The transmitting unit can also transmit a simplified signal if the user does not have expertise. The transmitting unit can also adjust the level of detail of the signal according to the user's level of expertise. In this way, by adjusting the level of detail of the transmission according to the user's level of expertise, a more appropriate electrical signal can be transmitted. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input the user's level of expertise data into a generating AI and cause the generating AI to adjust the level of detail of the transmission.

[0058] When monitoring brainwaves, the monitoring unit can improve the accuracy of the monitoring by referring to the user's past brainwave data. When monitoring brainwaves, the monitoring unit can improve the accuracy of the monitoring by referring to the user's past brainwave data. The monitoring unit, for example, uses data mining technology to analyze the past brainwave data and optimize the monitoring algorithm based on the data. For example, the monitoring unit adopts a similar monitoring method based on the user's past brainwave data. The monitoring unit can also analyze the user's past brainwave data and optimize the monitoring algorithm. The monitoring unit can also improve the accuracy of the monitoring by referring to the user's preferred monitoring method in the past. In this way, the accuracy of the monitoring can be improved by referring to the user's past brainwave data. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input past brainwave data into a generation AI and cause the generation AI to improve the accuracy of the monitoring.

[0059] When monitoring brain waves, the monitoring unit can determine the monitoring priority taking into account the user's geographical location information. When monitoring brain waves, the monitoring unit can determine the monitoring priority taking into account the user's geographical location information. The monitoring unit, for example, acquires the user's geographical location information using GPS data and determines the monitoring priority based on the acquired information. For example, the monitoring unit can lower the monitoring priority when the user is at home. The monitoring unit can also raise the monitoring priority when the user is traveling. The monitoring unit can also adjust the monitoring priority according to the user's location when the user is in a specific location. This allows for more appropriate dream content to be generated by taking the user's geographical location information into account. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input the user's geographical location information to the generation AI and have the generation AI determine the monitoring priority.

[0060] When monitoring brain waves, the monitoring unit can analyze the user's social media activities and acquire related monitoring data. When monitoring brain waves, the monitoring unit can analyze the user's social media activities and acquire related monitoring data. For example, the monitoring unit can analyze social media posts and acquire monitoring data based on the content. For example, the monitoring unit can acquire related monitoring data based on content shared by the user on social media. The monitoring unit can also analyze the user's social media activity history and acquire related monitoring data. The monitoring unit can also acquire related monitoring data by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activities, more appropriate dream content can be generated. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input social media data into a generation AI and cause the generation AI to acquire related monitoring data.

[0061] When adjusting the content of a dream, the adjustment unit can improve the accuracy of the adjustment by referring to data on the user's past dreams. When adjusting the content of a dream, the adjustment unit can improve the accuracy of the adjustment by referring to data on the user's past dreams. The adjustment unit, for example, uses data mining technology to analyze past dream data and optimize the adjustment algorithm based on the data. For example, the adjustment unit adjusts the content of a dream to a similar dream based on data on the user's past dreams. The adjustment unit can also analyze data on the user's past dreams and optimize the adjustment algorithm. The adjustment unit can also improve the accuracy of the adjustment by referring to dream patterns that the user has preferred in the past. In this way, the accuracy of the adjustment can be improved by referring to data on the user's past dreams. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data on past dreams into the generation AI and cause the generation AI to improve the accuracy of the adjustment.

[0062] When adjusting the content of a dream, the adjustment unit can determine the priority of the adjustment taking into account the user's geographical location information. When adjusting the content of a dream, the adjustment unit can determine the priority of the adjustment taking into account the user's geographical location information. The adjustment unit, for example, acquires the user's geographical location information using GPS data and determines the priority of the adjustment based on the acquired information. For example, the adjustment unit can lower the priority of the adjustment when the user is at home. The adjustment unit can also raise the priority of the adjustment when the user is traveling. The adjustment unit can also adjust the priority of the adjustment according to the location when the user is in a specific location. This makes it possible to generate more appropriate dream content by taking into account the user's geographical location information. Some or all of the above-described processing in the adjustment unit can be performed using, for example, AI, or without AI. For example, the adjustment unit can input the user's geographical location information to the generation AI and cause the generation AI to determine the priority of the adjustment.

[0063] When adjusting the dream content, the adjustment unit can analyze the user's social media activity and acquire related adjustment data. When adjusting the dream content, the adjustment unit can analyze the user's social media activity and acquire related adjustment data. The adjustment unit, for example, analyzes social media posts and acquires adjustment data based thereon. For example, the adjustment unit acquires related adjustment data based on content shared by the user on social media. The adjustment unit can also analyze the user's social media activity history and acquire related adjustment data. The adjustment unit can also acquire related adjustment data by referring to the activities of the user's friends on social media. In this way, more appropriate dream content can be generated by analyzing the user's social media activity. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input social media data into the generation AI and cause the generation AI to acquire related adjustment data.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The reception unit can analyze the user's past dream history and suggest the optimal prompt input method. For example, the reception unit can analyze the user's past dream history using data mining technology and automatically suggest similar prompts based on the content of dreams previously input by the user. The reception unit can also preferentially suggest specific themes or scenarios based on the user's past dream history. Furthermore, the reception unit can analyze the user's preferred dream patterns in the past and customize the optimal input method. In this way, the analysis of the user's past dream history can suggest the optimal prompt input method for the user.

[0066] When generating a dream, the generation unit can apply different generation algorithms depending on the category of the prompt. For example, the generation unit classifies the category of the prompt and applies an appropriate generation algorithm accordingly. A fantasy-specific generation algorithm can be applied to fantasy prompts, and a reality-based generation algorithm can be applied to realistic prompts. Also, a horror-specific generation algorithm can be applied to horror prompts. In this way, by applying a generation algorithm depending on the prompt category, more appropriate dream content can be generated.

[0067] When converting a dream scenario, the conversion unit can apply different conversion algorithms depending on the category of the scenario. For example, the conversion unit classifies the category of the scenario and applies an appropriate conversion algorithm accordingly. A conversion algorithm specialized for fantasy can be applied to fantasy scenarios, and a conversion algorithm based on reality can be applied to realistic scenarios. Also, a conversion algorithm specialized for horror can be applied to horror scenarios. In this way, by applying a conversion algorithm depending on the category of the scenario, a more appropriate electrical signal can be generated.

[0068] When inputting prompts, the reception unit can prioritize receiving highly relevant prompts by taking into account the user's geographical location information. For example, the reception unit obtains the user's geographical location information using GPS data and filters prompts based on the geographical location information. If the user is traveling, the reception unit can prioritize receiving dream prompts related to the travel destination. Also, if the user is at home, the reception unit can prioritize receiving dream prompts related to the home. Furthermore, if the user is in a specific location, the reception unit can prioritize receiving dream prompts related to that location. In this way, more relevant dream content can be generated by taking into account the user's geographical location information.

[0069] The processing flow of the first embodiment will be briefly explained below.

[0070] Step 1: The reception unit receives a prompt from the user to input the content of the dream they want to see. The prompt may include a scenario, a theme, characters, etc. The reception unit can receive the prompt by text input, voice input, multiple choice format, etc. Step 2: The generation unit uses the generation AI to analyze the prompts received by the reception unit and generate the content of the dream. The generation AI analyzes the prompts using natural language processing technology and keyword extraction technology, and generates the content of the dream using a scenario generation algorithm and storytelling technology. Step 3: The conversion unit converts the dream content generated by the generation unit into an electrical signal. The conversion unit converts the dream content into an electrical signal using signal processing technology and brain wave conversion technology. Step 4: The transmitter sends the electrical signal converted by the converter to the user's brain. The transmitter transmits the electrical signal to the brain using wireless communication technology and electrode placement technology.

[0071] (Example 2) A dream generation system according to an embodiment of the present invention prevents nightmares by allowing a user to input the dream they want to have into a generation AI, which then converts the dream into electrical signals and sends them to the brain. In the dream generation system, the user inputs the content of the dream they want to have as a prompt, and the generation AI analyzes the input prompt and generates the dream content. The generated dream content is converted into electrical signals and sent to the user's brain. This allows the user to dream the dream they want and sleep in a healthy state of mind and body without being troubled by nightmares. For example, the dream generation system can monitor the user's brain waves while they sleep and adjust the dream content in real time. For example, if the user encounters an unpleasant situation in a dream, the generation AI can detect this situation and change the dream content to maintain a comfortable dream. In this way, the user can dream the dream they want and sleep in a healthy state of mind and body without being troubled by nightmares. This allows the dream generation system to generate the dream the user wants to have and prevent nightmares. For example, by allowing the user to input the dream they want to have and convert the dream into electrical signals and send them to the brain, mental and physical health can be maintained. In addition, the device monitors the user's brain waves while they sleep and adjusts the content of their dreams in real time, helping to maintain a comfortable dream.

[0072] The dream creation system according to the embodiment includes a reception unit, a generation unit, a conversion unit, and a transmission unit. The reception unit receives a prompt for inputting the content of the dream the user wants to have. Examples of the prompt include, but are not limited to, a scenario, a theme, and characters. The reception unit can receive the prompt via, for example, text input, voice input, multiple-choice format, or other methods. The generation unit uses a generation AI to analyze the prompt received by the reception unit and generate the content of the dream. The generation AI analyzes the prompt using, for example, natural language processing technology or keyword extraction technology, and generates the content of the dream using a scenario generation algorithm or storytelling technology. The conversion unit converts the content of the dream generated by the generation unit into an electrical signal. The conversion unit converts the content of the dream into an electrical signal using, for example, signal processing technology or electroencephalogram conversion technology. The transmission unit sends the electrical signal converted by the conversion unit to the user's brain. The transmission unit transmits the electrical signal to the brain using, for example, wireless communication technology or electrode placement technology. This allows the dream creation system according to the embodiment to prevent nightmares and maintain physical and mental health by enabling the user to have the dream they want to have.

[0073] The dream generation system includes a monitoring unit that monitors the user's brain waves while sleeping. The monitoring unit monitors the user's brain waves while sleeping. For example, the monitoring unit measures brain waves using an EEG sensor and analyzes the brain wave data using data analysis technology. By monitoring the user's brain waves, the content of the dream can be adjusted in real time. For example, the monitoring unit can analyze the user's brain wave data in real time and provide feedback to adjust the content of the dream. The monitoring unit can also accumulate the user's brain wave data and analyze long-term changes in brain waves. This allows the user's brain waves to be understood in detail and the content of the dream to be optimized.

[0074] The dream generation system includes an adjustment unit that adjusts the content of a dream in real time based on the brain waves monitored by the monitoring unit. The adjustment unit adjusts the content of a dream in real time based on the brain waves monitored by the monitoring unit. The adjustment unit, for example, analyzes brain wave data using a feedback loop and executes an algorithm to adjust the content of the dream. For example, if the user encounters an unpleasant situation in a dream, the adjustment unit can detect the situation and change the content of the dream to maintain a comfortable dream. The adjustment unit can also dynamically change the content of the dream based on the user's brain wave data. This allows the user to maintain a comfortable state in the dream. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input brain wave data acquired by the monitoring unit into a generation AI and have the generation AI adjust the content of the dream.

[0075] The generation unit can create a detailed dream scenario based on the prompt. The generation unit uses a generation AI to create the detailed dream scenario based on the prompt. The generation AI creates the detailed dream scenario based on the prompt, for example, using a scenario generation algorithm or storytelling technology. For example, the generation AI analyzes keywords and phrases included in the prompt and builds a scenario based on them. The generation AI can also adjust the components of the scenario and the development of the story according to the content of the prompt. This allows the user to have a more specific dream by creating a detailed dream scenario. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input a prompt into the generation AI and cause the generation AI to create a detailed dream scenario.

[0076] The conversion unit can convert the generated dream scenario into an electrical signal. The conversion unit converts the dream scenario generated by the generation unit into an electrical signal. The conversion unit converts the dream scenario into an electrical signal using, for example, signal processing technology or electroencephalogram (EEG) conversion technology. For example, the conversion unit converts each element of the scenario into a corresponding electrical signal and then converts it into a signal to be transmitted to the brain. The conversion unit can also adjust the strength and pattern of the electrical signal depending on the content of the scenario. In this way, by converting the generated dream scenario into an electrical signal, the content of the dream can be transmitted to the user's brain. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input the generated dream scenario into a generation AI and have the generation AI convert it into an electrical signal.

[0077] The transmitting unit can send the converted electrical signal to the user's brain. The transmitting unit sends the electrical signal converted by the converting unit to the user's brain. The transmitting unit transmits the electrical signal to the brain using, for example, wireless communication technology or electrode placement technology. For example, the transmitting unit transmits the electrical signal wirelessly and transmits the signal through electrodes placed on the brain. The transmitting unit can also adjust the transmission timing and strength of the electrical signal. In this way, by sending the converted electrical signal to the user's brain, the user can have the dream they want to see. Some or all of the above-mentioned processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input the converted electrical signal to a generating AI and have the generating AI adjust the transmission method.

[0078] The reception unit can estimate the user's emotions and adjust the prompt input method based on the estimated user's emotions. The reception unit can estimate the user's emotions and adjust the prompt input method based on the estimated user's emotions. The reception unit estimates the user's emotions using, for example, facial expression recognition technology or voice analysis technology. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. If the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. If the user is in a hurry, the reception unit can prioritize voice input and enable prompt input quickly. This allows for the generation of more appropriate dream content by adjusting the prompt input method according to the user's emotions. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the user's emotion data into a generation AI and have the generation AI adjust the prompt input method.

[0079] The reception unit can analyze the user's past dream history and suggest an optimal prompt input method. The reception unit can analyze the user's past dream history and suggest an optimal prompt input method. The reception unit can analyze the past dream history using, for example, data mining technology. For example, the reception unit can automatically suggest similar prompts based on the content of dreams previously input by the user. The reception unit can also preferentially suggest specific themes or scenarios from the user's past dream history. The reception unit can also analyze the user's preferred dream patterns in the past and customize an optimal input method. In this way, the analysis of the past dream history can suggest an optimal prompt input method for the user. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input past dream history data into a generation AI and have the generation AI suggest an optimal prompt input method.

[0080] The reception unit can filter prompts based on the user's current psychological state when the prompts are input. The reception unit can filter prompts based on the user's current psychological state when the prompts are input. The reception unit can evaluate the user's psychological state using, for example, a psychological state evaluation method or a filtering algorithm, and filter the prompts based on the evaluation. For example, if the user is feeling anxious, the reception unit can preferentially suggest dream scenarios that alleviate the anxiety. Furthermore, if the user is excited, the reception unit can suggest dream scenarios that help the user relax. Furthermore, if the user is tired, the reception unit can suggest dream scenarios that have a soothing effect. In this way, by filtering prompts according to the user's psychological state, more appropriate dream content can be generated. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's psychological state data to a generation AI and have the generation AI perform prompt filtering.

[0081] The reception unit can select the optimal input means depending on the user's input method when inputting a prompt. The reception unit selects the optimal input means depending on the user's input method when inputting a prompt. The reception unit accepts prompts using methods such as voice input, text input, and image input. For example, when the user inputs a prompt by voice, the reception unit analyzes the input content using voice recognition technology. When the user inputs a prompt by text, the reception unit can analyze the input content using text analysis technology. When the user inputs a prompt by image, the reception unit can analyze the input content using image recognition technology. This allows the prompt to be input smoothly by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may input the user's input data to a generation AI and have the generation AI select the optimal input means.

[0082] The reception unit can estimate the user's emotions and determine the priority of input prompts based on the estimated user emotions. The reception unit can estimate the user's emotions and determine the priority of input prompts based on the estimated user emotions. The reception unit estimates the user's emotions using, for example, facial expression recognition technology or voice analysis technology. For example, if the user is feeling stressed, the reception unit can prioritize prompts with a relaxing effect. Also, if the user is excited, the reception unit can prioritize prompts with a stabilizing effect. Also, if the user is tired, the reception unit can prioritize prompts with a soothing effect. In this way, by prioritizing prompts according to the user's emotions, more appropriate dream content can be generated. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's emotion data to a generation AI and have the generation AI determine the priority of prompts.

[0083] The reception unit, when inputting a prompt, can prioritize receiving highly relevant prompts by taking into account the user's geographical location information. The reception unit, when inputting a prompt, prioritizes receiving highly relevant prompts by taking into account the user's geographical location information. The reception unit, for example, acquires the user's geographical location information using GPS data and filters prompts based on the acquired information. For example, when the user is traveling, the reception unit can prioritize receiving dream prompts related to the user's travel destination. Furthermore, when the user is at home, the reception unit can prioritize receiving dream prompts related to the user's home. Furthermore, when the user is in a specific location, the reception unit can prioritize receiving dream prompts related to that location. In this way, by taking the user's geographical location information into account, more relevant dream content can be generated. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize receiving highly relevant prompts.

[0084] The reception unit can analyze the user's social media activity and suggest related prompts when the user inputs a prompt. The reception unit can analyze the user's social media activity and suggest related prompts when the user inputs a prompt. The reception unit, for example, analyzes the content of social media posts and suggests prompts based on the content. For example, the reception unit can suggest related dream prompts based on content shared by the user on social media. The reception unit can also analyze the user's social media activity history and suggest related dream prompts. The reception unit can also suggest related dream prompts based on the activity of the user's friends on social media. In this way, more relevant dream content can be generated by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input social media data into a generation AI and have the generation AI suggest related prompts.

[0085] The reception unit can customize the input method by reflecting the user's past feedback when entering a prompt. The reception unit customizes the input method by reflecting the user's past feedback when entering a prompt. The reception unit, for example, analyzes feedback data and customizes the input method based on the feedback data. For example, the reception unit suggests an optimal input method based on the user's previously preferred input methods. The reception unit can also analyze the user's past feedback and improve the input method. The reception unit can also avoid input methods that the user has previously been dissatisfied with and suggest an optimal input method. In this way, a more appropriate input method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past feedback data to a generation AI and cause the generation AI to customize the input method.

[0086] The generation unit can estimate the user's emotions and adjust the way the dream scenario is expressed based on the estimated user emotions. The generation unit can use a generation AI to estimate the user's emotions and adjust the way the dream scenario is expressed based on the estimated user emotions. The generation AI can estimate the user's emotions using, for example, facial expression recognition technology or voice analysis technology. For example, the generation unit can generate a calm scenario if the user is relaxed. The generation unit can also generate a stimulating scenario if the user is excited. The generation unit can also generate a scenario that gives a sense of security if the user is feeling anxious. This allows for the generation of more appropriate dream content by adjusting the way the dream scenario is expressed based on the user's emotions. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the scenario is expressed.

[0087] The generation unit can adjust the level of detail of the scenario based on the importance of the prompt when generating a dream. The generation unit uses the generation AI to adjust the level of detail of the scenario based on the importance of the prompt when generating a dream. The generation AI, for example, evaluates the importance of the prompt and adjusts the level of detail of the scenario based on the evaluation. For example, the generation unit generates a detailed scenario for a prompt with high importance. The generation unit can also generate a simplified scenario for a prompt with low importance. The generation unit can also adjust the details of the scenario according to the importance of the prompt. In this way, by adjusting the level of detail of the scenario according to the importance of the prompt, more appropriate dream content can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input prompt importance data to the generation AI and cause the generation AI to adjust the level of detail of the scenario.

[0088] The generation unit can apply different generation algorithms depending on the category of the prompt when generating a dream. The generation unit uses a generation AI to apply different generation algorithms depending on the category of the prompt when generating a dream. The generation AI, for example, classifies the category of the prompt and applies an appropriate generation algorithm accordingly. For example, the generation unit applies a generation algorithm specialized for fantasy to a fantasy prompt. The generation unit can also apply a generation algorithm based on reality to a realistic prompt. The generation unit can also apply a generation algorithm specialized for horror to a horror prompt. In this way, by applying a generation algorithm depending on the category of the prompt, more appropriate dream content can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input prompt category data to the generation AI and cause the generation AI to apply the generation algorithm.

[0089] The generation unit can improve the accuracy of dream generation by referring to the results of the user's past dreams when generating dreams. The generation unit uses a generation AI to improve the accuracy of dream generation by referring to the results of the user's past dreams when generating dreams. The generation AI, for example, uses data mining technology to analyze the results of past dreams and optimizes the generation algorithm based on the results. For example, the generation unit generates similar scenarios based on the content of dreams the user has had in the past. The generation unit can also analyze the results of the user's past dreams and optimize the generation algorithm. The generation unit can also improve the accuracy of generation by referring to dream patterns that the user has preferred in the past. In this way, the accuracy of generation can be improved by referring to the results of the user's past dreams. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the results of past dreams into the generation AI and cause the generation AI to improve the accuracy of generation.

[0090] The generation unit can estimate the user's emotions and adjust the length of the dream scenario based on the estimated user emotions. The generation unit can use a generation AI to estimate the user's emotions and adjust the length of the dream scenario based on the estimated user emotions. The generation AI can estimate the user's emotions using, for example, facial expression recognition technology or voice analysis technology. For example, the generation unit can generate a longer scenario if the user is relaxed. The generation unit can also generate a shorter scenario if the user is in a hurry. The generation unit can also generate a scenario of appropriate length if the user is excited. This allows for more appropriate dream content to be generated by adjusting the length of the dream scenario according to the user's emotions. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's emotional data into the generation AI and have the generation AI adjust the length of the scenario.

[0091] The generation unit can determine the priority of scenarios based on the timing of prompt submission when generating a dream. The generation unit uses a generation AI to determine the priority of scenarios based on the timing of prompt submission when generating a dream. The generation AI evaluates, for example, the submission date and time and the frequency of submission, and determines the priority of scenarios based on the evaluation. For example, the generation unit prioritizes generating a scenario if a prompt is submitted early. The generation unit can also generate a scenario later if a prompt is submitted late. The generation unit can also adjust the order in which scenarios are generated based on the timing of prompt submission. In this way, by determining the priority of scenarios based on the timing of prompt submission, more appropriate dream content can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input prompt submission time data into the generation AI and have the generation AI determine the priority of scenarios.

[0092] The generation unit can adjust the order of scenarios based on the relevance of prompts when generating a dream. The generation unit uses a generation AI to adjust the order of scenarios based on the relevance of prompts when generating a dream. The generation AI evaluates, for example, the degree of theme agreement or the relevance of content, and adjusts the order of scenarios based on the evaluation. For example, the generation unit prioritizes generating scenarios for highly relevant prompts. The generation unit can also postpone generating scenarios for less relevant prompts. The generation unit can also adjust the order of scenario generation based on the relevance of prompts. In this way, by adjusting the order of scenarios based on the relevance of prompts, more appropriate dream content can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input prompt relevance data into the generation AI and cause the generation AI to adjust the order of scenarios.

[0093] The generation unit can adjust the use of technical terms in the scenario according to the user's level of expertise when generating a dream. The generation unit uses a generation AI to adjust the use of technical terms in the scenario according to the user's level of expertise when generating a dream. The generation AI evaluates the user's level of expertise, for example, using a questionnaire survey or past historical data, and adjusts the use of technical terms in the scenario based on that evaluation. For example, if the user has technical expertise, the generation unit generates a scenario that makes heavy use of technical terms. Alternatively, if the user does not have technical expertise, the generation unit can generate a scenario that avoids technical terms. The generation unit can also adjust the use of technical terms in the scenario according to the user's level of expertise. This allows for the generation of more appropriate dream content by adjusting the use of technical terms in the scenario according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms in the scenario.

[0094] The conversion unit can estimate the user's emotions and adjust the electrical signal conversion method based on the estimated user's emotions. The conversion unit can estimate the user's emotions and adjust the electrical signal conversion method based on the estimated user's emotions. The conversion unit estimates the user's emotions using, for example, facial expression recognition technology or voice analysis technology. For example, the conversion unit can generate a calming electrical signal if the user is relaxed. The conversion unit can also generate a stimulating electrical signal if the user is excited. The conversion unit can also generate an electrical signal that provides a sense of security if the user is feeling anxious. This allows the electrical signal conversion method to be adjusted according to the user's emotions, thereby transmitting more appropriate dream content. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without AI. For example, the conversion unit can input the user's emotion data into a generation AI and have the generation AI adjust the electrical signal conversion method.

[0095] When converting a dream scenario, the conversion unit can adjust the level of detail of the conversion based on the importance of the scenario. When converting a dream scenario, the conversion unit adjusts the level of detail of the conversion based on the importance of the scenario. The conversion unit, for example, evaluates the importance of the scenario and adjusts the level of detail of the conversion based on the evaluation. For example, the conversion unit generates a detailed electrical signal for a scenario with high importance. The conversion unit can also generate a simplified electrical signal for a scenario with low importance. The conversion unit can also adjust the details of the electrical signal according to the importance of the scenario. In this way, by adjusting the level of detail of the conversion according to the importance of the scenario, a more appropriate electrical signal can be generated. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input scenario importance data to a generation AI and cause the generation AI to adjust the level of detail of the conversion.

[0096] When converting a dream scenario, the conversion unit can apply different conversion algorithms depending on the category of the scenario. When converting a dream scenario, the conversion unit can apply different conversion algorithms depending on the category of the scenario. For example, the conversion unit classifies the scenario category and applies an appropriate conversion algorithm accordingly. For example, the conversion unit applies a conversion algorithm specialized for fantasy to a fantasy scenario. The conversion unit can also apply a conversion algorithm based on reality to a realistic scenario. The conversion unit can also apply a conversion algorithm specialized for horror to a horror scenario. In this way, by applying a conversion algorithm depending on the scenario category, a more appropriate electrical signal can be generated. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input scenario category data to a generation AI and cause the generation AI to apply the conversion algorithm.

[0097] When converting a dream scenario, the conversion unit can improve the accuracy of the conversion by referring to the user's past conversion results. When converting a dream scenario, the conversion unit can improve the accuracy of the conversion by referring to the user's past conversion results. The conversion unit, for example, uses data mining technology to analyze past conversion results and optimize the conversion algorithm based on the results. For example, the conversion unit generates similar electrical signals based on the content of dreams the user has had in the past. The conversion unit can also analyze the user's past conversion results and optimize the conversion algorithm. The conversion unit can also improve the accuracy of the conversion by referring to the user's favorite dream patterns in the past. In this way, the accuracy of the conversion can be improved by referring to the user's past conversion results. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without AI. For example, the conversion unit can input past conversion result data into a generation AI and cause the generation AI to improve the accuracy of the conversion.

[0098] The conversion unit can estimate the user's emotions and adjust the intensity of the electrical signal based on the estimated user's emotions. The conversion unit can estimate the user's emotions and adjust the intensity of the electrical signal based on the estimated user's emotions. The conversion unit estimates the user's emotions using, for example, facial expression recognition technology or voice analysis technology. For example, the conversion unit can generate an electrical signal with a moderate intensity when the user is relaxed. The conversion unit can also generate a stronger electrical signal when the user is excited. The conversion unit can also generate an electrical signal with an intensity that gives the user a sense of security when the user is anxious. This allows the intensity of the electrical signal to be adjusted according to the user's emotions, thereby transmitting more appropriate dream content. Some or all of the above-mentioned processing in the conversion unit can be performed using, for example, AI, or without AI. For example, the conversion unit can input the user's emotion data into a generation AI and have the generation AI adjust the intensity of the electrical signal.

[0099] When converting a dream scenario, the conversion unit can determine the conversion priority based on the time of submission of the scenario. When converting a dream scenario, the conversion unit determines the conversion priority based on the time of submission of the scenario. The conversion unit evaluates, for example, the submission date and time or the frequency of submission, and determines the conversion priority based on the evaluation. For example, if a scenario is submitted early, the conversion unit can convert it into an electrical signal preferentially. Also, if a scenario is submitted late, the conversion unit can convert it into an electrical signal later. Also, the conversion unit can adjust the conversion priority based on the time of submission of the scenario. In this way, by determining the conversion priority based on the time of submission of the scenario, a more appropriate electrical signal can be generated. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input scenario submission time data into a generation AI and have the generation AI determine the conversion priority.

[0100] When converting a dream scenario, the conversion unit can adjust the order of conversion based on the relevance of the scenarios. When converting a dream scenario, the conversion unit adjusts the order of conversion based on the relevance of the scenarios. The conversion unit, for example, evaluates the degree of theme consistency or the relevance of the content and adjusts the order of conversion based on the evaluation. For example, the conversion unit prioritizes conversion into electrical signals for highly relevant scenarios. The conversion unit can also convert into electrical signals less relevant scenarios at a later date. The conversion unit can also adjust the order of conversion based on the relevance of the scenarios. In this way, by adjusting the order of conversion based on the relevance of the scenarios, a more appropriate electrical signal can be generated. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input scenario relevance data to a generation AI and cause the generation AI to adjust the order of conversion.

[0101] When converting a dream scenario, the conversion unit can adjust the level of detail of the conversion according to the user's level of expertise. When converting a dream scenario, the conversion unit adjusts the level of detail of the conversion according to the user's level of expertise. The conversion unit evaluates the user's level of expertise using, for example, a questionnaire survey or past history data and adjusts the level of detail of the conversion based on the evaluation. For example, the conversion unit generates a detailed electrical signal if the user has expertise. The conversion unit can also generate a simplified electrical signal if the user does not have expertise. The conversion unit can also adjust the level of detail of the electrical signal according to the user's level of expertise. In this way, by adjusting the level of detail of the conversion according to the user's level of expertise, a more appropriate electrical signal can be generated. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without AI. For example, the conversion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the level of detail of the conversion.

[0102] The transmitting unit can estimate the user's emotions and adjust the transmission method of the electrical signal based on the estimated user's emotions. The transmitting unit can estimate the user's emotions and adjust the transmission method of the electrical signal based on the estimated user's emotions. The transmitting unit estimates the user's emotions using, for example, facial expression recognition technology or voice analysis technology. For example, the transmitting unit can adopt a gentle transmission method when the user is relaxed. The transmitting unit can also adopt a stronger transmission method when the user is excited. The transmitting unit can also adopt a transmission method that provides a sense of security when the user is feeling anxious. In this way, by adjusting the transmission method of the electrical signal according to the user's emotions, more appropriate dream content can be transmitted. Some or all of the above-mentioned processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input the user's emotion data into the generating AI and have the generating AI adjust the transmission method.

[0103] When transmitting an electrical signal, the transmitting unit can adjust the level of detail of the transmission based on the importance of the signal. When transmitting an electrical signal, the transmitting unit adjusts the level of detail of the transmission based on the importance of the signal. The transmitting unit, for example, evaluates the importance of the signal and adjusts the level of detail of the transmission based on the evaluation. For example, the transmitting unit may employ a detailed transmission method for a signal with high importance. The transmitting unit may also employ a simplified transmission method for a signal with low importance. The transmitting unit may also adjust the level of detail of the transmission according to the importance of the signal. In this way, by adjusting the level of detail of the transmission according to the importance of the signal, a more appropriate electrical signal can be transmitted. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit may input signal importance data to a generating AI and cause the generating AI to adjust the level of detail of the transmission.

[0104] When transmitting an electrical signal, the transmitting unit can apply different transmission algorithms depending on the signal category. When transmitting an electrical signal, the transmitting unit applies different transmission algorithms depending on the signal category. For example, the transmitting unit classifies the signal category and applies an appropriate transmission algorithm accordingly. For example, the transmitting unit applies a fantasy-specific transmission algorithm to a fantasy signal. The transmitting unit can also apply a reality-based transmission algorithm to a realistic signal. The transmitting unit can also apply a horror-specific transmission algorithm to a horror signal. In this way, by applying a transmission algorithm depending on the signal category, a more appropriate electrical signal can be transmitted. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input signal category data to a generation AI and cause the generation AI to apply the transmission algorithm.

[0105] When transmitting an electrical signal, the transmitting unit can improve the accuracy of the transmission by referring to the user's past transmission results. When transmitting an electrical signal, the transmitting unit can improve the accuracy of the transmission by referring to the user's past transmission results. The transmitting unit, for example, uses data mining technology to analyze past transmission results and optimize the transmission algorithm based on the results. For example, the transmitting unit may adopt a similar transmission method based on the content of signals the user has received in the past. The transmitting unit can also analyze the user's past transmission results and optimize the transmission algorithm. The transmitting unit can also improve the accuracy of the transmission by referring to signal patterns that the user has preferred in the past. In this way, the accuracy of the transmission can be improved by referring to the user's past transmission results. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without AI. For example, the transmitting unit may input past transmission result data into a generating AI and have the generating AI improve the accuracy of the transmission.

[0106] The transmitting unit can estimate the user's emotions and adjust the timing of transmitting the electrical signal based on the estimated user's emotions. The transmitting unit can estimate the user's emotions and adjust the timing of transmitting the electrical signal based on the estimated user's emotions. The transmitting unit estimates the user's emotions using, for example, facial expression recognition technology or voice analysis technology. For example, if the user is relaxed, the transmitting unit can transmit a signal at a gentle timing. Also, if the user is excited, the transmitting unit can transmit a signal at a rapid timing. Also, if the user is feeling anxious, the transmitting unit can transmit a signal at a timing that provides a sense of security. In this way, by adjusting the timing of transmitting the electrical signal according to the user's emotions, more appropriate dream content can be transmitted. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input the user's emotion data into the generating AI and have the generating AI adjust the transmission timing.

[0107] When transmitting an electrical signal, the transmitting unit can determine the transmission priority based on the time of signal submission. When transmitting an electrical signal, the transmitting unit determines the transmission priority based on the time of signal submission. The transmitting unit evaluates, for example, the submission date and time or the frequency of submission, and determines the transmission priority based on the evaluation. For example, the transmitting unit prioritizes transmission of a signal submitted early. The transmitting unit can also transmit a signal submitted late at a later date and time. The transmitting unit can also adjust the transmission priority based on the time of signal submission. In this way, by determining the transmission priority based on the time of signal submission, more appropriate electrical signals can be transmitted. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input signal submission time data to a generation AI and have the generation AI determine the transmission priority.

[0108] When transmitting electrical signals, the transmitting unit can adjust the order of transmission based on the relevance of the signals. When transmitting electrical signals, the transmitting unit adjusts the order of transmission based on the relevance of the signals. The transmitting unit evaluates, for example, the degree of theme agreement or the relevance of the content, and adjusts the order of transmission based on the evaluation. For example, the transmitting unit prioritizes transmission of highly relevant signals. The transmitting unit can also postpone transmission of less relevant signals. The transmitting unit can also adjust the order of transmission based on the relevance of the signals. In this way, by adjusting the order of transmission based on the relevance of the signals, more appropriate electrical signals can be transmitted. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input signal relevance data to a generating AI and cause the generating AI to adjust the order of transmission.

[0109] When transmitting an electrical signal, the transmitting unit can adjust the level of detail of the transmission according to the user's level of expertise. When transmitting an electrical signal, the transmitting unit adjusts the level of detail of the transmission according to the user's level of expertise. The transmitting unit evaluates the user's level of expertise using, for example, a questionnaire survey or past history data and adjusts the level of detail of the transmission based on the evaluation. For example, the transmitting unit transmits a detailed signal if the user has expertise. The transmitting unit can also transmit a simplified signal if the user does not have expertise. The transmitting unit can also adjust the level of detail of the signal according to the user's level of expertise. In this way, by adjusting the level of detail of the transmission according to the user's level of expertise, a more appropriate electrical signal can be transmitted. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input the user's level of expertise data into a generating AI and cause the generating AI to adjust the level of detail of the transmission.

[0110] The monitoring unit can estimate the user's emotions and adjust the brain wave monitoring method based on the estimated user's emotions. The monitoring unit can estimate the user's emotions and adjust the brain wave monitoring method based on the estimated user's emotions. The monitoring unit estimates the user's emotions using, for example, facial expression recognition technology or voice analysis technology. For example, the monitoring unit can adopt a gentle monitoring method when the user is relaxed. The monitoring unit can also adopt a detailed monitoring method when the user is excited. The monitoring unit can also adopt a monitoring method that provides a sense of security when the user is anxious. In this way, by adjusting the brain wave monitoring method according to the user's emotions, more appropriate dream content can be generated. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the monitoring method.

[0111] When monitoring brainwaves, the monitoring unit can improve the accuracy of the monitoring by referring to the user's past brainwave data. When monitoring brainwaves, the monitoring unit can improve the accuracy of the monitoring by referring to the user's past brainwave data. The monitoring unit, for example, uses data mining technology to analyze the past brainwave data and optimize the monitoring algorithm based on the data. For example, the monitoring unit adopts a similar monitoring method based on the user's past brainwave data. The monitoring unit can also analyze the user's past brainwave data and optimize the monitoring algorithm. The monitoring unit can also improve the accuracy of the monitoring by referring to the user's preferred monitoring method in the past. In this way, the accuracy of the monitoring can be improved by referring to the user's past brainwave data. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input past brainwave data into a generation AI and cause the generation AI to improve the accuracy of the monitoring.

[0112] When monitoring brain waves, the monitoring unit can adjust the level of monitoring detail based on the user's current psychological state. When monitoring brain waves, the monitoring unit adjusts the level of monitoring detail based on the user's current psychological state. The monitoring unit evaluates the user's current psychological state using, for example, a psychological test or behavioral analysis and adjusts the level of monitoring detail based on the evaluation. For example, the monitoring unit may employ a simplified monitoring method when the user is relaxed. Alternatively, the monitoring unit may employ a more detailed monitoring method when the user is excited. Alternatively, the monitoring unit may employ a monitoring method that provides a sense of security when the user is anxious. This allows for the generation of more appropriate dream content by adjusting the level of monitoring detail according to the user's psychological state. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit may input the user's psychological state data into a generation AI and cause the generation AI to adjust the level of monitoring detail.

[0113] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user's emotions. The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user's emotions. The monitoring unit estimates the user's emotions using, for example, facial expression recognition technology or voice analysis technology. For example, the monitoring unit can reduce the monitoring frequency when the user is relaxed. The monitoring unit can also increase the monitoring frequency when the user is excited. The monitoring unit can also adjust the monitoring frequency to provide a sense of security when the user is feeling anxious. In this way, by adjusting the monitoring frequency according to the user's emotions, more appropriate dream content can be generated. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the monitoring frequency.

[0114] When monitoring brain waves, the monitoring unit can determine the monitoring priority taking into account the user's geographical location information. When monitoring brain waves, the monitoring unit can determine the monitoring priority taking into account the user's geographical location information. The monitoring unit, for example, acquires the user's geographical location information using GPS data and determines the monitoring priority based on the acquired information. For example, the monitoring unit can lower the monitoring priority when the user is at home. The monitoring unit can also raise the monitoring priority when the user is traveling. The monitoring unit can also adjust the monitoring priority according to the user's location when the user is in a specific location. This allows for more appropriate dream content to be generated by taking the user's geographical location information into account. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input the user's geographical location information to the generation AI and have the generation AI determine the monitoring priority.

[0115] When monitoring brain waves, the monitoring unit can analyze the user's social media activities and acquire related monitoring data. When monitoring brain waves, the monitoring unit can analyze the user's social media activities and acquire related monitoring data. For example, the monitoring unit can analyze social media posts and acquire monitoring data based on the content. For example, the monitoring unit can acquire related monitoring data based on content shared by the user on social media. The monitoring unit can also analyze the user's social media activity history and acquire related monitoring data. The monitoring unit can also acquire related monitoring data by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activities, more appropriate dream content can be generated. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input social media data into a generation AI and cause the generation AI to acquire related monitoring data.

[0116] The adjustment unit can estimate the user's emotions and determine a method for adjusting the dream content based on the estimated user's emotions. The adjustment unit can estimate the user's emotions and determine a method for adjusting the dream content based on the estimated user's emotions. The adjustment unit estimates the user's emotions using, for example, facial expression recognition technology or voice analysis technology. For example, if the user is relaxed, the adjustment unit can adjust the dream content to be calm. If the user is excited, the adjustment unit can also adjust the dream content to be stimulating. If the user is feeling anxious, the adjustment unit can also adjust the dream content to be reassuring. In this way, by determining a method for adjusting the dream content according to the user's emotions, more appropriate dream content can be generated. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's emotional data into the generation AI and have the generation AI determine a method for adjusting the dream content.

[0117] When adjusting the content of a dream, the adjustment unit can improve the accuracy of the adjustment by referring to data on the user's past dreams. When adjusting the content of a dream, the adjustment unit can improve the accuracy of the adjustment by referring to data on the user's past dreams. The adjustment unit, for example, uses data mining technology to analyze past dream data and optimize the adjustment algorithm based on the data. For example, the adjustment unit adjusts the content of a dream to a similar dream based on data on the user's past dreams. The adjustment unit can also analyze data on the user's past dreams and optimize the adjustment algorithm. The adjustment unit can also improve the accuracy of the adjustment by referring to dream patterns that the user has preferred in the past. In this way, the accuracy of the adjustment can be improved by referring to data on the user's past dreams. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data on past dreams into the generation AI and cause the generation AI to improve the accuracy of the adjustment.

[0118] When adjusting the content of a dream, the adjustment unit can adjust the level of detail of the adjustment based on the user's current psychological state. When adjusting the content of a dream, the adjustment unit adjusts the level of detail of the adjustment based on the user's current psychological state. The adjustment unit evaluates the user's current psychological state using, for example, a psychological test or behavioral analysis, and adjusts the level of detail of the adjustment based on the evaluation. For example, if the user is relaxed, the adjustment unit adjusts the dream content to be more detailed. Also, if the user is excited, the adjustment unit can adjust the dream content to be more simplified. Also, if the user is feeling anxious, the adjustment unit can adjust the dream content to be more reassuring. In this way, by adjusting the level of detail of the adjustment according to the user's psychological state, more appropriate dream content can be generated. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit may input the user's psychological state data into the generation AI and cause the generation AI to adjust the level of detail of the adjustment.

[0119] The adjustment unit can estimate the user's emotions and determine the frequency of dream content adjustment based on the estimated user's emotions. The adjustment unit can estimate the user's emotions and determine the frequency of dream content adjustment based on the estimated user's emotions. The adjustment unit estimates the user's emotions using, for example, facial expression recognition technology or voice analysis technology. For example, the adjustment unit can lower the adjustment frequency when the user is relaxed. The adjustment unit can also increase the adjustment frequency when the user is excited. The adjustment unit can also adjust the adjustment frequency to provide a sense of security when the user is feeling anxious. In this way, by determining the frequency of dream content adjustment according to the user's emotions, more appropriate dream content can be generated. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's emotion data into the generation AI and have the generation AI determine the adjustment frequency.

[0120] When adjusting the content of a dream, the adjustment unit can determine the priority of the adjustment taking into account the user's geographical location information. When adjusting the content of a dream, the adjustment unit can determine the priority of the adjustment taking into account the user's geographical location information. The adjustment unit, for example, acquires the user's geographical location information using GPS data and determines the priority of the adjustment based on the acquired information. For example, the adjustment unit can lower the priority of the adjustment when the user is at home. The adjustment unit can also raise the priority of the adjustment when the user is traveling. The adjustment unit can also adjust the priority of the adjustment according to the location when the user is in a specific location. This makes it possible to generate more appropriate dream content by taking into account the user's geographical location information. Some or all of the above-described processing in the adjustment unit can be performed using, for example, AI, or without AI. For example, the adjustment unit can input the user's geographical location information to the generation AI and cause the generation AI to determine the priority of the adjustment.

[0121] When adjusting the dream content, the adjustment unit can analyze the user's social media activity and acquire related adjustment data. When adjusting the dream content, the adjustment unit can analyze the user's social media activity and acquire related adjustment data. The adjustment unit, for example, analyzes social media posts and acquires adjustment data based thereon. For example, the adjustment unit acquires related adjustment data based on content shared by the user on social media. The adjustment unit can also analyze the user's social media activity history and acquire related adjustment data. The adjustment unit can also acquire related adjustment data by referring to the activities of the user's friends on social media. In this way, more appropriate dream content can be generated by analyzing the user's social media activity. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input social media data into the generation AI and cause the generation AI to acquire related adjustment data. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, conversion unit, transmission unit, monitoring unit, and adjustment unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized using the reception device 38 of the smart device 14. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the conversion unit is realized by the specific processing unit 290 of the data processing device 12. For example, the transmission unit is realized by the communication I / F 44 of the smart device 14. For example, the monitoring unit is realized by the camera 42 or microphone 38B of the smart device 14. For example, the adjustment unit is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, conversion unit, transmission unit, monitoring unit, and adjustment unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized using the microphone 238 of the smart glasses 214. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the conversion unit is realized by the specific processing unit 290 of the data processing device 12. For example, the transmission unit is realized using the communication I / F 44 of the smart glasses 214. For example, the monitoring unit is realized using the camera 42 and the microphone 238 of the smart glasses 214. For example, the adjustment unit is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, conversion unit, transmission unit, monitoring unit, and adjustment unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized using the microphone 238 of the headset type terminal 314. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the conversion unit is realized by the specific processing unit 290 of the data processing device 12. For example, the transmission unit is realized using the communication I / F 44 of the headset type terminal 314. For example, the monitoring unit is realized using the camera 42 and microphone 238 of the headset type terminal 314. For example, the adjustment unit is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, conversion unit, transmission unit, monitoring unit, and adjustment unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized using the microphone 238 of the robot 414. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the conversion unit is realized by the specific processing unit 290 of the data processing device 12. For example, the transmission unit is realized using the communication I / F 44 of the robot 414. For example, the monitoring unit is realized using the camera 42 and microphone 238 of the robot 414. For example, the adjustment unit is realized by the specific processing unit 290 of the data processing device 12.

[0122] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0123] The reception unit can analyze the user's past dream history and suggest the optimal prompt input method. For example, the reception unit can analyze the user's past dream history using data mining technology and automatically suggest similar prompts based on the content of dreams previously input by the user. The reception unit can also preferentially suggest specific themes or scenarios based on the user's past dream history. Furthermore, the reception unit can analyze the user's preferred dream patterns in the past and customize the optimal input method. In this way, the analysis of the user's past dream history can suggest the optimal prompt input method for the user.

[0124] The monitoring unit can estimate the user's emotions and adjust the brain wave monitoring method based on the estimated user's emotions. For example, the monitoring unit can estimate the user's emotions using facial expression recognition technology or voice analysis technology, and adopt a gentle monitoring method if the user is relaxed. Alternatively, the monitoring unit can adopt a detailed monitoring method if the user is excited. Furthermore, the monitoring unit can adopt a monitoring method that provides a sense of security if the user is feeling anxious. In this way, by adjusting the brain wave monitoring method according to the user's emotions, more appropriate dream content can be generated.

[0125] When adjusting the dream content, the adjustment unit can adjust the level of detail of the adjustment based on the user's current psychological state. For example, the adjustment unit evaluates the user's current psychological state using a psychological test or behavioral analysis and adjusts the level of detail of the adjustment based on the evaluation. If the user is relaxed, the adjustment unit can adjust the dream content to be detailed, and if the user is excited, the adjustment unit can adjust the dream content to be simplified. Also, if the user is feeling anxious, the adjustment unit can adjust the dream content to be reassuring. In this way, by adjusting the level of detail of the adjustment according to the user's psychological state, more appropriate dream content can be generated.

[0126] When generating a dream, the generation unit can apply different generation algorithms depending on the category of the prompt. For example, the generation unit classifies the category of the prompt and applies an appropriate generation algorithm accordingly. A fantasy-specific generation algorithm can be applied to fantasy prompts, and a reality-based generation algorithm can be applied to realistic prompts. Also, a horror-specific generation algorithm can be applied to horror prompts. In this way, by applying a generation algorithm depending on the prompt category, more appropriate dream content can be generated.

[0127] When converting a dream scenario, the conversion unit can apply different conversion algorithms depending on the category of the scenario. For example, the conversion unit classifies the category of the scenario and applies an appropriate conversion algorithm accordingly. A conversion algorithm specialized for fantasy can be applied to fantasy scenarios, and a conversion algorithm based on reality can be applied to realistic scenarios. Also, a conversion algorithm specialized for horror can be applied to horror scenarios. In this way, by applying a conversion algorithm depending on the category of the scenario, a more appropriate electrical signal can be generated.

[0128] The transmitting unit can estimate the user's emotions and adjust the transmission method of the electrical signal based on the estimated user's emotions. For example, the transmitting unit can estimate the user's emotions using facial expression recognition technology or voice analysis technology, and if the user is relaxed, it can adopt a gentle transmission method. If the user is excited, it can also adopt a stronger transmission method. Furthermore, if the user is feeling anxious, it can adopt a transmission method that gives a sense of security. In this way, by adjusting the transmission method of the electrical signal according to the user's emotions, it is possible to transmit more appropriate dream content.

[0129] The reception unit can filter prompts based on the user's current psychological state when inputting them. For example, the reception unit evaluates the user's psychological state using a psychological state evaluation method or filtering algorithm, and filters the prompts based on that evaluation. If the user is feeling anxious, the reception unit can preferentially suggest dream scenarios that alleviate anxiety, and if the user is excited, the reception unit can suggest dream scenarios that help the user relax. Also, if the user is tired, the reception unit can suggest dream scenarios that have a soothing effect. In this way, by filtering prompts according to the user's psychological state, more appropriate dream content can be generated.

[0130] When inputting prompts, the reception unit can prioritize receiving highly relevant prompts by taking into account the user's geographical location information. For example, the reception unit obtains the user's geographical location information using GPS data and filters prompts based on the geographical location information. If the user is traveling, the reception unit can prioritize receiving dream prompts related to the travel destination. Also, if the user is at home, the reception unit can prioritize receiving dream prompts related to the home. Furthermore, if the user is in a specific location, the reception unit can prioritize receiving dream prompts related to that location. In this way, more relevant dream content can be generated by taking into account the user's geographical location information.

[0131] The generation unit can estimate the user's emotions and adjust the way the dream scenario is expressed based on the estimated user's emotions. For example, the generation unit can estimate the user's emotions using facial expression recognition technology or voice analysis technology, and generate a calm scenario if the user is relaxed. Also, the generation unit can generate a stimulating scenario if the user is excited. Furthermore, the generation unit can generate a scenario that gives a sense of security if the user is feeling anxious. In this way, by adjusting the way the dream scenario is expressed according to the user's emotions, more appropriate dream content can be generated.

[0132] The conversion unit can estimate the user's emotions and adjust the intensity of the electrical signal based on the estimated user's emotions. For example, the conversion unit can estimate the user's emotions using facial expression recognition technology or voice analysis technology, and generate an electrical signal with a moderate intensity if the user is relaxed. Alternatively, the conversion unit can generate a stronger electrical signal if the user is excited. Furthermore, the conversion unit can generate an electrical signal with an intensity that gives the user a sense of security if the user is feeling anxious. By adjusting the intensity of the electrical signal according to the user's emotions, more appropriate dream content can be transmitted.

[0133] The processing flow of the second embodiment will be briefly explained below.

[0134] Step 1: The reception unit receives a prompt from the user to input the content of the dream they want to see. The prompt may include a scenario, a theme, characters, etc. The reception unit can receive the prompt by text input, voice input, multiple choice format, etc. Step 2: The generation unit uses the generation AI to analyze the prompts received by the reception unit and generate the content of the dream. The generation AI analyzes the prompts using natural language processing technology and keyword extraction technology, and generates the content of the dream using a scenario generation algorithm and storytelling technology. Step 3: The conversion unit converts the dream content generated by the generation unit into an electrical signal. The conversion unit converts the dream content into an electrical signal using signal processing technology and brain wave conversion technology. Step 4: The transmitter sends the electrical signal converted by the converter to the user's brain. The transmitter transmits the electrical signal to the brain using wireless communication technology and electrode placement technology.

[0135] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0136] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0140] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0156] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] 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.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0172] 7, a 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.

[0173] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0174] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0175] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0176] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0177] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0178] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0179] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0180] 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.

[0181] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0182] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0183] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0184] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0185] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0186] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0187] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0188] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0189] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0190] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0191] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0192] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0193] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0194] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0195] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0196] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0197] 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.

[0198] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0199] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0200] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0201] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0202] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0203] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0204] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0205] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0206] [Explanation of symbols]

[0207] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives a prompt from a user to input the content of the dream that the user wants to see; a generation unit that analyzes the prompt received by the reception unit and generates dream content; A conversion unit that converts the content of the generated dream into an electrical signal; a transmitter that sends the converted electrical signal to the user's brain. A system characterized by:

2. Equipped with a monitoring unit that monitors the user's brain waves while they sleep 2. The system of claim 1.

3. and an adjustment unit that adjusts the content of dreams in real time based on the brain waves monitored by the monitoring unit.

3. The system of claim 2.

4. The generation unit Create a detailed dream scenario based on a prompt 2. The system of claim 1.

5. The conversion unit Converting the generated dream scenario into an electrical signal 2. The system of claim 1.

6. The transmission unit The converted electrical signals are sent to the user's brain.

2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the prompt input method based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyzes the user's past dream history and suggests the optimal prompt input method 2. The system of claim 1.

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