system

Abstract

We provide the system. [Solution] A means of receiving audio data and converting it into text data, A means of analyzing text data and generating visual data, A means of providing psychological interpretations based on text data, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventionally, the process of recording the content of a dream, visualizing it, and interpreting it psychologically requires manual work and specialized knowledge, making it difficult for individuals to use easily. In particular, there is a need for an effective means to automatically convert the content of an audio-input dream into text, generate visual data based on it, and psychologically analyze its content.

Means for Solving the Problems

[0005] According to the present invention, by providing means for receiving audio data and converting it into text data, means for analyzing the converted text data to generate visual data, and means for providing psychological interpretations based on the text data, a system is realized in which users can record the content of their dreams by voice, automate the subsequent process, and easily and effectively gain self-understanding and insights.

[0006] "Audio data" refers to data recorded as digital signals from human speech.

[0007] "Text data" refers to a collection of character information generated by analyzing audio data.

[0008] "Analysis" is the process of extracting useful information and patterns from given text data.

[0009] "Visual data" refers to images and graphic information generated based on text data and analysis results.

[0010] "Psychological interpretation" is the process of analyzing and explaining the psychological state and subconscious of a person who has a dream, based on the content and expression of the dream.

[0011] A "system" is a technical configuration that combines multiple devices and software for receiving, transcribing, analyzing, generating visual data, and performing psychological interpretation of audio data. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4]It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0014] First, the language used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface that includes a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] The system of the present invention is designed to allow users to record the content of their dreams as audio, automatically transcribe and visualize that content, and provide psychological interpretations. In an embodiment of this system, the following operations are performed.

[0034] When a user speaks about their dream into the device, the device records the audio and generates audio data. This audio data is sent to a server via the internet, where it is converted into text data using speech recognition technology. The server analyzes the text data and extracts the main keywords and themes of the dream.

[0035] Based on this analyzed text data, the server generates visual data. This visual data is created using generative AI technology, resulting in images and graphics corresponding to the extracted keywords. Simultaneously, the server performs a psychological interpretation, analyzing the inner state and themes of the person suggested by the dream's content.

[0036] The generated visual and interpretive data are sent to the device, displaying the visualized dream images and their interpretations to the user. This allows the user to visually confirm the content of their dreams and understand their psychological meaning.

[0037] For example, if a user says, "Last night I dreamt I was swimming in a huge ocean," the device records the audio and sends it to the server. The server converts the audio into text, "swimming in a huge ocean," and generates an image of the ocean based on that. It also interprets psychological themes such as "challenge" and "exploration of the unknown," and sends this data back to the device, allowing the user to receive both a visualized image and an interpretation. In this way, the system helps to grasp and understand the user's dreams from multiple perspectives.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The user speaks about the contents of their dream into the device. The device uses its built-in microphone to record this audio and digitizes it as audio data.

[0041] Step 2:

[0042] The terminal sends the generated audio data to the server via the internet. During this process, the audio data is compressed to ensure efficient transmission.

[0043] Step 3:

[0044] The server decompresses the received audio data and converts it into text data using a speech recognition algorithm. It also converts the audio waveform into text using a language model.

[0045] Step 4:

[0046] The server analyzes the generated text data and extracts keywords and important elements of the dream. Natural language processing techniques are used to grasp the meaning and emotional tone of the text.

[0047] Step 5:

[0048] The server generates visual data based on the analysis results. Based on the extracted keywords, a generative AI model is used to create related images and graphics.

[0049] Step 6:

[0050] The server uses the text and analysis results to perform a psychological interpretation of the dream's content. This includes explanations that reveal the dream's theme and underlying psychological state.

[0051] Step 7:

[0052] The server sends the generated visual data and psychological interpretation data to the terminal. The data is optimized for easy reception by the user.

[0053] Step 8:

[0054] The device displays the received data to the user. Along with the generated image, a psychological interpretation of the dream is presented on the screen.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] The challenge lies in accurately and efficiently recording, visualizing, and psychologically analyzing the content of dreams experienced by users, enabling them to understand the themes suggested by their own dreams. There is a need to automate this process, reducing user effort while simultaneously providing a deep, inner understanding.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes means for receiving audio information and converting it into text information, means for analyzing the text information and generating visual information, means for providing psychological analysis based on the text information, means for extracting concepts and creating visual information using an image generation model, and means for transmitting and displaying the generated visual and analytical information on a terminal. This enables the user to visualize the content of their dreams from multiple perspectives and to understand them more deeply.

[0060] "Audio information" refers to data that records the content of what a user has said in digital format.

[0061] "Textual information" refers to digital data that converts audio information into text using speech recognition technology.

[0062] "Visual information" refers to image data generated based on textual information and analysis results.

[0063] "Psychological analysis" refers to the analysis used to interpret a person's inner state and psychological themes derived from the content of their dreams.

[0064] A "concept" refers to important keywords or themes extracted from textual information.

[0065] An "image generation model" refers to an artificial intelligence model that generates relevant visual information based on a concept.

[0066] A "terminal" refers to a computer device used by users to input voice information and display generated visual and analytical information.

[0067] The embodiments for carrying out the present invention are shown below.

[0068] This system records the content of a user's dreams as audio information, converts that content into text information, and then generates visual information to provide psychological analysis. This system is realized through the cooperation of a terminal and a server.

[0069] The user speaks about their dream into the device. The device uses its built-in microphone to record the audio and saves it as audio data. The audio data is then transmitted to a server via the network.

[0070] The server converts speech information into text using speech recognition software (e.g., a common speech recognition API). Next, the server analyzes this text and extracts key concepts using natural language processing techniques. Based on the extracted concepts, it generates visual information using a generative AI model (e.g., a common image generation algorithm). An example of a prompt would be, "Generate illustrations related to the content of your dream."

[0071] Furthermore, the server applies psychological analysis algorithms to interpret the underlying psychological themes and inner states contained within the dream content. The results of this analysis and the generated visual information are then transmitted from the server to the terminal.

[0072] The device displays the received visual information and psychological analysis results to the user. This allows the user to gain a deeper understanding of the visualized images of their dreams and their psychological meanings.

[0073] For example, if a user says, "Last night I dreamt I was swimming in a huge ocean," the device records this and sends it to the server. The server converts the audio into text, "swimming in a huge ocean," and uses a generative AI model to generate related visual information. It also interprets psychological themes such as "challenge" and "exploration of the unknown" and provides this information to the user.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The user speaks the contents of their dream into the device. The device uses its built-in microphone to capture the audio in real time and saves it as digital audio data in a file. The input is what the user says, and the output is digital audio data. This saved audio data becomes the input for the next step.

[0077] Step 2:

[0078] The terminal transmits audio information to the server via the internet. The terminal uses a network module to establish a secure connection and upload the audio information to the server. The input is digital audio information, and the output is audio information that is accessible on the server.

[0079] Step 3:

[0080] The server inputs the received audio information into speech recognition software, which then converts it into text information. For example, a speech recognition API is used to convert audio data into text data. The input is digital audio information, and the output is the text data it has been converted into. This text information then becomes the input for the next step.

[0081] Step 4:

[0082] The server analyzes textual information and extracts important concepts using natural language processing techniques. This process extracts nouns and verbs from the text and detects highly relevant keywords. The input is textual information, and the output is a list of extracted concepts.

[0083] Step 5:

[0084] The server uses a generative AI model to generate visual information based on extracted concepts. The model is prompted with prompts such as "the open sea," which then execute the relevant visual data. The input is the extracted concepts, and the output is the visual information based on them.

[0085] Step 6:

[0086] The server performs psychological analysis on textual information and interprets the psychological themes contained within. This uses an algorithm that reveals the psychological tendencies and relationships indicated by specific keywords. The input is textual information and extracted concepts, and the output is the result of the psychological analysis.

[0087] Step 7:

[0088] The server sends the generated visual information and psychological analysis results to the terminal. The terminal can then display this information to the user. The input is the visual information and psychological analysis results, and the output is the data displayed on the terminal.

[0089] Step 8:

[0090] The user views visual information and psychological analysis results on their device. This visualized data allows the user to understand the content of their dreams from multiple perspectives. The input is the data displayed on the device, and the output is the user's perception and understanding.

[0091] (Application Example 1)

[0092] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0093] Conventional dream analysis systems merely record dreams, making it difficult to visually enjoy or psychologically understand them. This invention aims to solve the problem of providing personal inner growth and entertainment by enabling users to deeply understand the content of their dreams both visually and psychologically.

[0094] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0095] In this invention, the server includes means for receiving audio data and converting it into text data, means for analyzing the text data and generating visual data, means for providing psychological interpretations based on the text data, means for generating stories and videos based on the generated visual data, and means for presenting content with added psychological interpretations to the user. This makes it possible for the user to enjoy dreams visually while deeply understanding their meaning.

[0096] "Audio data" refers to digitized audio information used to record the content of dreams spoken by the user.

[0097] "Text data" refers to information in written form obtained by converting audio data using speech recognition technology.

[0098] "Visual data" refers to information in the form of images and graphics created using AI technology based on text data.

[0099] "Psychological interpretation" refers to explanations and analyses provided based on the content of a dream, examining the user's inner state and themes.

[0100] "Stories and images" refer to stories and visual content generated based on visual data and psychological interpretations.

[0101] "Content" is a general term for visually and psychologically interpreted information, including stories and images, that is presented to the user.

[0102] A "user" refers to an individual who uses the system to analyze and visualize the content of their dreams.

[0103] To implement this invention, a terminal equipped with a voice input function for the user and a server for processing the data are used. First, the user records voice data using the terminal. The terminal sends this voice data to the server via the internet. The server uses the Google® Cloud Speech-to-Text API to convert the received voice data into text data.

[0104] Subsequently, the server analyzes the converted text data and uses OpenAI's GPT-4™ to extract key keywords and themes. Based on the extracted keywords, visual data is generated using DALL-E. This creates an image that visually represents the content of the dream.

[0105] Furthermore, the server performs psychological interpretations based on the text data, analyzing the psychological themes of the dream and the user's inner state. The generated visual data and psychological interpretations are sent to the terminal and presented to the user.

[0106] For example, if a user describes a dream of swimming in the ocean, the system converts this audio into text and extracts keywords such as "ocean" and "swimming." As a result, images and videos with an ocean theme are generated, along with psychological themes such as "challenge" and "liberation." In this way, users can enjoy their dreams visually and gain a deeper psychological understanding of them.

[0107] Examples of prompts for a generative AI model:

[0108] Dream content: Swimming in the sea

[0109] Target image: The vast ocean, swimming

[0110] Examples of interpretations of psychological themes: Challenge, liberation

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The user inputs the content of their dream using voice via a device. This voice data is recorded on the device; at this point, the input is the user's actual speech, and the output is digitized voice data. The device utilizes its voice input function to save the user's speech as high-quality voice data.

[0114] Step 2:

[0115] The terminal transmits recorded audio data to the server via the information network. The input is audio data, and the output is the transmission of audio data to the server. The terminal uses network communication capabilities to ensure stable data transmission.

[0116] Step 3:

[0117] The server converts the received audio data into text data using the Google Cloud Speech-to-Text API. The input here is the audio data received by the server, and the output is text data. The server performs audio processing by calling the API, and obtains accurate text through speech recognition using a language model.

[0118] Step 4:

[0119] The server analyzes the obtained text data and extracts key keywords using OpenAI's GPT-4. The input to this process is text data, and the output is a list of extracted keywords. The server utilizes language models to analyze the semantic structure of the text and quickly identify important terms.

[0120] Step 5:

[0121] The server uses DALL-E to generate visual data based on extracted keywords. The input is a list of keywords, and the output is the generated visual data. The server runs a generative AI model to create images suitable for the specified theme.

[0122] Step 6:

[0123] The server simultaneously performs a psychological interpretation, analyzing the user's inner state based on the text data. At this stage, the input is text and keyword data, and the output is the text of the psychological interpretation. The server uses psychological algorithms to organize the themes suggested by the dream and articulate the interpretation.

[0124] Step 7:

[0125] The server transmits the generated visual data and psychological interpretations to the terminal. The input is this generated data, and the output is the delivery of the data to the user. The server uses its transmission function to deliver the data to the terminal quickly and securely.

[0126] Step 8:

[0127] The user views visual data and interpretations on the device, visually enjoying and psychologically understanding the content of their dreams. At this stage, the input is data received from the server, and the output is the user's visual experience and interpretation. The device utilizes an interface to display information to the user.

[0128] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0129] The present invention enables a user to record the content of their dreams as audio, transcribe that content into text, visualize it, interpret it psychologically, and further recognize and reflect the user's emotions in these processes. A specific form for implementing this is shown below.

[0130] The user first speaks about the content of their dream into the device. The device records this audio and generates digital audio data. This audio data is sent to a server via the internet. The server uses speech recognition software to convert the audio data into text data. At this time, the server uses an emotion engine to analyze the user's emotional tone from the audio data.

[0131] The server analyzes the generated text data and extracts keywords and key themes from the dream. During this analysis, it uses the results of an emotion engine to incorporate the emotional nuances expressed in the text. Based on the analyzed keywords and emotional information, the server generates visual data. This visual data generation incorporates emotional tone; for example, if the user expresses "anxiety," an image reflecting that emotion will be generated.

[0132] Simultaneously, the server performs a psychological interpretation of the dream content based on text and emotional information. This interpretation is adjusted to match the user's emotional state using emotions extracted by the emotion engine. The generated visual data and psychological interpretation are sent to the terminal, which then displays them to the user.

[0133] For example, if a user describes a dream where they were lost in a dark forest and felt scared, the device sends this to the server. The server then generates text containing the word "fear" and a dark image reflecting that emotion. Furthermore, "anxiety about an unknown challenge" is extracted as a psychological theme and presented to the user. In this way, the system captures the user's emotional experience and helps them to deeply understand the content of their dream.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] The user speaks about the content of their dream into the device. The device uses its built-in microphone to record the audio and generates audio data in digital format.

[0137] Step 2:

[0138] The device sends the generated audio data to the server via the internet. The audio data is compressed before transmission for efficient transfer.

[0139] Step 3:

[0140] The server decompresses the received audio data and converts it into text data using a speech recognition algorithm. Simultaneously, it activates an emotion engine to analyze the user's emotional tone from the audio data.

[0141] Step 4:

[0142] The server analyzes the text data converted from the speech and uses natural language processing to extract keywords and important themes. This process is combined with the results of the emotion engine to grasp the emotional nuances of the text.

[0143] Step 5:

[0144] The server generates visual data based on extracted keywords and emotional information. Using a generation AI, images reflecting the emotional tone are created. For example, if the user has a strong feeling of anxiety, a visual representing that anxiety will be generated.

[0145] Step 6:

[0146] The server performs a psychological interpretation of the dream's content based on text data and sentiment analysis results. This interpretation includes a psychological analysis that takes into account the user's emotional state.

[0147] Step 7:

[0148] The server sends the generated visual data and psychological interpretation to the terminal. The transmitted data is optimized for display.

[0149] Step 8:

[0150] The device displays the received data to the user. Along with visualized dream images, the screen presents insights into the psychological interpretation and emotions associated with the dream.

[0151] (Example 2)

[0152] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0153] This invention aims to solve the problem of insufficient systems for efficiently recording dream content as audio, transcribing and visualizing that content, and further interpreting it psychologically. Furthermore, there is a need for means to recognize and reflect the user's emotions in these processes.

[0154] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0155] In this invention, the server includes means for receiving audio data and converting it into text, means for analyzing emotional information from the audio data, and means for analyzing the text data and extracting keywords. This makes it possible to accurately understand the content of the user's dreams and provide visual and psychological feedback based on that understanding.

[0156] "Audio data" refers to information recorded in digital format using audio.

[0157] "Text data" refers to information obtained by converting audio data into text format.

[0158] "Emotional information" refers to information about the speaker's emotional tone and nuances, analyzed from audio data.

[0159] "Keywords" refer to important words or phrases extracted by analyzing text data.

[0160] "Visual data" refers to visual representations generated based on extracted keywords and emotional information.

[0161] "Psychological interpretation" refers to the analysis of dream content and the user's mental state, based on text data and emotional information.

[0162] "Communication network" refers to the infrastructure used to send and receive data, particularly the internet and networks.

[0163] "Computing equipment" refers to digital devices and servers that process and analyze data.

[0164] The system of this invention begins with the user recording the contents of their dream as audio on a terminal. The terminal uses a microphone to record this audio and saves it as digital audio data. This audio data is transmitted to a server, which is a computing device, via a communication network called the internet.

[0165] The server uses speech recognition software to convert the received audio data into text data. A commonly used speech recognition API is a typical example of this software. Next, the server uses an emotion engine to analyze the user's emotional tone from the text data. During this process, emotional information such as "joy" and "sadness" is extracted.

[0166] The server also analyzes text data to extract key keywords from the dream. Natural language processing techniques are used for this analysis. Subsequently, a generative AI model is used to generate visual data based on the keywords and emotional information. A specific example of such a model is an image generation model using deep learning.

[0167] Furthermore, the server provides a psychological interpretation of the dream based on text data and emotional information. This allows the user to gain a deeper understanding of the dream's content.

[0168] For example, if a user records in audio that they "dreamed of having fun in a field of colorful flowers," the device sends this as digital audio data to the server. The server generates text containing the word "happiness" and colorful visual data that reflects that emotion. Furthermore, the psychological theme of "peace of mind" is indicated. This system aims to give users emotional insights into their dreams.

[0169] Examples of prompts to input into a generative AI model include: "Analyze the content and emotions of your dreams, and generate visual and psychological feedback based on that."

[0170] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0171] Step 1:

[0172] The user inputs their dream by speaking it into the device. The device uses its built-in microphone to record this voice as digital audio data. The recorded audio data accurately captures the specific content of the user's dream. The output is digital data in audio format.

[0173] Step 2:

[0174] The terminal transmits the recorded audio data to the server via the internet. During this process, the audio data is properly encoded, and standard communication protocols are used to prevent data loss during transmission. The output is the digital audio data received by the server.

[0175] Step 3:

[0176] The server converts the received audio data into text data using speech recognition software. The speech recognition software uses an audio signal analysis algorithm to recognize spoken words as text. The input is digital audio data, and the output is text data.

[0177] Step 4:

[0178] The server uses an emotion engine to analyze emotional information from the converted text data. The emotion engine utilizes natural language processing techniques to determine keywords and context within the text and evaluate the emotional tone. The input is text data, and the output is emotional information.

[0179] Step 5:

[0180] The server analyzes text data and extracts important keywords. Text analysis techniques are used to select and highlight meaningful phrases and words. The input is text data, and the output is a list of extracted keywords.

[0181] Step 6:

[0182] The server uses a generative AI model to generate visual data based on keywords and sentiment information. The generative AI model leverages deep learning to construct images that combine keywords and sentiment information. The input is keywords and sentiment information, and the output is visual data.

[0183] Step 7:

[0184] The server performs a psychological interpretation of dreams based on text data and emotional information. This employs psycholinguistic methods and presents possible psychological themes. The input is text data and emotional information, and the output is a psychological interpretation.

[0185] Step 8:

[0186] The server sends the generated visual data and psychological interpretations to the terminal. The terminal receives this data and prepares to display it on the screen for the user. The displayed content consists of individually generated visual feedback and psychological interpretations. The output is the information displayed on the screen that the user views.

[0187] (Application Example 2)

[0188] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0189] Using smart devices to visually recreate the content of dreams experienced by users and providing it as content to deeply understand the emotions of those dreams has been difficult with conventional methods. To solve this problem, there is a need for a system that can transcribe and visualize dreams recorded as voices by users, and provide personalized psychological interpretations. In particular, it is necessary to enable users to directly experience their unique visual experiences through devices such as smart glasses.

[0190] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0191] In this invention, the server includes means for receiving audio data and converting it into text data, means for analyzing the text data and generating visual data, and means for inputting prompt data to provide user-specific visual content. This makes it possible to provide a personalized visual experience based on the content of the user's dreams.

[0192] "Audio data" refers to digital audio information generated when a user talks about the contents of their dreams.

[0193] "Text data" refers to string information obtained by analyzing audio data.

[0194] "Visual data" refers to digital information such as images and videos generated based on text data.

[0195] "Psychological interpretation" refers to the analysis and interpretation of a user's inner state based on text data and its emotional nuances.

[0196] "Generation processing" refers to the digital processing necessary to create user-specific visual content.

[0197] "Prompt data" refers to instructional information used when generating visual content, and it forms the basis for determining visual elements and themes.

[0198] This invention is a system that allows users to describe their dreams, which are then visually reproduced and psychologically analyzed through the collaboration of a smart device and a server. First, the user sends audio data to the server using a device such as smart glasses. The server converts the audio data into text data using a speech recognition solution (e.g., a cloud-based speech analysis service).

[0199] Next, the server uses a natural language processing library (e.g., spaCy) to extract keywords from the text data, and then uses a sentiment analysis tool (e.g., IBM Watson® Tone Analyzer) to analyze the emotional state. This generates data that can be associated with psychological patterns based on the content of the dreams.

[0200] Using a generative AI model (e.g., Stable Diffusion), visual data is generated based on analyzed text data and emotional information. The generated visual data is customized using user-specific prompt data and sent to the device. The device projects this visual data onto the user's display, allowing them to experience the dream content in real time.

[0201] For example, if a user says they "dreamed of flying," a video of them flying through a vast sky will be generated based on that description. An example of a prompt used for this purpose might be, "Generate a visual image of what it was like to dream of flying. Make the colors bright and vivid to express a sense of exhilaration." Through this visual data, the user can gain a unique visual experience based on their dream.

[0202] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0203] Step 1:

[0204] The user describes the content of their dream through smart glasses. The audio is collected by the device's microphone and stored as digital audio data. This audio data is the input, and the device outputs it by sending it to a server via the internet.

[0205] Step 2:

[0206] The server converts the received digital audio data into text data using speech recognition software. Specifically, it uses a cloud-based speech analysis service to convert speech into text. The input is audio data, and the output is text data.

[0207] Step 3:

[0208] The server performs natural language processing on text data to extract key keywords. It uses a natural language processing library to analyze the meaning and structure of the text, identifying important words and phrases. The input is text data, and the output is a keyword list.

[0209] Step 4:

[0210] The server uses a sentiment analysis tool to analyze the user's emotional state based on the extracted keywords. Specifically, it determines the emotional tone contained in the text and identifies the associated emotional patterns. The input is a list of keywords, and the output is emotional state data.

[0211] Step 5:

[0212] The server generates visual data based on keywords and emotional states using a generative AI model. This process involves supplying generated prompt sentences and creating visual content using computer vision technology. The input is keyword and emotional state data, and the output is visual data.

[0213] Step 6:

[0214] The server sends the generated visual data to the user's terminal, which then displays it on its screen so that the user can see it. The input is the visual data, and the output is the visual content presented to the user.

[0215] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0216] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0217] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0218] [Second Embodiment]

[0219] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0220] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0221] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0222] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0223] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0224] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0225] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0226] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0227] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0228] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0229] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0230] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0231] The system of the present invention is designed to allow users to record the content of their dreams as audio, automatically transcribe and visualize that content, and provide psychological interpretations. In an embodiment of this system, the following operations are performed.

[0232] When a user speaks about their dream into the device, the device records the audio and generates audio data. This audio data is sent to a server via the internet, where it is converted into text data using speech recognition technology. The server analyzes the text data and extracts the main keywords and themes of the dream.

[0233] Based on this analyzed text data, the server generates visual data. This visual data is created using generative AI technology, resulting in images and graphics corresponding to the extracted keywords. Simultaneously, the server performs a psychological interpretation, analyzing the inner state and themes of the person suggested by the dream's content.

[0234] The generated visual and interpretive data are sent to the device, displaying the visualized dream images and their interpretations to the user. This allows the user to visually confirm the content of their dreams and understand their psychological meaning.

[0235] For example, if a user says, "Last night I dreamt I was swimming in a huge ocean," the device records the audio and sends it to the server. The server converts the audio into text, "swimming in a huge ocean," and generates an image of the ocean based on that. It also interprets psychological themes such as "challenge" and "exploration of the unknown," and sends this data back to the device, allowing the user to receive both a visualized image and an interpretation. In this way, the system helps to grasp and understand the user's dreams from multiple perspectives.

[0236] The following describes the processing flow.

[0237] Step 1:

[0238] The user speaks about the contents of their dream into the device. The device uses its built-in microphone to record this audio and digitizes it as audio data.

[0239] Step 2:

[0240] The terminal sends the generated audio data to the server via the internet. During this process, the audio data is compressed to ensure efficient transmission.

[0241] Step 3:

[0242] The server decompresses the received audio data and converts it into text data using a speech recognition algorithm. It also converts the audio waveform into text using a language model.

[0243] Step 4:

[0244] The server analyzes the generated text data and extracts keywords and important elements of the dream. Natural language processing techniques are used to grasp the meaning and emotional tone of the text.

[0245] Step 5:

[0246] The server generates visual data based on the analysis results. Based on the extracted keywords, a generative AI model is used to create related images and graphics.

[0247] Step 6:

[0248] The server uses the text and analysis results to perform a psychological interpretation of the dream's content. This includes explanations that reveal the dream's theme and underlying psychological state.

[0249] Step 7:

[0250] The server sends the generated visual data and psychological interpretation data to the terminal. The data is optimized for easy reception by the user.

[0251] Step 8:

[0252] The device displays the received data to the user. Along with the generated image, a psychological interpretation of the dream is presented on the screen.

[0253] (Example 1)

[0254] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0255] The challenge lies in accurately and efficiently recording, visualizing, and psychologically analyzing the content of dreams experienced by users, enabling them to understand the themes suggested by their own dreams. There is a need to automate this process, reducing user effort while simultaneously providing a deep, inner understanding.

[0256] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0257] In this invention, the server includes means for receiving audio information and converting it into text information, means for analyzing the text information and generating visual information, means for providing psychological analysis based on the text information, means for extracting concepts and creating visual information using an image generation model, and means for transmitting and displaying the generated visual and analytical information on a terminal. This enables the user to visualize the content of their dreams from multiple perspectives and to understand them more deeply.

[0258] "Audio information" refers to data that records the content of what a user has said in digital format.

[0259] "Textual information" refers to digital data that converts audio information into text using speech recognition technology.

[0260] "Visual information" refers to image data generated based on textual information and analysis results.

[0261] "Psychological analysis" refers to the analysis used to interpret a person's inner state and psychological themes derived from the content of their dreams.

[0262] A "concept" refers to important keywords or themes extracted from textual information.

[0263] An "image generation model" refers to an artificial intelligence model that generates relevant visual information based on a concept.

[0264] A "terminal" refers to a computer device used by users to input voice information and display generated visual and analytical information.

[0265] The embodiments for carrying out the present invention are shown below.

[0266] This system records the content of a user's dreams as audio information, converts that content into text information, and then generates visual information to provide psychological analysis. This system is realized through the cooperation of a terminal and a server.

[0267] The user speaks about their dream into the device. The device uses its built-in microphone to record the audio and saves it as audio data. The audio data is then transmitted to a server via the network.

[0268] The server converts speech information into text using speech recognition software (e.g., a common speech recognition API). Next, the server analyzes this text and extracts key concepts using natural language processing techniques. Based on the extracted concepts, it generates visual information using a generative AI model (e.g., a common image generation algorithm). An example of a prompt would be, "Generate illustrations related to the content of your dream."

[0269] Furthermore, the server applies psychological analysis algorithms to interpret the underlying psychological themes and inner states contained within the dream content. The results of this analysis and the generated visual information are then transmitted from the server to the terminal.

[0270] The device displays the received visual information and psychological analysis results to the user. This allows the user to gain a deeper understanding of the visualized images of their dreams and their psychological meanings.

[0271] For example, if a user says, "Last night I dreamt I was swimming in a huge ocean," the device records this and sends it to the server. The server converts the audio into text, "swimming in a huge ocean," and uses a generative AI model to generate related visual information. It also interprets psychological themes such as "challenge" and "exploration of the unknown" and provides this information to the user.

[0272] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0273] Step 1:

[0274] The user speaks the contents of their dream into the device. The device uses its built-in microphone to capture the audio in real time and saves it as digital audio data in a file. The input is what the user says, and the output is digital audio data. This saved audio data becomes the input for the next step.

[0275] Step 2:

[0276] The terminal transmits audio information to the server via the internet. The terminal uses a network module to establish a secure connection and upload the audio information to the server. The input is digital audio information, and the output is audio information that is accessible on the server.

[0277] Step 3:

[0278] The server inputs the received audio information into speech recognition software, which then converts it into text information. For example, a speech recognition API is used to convert audio data into text data. The input is digital audio information, and the output is the text data it has been converted into. This text information then becomes the input for the next step.

[0279] Step 4:

[0280] The server analyzes the character information and extracts important concepts using natural language processing technology. In this process, nouns and verbs are extracted from the text, and highly relevant keywords are detected. The input is character information, and the output is the list of extracted concepts.

[0281] Step 5:

[0282] The server uses a generative AI model to generate visual information based on the extracted concepts. By using a prompt sentence such as "vast ocean" for the model, relevant visual data is executed. The input is the extracted concepts, and the output is the visual information based on them.

[0283] Step 6:

[0284] The server performs psychological analysis on the character information and interprets the psychological themes contained therein. For this, algorithms indicating the psychological tendencies and relevance shown by specific keywords are used. The input is the character information and the extracted concepts, and the output is the result of the psychological analysis.

[0285] Step 7:

[0286] The server sends the generated visual information and the result of the psychological analysis to the terminal. As a result, the terminal can display this information to the user. The input is the visual information and the result of the psychological analysis, and the output is the data displayed on the terminal.

[0287] Step 8:

[0288] The user checks the visual information and the result of the psychological analysis on the terminal. Based on this visualized data, the user can understand the content of the dream from multiple perspectives. The input is the data displayed on the terminal, and the output is the user's recognition and understanding.

[0289] (Application Example 1)

[0290] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0291] Conventional dream analysis systems merely record dreams, making it difficult to visually enjoy or psychologically understand them. This invention aims to solve the problem of providing personal inner growth and entertainment by enabling users to deeply understand the content of their dreams both visually and psychologically.

[0292] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0293] In this invention, the server includes means for receiving audio data and converting it into text data, means for analyzing the text data and generating visual data, means for providing psychological interpretations based on the text data, means for generating stories and videos based on the generated visual data, and means for presenting content with added psychological interpretations to the user. This makes it possible for the user to enjoy dreams visually while deeply understanding their meaning.

[0294] "Audio data" refers to digitized audio information used to record the content of dreams spoken by the user.

[0295] "Text data" refers to information in written form obtained by converting audio data using speech recognition technology.

[0296] "Visual data" refers to information in the form of images and graphics created using AI technology based on text data.

[0297] "Psychological interpretation" refers to explanations and analyses provided based on the content of a dream, examining the user's inner state and themes.

[0298] "Stories and images" refer to stories and visual content generated based on visual data and psychological interpretations.

[0299] "Content" is a general term for visually and psychologically interpreted information, including stories and images, that is presented to the user.

[0300] A "user" refers to an individual who uses the system to analyze and visualize the content of their dreams.

[0301] To implement this invention, a terminal equipped with a voice input function for the user and a server for processing the data are used. First, the user records voice data using the terminal. The terminal sends this voice data to the server via the internet. The server uses the Google Cloud Speech-to-Text API to convert the received voice data into text data.

[0302] The server then analyzes the converted text data, using OpenAI's GPT-4 to extract key keywords and themes. Based on these keywords, visual data is generated using DALL-E. This creates an image that visually represents the content of the dream.

[0303] Furthermore, the server performs psychological interpretations based on the text data, analyzing the psychological themes of the dream and the user's inner state. The generated visual data and psychological interpretations are sent to the terminal and presented to the user.

[0304] For example, if a user describes a dream of swimming in the ocean, the system converts this audio into text and extracts keywords such as "ocean" and "swimming." As a result, images and videos with an ocean theme are generated, along with psychological themes such as "challenge" and "liberation." In this way, users can enjoy their dreams visually and gain a deeper psychological understanding of them.

[0305] Examples of prompts for a generative AI model:

[0306] Dream content: Swimming in the sea

[0307] Target image: Vast ocean, swimming posture

[0308] Examples of interpretations of psychological themes: Challenge, sense of liberation

[0309] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0310] Step 1:

[0311] The user uses the terminal to input the content of the dream in voice. This voice data is recorded on the terminal, and at this point, the input is the user's speech itself and the output is digitized voice data. The terminal utilizes the voice input function and saves the user's speech as high-quality voice data.

[0312] Step 2:

[0313] The terminal transmits the recorded voice data to the server via the information network. The input at this time is voice data, and the output is the transmission of the voice data to the server. The terminal performs stable data transmission using the network communication function.

[0314] Step 3:

[0315] The server converts the received voice data into text data using the Google Cloud Speech-to-Text API. The input here is the voice data that reaches the server, and the output is text data. The server performs voice processing by calling the API and obtains accurate text through voice recognition processing using a language model.

[0316] Step 4:

[0317] The server analyzes the obtained text data and extracts key keywords using OpenAI's GPT-4. The input to this process is text data, and the output is a list of extracted keywords. The server utilizes language models to analyze the semantic structure of the text and quickly identify important terms.

[0318] Step 5:

[0319] The server uses DALL-E to generate visual data based on extracted keywords. The input is a list of keywords, and the output is the generated visual data. The server runs a generative AI model to create images suitable for the specified theme.

[0320] Step 6:

[0321] The server simultaneously performs a psychological interpretation, analyzing the user's inner state based on the text data. At this stage, the input is text and keyword data, and the output is the text of the psychological interpretation. The server uses psychological algorithms to organize the themes suggested by the dream and articulate the interpretation.

[0322] Step 7:

[0323] The server transmits the generated visual data and psychological interpretations to the terminal. The input is this generated data, and the output is the delivery of the data to the user. The server uses its transmission function to deliver the data to the terminal quickly and securely.

[0324] Step 8:

[0325] The user views visual data and interpretations on the device, visually enjoying and psychologically understanding the content of their dreams. At this stage, the input is data received from the server, and the output is the user's visual experience and interpretation. The device utilizes an interface to display information to the user.

[0326] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0327] The present invention enables a user to record the content of their dreams as audio, transcribe that content into text, visualize it, interpret it psychologically, and further recognize and reflect the user's emotions in these processes. A specific form for implementing this is shown below.

[0328] The user first speaks about the content of their dream into the device. The device records this audio and generates digital audio data. This audio data is sent to a server via the internet. The server uses speech recognition software to convert the audio data into text data. At this time, the server uses an emotion engine to analyze the user's emotional tone from the audio data.

[0329] The server analyzes the generated text data and extracts keywords and key themes from the dream. During this analysis, it uses the results of an emotion engine to incorporate the emotional nuances expressed in the text. Based on the analyzed keywords and emotional information, the server generates visual data. This visual data generation incorporates emotional tone; for example, if the user expresses "anxiety," an image reflecting that emotion will be generated.

[0330] Simultaneously, the server performs a psychological interpretation of the dream content based on text and emotional information. This interpretation is adjusted to match the user's emotional state using emotions extracted by the emotion engine. The generated visual data and psychological interpretation are sent to the terminal, which then displays them to the user.

[0331] For example, if a user describes a dream where they were lost in a dark forest and felt scared, the device sends this to the server. The server then generates text containing the word "fear" and a dark image reflecting that emotion. Furthermore, "anxiety about an unknown challenge" is extracted as a psychological theme and presented to the user. In this way, the system captures the user's emotional experience and helps them to deeply understand the content of their dream.

[0332] The following describes the processing flow.

[0333] Step 1:

[0334] The user speaks about the content of their dream into the device. The device uses its built-in microphone to record the audio and generates audio data in digital format.

[0335] Step 2:

[0336] The device sends the generated audio data to the server via the internet. The audio data is compressed before transmission for efficient transfer.

[0337] Step 3:

[0338] The server decompresses the received audio data and converts it into text data using a speech recognition algorithm. Simultaneously, it activates an emotion engine to analyze the user's emotional tone from the audio data.

[0339] Step 4:

[0340] The server analyzes the text data converted from the speech and uses natural language processing to extract keywords and important themes. This process is combined with the results of the emotion engine to grasp the emotional nuances of the text.

[0341] Step 5:

[0342] The server generates visual data based on extracted keywords and emotional information. Using a generation AI, images reflecting the emotional tone are created. For example, if the user has a strong feeling of anxiety, a visual representing that anxiety will be generated.

[0343] Step 6:

[0344] The server performs a psychological interpretation of the dream's content based on text data and sentiment analysis results. This interpretation includes a psychological analysis that takes into account the user's emotional state.

[0345] Step 7:

[0346] The server sends the generated visual data and psychological interpretation to the terminal. The transmitted data is optimized for display.

[0347] Step 8:

[0348] The device displays the received data to the user. Along with visualized dream images, the screen presents insights into the psychological interpretation and emotions associated with the dream.

[0349] (Example 2)

[0350] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0351] This invention aims to solve the problem of insufficient systems for efficiently recording dream content as audio, transcribing and visualizing that content, and further interpreting it psychologically. Furthermore, there is a need for means to recognize and reflect the user's emotions in these processes.

[0352] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0353] In this invention, the server includes means for receiving audio data and converting it into text, means for analyzing emotional information from the audio data, and means for analyzing the text data and extracting keywords. This makes it possible to accurately understand the content of the user's dreams and provide visual and psychological feedback based on that understanding.

[0354] "Audio data" refers to information recorded in digital format using audio.

[0355] "Text data" refers to information obtained by converting audio data into text format.

[0356] "Emotional information" refers to information about the speaker's emotional tone and nuances, analyzed from audio data.

[0357] "Keywords" refer to important words or phrases extracted by analyzing text data.

[0358] "Visual data" refers to visual representations generated based on extracted keywords and emotional information.

[0359] "Psychological interpretation" refers to the analysis of dream content and the user's mental state, based on text data and emotional information.

[0360] "Communication network" refers to the infrastructure used to send and receive data, particularly the internet and networks.

[0361] "Computing equipment" refers to digital devices and servers that process and analyze data.

[0362] The system of this invention begins with the user recording the contents of their dream as audio on a terminal. The terminal uses a microphone to record this audio and saves it as digital audio data. This audio data is transmitted to a server, which is a computing device, via a communication network called the internet.

[0363] The server uses speech recognition software to convert the received audio data into text data. A commonly used speech recognition API is a typical example of this software. Next, the server uses an emotion engine to analyze the user's emotional tone from the text data. During this process, emotional information such as "joy" and "sadness" is extracted.

[0364] The server also analyzes text data to extract key keywords from the dream. Natural language processing techniques are used for this analysis. Subsequently, a generative AI model is used to generate visual data based on the keywords and emotional information. A specific example of such a model is an image generation model using deep learning.

[0365] Furthermore, the server provides a psychological interpretation of the dream based on text data and emotional information. This allows the user to gain a deeper understanding of the dream's content.

[0366] For example, if a user records in audio that they "dreamed of having fun in a field of colorful flowers," the device sends this as digital audio data to the server. The server generates text containing the word "happiness" and colorful visual data that reflects that emotion. Furthermore, the psychological theme of "peace of mind" is indicated. This system aims to give users emotional insights into their dreams.

[0367] Examples of prompts to input into a generative AI model include: "Analyze the content and emotions of your dreams, and generate visual and psychological feedback based on that."

[0368] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0369] Step 1:

[0370] The user inputs their dream by speaking it into the device. The device uses its built-in microphone to record this voice as digital audio data. The recorded audio data accurately captures the specific content of the user's dream. The output is digital data in audio format.

[0371] Step 2:

[0372] The terminal transmits the recorded audio data to the server via the internet. During this process, the audio data is properly encoded, and standard communication protocols are used to prevent data loss during transmission. The output is the digital audio data received by the server.

[0373] Step 3:

[0374] The server converts the received audio data into text data using speech recognition software. The speech recognition software uses an audio signal analysis algorithm to recognize spoken words as text. The input is digital audio data, and the output is text data.

[0375] Step 4:

[0376] The server uses an emotion engine to analyze emotional information from the converted text data. The emotion engine utilizes natural language processing techniques to determine keywords and context within the text and evaluate the emotional tone. The input is text data, and the output is emotional information.

[0377] Step 5:

[0378] The server analyzes text data and extracts important keywords. Text analysis techniques are used to select and highlight meaningful phrases and words. The input is text data, and the output is a list of extracted keywords.

[0379] Step 6:

[0380] The server uses a generative AI model to generate visual data based on keywords and sentiment information. The generative AI model leverages deep learning to construct images that combine keywords and sentiment information. The input is keywords and sentiment information, and the output is visual data.

[0381] Step 7:

[0382] The server performs a psychological interpretation of dreams based on text data and emotional information. This employs psycholinguistic methods and presents possible psychological themes. The input is text data and emotional information, and the output is a psychological interpretation.

[0383] Step 8:

[0384] The server sends the generated visual data and psychological interpretations to the terminal. The terminal receives this data and prepares to display it on the screen for the user. The displayed content consists of individually generated visual feedback and psychological interpretations. The output is the information displayed on the screen that the user views.

[0385] (Application Example 2)

[0386] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0387] Using smart devices to visually recreate the content of dreams experienced by users and providing it as content to deeply understand the emotions of those dreams has been difficult with conventional methods. To solve this problem, there is a need for a system that can transcribe and visualize dreams recorded as voices by users, and provide personalized psychological interpretations. In particular, it is necessary to enable users to directly experience their unique visual experiences through devices such as smart glasses.

[0388] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0389] In this invention, the server includes means for receiving audio data and converting it into text data, means for analyzing the text data and generating visual data, and means for inputting prompt data to provide user-specific visual content. This makes it possible to provide a personalized visual experience based on the content of the user's dreams.

[0390] "Audio data" refers to digital audio information generated when a user talks about the contents of their dreams.

[0391] "Text data" refers to string information obtained by analyzing audio data.

[0392] "Visual data" refers to digital information such as images and videos generated based on text data.

[0393] "Psychological interpretation" refers to the analysis and interpretation of a user's inner state based on text data and its emotional nuances.

[0394] "Generation processing" refers to the digital processing necessary to create user-specific visual content.

[0395] "Prompt data" refers to instructional information used when generating visual content, and it forms the basis for determining visual elements and themes.

[0396] This invention is a system that allows users to describe their dreams, which are then visually reproduced and psychologically analyzed through the collaboration of a smart device and a server. First, the user sends audio data to the server using a device such as smart glasses. The server converts the audio data into text data using a speech recognition solution (e.g., a cloud-based speech analysis service).

[0397] Next, the server uses a natural language processing library (e.g., spaCy) to extract keywords from the text data and then uses a sentiment analysis tool (e.g., IBM Watson Tone Analyzer) to analyze the emotional state. This generates data that can be associated with psychological patterns based on the content of the dreams.

[0398] Using a generative AI model (e.g., Stable Diffusion), visual data is generated based on analyzed text data and emotional information. The generated visual data is customized using user-specific prompt data and sent to the device. The device projects this visual data onto the user's display, allowing them to experience the dream content in real time.

[0399] For example, if a user says they "dreamed of flying," a video of them flying through a vast sky will be generated based on that description. An example of a prompt used for this purpose might be, "Generate a visual image of what it was like to dream of flying. Make the colors bright and vivid to express a sense of exhilaration." Through this visual data, the user can gain a unique visual experience based on their dream.

[0400] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0401] Step 1:

[0402] The user describes the content of their dream through smart glasses. The audio is collected by the device's microphone and stored as digital audio data. This audio data is the input, and the device outputs it by sending it to a server via the internet.

[0403] Step 2:

[0404] The server converts the received digital audio data into text data using speech recognition software. Specifically, it uses a cloud-based speech analysis service to convert speech into text. The input is audio data, and the output is text data.

[0405] Step 3:

[0406] The server performs natural language processing on text data to extract key keywords. It uses a natural language processing library to analyze the meaning and structure of the text, identifying important words and phrases. The input is text data, and the output is a keyword list.

[0407] Step 4:

[0408] The server uses a sentiment analysis tool to analyze the user's emotional state based on the extracted keywords. Specifically, it determines the emotional tone contained in the text and identifies the associated emotional patterns. The input is a list of keywords, and the output is emotional state data.

[0409] Step 5:

[0410] The server generates visual data based on keywords and emotional states using a generative AI model. This process involves supplying generated prompt sentences and creating visual content using computer vision technology. The input is keyword and emotional state data, and the output is visual data.

[0411] Step 6:

[0412] The server sends the generated visual data to the user's terminal, which then displays it on its screen so that the user can see it. The input is the visual data, and the output is the visual content presented to the user.

[0413] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0414] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0415] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0416] [Third Embodiment]

[0417] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0418] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0419] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0420] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0421] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0422] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0423] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0424] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0425] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0426] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0427] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0428] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0429] The system of the present invention is designed to allow users to record the content of their dreams as audio, automatically transcribe and visualize that content, and provide psychological interpretations. In an embodiment of this system, the following operations are performed.

[0430] When a user speaks about their dream into the device, the device records the audio and generates audio data. This audio data is sent to a server via the internet, where it is converted into text data using speech recognition technology. The server analyzes the text data and extracts the main keywords and themes of the dream.

[0431] Based on this analyzed text data, the server generates visual data. This visual data is created using generative AI technology, resulting in images and graphics corresponding to the extracted keywords. Simultaneously, the server performs a psychological interpretation, analyzing the inner state and themes of the person suggested by the dream's content.

[0432] The generated visual and interpretive data are sent to the device, displaying the visualized dream images and their interpretations to the user. This allows the user to visually confirm the content of their dreams and understand their psychological meaning.

[0433] For example, if a user says, "Last night I dreamt I was swimming in a huge ocean," the device records the audio and sends it to the server. The server converts the audio into text, "swimming in a huge ocean," and generates an image of the ocean based on that. It also interprets psychological themes such as "challenge" and "exploration of the unknown," and sends this data back to the device, allowing the user to receive both a visualized image and an interpretation. In this way, the system helps to grasp and understand the user's dreams from multiple perspectives.

[0434] The following describes the processing flow.

[0435] Step 1:

[0436] The user speaks about the contents of their dream into the device. The device uses its built-in microphone to record this audio and digitizes it as audio data.

[0437] Step 2:

[0438] The terminal sends the generated audio data to the server via the internet. During this process, the audio data is compressed to ensure efficient transmission.

[0439] Step 3:

[0440] The server decompresses the received audio data and converts it into text data using a speech recognition algorithm. It also converts the audio waveform into text using a language model.

[0441] Step 4:

[0442] The server analyzes the generated text data and extracts keywords and important elements of the dream. Natural language processing techniques are used to grasp the meaning and emotional tone of the text.

[0443] Step 5:

[0444] The server generates visual data based on the analysis results. Based on the extracted keywords, a generative AI model is used to create related images and graphics.

[0445] Step 6:

[0446] The server uses the text and analysis results to perform a psychological interpretation of the dream's content. This includes explanations that reveal the dream's theme and underlying psychological state.

[0447] Step 7:

[0448] The server sends the generated visual data and psychological interpretation data to the terminal. The data is optimized for easy reception by the user.

[0449] Step 8:

[0450] The device displays the received data to the user. Along with the generated image, a psychological interpretation of the dream is presented on the screen.

[0451] (Example 1)

[0452] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0453] The challenge lies in accurately and efficiently recording, visualizing, and psychologically analyzing the content of dreams experienced by users, enabling them to understand the themes suggested by their own dreams. There is a need to automate this process, reducing user effort while simultaneously providing a deep, inner understanding.

[0454] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0455] In this invention, the server includes means for receiving audio information and converting it into text information, means for analyzing the text information and generating visual information, means for providing psychological analysis based on the text information, means for extracting concepts and creating visual information using an image generation model, and means for transmitting and displaying the generated visual and analytical information on a terminal. This enables the user to visualize the content of their dreams from multiple perspectives and to understand them more deeply.

[0456] "Audio information" refers to data that records the content of what a user has said in digital format.

[0457] "Textual information" refers to digital data that converts audio information into text using speech recognition technology.

[0458] "Visual information" refers to image data generated based on textual information and analysis results.

[0459] "Psychological analysis" refers to the analysis used to interpret a person's inner state and psychological themes derived from the content of their dreams.

[0460] A "concept" refers to important keywords or themes extracted from textual information.

[0461] An "image generation model" refers to an artificial intelligence model that generates relevant visual information based on a concept.

[0462] A "terminal" refers to a computer device used by users to input voice information and display generated visual and analytical information.

[0463] The embodiments for carrying out the present invention are shown below.

[0464] This system records the content of a user's dreams as audio information, converts that content into text information, and then generates visual information to provide psychological analysis. This system is realized through the cooperation of a terminal and a server.

[0465] The user speaks about their dream into the device. The device uses its built-in microphone to record the audio and saves it as audio data. The audio data is then transmitted to a server via the network.

[0466] The server converts speech information into text using speech recognition software (e.g., a common speech recognition API). Next, the server analyzes this text and extracts key concepts using natural language processing techniques. Based on the extracted concepts, it generates visual information using a generative AI model (e.g., a common image generation algorithm). An example of a prompt would be, "Generate illustrations related to the content of your dream."

[0467] Furthermore, the server applies psychological analysis algorithms to interpret the underlying psychological themes and inner states contained within the dream content. The results of this analysis and the generated visual information are then transmitted from the server to the terminal.

[0468] The device displays the received visual information and psychological analysis results to the user. This allows the user to gain a deeper understanding of the visualized images of their dreams and their psychological meanings.

[0469] For example, if a user says, "Last night I dreamt I was swimming in a huge ocean," the device records this and sends it to the server. The server converts the audio into text, "swimming in a huge ocean," and uses a generative AI model to generate related visual information. It also interprets psychological themes such as "challenge" and "exploration of the unknown" and provides this information to the user.

[0470] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0471] Step 1:

[0472] The user speaks the contents of their dream into the device. The device uses its built-in microphone to capture the audio in real time and saves it as digital audio data in a file. The input is what the user says, and the output is digital audio data. This saved audio data becomes the input for the next step.

[0473] Step 2:

[0474] The terminal transmits audio information to the server via the internet. The terminal uses a network module to establish a secure connection and upload the audio information to the server. The input is digital audio information, and the output is audio information that is accessible on the server.

[0475] Step 3:

[0476] The server inputs the received audio information into speech recognition software, which then converts it into text information. For example, a speech recognition API is used to convert audio data into text data. The input is digital audio information, and the output is the text data it has been converted into. This text information then becomes the input for the next step.

[0477] Step 4:

[0478] The server analyzes textual information and extracts important concepts using natural language processing techniques. This process extracts nouns and verbs from the text and detects highly relevant keywords. The input is textual information, and the output is a list of extracted concepts.

[0479] Step 5:

[0480] The server uses a generative AI model to generate visual information based on extracted concepts. The model is prompted with prompts such as "the open sea," which then execute the relevant visual data. The input is the extracted concepts, and the output is the visual information based on them.

[0481] Step 6:

[0482] The server performs psychological analysis on textual information and interprets the psychological themes contained within. This uses an algorithm that reveals the psychological tendencies and relationships indicated by specific keywords. The input is textual information and extracted concepts, and the output is the result of the psychological analysis.

[0483] Step 7:

[0484] The server sends the generated visual information and psychological analysis results to the terminal. The terminal can then display this information to the user. The input is the visual information and psychological analysis results, and the output is the data displayed on the terminal.

[0485] Step 8:

[0486] The user views visual information and psychological analysis results on their device. This visualized data allows the user to understand the content of their dreams from multiple perspectives. The input is the data displayed on the device, and the output is the user's perception and understanding.

[0487] (Application Example 1)

[0488] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0489] Conventional dream analysis systems merely record dreams, making it difficult to visually enjoy or psychologically understand them. This invention aims to solve the problem of providing personal inner growth and entertainment by enabling users to deeply understand the content of their dreams both visually and psychologically.

[0490] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0491] In this invention, the server includes means for receiving audio data and converting it into text data, means for analyzing the text data and generating visual data, means for providing psychological interpretations based on the text data, means for generating stories and videos based on the generated visual data, and means for presenting content with added psychological interpretations to the user. This makes it possible for the user to enjoy dreams visually while deeply understanding their meaning.

[0492] "Audio data" refers to digitized audio information used to record the content of dreams spoken by the user.

[0493] "Text data" refers to information in written form obtained by converting audio data using speech recognition technology.

[0494] "Visual data" refers to information in the form of images and graphics created using AI technology based on text data.

[0495] "Psychological interpretation" refers to explanations and analyses provided based on the content of a dream, examining the user's inner state and themes.

[0496] "Stories and images" refer to stories and visual content generated based on visual data and psychological interpretations.

[0497] "Content" is a general term for visually and psychologically interpreted information, including stories and images, that is presented to the user.

[0498] A "user" refers to an individual who uses the system to analyze and visualize the content of their dreams.

[0499] To implement this invention, a terminal equipped with a voice input function for the user and a server for processing the data are used. First, the user records voice data using the terminal. The terminal sends this voice data to the server via the internet. The server uses the Google Cloud Speech-to-Text API to convert the received voice data into text data.

[0500] The server then analyzes the converted text data, using OpenAI's GPT-4 to extract key keywords and themes. Based on these keywords, visual data is generated using DALL-E. This creates an image that visually represents the content of the dream.

[0501] Furthermore, the server performs psychological interpretations based on the text data, analyzing the psychological themes of the dream and the user's inner state. The generated visual data and psychological interpretations are sent to the terminal and presented to the user.

[0502] For example, if a user describes a dream of swimming in the ocean, the system converts this audio into text and extracts keywords such as "ocean" and "swimming." As a result, images and videos with an ocean theme are generated, along with psychological themes such as "challenge" and "liberation." In this way, users can enjoy their dreams visually and gain a deeper psychological understanding of them.

[0503] Examples of prompts for a generative AI model:

[0504] Dream content: Swimming in the sea

[0505] Target image: The vast ocean, swimming

[0506] Examples of interpretations of psychological themes: Challenge, liberation

[0507] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0508] Step 1:

[0509] The user inputs the content of their dream using voice via a device. This voice data is recorded on the device; at this point, the input is the user's actual speech, and the output is digitized voice data. The device utilizes its voice input function to save the user's speech as high-quality voice data.

[0510] Step 2:

[0511] The terminal transmits recorded audio data to the server via the information network. The input is audio data, and the output is the transmission of audio data to the server. The terminal uses network communication capabilities to ensure stable data transmission.

[0512] Step 3:

[0513] The server converts the received audio data into text data using the Google Cloud Speech-to-Text API. The input here is the audio data received by the server, and the output is text data. The server performs audio processing by calling the API, and obtains accurate text through speech recognition using a language model.

[0514] Step 4:

[0515] The server analyzes the obtained text data and extracts key keywords using OpenAI's GPT-4. The input to this process is text data, and the output is a list of extracted keywords. The server utilizes language models to analyze the semantic structure of the text and quickly identify important terms.

[0516] Step 5:

[0517] The server uses DALL-E to generate visual data based on extracted keywords. The input is a list of keywords, and the output is the generated visual data. The server runs a generative AI model to create images suitable for the specified theme.

[0518] Step 6:

[0519] The server simultaneously performs a psychological interpretation, analyzing the user's inner state based on the text data. At this stage, the input is text and keyword data, and the output is the text of the psychological interpretation. The server uses psychological algorithms to organize the themes suggested by the dream and articulate the interpretation.

[0520] Step 7:

[0521] The server transmits the generated visual data and psychological interpretations to the terminal. The input is this generated data, and the output is the delivery of the data to the user. The server uses its transmission function to deliver the data to the terminal quickly and securely.

[0522] Step 8:

[0523] The user views visual data and interpretations on the device, visually enjoying and psychologically understanding the content of their dreams. At this stage, the input is data received from the server, and the output is the user's visual experience and interpretation. The device utilizes an interface to display information to the user.

[0524] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0525] The present invention enables a user to record the content of their dreams as audio, transcribe that content into text, visualize it, interpret it psychologically, and further recognize and reflect the user's emotions in these processes. A specific form for implementing this is shown below.

[0526] The user first speaks about the content of their dream into the device. The device records this audio and generates digital audio data. This audio data is sent to a server via the internet. The server uses speech recognition software to convert the audio data into text data. At this time, the server uses an emotion engine to analyze the user's emotional tone from the audio data.

[0527] The server analyzes the generated text data and extracts keywords and key themes from the dream. During this analysis, it uses the results of an emotion engine to incorporate the emotional nuances expressed in the text. Based on the analyzed keywords and emotional information, the server generates visual data. This visual data generation incorporates emotional tone; for example, if the user expresses "anxiety," an image reflecting that emotion will be generated.

[0528] Simultaneously, the server performs a psychological interpretation of the dream content based on text and emotional information. This interpretation is adjusted to match the user's emotional state using emotions extracted by the emotion engine. The generated visual data and psychological interpretation are sent to the terminal, which then displays them to the user.

[0529] For example, if a user describes a dream where they were lost in a dark forest and felt scared, the device sends this to the server. The server then generates text containing the word "fear" and a dark image reflecting that emotion. Furthermore, "anxiety about an unknown challenge" is extracted as a psychological theme and presented to the user. In this way, the system captures the user's emotional experience and helps them to deeply understand the content of their dream.

[0530] The following describes the processing flow.

[0531] Step 1:

[0532] The user speaks about the content of their dream into the device. The device uses its built-in microphone to record the audio and generates audio data in digital format.

[0533] Step 2:

[0534] The device sends the generated audio data to the server via the internet. The audio data is compressed before transmission for efficient transfer.

[0535] Step 3:

[0536] The server decompresses the received audio data and converts it into text data using a speech recognition algorithm. Simultaneously, it activates an emotion engine to analyze the user's emotional tone from the audio data.

[0537] Step 4:

[0538] The server analyzes the text data converted from the speech and uses natural language processing to extract keywords and important themes. This process is combined with the results of the emotion engine to grasp the emotional nuances of the text.

[0539] Step 5:

[0540] The server generates visual data based on extracted keywords and emotional information. Using a generation AI, images reflecting the emotional tone are created. For example, if the user has a strong feeling of anxiety, a visual representing that anxiety will be generated.

[0541] Step 6:

[0542] The server performs a psychological interpretation of the dream's content based on text data and sentiment analysis results. This interpretation includes a psychological analysis that takes into account the user's emotional state.

[0543] Step 7:

[0544] The server sends the generated visual data and psychological interpretation to the terminal. The transmitted data is optimized for display.

[0545] Step 8:

[0546] The device displays the received data to the user. Along with visualized dream images, the screen presents insights into the psychological interpretation and emotions associated with the dream.

[0547] (Example 2)

[0548] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0549] This invention aims to solve the problem of insufficient systems for efficiently recording dream content as audio, transcribing and visualizing that content, and further interpreting it psychologically. Furthermore, there is a need for means to recognize and reflect the user's emotions in these processes.

[0550] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0551] In this invention, the server includes means for receiving audio data and converting it into text, means for analyzing emotional information from the audio data, and means for analyzing the text data and extracting keywords. This makes it possible to accurately understand the content of the user's dreams and provide visual and psychological feedback based on that understanding.

[0552] "Audio data" refers to information recorded in digital format using audio.

[0553] "Text data" refers to information obtained by converting audio data into text format.

[0554] "Emotional information" refers to information about the speaker's emotional tone and nuances, analyzed from audio data.

[0555] "Keywords" refer to important words or phrases extracted by analyzing text data.

[0556] "Visual data" refers to visual representations generated based on extracted keywords and emotional information.

[0557] "Psychological interpretation" refers to the analysis of dream content and the user's mental state, based on text data and emotional information.

[0558] "Communication network" refers to the infrastructure used to send and receive data, particularly the internet and networks.

[0559] "Computing equipment" refers to digital devices and servers that process and analyze data.

[0560] The system of this invention begins with the user recording the contents of their dream as audio on a terminal. The terminal uses a microphone to record this audio and saves it as digital audio data. This audio data is transmitted to a server, which is a computing device, via a communication network called the internet.

[0561] The server uses speech recognition software to convert the received audio data into text data. A commonly used speech recognition API is a typical example of this software. Next, the server uses an emotion engine to analyze the user's emotional tone from the text data. During this process, emotional information such as "joy" and "sadness" is extracted.

[0562] The server also analyzes text data to extract key keywords from the dream. Natural language processing techniques are used for this analysis. Subsequently, a generative AI model is used to generate visual data based on the keywords and emotional information. A specific example of such a model is an image generation model using deep learning.

[0563] Furthermore, the server provides a psychological interpretation of the dream based on text data and emotional information. This allows the user to gain a deeper understanding of the dream's content.

[0564] For example, if a user records in audio that they "dreamed of having fun in a field of colorful flowers," the device sends this as digital audio data to the server. The server generates text containing the word "happiness" and colorful visual data that reflects that emotion. Furthermore, the psychological theme of "peace of mind" is indicated. This system aims to give users emotional insights into their dreams.

[0565] Examples of prompts to input into a generative AI model include: "Analyze the content and emotions of your dreams, and generate visual and psychological feedback based on that."

[0566] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0567] Step 1:

[0568] The user inputs their dream by speaking it into the device. The device uses its built-in microphone to record this voice as digital audio data. The recorded audio data accurately captures the specific content of the user's dream. The output is digital data in audio format.

[0569] Step 2:

[0570] The terminal transmits the recorded audio data to the server via the internet. During this process, the audio data is properly encoded, and standard communication protocols are used to prevent data loss during transmission. The output is the digital audio data received by the server.

[0571] Step 3:

[0572] The server converts the received audio data into text data using speech recognition software. The speech recognition software uses an audio signal analysis algorithm to recognize spoken words as text. The input is digital audio data, and the output is text data.

[0573] Step 4:

[0574] The server uses an emotion engine to analyze emotional information from the converted text data. The emotion engine utilizes natural language processing techniques to determine keywords and context within the text and evaluate the emotional tone. The input is text data, and the output is emotional information.

[0575] Step 5:

[0576] The server analyzes text data and extracts important keywords. Text analysis techniques are used to select and highlight meaningful phrases and words. The input is text data, and the output is a list of extracted keywords.

[0577] Step 6:

[0578] The server uses a generative AI model to generate visual data based on keywords and sentiment information. The generative AI model leverages deep learning to construct images that combine keywords and sentiment information. The input is keywords and sentiment information, and the output is visual data.

[0579] Step 7:

[0580] The server performs a psychological interpretation of dreams based on text data and emotional information. This employs psycholinguistic methods and presents possible psychological themes. The input is text data and emotional information, and the output is a psychological interpretation.

[0581] Step 8:

[0582] The server sends the generated visual data and psychological interpretations to the terminal. The terminal receives this data and prepares to display it on the screen for the user. The displayed content consists of individually generated visual feedback and psychological interpretations. The output is the information displayed on the screen that the user views.

[0583] (Application Example 2)

[0584] Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0585] Using smart devices to visually recreate the content of dreams experienced by users and providing it as content to deeply understand the emotions of those dreams has been difficult with conventional methods. To solve this problem, there is a need for a system that can transcribe and visualize dreams recorded as voices by users, and provide personalized psychological interpretations. In particular, it is necessary to enable users to directly experience their unique visual experiences through devices such as smart glasses.

[0586] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0587] In this invention, the server includes means for receiving audio data and converting it into text data, means for analyzing the text data and generating visual data, and means for inputting prompt data to provide user-specific visual content. This makes it possible to provide a personalized visual experience based on the content of the user's dreams.

[0588] "Audio data" refers to digital audio information generated when a user talks about the contents of their dreams.

[0589] "Text data" refers to string information obtained by analyzing audio data.

[0590] "Visual data" refers to digital information such as images and videos generated based on text data.

[0591] "Psychological interpretation" refers to the analysis and interpretation of a user's inner state based on text data and its emotional nuances.

[0592] "Generation processing" refers to the digital processing necessary to create user-specific visual content.

[0593] "Prompt data" refers to instructional information used when generating visual content, and it forms the basis for determining visual elements and themes.

[0594] This invention is a system that allows users to describe their dreams, which are then visually reproduced and psychologically analyzed through the collaboration of a smart device and a server. First, the user sends audio data to the server using a device such as smart glasses. The server converts the audio data into text data using a speech recognition solution (e.g., a cloud-based speech analysis service).

[0595] Next, the server uses a natural language processing library (e.g., spaCy) to extract keywords from the text data and then uses a sentiment analysis tool (e.g., IBM Watson Tone Analyzer) to analyze the emotional state. This generates data that can be associated with psychological patterns based on the content of the dreams.

[0596] Using a generative AI model (e.g., Stable Diffusion), visual data is generated based on analyzed text data and emotional information. The generated visual data is customized using user-specific prompt data and sent to the device. The device projects this visual data onto the user's display, allowing them to experience the dream content in real time.

[0597] For example, if a user says they "dreamed of flying," a video of them flying through a vast sky will be generated based on that description. An example of a prompt used for this purpose might be, "Generate a visual image of what it was like to dream of flying. Make the colors bright and vivid to express a sense of exhilaration." Through this visual data, the user can gain a unique visual experience based on their dream.

[0598] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0599] Step 1:

[0600] The user describes the content of their dream through smart glasses. The audio is collected by the device's microphone and stored as digital audio data. This audio data is the input, and the device outputs it by sending it to a server via the internet.

[0601] Step 2:

[0602] The server converts the received digital audio data into text data using speech recognition software. Specifically, it uses a cloud-based speech analysis service to convert speech into text. The input is audio data, and the output is text data.

[0603] Step 3:

[0604] The server performs natural language processing on text data to extract key keywords. It uses a natural language processing library to analyze the meaning and structure of the text, identifying important words and phrases. The input is text data, and the output is a keyword list.

[0605] Step 4:

[0606] The server uses a sentiment analysis tool to analyze the user's emotional state based on the extracted keywords. Specifically, it determines the emotional tone contained in the text and identifies the associated emotional patterns. The input is a list of keywords, and the output is emotional state data.

[0607] Step 5:

[0608] The server generates visual data based on keywords and emotional states using a generative AI model. This process involves supplying generated prompt sentences and creating visual content using computer vision technology. The input is keyword and emotional state data, and the output is visual data.

[0609] Step 6:

[0610] The server sends the generated visual data to the user's terminal, which then displays it on its screen so that the user can see it. The input is the visual data, and the output is the visual content presented to the user.

[0611] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0612] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0613] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0614] [Fourth Embodiment]

[0615] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0616] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0617] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0618] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0619] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0620] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0621] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0622] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0623] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0624] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0625] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0626] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0627] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0628] The system of the present invention is designed to allow users to record the content of their dreams as audio, automatically transcribe and visualize that content, and provide psychological interpretations. In an embodiment of this system, the following operations are performed.

[0629] When a user speaks about their dream into the device, the device records the audio and generates audio data. This audio data is sent to a server via the internet, where it is converted into text data using speech recognition technology. The server analyzes the text data and extracts the main keywords and themes of the dream.

[0630] Based on this analyzed text data, the server generates visual data. This visual data is created using generative AI technology, resulting in images and graphics corresponding to the extracted keywords. Simultaneously, the server performs a psychological interpretation, analyzing the inner state and themes of the person suggested by the dream's content.

[0631] The generated visual and interpretive data are sent to the device, displaying the visualized dream images and their interpretations to the user. This allows the user to visually confirm the content of their dreams and understand their psychological meaning.

[0632] For example, if a user says, "Last night I dreamt I was swimming in a huge ocean," the device records the audio and sends it to the server. The server converts the audio into text, "swimming in a huge ocean," and generates an image of the ocean based on that. It also interprets psychological themes such as "challenge" and "exploration of the unknown," and sends this data back to the device, allowing the user to receive both a visualized image and an interpretation. In this way, the system helps to grasp and understand the user's dreams from multiple perspectives.

[0633] The following describes the processing flow.

[0634] Step 1:

[0635] The user speaks about the contents of their dream into the device. The device uses its built-in microphone to record this audio and digitizes it as audio data.

[0636] Step 2:

[0637] The terminal sends the generated audio data to the server via the internet. During this process, the audio data is compressed to ensure efficient transmission.

[0638] Step 3:

[0639] The server decompresses the received audio data and converts it into text data using a speech recognition algorithm. It also converts the audio waveform into text using a language model.

[0640] Step 4:

[0641] The server analyzes the generated text data and extracts keywords and important elements of the dream. Natural language processing techniques are used to grasp the meaning and emotional tone of the text.

[0642] Step 5:

[0643] The server generates visual data based on the analysis results. Based on the extracted keywords, a generative AI model is used to create related images and graphics.

[0644] Step 6:

[0645] The server uses the text and analysis results to perform a psychological interpretation of the dream's content. This includes explanations that reveal the dream's theme and underlying psychological state.

[0646] Step 7:

[0647] The server sends the generated visual data and psychological interpretation data to the terminal. The data is optimized for easy reception by the user.

[0648] Step 8:

[0649] The device displays the received data to the user. Along with the generated image, a psychological interpretation of the dream is presented on the screen.

[0650] (Example 1)

[0651] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0652] The challenge lies in accurately and efficiently recording, visualizing, and psychologically analyzing the content of dreams experienced by users, enabling them to understand the themes suggested by their own dreams. There is a need to automate this process, reducing user effort while simultaneously providing a deep, inner understanding.

[0653] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0654] In this invention, the server includes means for receiving audio information and converting it into text information, means for analyzing the text information and generating visual information, means for providing psychological analysis based on the text information, means for extracting concepts and creating visual information using an image generation model, and means for transmitting and displaying the generated visual and analytical information on a terminal. This enables the user to visualize the content of their dreams from multiple perspectives and to understand them more deeply.

[0655] "Audio information" refers to data that records the content of what a user has said in digital format.

[0656] "Textual information" refers to digital data that converts audio information into text using speech recognition technology.

[0657] "Visual information" refers to image data generated based on textual information and analysis results.

[0658] "Psychological analysis" refers to the analysis used to interpret a person's inner state and psychological themes derived from the content of their dreams.

[0659] A "concept" refers to important keywords or themes extracted from textual information.

[0660] An "image generation model" refers to an artificial intelligence model that generates relevant visual information based on a concept.

[0661] A "terminal" refers to a computer device used by users to input voice information and display generated visual and analytical information.

[0662] The embodiments for carrying out the present invention are shown below.

[0663] This system records the content of a user's dreams as audio information, converts that content into text information, and then generates visual information to provide psychological analysis. This system is realized through the cooperation of a terminal and a server.

[0664] The user speaks about their dream into the device. The device uses its built-in microphone to record the audio and saves it as audio data. The audio data is then transmitted to a server via the network.

[0665] The server converts speech information into text using speech recognition software (e.g., a common speech recognition API). Next, the server analyzes this text and extracts key concepts using natural language processing techniques. Based on the extracted concepts, it generates visual information using a generative AI model (e.g., a common image generation algorithm). An example of a prompt would be, "Generate illustrations related to the content of your dream."

[0666] Furthermore, the server applies psychological analysis algorithms to interpret the underlying psychological themes and inner states contained within the dream content. The results of this analysis and the generated visual information are then transmitted from the server to the terminal.

[0667] The device displays the received visual information and psychological analysis results to the user. This allows the user to gain a deeper understanding of the visualized images of their dreams and their psychological meanings.

[0668] For example, if a user says, "Last night I dreamt I was swimming in a huge ocean," the device records this and sends it to the server. The server converts the audio into text, "swimming in a huge ocean," and uses a generative AI model to generate related visual information. It also interprets psychological themes such as "challenge" and "exploration of the unknown" and provides this information to the user.

[0669] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0670] Step 1:

[0671] The user speaks the contents of their dream into the device. The device uses its built-in microphone to capture the audio in real time and saves it as digital audio data in a file. The input is what the user says, and the output is digital audio data. This saved audio data becomes the input for the next step.

[0672] Step 2:

[0673] The terminal transmits audio information to the server via the internet. The terminal uses a network module to establish a secure connection and upload the audio information to the server. The input is digital audio information, and the output is audio information that is accessible on the server.

[0674] Step 3:

[0675] The server inputs the received audio information into speech recognition software, which then converts it into text information. For example, a speech recognition API is used to convert audio data into text data. The input is digital audio information, and the output is the text data it has been converted into. This text information then becomes the input for the next step.

[0676] Step 4:

[0677] The server analyzes textual information and extracts important concepts using natural language processing techniques. This process extracts nouns and verbs from the text and detects highly relevant keywords. The input is textual information, and the output is a list of extracted concepts.

[0678] Step 5:

[0679] The server uses a generative AI model to generate visual information based on extracted concepts. The model is prompted with prompts such as "the open sea," which then execute the relevant visual data. The input is the extracted concepts, and the output is the visual information based on them.

[0680] Step 6:

[0681] The server performs psychological analysis on textual information and interprets the psychological themes contained within. This uses an algorithm that reveals the psychological tendencies and relationships indicated by specific keywords. The input is textual information and extracted concepts, and the output is the result of the psychological analysis.

[0682] Step 7:

[0683] The server sends the generated visual information and psychological analysis results to the terminal. The terminal can then display this information to the user. The input is the visual information and psychological analysis results, and the output is the data displayed on the terminal.

[0684] Step 8:

[0685] The user views visual information and psychological analysis results on their device. This visualized data allows the user to understand the content of their dreams from multiple perspectives. The input is the data displayed on the device, and the output is the user's perception and understanding.

[0686] (Application Example 1)

[0687] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0688] Conventional dream analysis systems merely record dreams, making it difficult to visually enjoy or psychologically understand them. This invention aims to solve the problem of providing personal inner growth and entertainment by enabling users to deeply understand the content of their dreams both visually and psychologically.

[0689] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0690] In this invention, the server includes means for receiving audio data and converting it into text data, means for analyzing the text data and generating visual data, means for providing psychological interpretations based on the text data, means for generating stories and videos based on the generated visual data, and means for presenting content with added psychological interpretations to the user. This makes it possible for the user to enjoy dreams visually while deeply understanding their meaning.

[0691] "Audio data" refers to digitized audio information used to record the content of dreams spoken by the user.

[0692] "Text data" refers to information in written form obtained by converting audio data using speech recognition technology.

[0693] "Visual data" refers to information in the form of images and graphics created using AI technology based on text data.

[0694] "Psychological interpretation" refers to explanations and analyses provided based on the content of a dream, examining the user's inner state and themes.

[0695] "Stories and images" refer to stories and visual content generated based on visual data and psychological interpretations.

[0696] "Content" is a general term for visually and psychologically interpreted information, including stories and images, that is presented to the user.

[0697] A "user" refers to an individual who uses the system to analyze and visualize the content of their dreams.

[0698] To implement this invention, a terminal equipped with a voice input function for the user and a server for processing the data are used. First, the user records voice data using the terminal. The terminal sends this voice data to the server via the internet. The server uses the Google Cloud Speech-to-Text API to convert the received voice data into text data.

[0699] The server then analyzes the converted text data, using OpenAI's GPT-4 to extract key keywords and themes. Based on these keywords, visual data is generated using DALL-E. This creates an image that visually represents the content of the dream.

[0700] Furthermore, the server performs psychological interpretations based on the text data, analyzing the psychological themes of the dream and the user's inner state. The generated visual data and psychological interpretations are sent to the terminal and presented to the user.

[0701] For example, if a user describes a dream of swimming in the ocean, the system converts this audio into text and extracts keywords such as "ocean" and "swimming." As a result, images and videos with an ocean theme are generated, along with psychological themes such as "challenge" and "liberation." In this way, users can enjoy their dreams visually and gain a deeper psychological understanding of them.

[0702] Examples of prompts for a generative AI model:

[0703] Dream content: Swimming in the sea

[0704] Target image: The vast ocean, swimming

[0705] Examples of interpretations of psychological themes: Challenge, liberation

[0706] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0707] Step 1:

[0708] The user inputs the content of their dream using voice via a device. This voice data is recorded on the device; at this point, the input is the user's actual speech, and the output is digitized voice data. The device utilizes its voice input function to save the user's speech as high-quality voice data.

[0709] Step 2:

[0710] The terminal transmits recorded audio data to the server via the information network. The input is audio data, and the output is the transmission of audio data to the server. The terminal uses network communication capabilities to ensure stable data transmission.

[0711] Step 3:

[0712] The server converts the received audio data into text data using the Google Cloud Speech-to-Text API. The input here is the audio data received by the server, and the output is text data. The server performs audio processing by calling the API, and obtains accurate text through speech recognition using a language model.

[0713] Step 4:

[0714] The server analyzes the obtained text data and extracts key keywords using OpenAI's GPT-4. The input to this process is text data, and the output is a list of extracted keywords. The server utilizes language models to analyze the semantic structure of the text and quickly identify important terms.

[0715] Step 5:

[0716] The server uses DALL-E to generate visual data based on extracted keywords. The input is a list of keywords, and the output is the generated visual data. The server runs a generative AI model to create images suitable for the specified theme.

[0717] Step 6:

[0718] The server simultaneously performs a psychological interpretation, analyzing the user's inner state based on the text data. At this stage, the input is text and keyword data, and the output is the text of the psychological interpretation. The server uses psychological algorithms to organize the themes suggested by the dream and articulate the interpretation.

[0719] Step 7:

[0720] The server transmits the generated visual data and psychological interpretations to the terminal. The input is this generated data, and the output is the delivery of the data to the user. The server uses its transmission function to deliver the data to the terminal quickly and securely.

[0721] Step 8:

[0722] The user views visual data and interpretations on the device, visually enjoying and psychologically understanding the content of their dreams. At this stage, the input is data received from the server, and the output is the user's visual experience and interpretation. The device utilizes an interface to display information to the user.

[0723] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0724] The present invention enables a user to record the content of their dreams as audio, transcribe that content into text, visualize it, interpret it psychologically, and further recognize and reflect the user's emotions in these processes. A specific form for implementing this is shown below.

[0725] The user first speaks about the content of their dream into the device. The device records this audio and generates digital audio data. This audio data is sent to a server via the internet. The server uses speech recognition software to convert the audio data into text data. At this time, the server uses an emotion engine to analyze the user's emotional tone from the audio data.

[0726] The server analyzes the generated text data and extracts keywords and key themes from the dream. During this analysis, it uses the results of an emotion engine to incorporate the emotional nuances expressed in the text. Based on the analyzed keywords and emotional information, the server generates visual data. This visual data generation incorporates emotional tone; for example, if the user expresses "anxiety," an image reflecting that emotion will be generated.

[0727] Simultaneously, the server performs a psychological interpretation of the dream content based on text and emotional information. This interpretation is adjusted to match the user's emotional state using emotions extracted by the emotion engine. The generated visual data and psychological interpretation are sent to the terminal, which then displays them to the user.

[0728] For example, if a user describes a dream where they were lost in a dark forest and felt scared, the device sends this to the server. The server then generates text containing the word "fear" and a dark image reflecting that emotion. Furthermore, "anxiety about an unknown challenge" is extracted as a psychological theme and presented to the user. In this way, the system captures the user's emotional experience and helps them to deeply understand the content of their dream.

[0729] The following describes the processing flow.

[0730] Step 1:

[0731] The user speaks about the content of their dream into the device. The device uses its built-in microphone to record the audio and generates audio data in digital format.

[0732] Step 2:

[0733] The device sends the generated audio data to the server via the internet. The audio data is compressed before transmission for efficient transfer.

[0734] Step 3:

[0735] The server decompresses the received audio data and converts it into text data using a speech recognition algorithm. Simultaneously, it activates an emotion engine to analyze the user's emotional tone from the audio data.

[0736] Step 4:

[0737] The server analyzes the text data converted from the speech and uses natural language processing to extract keywords and important themes. This process is combined with the results of the emotion engine to grasp the emotional nuances of the text.

[0738] Step 5:

[0739] The server generates visual data based on extracted keywords and emotional information. Using a generation AI, images reflecting the emotional tone are created. For example, if the user has a strong feeling of anxiety, a visual representing that anxiety will be generated.

[0740] Step 6:

[0741] The server performs a psychological interpretation of the dream's content based on text data and sentiment analysis results. This interpretation includes a psychological analysis that takes into account the user's emotional state.

[0742] Step 7:

[0743] The server sends the generated visual data and psychological interpretation to the terminal. The transmitted data is optimized for display.

[0744] Step 8:

[0745] The device displays the received data to the user. Along with visualized dream images, the screen presents insights into the psychological interpretation and emotions associated with the dream.

[0746] (Example 2)

[0747] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0748] This invention aims to solve the problem of insufficient systems for efficiently recording dream content as audio, transcribing and visualizing that content, and further interpreting it psychologically. Furthermore, there is a need for means to recognize and reflect the user's emotions in these processes.

[0749] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0750] In this invention, the server includes means for receiving audio data and converting it into text, means for analyzing emotional information from the audio data, and means for analyzing the text data and extracting keywords. This makes it possible to accurately understand the content of the user's dreams and provide visual and psychological feedback based on that understanding.

[0751] "Audio data" refers to information recorded in digital format using audio.

[0752] "Text data" refers to information obtained by converting audio data into text format.

[0753] "Emotional information" refers to information about the speaker's emotional tone and nuances, analyzed from audio data.

[0754] "Keywords" refer to important words or phrases extracted by analyzing text data.

[0755] "Visual data" refers to visual representations generated based on extracted keywords and emotional information.

[0756] "Psychological interpretation" refers to the analysis of dream content and the user's mental state, based on text data and emotional information.

[0757] "Communication network" refers to the infrastructure used to send and receive data, particularly the internet and networks.

[0758] "Computing equipment" refers to digital devices and servers that process and analyze data.

[0759] The system of this invention begins with the user recording the contents of their dream as audio on a terminal. The terminal uses a microphone to record this audio and saves it as digital audio data. This audio data is transmitted to a server, which is a computing device, via a communication network called the internet.

[0760] The server uses speech recognition software to convert the received audio data into text data. A commonly used speech recognition API is a typical example of this software. Next, the server uses an emotion engine to analyze the user's emotional tone from the text data. During this process, emotional information such as "joy" and "sadness" is extracted.

[0761] The server also analyzes text data to extract key keywords from the dream. Natural language processing techniques are used for this analysis. Subsequently, a generative AI model is used to generate visual data based on the keywords and emotional information. A specific example of such a model is an image generation model using deep learning.

[0762] Furthermore, the server provides a psychological interpretation of the dream based on text data and emotional information. This allows the user to gain a deeper understanding of the dream's content.

[0763] For example, if a user records in audio that they "dreamed of having fun in a field of colorful flowers," the device sends this as digital audio data to the server. The server generates text containing the word "happiness" and colorful visual data that reflects that emotion. Furthermore, the psychological theme of "peace of mind" is indicated. This system aims to give users emotional insights into their dreams.

[0764] Examples of prompts to input into a generative AI model include: "Analyze the content and emotions of your dreams, and generate visual and psychological feedback based on that."

[0765] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0766] Step 1:

[0767] The user inputs their dream by speaking it into the device. The device uses its built-in microphone to record this voice as digital audio data. The recorded audio data accurately captures the specific content of the user's dream. The output is digital data in audio format.

[0768] Step 2:

[0769] The terminal transmits the recorded audio data to the server via the internet. During this process, the audio data is properly encoded, and standard communication protocols are used to prevent data loss during transmission. The output is the digital audio data received by the server.

[0770] Step 3:

[0771] The server converts the received audio data into text data using speech recognition software. The speech recognition software uses an audio signal analysis algorithm to recognize spoken words as text. The input is digital audio data, and the output is text data.

[0772] Step 4:

[0773] The server uses an emotion engine to analyze emotional information from the converted text data. The emotion engine utilizes natural language processing techniques to determine keywords and context within the text and evaluate the emotional tone. The input is text data, and the output is emotional information.

[0774] Step 5:

[0775] The server analyzes text data and extracts important keywords. Text analysis techniques are used to select and highlight meaningful phrases and words. The input is text data, and the output is a list of extracted keywords.

[0776] Step 6:

[0777] The server uses a generative AI model to generate visual data based on keywords and sentiment information. The generative AI model leverages deep learning to construct images that combine keywords and sentiment information. The input is keywords and sentiment information, and the output is visual data.

[0778] Step 7:

[0779] The server performs a psychological interpretation of dreams based on text data and emotional information. This employs psycholinguistic methods and presents possible psychological themes. The input is text data and emotional information, and the output is a psychological interpretation.

[0780] Step 8:

[0781] The server sends the generated visual data and psychological interpretations to the terminal. The terminal receives this data and prepares to display it on the screen for the user. The displayed content consists of individually generated visual feedback and psychological interpretations. The output is the information displayed on the screen that the user views.

[0782] (Application Example 2)

[0783] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0784] Using smart devices to visually recreate the content of dreams experienced by users and providing it as content to deeply understand the emotions of those dreams has been difficult with conventional methods. To solve this problem, there is a need for a system that can transcribe and visualize dreams recorded as voices by users, and provide personalized psychological interpretations. In particular, it is necessary to enable users to directly experience their unique visual experiences through devices such as smart glasses.

[0785] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0786] In this invention, the server includes means for receiving audio data and converting it into text data, means for analyzing the text data and generating visual data, and means for inputting prompt data to provide user-specific visual content. This makes it possible to provide a personalized visual experience based on the content of the user's dreams.

[0787] "Audio data" refers to digital audio information generated when a user talks about the contents of their dreams.

[0788] "Text data" refers to string information obtained by analyzing audio data.

[0789] "Visual data" refers to digital information such as images and videos generated based on text data.

[0790] "Psychological interpretation" refers to the analysis and interpretation of a user's inner state based on text data and its emotional nuances.

[0791] "Generation processing" refers to the digital processing necessary to create user-specific visual content.

[0792] "Prompt data" refers to instructional information used when generating visual content, and it forms the basis for determining visual elements and themes.

[0793] This invention is a system that allows users to describe their dreams, which are then visually reproduced and psychologically analyzed through the collaboration of a smart device and a server. First, the user sends audio data to the server using a device such as smart glasses. The server converts the audio data into text data using a speech recognition solution (e.g., a cloud-based speech analysis service).

[0794] Next, the server uses a natural language processing library (e.g., spaCy) to extract keywords from the text data and then uses a sentiment analysis tool (e.g., IBM Watson Tone Analyzer) to analyze the emotional state. This generates data that can be associated with psychological patterns based on the content of the dreams.

[0795] Using a generative AI model (e.g., Stable Diffusion), visual data is generated based on analyzed text data and emotional information. The generated visual data is customized using user-specific prompt data and sent to the device. The device projects this visual data onto the user's display, allowing them to experience the dream content in real time.

[0796] For example, if a user says they "dreamed of flying," a video of them flying through a vast sky will be generated based on that description. An example of a prompt used for this purpose might be, "Generate a visual image of what it was like to dream of flying. Make the colors bright and vivid to express a sense of exhilaration." Through this visual data, the user can gain a unique visual experience based on their dream.

[0797] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0798] Step 1:

[0799] The user describes the content of their dream through smart glasses. The audio is collected by the device's microphone and stored as digital audio data. This audio data is the input, and the device outputs it by sending it to a server via the internet.

[0800] Step 2:

[0801] The server converts the received digital audio data into text data using speech recognition software. Specifically, it uses a cloud-based speech analysis service to convert speech into text. The input is audio data, and the output is text data.

[0802] Step 3:

[0803] The server performs natural language processing on text data to extract key keywords. It uses a natural language processing library to analyze the meaning and structure of the text, identifying important words and phrases. The input is text data, and the output is a keyword list.

[0804] Step 4:

[0805] The server uses a sentiment analysis tool to analyze the user's emotional state based on the extracted keywords. Specifically, it determines the emotional tone contained in the text and identifies the associated emotional patterns. The input is a list of keywords, and the output is emotional state data.

[0806] Step 5:

[0807] The server generates visual data based on keywords and emotional states using a generative AI model. This process involves supplying generated prompt sentences and creating visual content using computer vision technology. The input is keyword and emotional state data, and the output is visual data.

[0808] Step 6:

[0809] The server sends the generated visual data to the user's terminal, which then displays it on its screen so that the user can see it. The input is the visual data, and the output is the visual content presented to the user.

[0810] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0811] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0812] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0813] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0814] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0815] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0816] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0817] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0818] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0819] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0820] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0821] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0822] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0823] 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.

[0824] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0825] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0826] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0827] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0828] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0829] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0830] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0831] The following is further disclosed regarding the embodiments described above.

[0832] (Claim 1)

[0833] A means of receiving audio data and converting it into text data,

[0834] A means of analyzing text data and generating visual data,

[0835] A means of providing psychological interpretations based on text data,

[0836] A system that includes this.

[0837] (Claim 2)

[0838] The system according to claim 1, which transmits audio data to a server via the internet.

[0839] (Claim 3)

[0840] The system according to claim 1 for extracting keywords from analyzed text data.

[0841] "Example 1"

[0842] (Claim 1)

[0843] A means for receiving audio information and converting it into text information,

[0844] A means for analyzing textual information and generating visual information,

[0845] A means of providing psychological analysis based on textual information,

[0846] A means of extracting concepts and creating visual information using an image generation model,

[0847] A means for transmitting and displaying the generated visual and analytical information on a terminal,

[0848] A system that includes this.

[0849] (Claim 2)

[0850] The system according to claim 1, which transmits voice information to a processing device via a communication network.

[0851] (Claim 3)

[0852] The system according to claim 1 for extracting concepts from analyzed character information.

[0853] "Application Example 1"

[0854] (Claim 1)

[0855] A means of receiving audio data and converting it into text data,

[0856] A means of analyzing text data and generating visual data,

[0857] A means of providing psychological interpretations based on text data,

[0858] A means of generating stories and images based on the generated visual data,

[0859] A means of presenting content to users with added psychological interpretations,

[0860] A system that includes this.

[0861] (Claim 2)

[0862] The system according to claim 1, which transmits audio data to a server via an information network.

[0863] (Claim 3)

[0864] The system according to claim 1 for extracting important words and phrases from analyzed text data.

[0865] "Example 2 of combining an emotion engine"

[0866] (Claim 1)

[0867] A means of receiving audio data and converting it to text,

[0868] A means for analyzing emotional information from the audio data,

[0869] A means for analyzing the text data and extracting keywords,

[0870] Means for generating visual data based on the keywords and sentiment information,

[0871] A means for providing psychological interpretations based on the text data and emotional information,

[0872] ...

[0873] A system that includes this.

[0874] (Claim 2)

[0875] The system according to claim 1, which transmits voice data to a computing device via a communication network.

[0876] (Claim 3)

[0877] The system according to claim 1, which uses the emotional information to reflect the generation of visual data.

[0878] "Application example 2 when combining with an emotional engine"

[0879] (Claim 1)

[0880] A means of receiving audio data and converting it into text data,

[0881] A means of analyzing text data and generating visual data,

[0882] A means of providing psychological interpretations based on text data,

[0883] A means for performing generation processing to project visual data onto a terminal,

[0884] To provide user-specific visual content, a means for entering prompt data is provided,

[0885] A system that includes this.

[0886] (Claim 2)

[0887] The system according to claim 1, which transmits audio data to a server via the internet.

[0888] (Claim 3)

[0889] The system according to claim 1, which extracts keywords from analyzed text data and generates visual content based on those keywords. [Explanation of Symbols]

[0890] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving audio data and converting it into text data, A means of analyzing text data and generating visual data, A means of providing psychological interpretations based on text data, A system that includes this.

2. The system according to claim 1, which transmits audio data to a server via the internet.

3. The system according to claim 1 for extracting keywords from analyzed text data.

Images