system

Abstract

We provide the system. [Solution] A means of obtaining dream records from users in voice or text format, A means of processing the acquired dream records and saving them to a database, A means of running a generative AI model to analyze saved dream records, A means of providing feedback to the user based on the analysis results, A means of generating mental health advice for the user based on the analysis results, A system that includes this.

Description

Technical Field

[0004] , , , ,

[0005] , , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Although the importance of self-understanding and mental health improvement through dreams has been recognized, the conventional technologies have insufficiently supported users in efficiently recording their own dreams and obtaining psychological insights. In particular, it has been difficult for users to effectively grasp the results of dream recording and analysis and receive advice for taking specific actions. Furthermore, there has been a lack of means for presenting complex dream contents simply and understandably.

Means for Solving the Problems

[0005] This invention provides a means for acquiring dream records from a user in voice or text format and storing them in a database. Furthermore, it includes a means for analyzing the stored dream records using a generative AI model and providing the analysis results to the user as easily understandable feedback. This makes it easier for users to gain psychological insights through their dreams. In addition, it provides customized mental health advice based on the analysis results to support users in taking concrete actions. Furthermore, it includes a means for visualizing the analysis results to the user in the form of diagrams or charts, enabling them to grasp complex information concisely.

[0006] A "user" refers to an individual who operates the system, records their dreams, and receives the analysis results.

[0007] "Voice or text" refers to the input method used by the user to express the content of their dreams, with the audio being later converted to text format.

[0008] "Means of obtaining dream records" refers to a system function that allows users to input the content of their dreams into the system via voice or text, and collects that information.

[0009] A "database" refers to an information storage device for safely and efficiently storing and managing the records and analysis results of acquired dreams.

[0010] A "generative AI model" refers to an artificial intelligence program that uses natural language processing algorithms to analyze dream records and extract psychological patterns and insights.

[0011] "Analysis results" refer to the information obtained after the generating AI model analyzes the dream record, and form the basis for the feedback provided to the user.

[0012] "Means of providing feedback" refers to system functions that display analysis results in a format that is easy for users to understand and present information that allows users to gain psychological insights.

[0013] "Mental health advice" refers to information provided to users based on analysis results, including individualized recommended behaviors and measures to improve psychological health.

[0014] "Illustrated diagrams and charts" refer to graphical display methods that visually represent analysis results and provide them in a way that users can easily understand. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the 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 the emotion engine is combined.

Mode for Carrying Out the Invention

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

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

[0018] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

[0020] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

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

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

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

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention is a system that allows users to effectively record, understand, and use their dreams to improve their mental health. The system provides a consistent flow of dream recording, storage, analysis, feedback, and advice.

[0037] First, the device provides an application that allows the user to input the content of their dreams. The user can input their dreams directly in text format or in voice format, and the device can convert this into text using speech recognition technology. As a result, the content of the dreams is acquired as digital data.

[0038] Next, this digital data is sent from the terminal to the server, where the server verifies that the data is properly formatted. Once verified, the data is securely stored in the database. This storage process prepares the dream record for analysis.

[0039] The server then inputs the saved dream record data into a generating AI model. This AI model uses natural language processing (NLP) techniques to analyze the dreams and extract themes and underlying psychological elements. Specifically, the AI ​​analyzes the language patterns and key words used in the dreams and adds psychological meanings.

[0040] Once the analysis is complete, the server summarizes the results and generates feedback in an easy-to-understand format for the user. This feedback not only provides a text-based summary but also uses visual diagrams and charts to help the user intuitively understand the analysis results. The server also takes the user's past data into consideration to provide individually customized mental health advice.

[0041] The device presents the user with generated feedback and advice. For example, if a user dreams of being chased by waves, the system might interpret this as a symbol of anxiety or stress and display feedback recommending that the user practice relaxation techniques.

[0042] This process allows users to understand the psychological state hidden in their dreams and obtain information that can help improve their mental health. The system provides users with opportunities to deepen their self-understanding on a daily basis and helps them maintain a healthier mental state.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] After waking up, the user launches the application to record their dreams. The user enters the content of their dreams into the device via voice input or text input. If using voice input, the user should describe the dream in detail.

[0046] Step 2:

[0047] When the device receives voice input, it uses its built-in speech recognition system to convert the voice data into text. The converted text is then displayed in a format that allows the user to review its content.

[0048] Step 3:

[0049] The terminal formats the verified text data in JSON format and sends it to the server using an endpoint-secure protocol.

[0050] Step 4:

[0051] The server temporarily records the received text data for analysis and automatically verifies that the format is correct. If there are any problems, it sends an error message to the terminal.

[0052] Step 5:

[0053] The server inputs the stored dream data into a generating AI model. The AI ​​model processes this data and extracts patterns and keywords from the text that indicate psychological tendencies.

[0054] Step 6:

[0055] The server receives analysis results from the AI ​​model and generates feedback for the user. This includes creating a text summary based on the analysis results and generating visual diagrams and charts.

[0056] Step 7:

[0057] The server considers the analysis results and generates customized mental health advice based on the user's psychological state.

[0058] Step 8:

[0059] The server sends the completed feedback and advice to the terminal. The terminal displays this on its user interface, allowing the user to view the information.

[0060] Step 9:

[0061] Users review the displayed feedback and advice, and strive to improve their mental health by implementing recommended action plans and relaxation methods as needed.

[0062] (Example 1)

[0063] 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."

[0064] In modern society, it is important for users to effectively record their dreams and understand the underlying psychological states they represent. However, manually recording dreams or undergoing professional psychological analysis is time-consuming and costly. Therefore, there is a need for a way for users to easily record their dreams and improve their mental health through the analysis results.

[0065] 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.

[0066] In this invention, the server includes means for receiving dream content from a user in voice or text, means for processing the acquired dream content and storing it in an information database, and means for executing a generative AI model using natural language processing technology to analyze the stored dream content. This allows users to easily record their dreams and improve their mental health by receiving feedback on their psychological state based on the analysis.

[0067] A "user" refers to an individual or group that uses the system to record the content of their dreams and receives analysis results and feedback.

[0068] "Audio or text" refers to the communication format used by users to record the content of their dreams, and is entered as either audio or text information.

[0069] "Information acquisition means" refers to functions and methods for receiving dream content from users in audio or text format.

[0070] An "information database" refers to a data storage system that systematically stores the content and analysis results of acquired dreams for use in subsequent processing and analysis.

[0071] A "generative AI model" refers to an artificial intelligence model that uses natural language processing technology to analyze the content of a user's dreams and extract psychological themes and elements.

[0072] "Natural language processing technology" refers to the technology used to process, analyze, and understand human language using computers.

[0073] "Analysis results" refer to themes and psychological insights extracted from dream records by the generative AI model, and include information provided to the user as feedback.

[0074] "Feedback" refers to information provided to the user based on the analysis results, including guidelines and advice to deepen their understanding of the content of their dreams.

[0075] "Mental health advice" refers to guidance and suggestions aimed at improving a user's psychological state and enhancing their mental well-being.

[0076] "Visual diagrams and graphs" refer to visual representations used to present analysis results to users in an easily understandable way.

[0077] This invention is a system that allows users to easily record the content of their dreams, deepen their understanding of their psychological state through analysis, and use this information to improve their mental health. Users input their dream records in voice or text format via a device such as a smartphone or tablet. Voice input is converted to text using speech recognition technology on the device. General speech analysis software is used for this speech recognition.

[0078] The content of the dream entered is sent from the terminal to the server. The server securely receives the data via HTTPS communication, standardizes the format, and stores it in an information database. This stored data is then supplied to a generative AI model that uses natural language processing technology for analysis. A general-purpose AI model with excellent natural language processing capabilities is used for the generative AI model.

[0079] The server analyzes dream records using a generative AI model and generates feedback based on themes and psychological insights derived from them. This feedback utilizes visual diagrams and graphs to ensure intuitive understanding for the user. In addition, it compares the current data with previously recorded data to provide personalized mental health advice.

[0080] The device displays feedback and advice received from the server to the user. For example, if a user dreams of being chased by waves, the system interprets this as a symbol of anxiety or stress and displays feedback suggesting the introduction of relaxation techniques.

[0081] An example of a prompt message could be, "Please tell me about the content of a recent dream you had. I want to know what kind of psychological state it represents." Based on this prompt message, the system will understand the content of the user's dream and provide appropriate analysis and feedback.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The user inputs the content of their dream using a terminal application. The user can input directly in text format or in voice format. In the case of voice input, the terminal uses speech recognition technology to convert the voice to text. During input, the user freely describes the content of their dream from the previous night, and the result is captured as text data. The output is text data containing the input dream record.

[0085] Step 2:

[0086] The device sends the acquired text data to the server. A secure HTTPS protocol is used for transmission. The data processing performed here is encryption to protect user privacy. The input is text data from the device, and the output is data in a securely encrypted format.

[0087] Step 3:

[0088] The server performs format validation before saving the received data to the database. This process verifies the data's integrity and checks for any abnormal formats. The input is the received encrypted data, and the output is the parsing-ready data stored in the database.

[0089] Step 4:

[0090] The server inputs stored dream data into a generative AI model. The generative AI model uses natural language processing techniques to extract dream themes and underlying psychological elements. This data analysis includes word frequency analysis and contextual analysis. The input is dream data from a database, and the output is the analyzed themes and psychological insights.

[0091] Step 5:

[0092] The server generates user-facing feedback based on the analysis results. This feedback includes comparisons with past dream data and is presented in visual diagrams and graphs. The data processing performed here involves summarizing and visualizing the analysis information to make it easier for the user to understand. The input is the analysis information, and the output is visual data including feedback and advice.

[0093] Step 6:

[0094] The device displays the generated feedback and advice to the user. The device's application provides a user-friendly design and navigation to make the feedback easy to understand. In the example of a dream where the user is "chased by waves," the user is offered specific relaxation techniques to reduce stress. The input is visual data from the server, and the output is the feedback information displayed to the user.

[0095] (Application Example 1)

[0096] 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."

[0097] There is a challenge in that there are insufficient methods provided for users to understand their psychological state and improve their mental health through the process of effectively saving and analyzing dream records. Furthermore, there is a need for a means to analyze dream content in a short period of time and present it in a visually easy-to-understand format. In addition, there is a need for a system that allows users to continuously track their psychological changes by comparing current dream data with past dream data.

[0098] 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.

[0099] In this invention, the server includes means for acquiring dream information from a user in digital format, means for processing the acquired dream information and storing it in a memory area, means for executing generative AI technology to analyze the stored dream information, means for generating graphs and charts for visualizing the analysis results, and means for analyzing the dream information based on the passage of time and comparing it with past data. This makes it possible for the user to easily understand the analysis results based on their dreams visually and grasp the changes in their own psychological state.

[0100] "Digital format" refers to representing information in a way that can be processed by a computer, using electrical signals or numerical data.

[0101] "Dream information" refers to the content of dreams experienced by the user, and is recorded in audio or text format.

[0102] "Storage area" refers to a part of a computer or database where data can be stored for a long or short period of time.

[0103] "Generative AI technology" is a process that uses artificial intelligence to analyze data and generate analysis results.

[0104] "Graphs and charts" are shapes and diagrams used to visually represent data and analysis results.

[0105] "Analyzing based on the passage of time" means analyzing how specific data has changed over time.

[0106] The system for carrying out the present invention helps users input dream records and understand their psychological state based on that information. This system includes a terminal, a server, and generative AI technology.

[0107] First, the device is a mobile device such as a smartphone, and the user can use this device to input the content of their dream. Input can be done by voice or text, and in the case of voice input, it is converted to text using the Google® Speech-to-Text API.

[0108] Next, the dream information acquired by the device is sent to a cloud server via the internet. Specifically, AWS® Lambda receives the data, and then the data is analyzed by the Google Cloud Natural Language API. This analysis extracts the themes and psychological elements within the dream.

[0109] Once the analysis is complete, the server generates visually easy-to-understand feedback for the user based on the analysis results. Using the D3.js library, the results are visualized in graph and chart format. This allows the user to gain psychological insights based on the content of their dreams.

[0110] Furthermore, the server compares current dream data with past dream data, providing advice that tracks psychological changes over time. This information is extremely useful in supporting the improvement of the user's mental health.

[0111] For example, if a user enters that they "dreamed of walking in water," the system can analyze this as a symbol of calmness and mental purification, and generate advice encouraging relaxation techniques and self-reflection.

[0112] An example of a prompt might be, "Analyze the content of the dream and provide psychological elements and related advice." The system can use this prompt to generate specific feedback for the user.

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The user enters the content of their dream into the device either by voice or text. In the case of voice input, the device uses the Google Speech-to-Text API to convert the voice into text and generate text data. This text data is then used as input for the next step.

[0116] Step 2:

[0117] The device sends the acquired text data to the cloud server. The cloud server receives the data and verifies that it is properly formatted. The received, formatted text data becomes the input for the next step.

[0118] Step 3:

[0119] The server uses the Google Cloud Natural Language API to analyze the received text data. Here, data analysis techniques are used to extract themes and psychological elements of the dream. The results of this analysis will serve as input for the next step.

[0120] Step 4:

[0121] The server uses the D3.js library to visualize the analysis results. Specifically, it generates graphs and charts based on the analysis results, making them intuitively understandable to the user. This visualized data then serves as input for the next step.

[0122] Step 5:

[0123] The server compares past dream data with current analysis results to analyze changes in the user's psychological state. Based on this comparison, it generates personalized mental health advice. This advice serves as input for the next step.

[0124] Step 6:

[0125] The terminal displays visualized data received from the server along with generated advice to the user. The user receives psychological analysis results based on their dreams, which can help improve their mental health.

[0126] 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.

[0127] This invention is a system that effectively analyzes dream records entered by users via voice or text, enabling them to gain psychological insights. Furthermore, by combining it with an emotion engine, it analyzes the user's emotions and provides personalized advice to help improve their mental health.

[0128] First, the user can input the content of their dream through the application. If the user chooses voice input, the device uses speech recognition to convert the voice into text data. At this stage, the emotion engine evaluates the specific tone and intonation of the voice data to identify the emotions the user is experiencing.

[0129] The terminal correctly formats the acquired text data in JSON format and then sends it to the server using a secure communication protocol. The server verifies the received data, saves it, and then prepares it for analysis.

[0130] The server inputs the stored dream record data into a generative AI model. The AI ​​model uses natural language processing algorithms to extract dream themes and psychological tendencies from this data. Simultaneously, the emotion engine analyzes the user's emotions and adds insights into their psychological state.

[0131] Once the analysis results are obtained, the server generates feedback based on those results. This feedback includes a text-based summary, and the information is further presented in the form of diagrams and charts. This allows the user to intuitively understand the extracted information. The feedback also includes additional sentiment analysis results from the sentiment engine, which helps to provide a deeper understanding of the user's emotional background.

[0132] Based on feedback, analysis results, and emotional state, the server generates personalized mental health advice for the user. This advice is tailored to the user's current emotional state and includes specific action suggestions and relaxation techniques.

[0133] Finally, the device displays the generated feedback and advice to the user. For example, if the user experienced fear in a dream, the system recognizes this as anxiety or tension and recommends practicing relaxation techniques.

[0134] The introduction of this system will allow users to deepen their psychological insights gained through dreams, take a more conscious approach to their own emotions, and contribute to improving their mental health.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] The user launches the application and enters information about their dream from the previous night via voice or text. If they choose voice input, they describe the dream in detail.

[0138] Step 2:

[0139] The device converts voice input into text data. This conversion uses speech recognition technology, and simultaneously, an emotion engine analyzes the voice tone to estimate the user's emotional state.

[0140] Step 3:

[0141] The terminal organizes the converted text data and formats it into JSON format. This data is then sent to the server using a secure communication channel.

[0142] Step 4:

[0143] The server validates the received JSON data. Once the data's validity is confirmed, it is saved to the database.

[0144] Step 5:

[0145] The server analyzes the stored dream records using a generated AI model. The model identifies keywords and patterns related to the dreams and extracts psychological themes.

[0146] Step 6:

[0147] The server integrates the emotional data obtained by the emotion engine into the analysis results. This visualizes the relationship between the user's emotional state and the content of their dreams.

[0148] Step 7:

[0149] The server generates feedback based on the analysis results and sentiment data. The feedback is summarized in text format and also visually represented in diagrams and charts.

[0150] Step 8:

[0151] The server incorporates customized mental health advice into the feedback, tailored to the user's emotional state. This advice includes specific relaxation techniques and positive behavioral patterns.

[0152] Step 9:

[0153] The device displays final feedback and advice on the user's screen. Based on this information, the user can review the psychological insights and emotional management methods gained from their dreams and apply them to their daily life.

[0154] (Example 2)

[0155] 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".

[0156] There is a need to provide users with a means to deeply understand their own psychological state and improve their mental health by recording and analyzing the content of their dreams. Conventional technologies have limitations in properly analyzing dream content and providing users with appropriate feedback and advice, so a new method is needed to solve this problem.

[0157] 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.

[0158] This invention includes a server that acquires dream records from a user in text or voice, converts voice to text data, performs sentiment analysis, organizes the sentiment analysis results and dream records in a specific format and stores them in a memory device, and runs a generative model to analyze the stored dream records. This makes it possible to provide feedback and customized mental health advice based on the user's psychological state.

[0159] A "user" refers to someone who uses the system to input records of their dreams and receives analysis results and advice.

[0160] "Dream records" refer to the specific details of dreams experienced by the user while awake, and are entered in text or audio format.

[0161] "Voice data" refers to information that a user inputs as voice and that is converted into text data by a speech recognition system.

[0162] "Character data" refers to information converted by a character recognition system from text or voice data entered by a user.

[0163] "Sentiment analysis" refers to the process of analyzing the tone and content of input data to identify the user's emotional state.

[0164] A "generative model" is a system that uses machine learning algorithms and refers to a technology that extracts themes and psychological tendencies from dream records through natural language processing.

[0165] "Feedback" refers to information provided to users based on analysis results, including psychological insights and suggestions for improvement.

[0166] "Mental health advice" refers to advice that includes customized behavioral suggestions and relaxation methods based on the user's emotional state and dream content.

[0167] A "storage device" refers to a hardware or software system used to store information.

[0168] "Visualization" refers to presenting analysis results in graph or chart format to make them easy for users to understand.

[0169] This invention is a system that allows users to input dream records in voice or text, analyzes the content, and provides psychological insights and mental health advice. The following hardware and software are used for implementation.

[0170] Users launch a dedicated application using a mobile device or tablet and input the content of their dream. In the case of voice input, voice data is acquired through the device's microphone and converted into text data using speech recognition software (e.g., speech recognition API).

[0171] The device passes the converted text data to an emotion analysis engine for tone and intonation analysis. The analysis results and the dream text data are formatted into JSON format. The device then sends this data to the server via a security protocol (e.g., HTTPS).

[0172] The server stores the received data in storage and analyzes it by running a generative model (e.g., a natural language processing model). The generative model uses machine learning algorithms to analyze dream themes and psychological tendencies. Simultaneously, an emotion analysis engine adds user emotion information.

[0173] Based on the analysis results, the server generates feedback, which is then visualized using graphs and charts. This feedback includes personalized advice for mental health, taking into account the user's current psychological state.

[0174] The device displays this feedback and advice to the user, helping them deepen the psychological insights gained through dream recording.

[0175] For example, if a user dreams of getting lost in the rain, the system analyzes this as a symbol of anxiety or a sense of loss. Based on statistical methods, the generative model prompts the user with the question, "Do you feel unsure of which direction to go right now?" The emotion analysis engine also suggests relaxation techniques, such as, "Try deep breathing or meditation to regain your composure."

[0176] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0177] Step 1:

[0178] The user launches the application and enters a record of their dream. Input can be done via voice or text. For voice input, the user speaks the dream content using the device's microphone. The device receives the voice input and stores it internally as audio data. Both input and output are audio data.

[0179] Step 2:

[0180] The device launches speech recognition software to acquire speech data and convert it into text data. Specifically, the device passes the speech data to an API and receives the converted text result. The input is speech data, and the output is text data. In this process, speech-specific tones and intonation are also used in the analysis.

[0181] Step 3:

[0182] The terminal passes the converted text data to the sentiment analysis engine, which then performs sentiment analysis. The sentiment analysis engine receives the text data as input, identifies the user's emotional state, and generates analysis results. The output is the sentiment analysis result. This process supplements the emotional information extracted from the text data.

[0183] Step 4:

[0184] The terminal integrates text data and sentiment analysis results in JSON format, and then formats the data. The formatted data is prepared for communication with the server. The input is text data and sentiment analysis results, and the output is formatted JSON data. Specifically, the data structure is converted to JSON and adapted to a secure communication protocol.

[0185] Step 5:

[0186] The terminal sends formatted JSON data to the server using a security protocol. The data being sent is already properly formatted JSON data. Through this communication process, the terminal securely transmits information to the server while preventing data leakage.

[0187] Step 6:

[0188] The server receives JSON data sent from the terminal and verifies its contents. The server checks the received data for tampering or omissions and confirms that it is valid data. The input is the received JSON data, and the output is the verified data.

[0189] Step 7:

[0190] The server saves the verified data to storage and starts the generative AI model to begin data analysis. The generative model performs natural language processing on the saved data to extract dream themes and psychological tendencies. The input is the verified data, and the output is the analysis results. Specifically, the generative model uses that data to generate insights.

[0191] Step 8:

[0192] The server generates feedback and prompts based on the analysis results of the generated AI model. The results of the sentiment analysis engine are also reflected in the feedback. The input is the analysis results, and the output is the feedback and prompts for the user. In this process, information is presented in a format that is easy for the user to understand.

[0193] Step 9:

[0194] The server creates customized mental health advice for the user based on the generated feedback and prompts. The input is the generated feedback and prompts, and the output is the customized advice. Specific examples include comforting messages and action plans that look to the distant future.

[0195] Step 10:

[0196] The terminal displays feedback, prompts, and mental health advice received from the server to the user. Through this, the user can instantly see ways to improve their psychological state in daily life. Input is data from the server, and output is a visual display to the user. In this process, the information is presented in a format that is easy for the user to understand.

[0197] (Application Example 2)

[0198] 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".

[0199] In modern life, managing the mental health of brick-and-mortar store staff is a crucial issue. However, a lack of effective means to identify the stress and anxiety staff experience at work and provide appropriate support can lead to decreased job satisfaction and productivity.

[0200] 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.

[0201] In this invention, the server includes means for acquiring emotions and events from a user in voice or text, means for processing the acquired records of emotions and events and storing them in an information aggregation device, and means for running a generative AI model to analyze the stored records of emotions and events. This enables staff at physical stores to regularly record their own emotions and experiences and receive appropriate feedback and action suggestions based on the data.

[0202] A "user" refers to an individual who uses the system to record their emotions and experiences, and to receive feedback and advice.

[0203] "Audio or text" refers to the format of information that a user provides to the system, including spoken word and written text.

[0204] "Records of emotions and events" refers to specific data about the emotions and experiences that users have felt on a daily basis.

[0205] An "information aggregation device" refers to a data storage device that stores records of acquired emotions and events for later analysis.

[0206] A "generative AI model" refers to an artificial intelligence algorithm that analyzes input information and identifies patterns.

[0207] "Feedback" refers to responses, including suggestions and evaluations, provided to the user based on the analysis results.

[0208] "Action suggestions and stress management methods" refer to specific action guidelines and methods for reducing stress that are provided to users based on the analysis results.

[0209] This invention is a system that allows staff at physical stores to record emotions and events and receive feedback and action suggestions based on that information. The system consists of a voice or text input device, an information aggregation device, a server that runs a generative AI model, and a user terminal.

[0210] Users record their emotions and events using devices such as smartphones via voice or text input. During this process, speech recognition software, such as Google Cloud Speech-to-Text, installed on the smartphone is used to convert the voice data into text. The converted data is then securely stored on an information aggregation device via the internet.

[0211] Data stored in the information aggregation device is received by a server and analyzed using a generating AI model. Natural language processing algorithms such as OpenAI's GPT-3 (registered trademark) are used for the analysis to extract specific emotional patterns and psychological tendencies. Furthermore, feedback is generated based on the analysis results to provide a deeper understanding of the user's psychological state. Behavioral suggestions and stress management methods are also generated and provided to the user.

[0212] The feedback results are displayed visually to the user on their device. Specifically, guidance related to the analyzed emotions and events is displayed as text and graphs. This allows users to understand their own psychological state and obtain information to take appropriate actions in different situations.

[0213] For example, if a staff member at a store records their stress levels during work and the system analyzes this as "peak tension," it will then provide specific suggestions regarding relaxation techniques or adjustments to working hours.

[0214] For the generative AI model, the following prompt can be used: "A staff member reported feeling stressed during peak hours. Analyze their emotional tone and provide recommendations for stress management and relaxation techniques." This will generate appropriate feedback and provide it to the user.

[0215] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0216] Step 1:

[0217] Users record their emotions and events using their smartphones via voice or text. For voice input, the device uses Google Cloud Speech-to-Text to convert the speech to text. The input for this step is the user's voice or text, and the output is text data. Speech recognition-based data conversion takes place here.

[0218] Step 2:

[0219] The terminal transmits the converted text data to the information aggregation device using a secure communication protocol. The input to this step is the text data generated in step 1, and the output is the storage of the data in the information aggregation device. HTTPS communication is used to ensure the security of the information during data transmission.

[0220] Step 3:

[0221] The server receives text data stored in the information aggregation device and inputs it into the generating AI model. In this step, the input is data from the information aggregation device, and the output is the analysis result by the AI ​​model. Natural language processing is performed using OpenAI GPT-3 for data analysis, and emotional patterns and psychological tendencies are extracted.

[0222] Step 4:

[0223] The server generates feedback and action suggestions for the user using the analysis results of the generated AI model. The input is the analysis results obtained in step 3, and the output is feedback information. In feedback creation, data processing is performed to convert the analysis results into a format that is easy for the user to understand.

[0224] Step 5:

[0225] The terminal visually displays feedback sent from the server to the user. The input is feedback data from the server, and the output is the display of information to the user. Here, the data is provided to the user in the form of text or graphs.

[0226] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0227] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0228] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0229] [Second Embodiment]

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

[0231] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0232] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0233] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0234] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0235] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0236] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0237] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0238] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0239] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0240] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0241] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0242] This invention is a system that allows users to effectively record, understand, and use their dreams to improve their mental health. The system provides a consistent flow of dream recording, storage, analysis, feedback, and advice.

[0243] First, the device provides an application that allows the user to input the content of their dreams. The user can input their dreams directly in text format or in voice format, and the device can convert this into text using speech recognition technology. As a result, the content of the dreams is acquired as digital data.

[0244] Next, this digital data is sent from the terminal to the server, where the server verifies that the data is properly formatted. Once verified, the data is securely stored in the database. This storage process prepares the dream record for analysis.

[0245] The server then inputs the saved dream record data into a generating AI model. This AI model uses natural language processing (NLP) techniques to analyze the dreams and extract themes and underlying psychological elements. Specifically, the AI ​​analyzes the language patterns and key words used in the dreams and adds psychological meanings.

[0246] Once the analysis is complete, the server summarizes the results and generates feedback in an easy-to-understand format for the user. This feedback not only provides a text-based summary but also uses visual diagrams and charts to help the user intuitively understand the analysis results. The server also takes the user's past data into consideration to provide individually customized mental health advice.

[0247] The device presents the user with generated feedback and advice. For example, if a user dreams of being chased by waves, the system might interpret this as a symbol of anxiety or stress and display feedback recommending that the user practice relaxation techniques.

[0248] This process allows users to understand the psychological state hidden in their dreams and obtain information that can help improve their mental health. The system provides users with opportunities to deepen their self-understanding on a daily basis and helps them maintain a healthier mental state.

[0249] The following describes the processing flow.

[0250] Step 1:

[0251] After waking up, the user launches the application to record their dreams. The user enters the content of their dreams into the device via voice input or text input. If using voice input, the user should describe the dream in detail.

[0252] Step 2:

[0253] When the device receives voice input, it uses its built-in speech recognition system to convert the voice data into text. The converted text is then displayed in a format that allows the user to review its content.

[0254] Step 3:

[0255] The terminal formats the verified text data in JSON format and sends it to the server using an endpoint-secure protocol.

[0256] Step 4:

[0257] The server temporarily records the received text data for analysis and automatically verifies that the format is correct. If there are any problems, it sends an error message to the terminal.

[0258] Step 5:

[0259] The server inputs the stored dream data into a generating AI model. The AI ​​model processes this data and extracts patterns and keywords from the text that indicate psychological tendencies.

[0260] Step 6:

[0261] The server receives analysis results from the AI ​​model and generates feedback for the user. This includes creating a text summary based on the analysis results and generating visual diagrams and charts.

[0262] Step 7:

[0263] The server considers the analysis results and generates customized mental health advice based on the user's psychological state.

[0264] Step 8:

[0265] The server sends the completed feedback and advice to the terminal. The terminal displays this on its user interface, allowing the user to view the information.

[0266] Step 9:

[0267] Users review the displayed feedback and advice, and strive to improve their mental health by implementing recommended action plans and relaxation methods as needed.

[0268] (Example 1)

[0269] 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."

[0270] In modern society, it is important for users to effectively record their dreams and understand the underlying psychological states they represent. However, manually recording dreams or undergoing professional psychological analysis is time-consuming and costly. Therefore, there is a need for a way for users to easily record their dreams and improve their mental health through the analysis results.

[0271] 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.

[0272] In this invention, the server includes means for receiving dream content from a user in voice or text, means for processing the acquired dream content and storing it in an information database, and means for executing a generative AI model using natural language processing technology to analyze the stored dream content. This allows users to easily record their dreams and improve their mental health by receiving feedback on their psychological state based on the analysis.

[0273] A "user" refers to an individual or group that uses the system to record the content of their dreams and receives analysis results and feedback.

[0274] "Audio or text" refers to the communication format used by users to record the content of their dreams, and is entered as either audio or text information.

[0275] "Information acquisition means" refers to functions and methods for receiving dream content from users in audio or text format.

[0276] An "information database" refers to a data storage system that systematically stores the content and analysis results of acquired dreams for use in subsequent processing and analysis.

[0277] A "generative AI model" refers to an artificial intelligence model that uses natural language processing technology to analyze the content of a user's dreams and extract psychological themes and elements.

[0278] "Natural language processing technology" refers to the technology used to process, analyze, and understand human language using computers.

[0279] "Analysis results" refer to themes and psychological insights extracted from dream records by the generative AI model, and include information provided to the user as feedback.

[0280] "Feedback" refers to information provided to the user based on the analysis results, including guidelines and advice to deepen their understanding of the content of their dreams.

[0281] "Mental health advice" refers to guidance and suggestions aimed at improving the user's mental state and enhancing mental health.

[0282] "Visual illustrations or graph formats" refer to visual representation methods used to present analysis results in an easy-to-understand manner for the user.

[0283] This invention is a system for users to easily record the content of their dreams, deepen their understanding of their mental state through analysis, and utilize it to improve mental health. The user inputs the dream record in voice or text format via a terminal such as a smartphone or tablet. The voice format input is performed by the terminal converting it to text using voice recognition technology. General voice analysis software is used for this voice recognition.

[0284] The input dream content is sent from the terminal to the server. The server securely receives the data via HTTPS communication, unifies the format, and stores it in the information database. This stored data is supplied to a generative AI model using natural language processing technology for analysis. A general AI model with excellent natural language processing is used for the generative AI model.

[0285] The server analyzes the dream record using the generative AI model and generates feedback based on the themes and psychological insights obtained therefrom. This feedback also utilizes visual illustrations or graph formats so that the user can intuitively understand it. In addition, a comparison is made with the data recorded in the past, and optimized mental health advice is provided to the user.

[0286] The terminal presents the feedback and advice received from the server to the user. As a specific example, when the user has a dream of "being chased by waves", the system captures it as a symbol of anxiety or stress and displays feedback suggesting the introduction of relaxation techniques.

[0287] An example of a prompt message could be, "Please tell me about the content of a recent dream you had. I want to know what kind of psychological state it represents." Based on this prompt message, the system will understand the content of the user's dream and provide appropriate analysis and feedback.

[0288] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0289] Step 1:

[0290] The user inputs the content of their dream using a terminal application. The user can input directly in text format or in voice format. In the case of voice input, the terminal uses speech recognition technology to convert the voice to text. During input, the user freely describes the content of their dream from the previous night, and the result is captured as text data. The output is text data containing the input dream record.

[0291] Step 2:

[0292] The device sends the acquired text data to the server. A secure HTTPS protocol is used for transmission. The data processing performed here is encryption to protect user privacy. The input is text data from the device, and the output is data in a securely encrypted format.

[0293] Step 3:

[0294] The server performs format validation before saving the received data to the database. This process verifies the data's integrity and checks for any abnormal formats. The input is the received encrypted data, and the output is the parsing-ready data stored in the database.

[0295] Step 4:

[0296] The server inputs stored dream data into a generative AI model. The generative AI model uses natural language processing techniques to extract dream themes and underlying psychological elements. This data analysis includes word frequency analysis and contextual analysis. The input is dream data from a database, and the output is the analyzed themes and psychological insights.

[0297] Step 5:

[0298] The server generates user-facing feedback based on the analysis results. This feedback includes comparisons with past dream data and is presented in visual diagrams and graphs. The data processing performed here involves summarizing and visualizing the analysis information to make it easier for the user to understand. The input is the analysis information, and the output is visual data including feedback and advice.

[0299] Step 6:

[0300] The device displays the generated feedback and advice to the user. The device's application provides a user-friendly design and navigation to make the feedback easy to understand. In the example of a dream where the user is "chased by waves," the user is offered specific relaxation techniques to reduce stress. The input is visual data from the server, and the output is the feedback information displayed to the user.

[0301] (Application Example 1)

[0302] 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."

[0303] There is a problem that through the process of effectively storing and analyzing dream records, a method for users to understand their own mental state and improve mental health is not sufficiently provided. Furthermore, there is a need to realize a means for analyzing the content of dreams in a short period of time and providing it in a visually understandable form. In addition, a mechanism is required that allows users to continuously track their own psychological changes by comparing them with past dream data.

[0304] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0305] In this invention, the server includes means for acquiring dream information in digital form from a user, means for processing the acquired dream information and storing it in a storage area, means for executing a generative AI technique for analyzing the stored dream information, means for generating graphs and charts for visualizing the analysis results, and means for analyzing dream information based on the passage of time and comparing it with past data. As a result, it becomes possible for users to easily understand the analysis results based on their own dreams visually and grasp the changes in their own mental states.

[0306] "Digital form" means expressing information in the form of electrical signals or numerical data that can be processed by a computer.

[0307] "Dream information" refers to the content of dreams experienced by a user and is recorded by voice or text.

[0308] "Storage area" refers to a part of a computer or a database that can store data for a long or short period of time.

[0309] "Generative AI technique" is a process of analyzing data using artificial intelligence and generating analysis results.

[0310] "Graphs and charts" are figures and diagrams for visually expressing data and analysis results.

[0311] "Analyzing based on the passage of time" means analyzing how specific data has changed over time.

[0312] The system for carrying out the present invention helps users input dream records and understand their psychological state based on that information. This system includes a terminal, a server, and generative AI technology.

[0313] First, the device is a mobile device such as a smartphone, and the user can use this device to input the content of their dream. Input can be done by voice or text, and if voice input is used, it will be converted to text using the Google Speech-to-Text API.

[0314] Next, the dream information acquired by the device is sent to a cloud server via the internet. Specifically, AWS Lambda receives the data, and then the data is analyzed by the Google Cloud Natural Language API. This analysis extracts the themes and psychological elements within the dream.

[0315] Once the analysis is complete, the server generates visually easy-to-understand feedback for the user based on the analysis results. Using the D3.js library, the results are visualized in graph and chart format. This allows the user to gain psychological insights based on the content of their dreams.

[0316] Furthermore, the server compares current dream data with past dream data, providing advice that tracks psychological changes over time. This information is extremely useful in supporting the improvement of the user's mental health.

[0317] For example, if a user enters that they "dreamed of walking in water," the system can analyze this as a symbol of calmness and mental purification, and generate advice encouraging relaxation techniques and self-reflection.

[0318] An example of a prompt might be, "Analyze the content of the dream and provide psychological elements and related advice." The system can use this prompt to generate specific feedback for the user.

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

[0320] Step 1:

[0321] The user enters the content of their dream into the device either by voice or text. In the case of voice input, the device uses the Google Speech-to-Text API to convert the voice into text and generate text data. This text data is then used as input for the next step.

[0322] Step 2:

[0323] The device sends the acquired text data to the cloud server. The cloud server receives the data and verifies that it is properly formatted. The received, formatted text data becomes the input for the next step.

[0324] Step 3:

[0325] The server uses the Google Cloud Natural Language API to analyze the received text data. Here, data analysis techniques are used to extract themes and psychological elements of the dream. The results of this analysis will serve as input for the next step.

[0326] Step 4:

[0327] The server uses the D3.js library to visualize the analysis results. Specifically, it generates graphs and charts based on the analysis results, making them intuitively understandable to the user. This visualized data then serves as input for the next step.

[0328] Step 5:

[0329] The server compares past dream data with current analysis results to analyze changes in the user's psychological state. Based on this comparison, it generates personalized mental health advice. This advice serves as input for the next step.

[0330] Step 6:

[0331] The terminal displays visualized data received from the server along with generated advice to the user. The user receives psychological analysis results based on their dreams, which can help improve their mental health.

[0332] 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.

[0333] This invention is a system that effectively analyzes dream records entered by users via voice or text, enabling them to gain psychological insights. Furthermore, by combining it with an emotion engine, it analyzes the user's emotions and provides personalized advice to help improve their mental health.

[0334] First, the user can input the content of their dream through the application. If the user chooses voice input, the device uses speech recognition to convert the voice into text data. At this stage, the emotion engine evaluates the specific tone and intonation of the voice data to identify the emotions the user is experiencing.

[0335] The terminal correctly formats the acquired text data in JSON format and then sends it to the server using a secure communication protocol. The server verifies the received data, saves it, and then prepares it for analysis.

[0336] The server inputs the stored dream record data into a generative AI model. The AI ​​model uses natural language processing algorithms to extract dream themes and psychological tendencies from this data. Simultaneously, the emotion engine analyzes the user's emotions and adds insights into their psychological state.

[0337] Once the analysis results are obtained, the server generates feedback based on those results. This feedback includes a text-based summary, and the information is further presented in the form of diagrams and charts. This allows the user to intuitively understand the extracted information. The feedback also includes additional sentiment analysis results from the sentiment engine, which helps to provide a deeper understanding of the user's emotional background.

[0338] Based on feedback, analysis results, and emotional state, the server generates personalized mental health advice for the user. This advice is tailored to the user's current emotional state and includes specific action suggestions and relaxation techniques.

[0339] Finally, the device displays the generated feedback and advice to the user. For example, if the user experienced fear in a dream, the system recognizes this as anxiety or tension and recommends practicing relaxation techniques.

[0340] The introduction of this system will allow users to deepen their psychological insights gained through dreams, take a more conscious approach to their own emotions, and contribute to improving their mental health.

[0341] The following describes the processing flow.

[0342] Step 1:

[0343] The user launches the application and enters information about their dream from the previous night via voice or text. If they choose voice input, they describe the dream in detail.

[0344] Step 2:

[0345] The device converts voice input into text data. This conversion uses speech recognition technology, and simultaneously, an emotion engine analyzes the voice tone to estimate the user's emotional state.

[0346] Step 3:

[0347] The terminal organizes the converted text data and formats it into JSON format. This data is then sent to the server using a secure communication channel.

[0348] Step 4:

[0349] The server validates the received JSON data. Once the data's validity is confirmed, it is saved to the database.

[0350] Step 5:

[0351] The server analyzes the stored dream records using a generated AI model. The model identifies keywords and patterns related to the dreams and extracts psychological themes.

[0352] Step 6:

[0353] The server integrates the emotional data obtained by the emotion engine into the analysis results. This visualizes the relationship between the user's emotional state and the content of their dreams.

[0354] Step 7:

[0355] The server generates feedback based on the analysis results and sentiment data. The feedback is summarized in text format and also visually represented in diagrams and charts.

[0356] Step 8:

[0357] The server incorporates customized mental health advice into the feedback, tailored to the user's emotional state. This advice includes specific relaxation techniques and positive behavioral patterns.

[0358] Step 9:

[0359] The device displays final feedback and advice on the user's screen. Based on this information, the user can review the psychological insights and emotional management methods gained from their dreams and apply them to their daily life.

[0360] (Example 2)

[0361] 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".

[0362] There is a need to provide users with a means to deeply understand their own psychological state and improve their mental health by recording and analyzing the content of their dreams. Conventional technologies have limitations in properly analyzing dream content and providing users with appropriate feedback and advice, so a new method is needed to solve this problem.

[0363] 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.

[0364] This invention includes a server that acquires dream records from a user in text or voice, converts voice to text data, performs sentiment analysis, organizes the sentiment analysis results and dream records in a specific format and stores them in a memory device, and runs a generative model to analyze the stored dream records. This makes it possible to provide feedback and customized mental health advice based on the user's psychological state.

[0365] A "user" refers to someone who uses the system to input records of their dreams and receives analysis results and advice.

[0366] "Dream records" refer to the specific details of dreams experienced by the user while awake, and are entered in text or audio format.

[0367] "Voice data" refers to information that a user inputs as voice and that is converted into text data by a speech recognition system.

[0368] "Character data" refers to information converted by a character recognition system from text or voice data entered by a user.

[0369] "Sentiment analysis" refers to the process of analyzing the tone and content of input data to identify the user's emotional state.

[0370] A "generative model" is a system that uses machine learning algorithms and refers to a technology that extracts themes and psychological tendencies from dream records through natural language processing.

[0371] "Feedback" refers to information provided to users based on analysis results, including psychological insights and suggestions for improvement.

[0372] "Mental health advice" refers to advice that includes customized behavioral suggestions and relaxation methods based on the user's emotional state and dream content.

[0373] A "storage device" refers to a hardware or software system used to store information.

[0374] "Visualization" refers to presenting analysis results in graph or chart format to make them easy for users to understand.

[0375] This invention is a system that allows users to input dream records in voice or text, analyzes the content, and provides psychological insights and mental health advice. The following hardware and software are used for implementation.

[0376] Users launch a dedicated application using a mobile device or tablet and input the content of their dream. In the case of voice input, voice data is acquired through the device's microphone and converted into text data using speech recognition software (e.g., speech recognition API).

[0377] The device passes the converted text data to an emotion analysis engine for tone and intonation analysis. The analysis results and the dream text data are formatted into JSON format. The device then sends this data to the server via a security protocol (e.g., HTTPS).

[0378] The server stores the received data in storage and analyzes it by running a generative model (e.g., a natural language processing model). The generative model uses machine learning algorithms to analyze dream themes and psychological tendencies. Simultaneously, an emotion analysis engine adds user emotion information.

[0379] Based on the analysis results, the server generates feedback, which is then visualized using graphs and charts. This feedback includes personalized advice for mental health, taking into account the user's current psychological state.

[0380] The device displays this feedback and advice to the user, helping them deepen the psychological insights gained through dream recording.

[0381] For example, if a user dreams of getting lost in the rain, the system analyzes this as a symbol of anxiety or a sense of loss. Based on statistical methods, the generative model prompts the user with the question, "Do you feel unsure of which direction to go right now?" The emotion analysis engine also suggests relaxation techniques, such as, "Try deep breathing or meditation to regain your composure."

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

[0383] Step 1:

[0384] The user launches the application and enters a record of their dream. Input can be done via voice or text. For voice input, the user speaks the dream content using the device's microphone. The device receives the voice input and stores it internally as audio data. Both input and output are audio data.

[0385] Step 2:

[0386] The device launches speech recognition software to acquire speech data and convert it into text data. Specifically, the device passes the speech data to an API and receives the converted text result. The input is speech data, and the output is text data. In this process, speech-specific tones and intonation are also used in the analysis.

[0387] Step 3:

[0388] The terminal passes the converted text data to the sentiment analysis engine, which then performs sentiment analysis. The sentiment analysis engine receives the text data as input, identifies the user's emotional state, and generates analysis results. The output is the sentiment analysis result. This process supplements the emotional information extracted from the text data.

[0389] Step 4:

[0390] The terminal integrates text data and sentiment analysis results in JSON format, and then formats the data. The formatted data is prepared for communication with the server. The input is text data and sentiment analysis results, and the output is formatted JSON data. Specifically, the data structure is converted to JSON and adapted to a secure communication protocol.

[0391] Step 5:

[0392] The terminal sends formatted JSON data to the server using a security protocol. The data being sent is already properly formatted JSON data. Through this communication process, the terminal securely transmits information to the server while preventing data leakage.

[0393] Step 6:

[0394] The server receives JSON data sent from the terminal and verifies its contents. The server checks the received data for tampering or omissions and confirms that it is valid data. The input is the received JSON data, and the output is the verified data.

[0395] Step 7:

[0396] The server saves the verified data to storage and starts the generative AI model to begin data analysis. The generative model performs natural language processing on the saved data to extract dream themes and psychological tendencies. The input is the verified data, and the output is the analysis results. Specifically, the generative model uses that data to generate insights.

[0397] Step 8:

[0398] The server generates feedback and prompts based on the analysis results of the generated AI model. The results of the sentiment analysis engine are also reflected in the feedback. The input is the analysis results, and the output is the feedback and prompts for the user. In this process, information is presented in a format that is easy for the user to understand.

[0399] Step 9:

[0400] The server creates customized mental health advice for the user based on the generated feedback and prompts. The input is the generated feedback and prompts, and the output is the customized advice. Specific examples include comforting messages and action plans that look to the distant future.

[0401] Step 10:

[0402] The terminal displays feedback, prompts, and mental health advice received from the server to the user. Through this, the user can instantly see ways to improve their psychological state in daily life. Input is data from the server, and output is a visual display to the user. In this process, the information is presented in a format that is easy for the user to understand.

[0403] (Application Example 2)

[0404] 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."

[0405] In modern life, managing the mental health of brick-and-mortar store staff is a crucial issue. However, a lack of effective means to identify the stress and anxiety staff experience at work and provide appropriate support can lead to decreased job satisfaction and productivity.

[0406] 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.

[0407] In this invention, the server includes means for acquiring emotions and events from a user in voice or text, means for processing the acquired records of emotions and events and storing them in an information aggregation device, and means for running a generative AI model to analyze the stored records of emotions and events. This enables staff at physical stores to regularly record their own emotions and experiences and receive appropriate feedback and action suggestions based on the data.

[0408] A "user" refers to an individual who uses the system to record their emotions and experiences, and to receive feedback and advice.

[0409] "Audio or text" refers to the format of information that a user provides to the system, including spoken word and written text.

[0410] "Records of emotions and events" refers to specific data about the emotions and experiences that users have felt on a daily basis.

[0411] An "information aggregation device" refers to a data storage device that stores records of acquired emotions and events for later analysis.

[0412] A "generative AI model" refers to an artificial intelligence algorithm that analyzes input information and identifies patterns.

[0413] "Feedback" refers to responses, including suggestions and evaluations, provided to the user based on the analysis results.

[0414] "Action suggestions and stress management methods" refer to specific action guidelines and methods for reducing stress that are provided to users based on the analysis results.

[0415] This invention is a system that allows staff at physical stores to record emotions and events and receive feedback and action suggestions based on that information. The system consists of a voice or text input device, an information aggregation device, a server that runs a generative AI model, and a user terminal.

[0416] Users record their emotions and events using devices such as smartphones via voice or text input. During this process, speech recognition software, such as Google Cloud Speech-to-Text, installed on the smartphone is used to convert the voice data into text. The converted data is then securely stored on an information aggregation device via the internet.

[0417] Data stored in the information aggregation device is received by a server and analyzed using a generating AI model. Natural language processing algorithms such as OpenAI's GPT-3 are used for the analysis to extract specific emotional patterns and psychological tendencies. Furthermore, feedback is generated based on the analysis results to provide a deeper understanding of the user's psychological state. Behavioral suggestions and stress management methods are also generated and provided to the user.

[0418] The feedback results are displayed visually to the user on their device. Specifically, guidance related to the analyzed emotions and events is displayed as text and graphs. This allows users to understand their own psychological state and obtain information to take appropriate actions in different situations.

[0419] For example, if a staff member at a store records their stress levels during work and the system analyzes this as "peak tension," it will then provide specific suggestions regarding relaxation techniques or adjustments to working hours.

[0420] For the generative AI model, the following prompt can be used: "A staff member reported feeling stressed during peak hours. Analyze their emotional tone and provide recommendations for stress management and relaxation techniques." This will generate appropriate feedback and provide it to the user.

[0421] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0422] Step 1:

[0423] Users record their emotions and events using their smartphones via voice or text. For voice input, the device uses Google Cloud Speech-to-Text to convert the speech to text. The input for this step is the user's voice or text, and the output is text data. Speech recognition-based data conversion takes place here.

[0424] Step 2:

[0425] The terminal transmits the converted text data to the information aggregation device using a secure communication protocol. The input to this step is the text data generated in step 1, and the output is the storage of the data in the information aggregation device. HTTPS communication is used to ensure the security of the information during data transmission.

[0426] Step 3:

[0427] The server receives text data stored in the information aggregation device and inputs it into the generating AI model. In this step, the input is data from the information aggregation device, and the output is the analysis result by the AI ​​model. Natural language processing is performed using OpenAI GPT-3 for data analysis, and emotional patterns and psychological tendencies are extracted.

[0428] Step 4:

[0429] The server generates feedback and action suggestions for the user using the analysis results of the generated AI model. The input is the analysis results obtained in step 3, and the output is feedback information. In feedback creation, data processing is performed to convert the analysis results into a format that is easy for the user to understand.

[0430] Step 5:

[0431] The terminal visually displays feedback sent from the server to the user. The input is feedback data from the server, and the output is the display of information to the user. Here, the data is provided to the user in the form of text or graphs.

[0432] 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.

[0433] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0434] 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.

[0435] [Third Embodiment]

[0436] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0437] 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.

[0438] 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).

[0439] 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.

[0440] 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.

[0441] 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).

[0442] 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.

[0443] 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.

[0444] 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.

[0445] 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.

[0446] 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.

[0447] 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".

[0448] This invention is a system that allows users to effectively record, understand, and use their dreams to improve their mental health. The system provides a consistent flow of dream recording, storage, analysis, feedback, and advice.

[0449] First, the device provides an application that allows the user to input the content of their dreams. The user can input their dreams directly in text format or in voice format, and the device can convert this into text using speech recognition technology. As a result, the content of the dreams is acquired as digital data.

[0450] Next, this digital data is sent from the terminal to the server, where the server verifies that the data is properly formatted. Once verified, the data is securely stored in the database. This storage process prepares the dream record for analysis.

[0451] The server then inputs the saved dream record data into a generating AI model. This AI model uses natural language processing (NLP) techniques to analyze the dreams and extract themes and underlying psychological elements. Specifically, the AI ​​analyzes the language patterns and key words used in the dreams and adds psychological meanings.

[0452] Once the analysis is complete, the server summarizes the results and generates feedback in an easy-to-understand format for the user. This feedback not only provides a text-based summary but also uses visual diagrams and charts to help the user intuitively understand the analysis results. The server also takes the user's past data into consideration to provide individually customized mental health advice.

[0453] The device presents the user with generated feedback and advice. For example, if a user dreams of being chased by waves, the system might interpret this as a symbol of anxiety or stress and display feedback recommending that the user practice relaxation techniques.

[0454] This process allows users to understand the psychological state hidden in their dreams and obtain information that can help improve their mental health. The system provides users with opportunities to deepen their self-understanding on a daily basis and helps them maintain a healthier mental state.

[0455] The following describes the processing flow.

[0456] Step 1:

[0457] After waking up, the user launches the application to record their dreams. The user enters the content of their dreams into the device via voice input or text input. If using voice input, the user should describe the dream in detail.

[0458] Step 2:

[0459] When the device receives voice input, it uses its built-in speech recognition system to convert the voice data into text. The converted text is then displayed in a format that allows the user to review its content.

[0460] Step 3:

[0461] The terminal formats the verified text data in JSON format and sends it to the server using an endpoint-secure protocol.

[0462] Step 4:

[0463] The server temporarily records the received text data for analysis and automatically verifies that the format is correct. If there are any problems, it sends an error message to the terminal.

[0464] Step 5:

[0465] The server inputs the stored dream data into a generating AI model. The AI ​​model processes this data and extracts patterns and keywords from the text that indicate psychological tendencies.

[0466] Step 6:

[0467] The server receives analysis results from the AI ​​model and generates feedback for the user. This includes creating a text summary based on the analysis results and generating visual diagrams and charts.

[0468] Step 7:

[0469] The server considers the analysis results and generates customized mental health advice based on the user's psychological state.

[0470] Step 8:

[0471] The server sends the completed feedback and advice to the terminal. The terminal displays this on its user interface, allowing the user to view the information.

[0472] Step 9:

[0473] Users review the displayed feedback and advice, and strive to improve their mental health by implementing recommended action plans and relaxation methods as needed.

[0474] (Example 1)

[0475] 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."

[0476] In modern society, it is important for users to effectively record their dreams and understand the underlying psychological states they represent. However, manually recording dreams or undergoing professional psychological analysis is time-consuming and costly. Therefore, there is a need for a way for users to easily record their dreams and improve their mental health through the analysis results.

[0477] 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.

[0478] In this invention, the server includes means for receiving dream content from a user in voice or text, means for processing the acquired dream content and storing it in an information database, and means for executing a generative AI model using natural language processing technology to analyze the stored dream content. This allows users to easily record their dreams and improve their mental health by receiving feedback on their psychological state based on the analysis.

[0479] A "user" refers to an individual or group that uses the system to record the content of their dreams and receives analysis results and feedback.

[0480] "Audio or text" refers to the communication format used by users to record the content of their dreams, and is entered as either audio or text information.

[0481] "Information acquisition means" refers to functions and methods for receiving dream content from users in audio or text format.

[0482] An "information database" refers to a data storage system that systematically stores the content and analysis results of acquired dreams for use in subsequent processing and analysis.

[0483] A "generative AI model" refers to an artificial intelligence model that uses natural language processing technology to analyze the content of a user's dreams and extract psychological themes and elements.

[0484] "Natural language processing technology" refers to the technology used to process, analyze, and understand human language using computers.

[0485] "Analysis results" refer to themes and psychological insights extracted from dream records by the generative AI model, and include information provided to the user as feedback.

[0486] "Feedback" refers to information provided to the user based on the analysis results, including guidelines and advice to deepen their understanding of the content of their dreams.

[0487] "Mental health advice" refers to guidance and suggestions aimed at improving a user's psychological state and enhancing their mental well-being.

[0488] "Visual diagrams and graphs" refer to visual representations used to present analysis results to users in an easily understandable way.

[0489] This invention is a system that allows users to easily record the content of their dreams, deepen their understanding of their psychological state through analysis, and use this information to improve their mental health. Users input their dream records in voice or text format via a device such as a smartphone or tablet. Voice input is converted to text using speech recognition technology on the device. General speech analysis software is used for this speech recognition.

[0490] The content of the dream entered is sent from the terminal to the server. The server securely receives the data via HTTPS communication, standardizes the format, and stores it in an information database. This stored data is then supplied to a generative AI model that uses natural language processing technology for analysis. A general-purpose AI model with excellent natural language processing capabilities is used for the generative AI model.

[0491] The server analyzes dream records using a generative AI model and generates feedback based on themes and psychological insights derived from them. This feedback utilizes visual diagrams and graphs to ensure intuitive understanding for the user. In addition, it compares the current data with previously recorded data to provide personalized mental health advice.

[0492] The device displays feedback and advice received from the server to the user. For example, if a user dreams of being chased by waves, the system interprets this as a symbol of anxiety or stress and displays feedback suggesting the introduction of relaxation techniques.

[0493] An example of a prompt message could be, "Please tell me about the content of a recent dream you had. I want to know what kind of psychological state it represents." Based on this prompt message, the system will understand the content of the user's dream and provide appropriate analysis and feedback.

[0494] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0495] Step 1:

[0496] The user inputs the content of their dream using a terminal application. The user can input directly in text format or in voice format. In the case of voice input, the terminal uses speech recognition technology to convert the voice to text. During input, the user freely describes the content of their dream from the previous night, and the result is captured as text data. The output is text data containing the input dream record.

[0497] Step 2:

[0498] The device sends the acquired text data to the server. A secure HTTPS protocol is used for transmission. The data processing performed here is encryption to protect user privacy. The input is text data from the device, and the output is data in a securely encrypted format.

[0499] Step 3:

[0500] The server performs format validation before saving the received data to the database. This process verifies the data's integrity and checks for any abnormal formats. The input is the received encrypted data, and the output is the parsing-ready data stored in the database.

[0501] Step 4:

[0502] The server inputs stored dream data into a generative AI model. The generative AI model uses natural language processing techniques to extract dream themes and underlying psychological elements. This data analysis includes word frequency analysis and contextual analysis. The input is dream data from a database, and the output is the analyzed themes and psychological insights.

[0503] Step 5:

[0504] The server generates user-facing feedback based on the analysis results. This feedback includes comparisons with past dream data and is presented in visual diagrams and graphs. The data processing performed here involves summarizing and visualizing the analysis information to make it easier for the user to understand. The input is the analysis information, and the output is visual data including feedback and advice.

[0505] Step 6:

[0506] The device displays the generated feedback and advice to the user. The device's application provides a user-friendly design and navigation to make the feedback easy to understand. In the example of a dream where the user is "chased by waves," the user is offered specific relaxation techniques to reduce stress. The input is visual data from the server, and the output is the feedback information displayed to the user.

[0507] (Application Example 1)

[0508] 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."

[0509] There is a challenge in that there are insufficient methods provided for users to understand their psychological state and improve their mental health through the process of effectively saving and analyzing dream records. Furthermore, there is a need for a means to analyze dream content in a short period of time and present it in a visually easy-to-understand format. In addition, there is a need for a system that allows users to continuously track their psychological changes by comparing current dream data with past dream data.

[0510] 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.

[0511] In this invention, the server includes means for acquiring dream information from a user in digital format, means for processing the acquired dream information and storing it in a memory area, means for executing generative AI technology to analyze the stored dream information, means for generating graphs and charts for visualizing the analysis results, and means for analyzing the dream information based on the passage of time and comparing it with past data. This makes it possible for the user to easily understand the analysis results based on their dreams visually and grasp the changes in their own psychological state.

[0512] "Digital format" refers to representing information in a way that can be processed by a computer, using electrical signals or numerical data.

[0513] "Dream information" refers to the content of dreams experienced by the user, and is recorded in audio or text format.

[0514] "Storage area" refers to a part of a computer or database where data can be stored for a long or short period of time.

[0515] "Generative AI technology" is a process that uses artificial intelligence to analyze data and generate analysis results.

[0516] "Graphs and charts" are shapes and diagrams used to visually represent data and analysis results.

[0517] "Analyzing based on the passage of time" means analyzing how specific data has changed over time.

[0518] The system for carrying out the present invention helps users input dream records and understand their psychological state based on that information. This system includes a terminal, a server, and generative AI technology.

[0519] First, the device is a mobile device such as a smartphone, and the user can use this device to input the content of their dream. Input can be done by voice or text, and if voice input is used, it will be converted to text using the Google Speech-to-Text API.

[0520] Next, the dream information acquired by the device is sent to a cloud server via the internet. Specifically, AWS Lambda receives the data, and then the data is analyzed by the Google Cloud Natural Language API. This analysis extracts the themes and psychological elements within the dream.

[0521] Once the analysis is complete, the server generates visually easy-to-understand feedback for the user based on the analysis results. Using the D3.js library, the results are visualized in graph and chart format. This allows the user to gain psychological insights based on the content of their dreams.

[0522] Furthermore, the server compares current dream data with past dream data, providing advice that tracks psychological changes over time. This information is extremely useful in supporting the improvement of the user's mental health.

[0523] For example, if a user enters that they "dreamed of walking in water," the system can analyze this as a symbol of calmness and mental purification, and generate advice encouraging relaxation techniques and self-reflection.

[0524] An example of a prompt might be, "Analyze the content of the dream and provide psychological elements and related advice." The system can use this prompt to generate specific feedback for the user.

[0525] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0526] Step 1:

[0527] The user enters the content of their dream into the device either by voice or text. In the case of voice input, the device uses the Google Speech-to-Text API to convert the voice into text and generate text data. This text data is then used as input for the next step.

[0528] Step 2:

[0529] The device sends the acquired text data to the cloud server. The cloud server receives the data and verifies that it is properly formatted. The received, formatted text data becomes the input for the next step.

[0530] Step 3:

[0531] The server uses the Google Cloud Natural Language API to analyze the received text data. Here, data analysis techniques are used to extract themes and psychological elements of the dream. The results of this analysis will serve as input for the next step.

[0532] Step 4:

[0533] The server uses the D3.js library to visualize the analysis results. Specifically, it generates graphs and charts based on the analysis results, making them intuitively understandable to the user. This visualized data then serves as input for the next step.

[0534] Step 5:

[0535] The server compares past dream data with current analysis results to analyze changes in the user's psychological state. Based on this comparison, it generates personalized mental health advice. This advice serves as input for the next step.

[0536] Step 6:

[0537] The terminal displays visualized data received from the server along with generated advice to the user. The user receives psychological analysis results based on their dreams, which can help improve their mental health.

[0538] 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.

[0539] This invention is a system that effectively analyzes dream records entered by users via voice or text, enabling them to gain psychological insights. Furthermore, by combining it with an emotion engine, it analyzes the user's emotions and provides personalized advice to help improve their mental health.

[0540] First, the user can input the content of their dream through the application. If the user chooses voice input, the device uses speech recognition to convert the voice into text data. At this stage, the emotion engine evaluates the specific tone and intonation of the voice data to identify the emotions the user is experiencing.

[0541] The terminal correctly formats the acquired text data in JSON format and then sends it to the server using a secure communication protocol. The server verifies the received data, saves it, and then prepares it for analysis.

[0542] The server inputs the stored dream record data into a generative AI model. The AI ​​model uses natural language processing algorithms to extract dream themes and psychological tendencies from this data. Simultaneously, the emotion engine analyzes the user's emotions and adds insights into their psychological state.

[0543] Once the analysis results are obtained, the server generates feedback based on those results. This feedback includes a text-based summary, and the information is further presented in the form of diagrams and charts. This allows the user to intuitively understand the extracted information. The feedback also includes additional sentiment analysis results from the sentiment engine, which helps to provide a deeper understanding of the user's emotional background.

[0544] Based on feedback, analysis results, and emotional state, the server generates personalized mental health advice for the user. This advice is tailored to the user's current emotional state and includes specific action suggestions and relaxation techniques.

[0545] Finally, the device displays the generated feedback and advice to the user. For example, if the user experienced fear in a dream, the system recognizes this as anxiety or tension and recommends practicing relaxation techniques.

[0546] The introduction of this system will allow users to deepen their psychological insights gained through dreams, take a more conscious approach to their own emotions, and contribute to improving their mental health.

[0547] The following describes the processing flow.

[0548] Step 1:

[0549] The user launches the application and enters information about their dream from the previous night via voice or text. If they choose voice input, they describe the dream in detail.

[0550] Step 2:

[0551] The device converts voice input into text data. This conversion uses speech recognition technology, and simultaneously, an emotion engine analyzes the voice tone to estimate the user's emotional state.

[0552] Step 3:

[0553] The terminal organizes the converted text data and formats it into JSON format. This data is then sent to the server using a secure communication channel.

[0554] Step 4:

[0555] The server validates the received JSON data. Once the data's validity is confirmed, it is saved to the database.

[0556] Step 5:

[0557] The server analyzes the stored dream records using a generated AI model. The model identifies keywords and patterns related to the dreams and extracts psychological themes.

[0558] Step 6:

[0559] The server integrates the emotional data obtained by the emotion engine into the analysis results. This visualizes the relationship between the user's emotional state and the content of their dreams.

[0560] Step 7:

[0561] The server generates feedback based on the analysis results and sentiment data. The feedback is summarized in text format and also visually represented in diagrams and charts.

[0562] Step 8:

[0563] The server incorporates customized mental health advice into the feedback, tailored to the user's emotional state. This advice includes specific relaxation techniques and positive behavioral patterns.

[0564] Step 9:

[0565] The device displays final feedback and advice on the user's screen. Based on this information, the user can review the psychological insights and emotional management methods gained from their dreams and apply them to their daily life.

[0566] (Example 2)

[0567] 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."

[0568] There is a need to provide users with a means to deeply understand their own psychological state and improve their mental health by recording and analyzing the content of their dreams. Conventional technologies have limitations in properly analyzing dream content and providing users with appropriate feedback and advice, so a new method is needed to solve this problem.

[0569] 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.

[0570] This invention includes a server that acquires dream records from a user in text or voice, converts voice to text data, performs sentiment analysis, organizes the sentiment analysis results and dream records in a specific format and stores them in a memory device, and runs a generative model to analyze the stored dream records. This makes it possible to provide feedback and customized mental health advice based on the user's psychological state.

[0571] A "user" refers to someone who uses the system to input records of their dreams and receives analysis results and advice.

[0572] "Dream records" refer to the specific details of dreams experienced by the user while awake, and are entered in text or audio format.

[0573] "Voice data" refers to information that a user inputs as voice and that is converted into text data by a speech recognition system.

[0574] "Character data" refers to information converted by a character recognition system from text or voice data entered by a user.

[0575] "Sentiment analysis" refers to the process of analyzing the tone and content of input data to identify the user's emotional state.

[0576] A "generative model" is a system that uses machine learning algorithms and refers to a technology that extracts themes and psychological tendencies from dream records through natural language processing.

[0577] "Feedback" refers to information provided to users based on analysis results, including psychological insights and suggestions for improvement.

[0578] "Mental health advice" refers to advice that includes customized behavioral suggestions and relaxation methods based on the user's emotional state and dream content.

[0579] A "storage device" refers to a hardware or software system used to store information.

[0580] "Visualization" refers to presenting analysis results in graph or chart format to make them easy for users to understand.

[0581] This invention is a system that allows users to input dream records in voice or text, analyzes the content, and provides psychological insights and mental health advice. The following hardware and software are used for implementation.

[0582] Users launch a dedicated application using a mobile device or tablet and input the content of their dream. In the case of voice input, voice data is acquired through the device's microphone and converted into text data using speech recognition software (e.g., speech recognition API).

[0583] The device passes the converted text data to an emotion analysis engine for tone and intonation analysis. The analysis results and the dream text data are formatted into JSON format. The device then sends this data to the server via a security protocol (e.g., HTTPS).

[0584] The server stores the received data in storage and analyzes it by running a generative model (e.g., a natural language processing model). The generative model uses machine learning algorithms to analyze dream themes and psychological tendencies. Simultaneously, an emotion analysis engine adds user emotion information.

[0585] Based on the analysis results, the server generates feedback, which is then visualized using graphs and charts. This feedback includes personalized advice for mental health, taking into account the user's current psychological state.

[0586] The device displays this feedback and advice to the user, helping them deepen the psychological insights gained through dream recording.

[0587] For example, if a user dreams of getting lost in the rain, the system analyzes this as a symbol of anxiety or a sense of loss. Based on statistical methods, the generative model prompts the user with the question, "Do you feel unsure of which direction to go right now?" The emotion analysis engine also suggests relaxation techniques, such as, "Try deep breathing or meditation to regain your composure."

[0588] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0589] Step 1:

[0590] The user launches the application and enters a record of their dream. Input can be done via voice or text. For voice input, the user speaks the dream content using the device's microphone. The device receives the voice input and stores it internally as audio data. Both input and output are audio data.

[0591] Step 2:

[0592] The device launches speech recognition software to acquire speech data and convert it into text data. Specifically, the device passes the speech data to an API and receives the converted text result. The input is speech data, and the output is text data. In this process, speech-specific tones and intonation are also used in the analysis.

[0593] Step 3:

[0594] The terminal passes the converted text data to the sentiment analysis engine, which then performs sentiment analysis. The sentiment analysis engine receives the text data as input, identifies the user's emotional state, and generates analysis results. The output is the sentiment analysis result. This process supplements the emotional information extracted from the text data.

[0595] Step 4:

[0596] The terminal integrates text data and sentiment analysis results in JSON format, and then formats the data. The formatted data is prepared for communication with the server. The input is text data and sentiment analysis results, and the output is formatted JSON data. Specifically, the data structure is converted to JSON and adapted to a secure communication protocol.

[0597] Step 5:

[0598] The terminal sends formatted JSON data to the server using a security protocol. The data being sent is already properly formatted JSON data. Through this communication process, the terminal securely transmits information to the server while preventing data leakage.

[0599] Step 6:

[0600] The server receives JSON data sent from the terminal and verifies its contents. The server checks the received data for tampering or omissions and confirms that it is valid data. The input is the received JSON data, and the output is the verified data.

[0601] Step 7:

[0602] The server saves the verified data to storage and starts the generative AI model to begin data analysis. The generative model performs natural language processing on the saved data to extract dream themes and psychological tendencies. The input is the verified data, and the output is the analysis results. Specifically, the generative model uses that data to generate insights.

[0603] Step 8:

[0604] The server generates feedback and prompts based on the analysis results of the generated AI model. The results of the sentiment analysis engine are also reflected in the feedback. The input is the analysis results, and the output is the feedback and prompts for the user. In this process, information is presented in a format that is easy for the user to understand.

[0605] Step 9:

[0606] The server creates customized mental health advice for the user based on the generated feedback and prompts. The input is the generated feedback and prompts, and the output is the customized advice. Specific examples include comforting messages and action plans that look to the distant future.

[0607] Step 10:

[0608] The terminal displays feedback, prompts, and mental health advice received from the server to the user. Through this, the user can instantly see ways to improve their psychological state in daily life. Input is data from the server, and output is a visual display to the user. In this process, the information is presented in a format that is easy for the user to understand.

[0609] (Application Example 2)

[0610] 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."

[0611] In modern life, managing the mental health of brick-and-mortar store staff is a crucial issue. However, a lack of effective means to identify the stress and anxiety staff experience at work and provide appropriate support can lead to decreased job satisfaction and productivity.

[0612] 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.

[0613] In this invention, the server includes means for acquiring emotions and events from a user in voice or text, means for processing the acquired records of emotions and events and storing them in an information aggregation device, and means for running a generative AI model to analyze the stored records of emotions and events. This enables staff at physical stores to regularly record their own emotions and experiences and receive appropriate feedback and action suggestions based on the data.

[0614] A "user" refers to an individual who uses the system to record their emotions and experiences, and to receive feedback and advice.

[0615] "Audio or text" refers to the format of information that a user provides to the system, including spoken word and written text.

[0616] "Records of emotions and events" refers to specific data about the emotions and experiences that users have felt on a daily basis.

[0617] An "information aggregation device" refers to a data storage device that stores records of acquired emotions and events for later analysis.

[0618] A "generative AI model" refers to an artificial intelligence algorithm that analyzes input information and identifies patterns.

[0619] "Feedback" refers to responses, including suggestions and evaluations, provided to the user based on the analysis results.

[0620] "Action suggestions and stress management methods" refer to specific action guidelines and methods for reducing stress that are provided to users based on the analysis results.

[0621] This invention is a system that allows staff at physical stores to record emotions and events and receive feedback and action suggestions based on that information. The system consists of a voice or text input device, an information aggregation device, a server that runs a generative AI model, and a user terminal.

[0622] Users record their emotions and events using devices such as smartphones via voice or text input. During this process, speech recognition software, such as Google Cloud Speech-to-Text, installed on the smartphone is used to convert the voice data into text. The converted data is then securely stored on an information aggregation device via the internet.

[0623] Data stored in the information aggregation device is received by a server and analyzed using a generating AI model. Natural language processing algorithms such as OpenAI's GPT-3 are used for the analysis to extract specific emotional patterns and psychological tendencies. Furthermore, feedback is generated based on the analysis results to provide a deeper understanding of the user's psychological state. Behavioral suggestions and stress management methods are also generated and provided to the user.

[0624] The feedback results are displayed visually to the user on their device. Specifically, guidance related to the analyzed emotions and events is displayed as text and graphs. This allows users to understand their own psychological state and obtain information to take appropriate actions in different situations.

[0625] For example, if a staff member at a store records their stress levels during work and the system analyzes this as "peak tension," it will then provide specific suggestions regarding relaxation techniques or adjustments to working hours.

[0626] For the generative AI model, the following prompt can be used: "A staff member reported feeling stressed during peak hours. Analyze their emotional tone and provide recommendations for stress management and relaxation techniques." This will generate appropriate feedback and provide it to the user.

[0627] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0628] Step 1:

[0629] Users record their emotions and events using their smartphones via voice or text. For voice input, the device uses Google Cloud Speech-to-Text to convert the speech to text. The input for this step is the user's voice or text, and the output is text data. Speech recognition-based data conversion takes place here.

[0630] Step 2:

[0631] The terminal transmits the converted text data to the information aggregation device using a secure communication protocol. The input to this step is the text data generated in step 1, and the output is the storage of the data in the information aggregation device. HTTPS communication is used to ensure the security of the information during data transmission.

[0632] Step 3:

[0633] The server receives text data stored in the information aggregation device and inputs it into the generating AI model. In this step, the input is data from the information aggregation device, and the output is the analysis result by the AI ​​model. Natural language processing is performed using OpenAI GPT-3 for data analysis, and emotional patterns and psychological tendencies are extracted.

[0634] Step 4:

[0635] The server generates feedback and action suggestions for the user using the analysis results of the generated AI model. The input is the analysis results obtained in step 3, and the output is feedback information. In feedback creation, data processing is performed to convert the analysis results into a format that is easy for the user to understand.

[0636] Step 5:

[0637] The terminal visually displays feedback sent from the server to the user. The input is feedback data from the server, and the output is the display of information to the user. Here, the data is provided to the user in the form of text or graphs.

[0638] 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.

[0639] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0640] 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.

[0641] [Fourth Embodiment]

[0642] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0643] 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.

[0644] 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).

[0645] 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.

[0646] 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.

[0647] 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).

[0648] 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.

[0649] 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.

[0650] 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.

[0651] 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.

[0652] 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.

[0653] 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.

[0654] 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".

[0655] This invention is a system that allows users to effectively record, understand, and use their dreams to improve their mental health. The system provides a consistent flow of dream recording, storage, analysis, feedback, and advice.

[0656] First, the device provides an application that allows the user to input the content of their dreams. The user can input their dreams directly in text format or in voice format, and the device can convert this into text using speech recognition technology. As a result, the content of the dreams is acquired as digital data.

[0657] Next, this digital data is sent from the terminal to the server, where the server verifies that the data is properly formatted. Once verified, the data is securely stored in the database. This storage process prepares the dream record for analysis.

[0658] The server then inputs the saved dream record data into a generating AI model. This AI model uses natural language processing (NLP) techniques to analyze the dreams and extract themes and underlying psychological elements. Specifically, the AI ​​analyzes the language patterns and key words used in the dreams and adds psychological meanings.

[0659] Once the analysis is complete, the server summarizes the results and generates feedback in an easy-to-understand format for the user. This feedback not only provides a text-based summary but also uses visual diagrams and charts to help the user intuitively understand the analysis results. The server also takes the user's past data into consideration to provide individually customized mental health advice.

[0660] The device presents the user with generated feedback and advice. For example, if a user dreams of being chased by waves, the system might interpret this as a symbol of anxiety or stress and display feedback recommending that the user practice relaxation techniques.

[0661] This process allows users to understand the psychological state hidden in their dreams and obtain information that can help improve their mental health. The system provides users with opportunities to deepen their self-understanding on a daily basis and helps them maintain a healthier mental state.

[0662] The following describes the processing flow.

[0663] Step 1:

[0664] After waking up, the user launches the application to record their dreams. The user enters the content of their dreams into the device via voice input or text input. If using voice input, the user should describe the dream in detail.

[0665] Step 2:

[0666] When the device receives voice input, it uses its built-in speech recognition system to convert the voice data into text. The converted text is then displayed in a format that allows the user to review its content.

[0667] Step 3:

[0668] The terminal formats the verified text data in JSON format and sends it to the server using an endpoint-secure protocol.

[0669] Step 4:

[0670] The server temporarily records the received text data for analysis and automatically verifies that the format is correct. If there are any problems, it sends an error message to the terminal.

[0671] Step 5:

[0672] The server inputs the stored dream data into a generating AI model. The AI ​​model processes this data and extracts patterns and keywords from the text that indicate psychological tendencies.

[0673] Step 6:

[0674] The server receives analysis results from the AI ​​model and generates feedback for the user. This includes creating a text summary based on the analysis results and generating visual diagrams and charts.

[0675] Step 7:

[0676] The server considers the analysis results and generates customized mental health advice based on the user's psychological state.

[0677] Step 8:

[0678] The server sends the completed feedback and advice to the terminal. The terminal displays this on its user interface, allowing the user to view the information.

[0679] Step 9:

[0680] Users review the displayed feedback and advice, and strive to improve their mental health by implementing recommended action plans and relaxation methods as needed.

[0681] (Example 1)

[0682] 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".

[0683] In modern society, it is important for users to effectively record their dreams and understand the underlying psychological states they represent. However, manually recording dreams or undergoing professional psychological analysis is time-consuming and costly. Therefore, there is a need for a way for users to easily record their dreams and improve their mental health through the analysis results.

[0684] 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.

[0685] In this invention, the server includes means for receiving dream content from a user in voice or text, means for processing the acquired dream content and storing it in an information database, and means for executing a generative AI model using natural language processing technology to analyze the stored dream content. This allows users to easily record their dreams and improve their mental health by receiving feedback on their psychological state based on the analysis.

[0686] A "user" refers to an individual or group that uses the system to record the content of their dreams and receives analysis results and feedback.

[0687] "Audio or text" refers to the communication format used by users to record the content of their dreams, and is entered as either audio or text information.

[0688] "Information acquisition means" refers to functions and methods for receiving dream content from users in audio or text format.

[0689] An "information database" refers to a data storage system that systematically stores the content and analysis results of acquired dreams for use in subsequent processing and analysis.

[0690] A "generative AI model" refers to an artificial intelligence model that uses natural language processing technology to analyze the content of a user's dreams and extract psychological themes and elements.

[0691] "Natural language processing technology" refers to the technology used to process, analyze, and understand human language using computers.

[0692] "Analysis results" refer to themes and psychological insights extracted from dream records by the generative AI model, and include information provided to the user as feedback.

[0693] "Feedback" refers to information provided to the user based on the analysis results, including guidelines and advice to deepen their understanding of the content of their dreams.

[0694] "Mental health advice" refers to guidance and suggestions aimed at improving a user's psychological state and enhancing their mental well-being.

[0695] "Visual diagrams and graphs" refer to visual representations used to present analysis results to users in an easily understandable way.

[0696] This invention is a system that allows users to easily record the content of their dreams, deepen their understanding of their psychological state through analysis, and use this information to improve their mental health. Users input their dream records in voice or text format via a device such as a smartphone or tablet. Voice input is converted to text using speech recognition technology on the device. General speech analysis software is used for this speech recognition.

[0697] The content of the dream entered is sent from the terminal to the server. The server securely receives the data via HTTPS communication, standardizes the format, and stores it in an information database. This stored data is then supplied to a generative AI model that uses natural language processing technology for analysis. A general-purpose AI model with excellent natural language processing capabilities is used for the generative AI model.

[0698] The server analyzes dream records using a generative AI model and generates feedback based on themes and psychological insights derived from them. This feedback utilizes visual diagrams and graphs to ensure intuitive understanding for the user. In addition, it compares the current data with previously recorded data to provide personalized mental health advice.

[0699] The device displays feedback and advice received from the server to the user. For example, if a user dreams of being chased by waves, the system interprets this as a symbol of anxiety or stress and displays feedback suggesting the introduction of relaxation techniques.

[0700] An example of a prompt message could be, "Please tell me about the content of a recent dream you had. I want to know what kind of psychological state it represents." Based on this prompt message, the system will understand the content of the user's dream and provide appropriate analysis and feedback.

[0701] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0702] Step 1:

[0703] The user inputs the content of their dream using a terminal application. The user can input directly in text format or in voice format. In the case of voice input, the terminal uses speech recognition technology to convert the voice to text. During input, the user freely describes the content of their dream from the previous night, and the result is captured as text data. The output is text data containing the input dream record.

[0704] Step 2:

[0705] The device sends the acquired text data to the server. A secure HTTPS protocol is used for transmission. The data processing performed here is encryption to protect user privacy. The input is text data from the device, and the output is data in a securely encrypted format.

[0706] Step 3:

[0707] The server performs format validation before saving the received data to the database. This process verifies the data's integrity and checks for any abnormal formats. The input is the received encrypted data, and the output is the parsing-ready data stored in the database.

[0708] Step 4:

[0709] The server inputs stored dream data into a generative AI model. The generative AI model uses natural language processing techniques to extract dream themes and underlying psychological elements. This data analysis includes word frequency analysis and contextual analysis. The input is dream data from a database, and the output is the analyzed themes and psychological insights.

[0710] Step 5:

[0711] The server generates user-facing feedback based on the analysis results. This feedback includes comparisons with past dream data and is presented in visual diagrams and graphs. The data processing performed here involves summarizing and visualizing the analysis information to make it easier for the user to understand. The input is the analysis information, and the output is visual data including feedback and advice.

[0712] Step 6:

[0713] The device displays the generated feedback and advice to the user. The device's application provides a user-friendly design and navigation to make the feedback easy to understand. In the example of a dream where the user is "chased by waves," the user is offered specific relaxation techniques to reduce stress. The input is visual data from the server, and the output is the feedback information displayed to the user.

[0714] (Application Example 1)

[0715] 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".

[0716] There is a challenge in that there are insufficient methods provided for users to understand their psychological state and improve their mental health through the process of effectively saving and analyzing dream records. Furthermore, there is a need for a means to analyze dream content in a short period of time and present it in a visually easy-to-understand format. In addition, there is a need for a system that allows users to continuously track their psychological changes by comparing current dream data with past dream data.

[0717] 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.

[0718] In this invention, the server includes means for acquiring dream information from a user in digital format, means for processing the acquired dream information and storing it in a memory area, means for executing generative AI technology to analyze the stored dream information, means for generating graphs and charts for visualizing the analysis results, and means for analyzing the dream information based on the passage of time and comparing it with past data. This makes it possible for the user to easily understand the analysis results based on their dreams visually and grasp the changes in their own psychological state.

[0719] "Digital format" refers to representing information in a way that can be processed by a computer, using electrical signals or numerical data.

[0720] "Dream information" refers to the content of dreams experienced by the user, and is recorded in audio or text format.

[0721] "Storage area" refers to a part of a computer or database where data can be stored for a long or short period of time.

[0722] "Generative AI technology" is a process that uses artificial intelligence to analyze data and generate analysis results.

[0723] "Graphs and charts" are shapes and diagrams used to visually represent data and analysis results.

[0724] "Analyzing based on the passage of time" means analyzing how specific data has changed over time.

[0725] The system for carrying out the present invention helps users input dream records and understand their psychological state based on that information. This system includes a terminal, a server, and generative AI technology.

[0726] First, the device is a mobile device such as a smartphone, and the user can use this device to input the content of their dream. Input can be done by voice or text, and if voice input is used, it will be converted to text using the Google Speech-to-Text API.

[0727] Next, the dream information acquired by the device is sent to a cloud server via the internet. Specifically, AWS Lambda receives the data, and then the data is analyzed by the Google Cloud Natural Language API. This analysis extracts the themes and psychological elements within the dream.

[0728] Once the analysis is complete, the server generates visually easy-to-understand feedback for the user based on the analysis results. Using the D3.js library, the results are visualized in graph and chart format. This allows the user to gain psychological insights based on the content of their dreams.

[0729] Furthermore, the server compares current dream data with past dream data, providing advice that tracks psychological changes over time. This information is extremely useful in supporting the improvement of the user's mental health.

[0730] For example, if a user enters that they "dreamed of walking in water," the system can analyze this as a symbol of calmness and mental purification, and generate advice encouraging relaxation techniques and self-reflection.

[0731] An example of a prompt might be, "Analyze the content of the dream and provide psychological elements and related advice." The system can use this prompt to generate specific feedback for the user.

[0732] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0733] Step 1:

[0734] The user enters the content of their dream into the device either by voice or text. In the case of voice input, the device uses the Google Speech-to-Text API to convert the voice into text and generate text data. This text data is then used as input for the next step.

[0735] Step 2:

[0736] The device sends the acquired text data to the cloud server. The cloud server receives the data and verifies that it is properly formatted. The received, formatted text data becomes the input for the next step.

[0737] Step 3:

[0738] The server uses the Google Cloud Natural Language API to analyze the received text data. Here, data analysis techniques are used to extract themes and psychological elements of the dream. The results of this analysis will serve as input for the next step.

[0739] Step 4:

[0740] The server uses the D3.js library to visualize the analysis results. Specifically, it generates graphs and charts based on the analysis results, making them intuitively understandable to the user. This visualized data then serves as input for the next step.

[0741] Step 5:

[0742] The server compares past dream data with current analysis results to analyze changes in the user's psychological state. Based on this comparison, it generates personalized mental health advice. This advice serves as input for the next step.

[0743] Step 6:

[0744] The terminal displays visualized data received from the server along with generated advice to the user. The user receives psychological analysis results based on their dreams, which can help improve their mental health.

[0745] 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.

[0746] This invention is a system that effectively analyzes dream records entered by users via voice or text, enabling them to gain psychological insights. Furthermore, by combining it with an emotion engine, it analyzes the user's emotions and provides personalized advice to help improve their mental health.

[0747] First, the user can input the content of their dream through the application. If the user chooses voice input, the device uses speech recognition to convert the voice into text data. At this stage, the emotion engine evaluates the specific tone and intonation of the voice data to identify the emotions the user is experiencing.

[0748] The terminal correctly formats the acquired text data in JSON format and then sends it to the server using a secure communication protocol. The server verifies the received data, saves it, and then prepares it for analysis.

[0749] The server inputs the stored dream record data into a generative AI model. The AI ​​model uses natural language processing algorithms to extract dream themes and psychological tendencies from this data. Simultaneously, the emotion engine analyzes the user's emotions and adds insights into their psychological state.

[0750] Once the analysis results are obtained, the server generates feedback based on those results. This feedback includes a text-based summary, and the information is further presented in the form of diagrams and charts. This allows the user to intuitively understand the extracted information. The feedback also includes additional sentiment analysis results from the sentiment engine, which helps to provide a deeper understanding of the user's emotional background.

[0751] Based on feedback, analysis results, and emotional state, the server generates personalized mental health advice for the user. This advice is tailored to the user's current emotional state and includes specific action suggestions and relaxation techniques.

[0752] Finally, the device displays the generated feedback and advice to the user. For example, if the user experienced fear in a dream, the system recognizes this as anxiety or tension and recommends practicing relaxation techniques.

[0753] The introduction of this system will allow users to deepen their psychological insights gained through dreams, take a more conscious approach to their own emotions, and contribute to improving their mental health.

[0754] The following describes the processing flow.

[0755] Step 1:

[0756] The user launches the application and enters information about their dream from the previous night via voice or text. If they choose voice input, they describe the dream in detail.

[0757] Step 2:

[0758] The device converts voice input into text data. This conversion uses speech recognition technology, and simultaneously, an emotion engine analyzes the voice tone to estimate the user's emotional state.

[0759] Step 3:

[0760] The terminal organizes the converted text data and formats it into JSON format. This data is then sent to the server using a secure communication channel.

[0761] Step 4:

[0762] The server validates the received JSON data. Once the data's validity is confirmed, it is saved to the database.

[0763] Step 5:

[0764] The server analyzes the stored dream records using a generated AI model. The model identifies keywords and patterns related to the dreams and extracts psychological themes.

[0765] Step 6:

[0766] The server integrates the emotional data obtained by the emotion engine into the analysis results. This visualizes the relationship between the user's emotional state and the content of their dreams.

[0767] Step 7:

[0768] The server generates feedback based on the analysis results and sentiment data. The feedback is summarized in text format and also visually represented in diagrams and charts.

[0769] Step 8:

[0770] The server incorporates customized mental health advice into the feedback, tailored to the user's emotional state. This advice includes specific relaxation techniques and positive behavioral patterns.

[0771] Step 9:

[0772] The device displays final feedback and advice on the user's screen. Based on this information, the user can review the psychological insights and emotional management methods gained from their dreams and apply them to their daily life.

[0773] (Example 2)

[0774] 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".

[0775] There is a need to provide users with a means to deeply understand their own psychological state and improve their mental health by recording and analyzing the content of their dreams. Conventional technologies have limitations in properly analyzing dream content and providing users with appropriate feedback and advice, so a new method is needed to solve this problem.

[0776] 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.

[0777] This invention includes a server that acquires dream records from a user in text or voice, converts voice to text data, performs sentiment analysis, organizes the sentiment analysis results and dream records in a specific format and stores them in a memory device, and runs a generative model to analyze the stored dream records. This makes it possible to provide feedback and customized mental health advice based on the user's psychological state.

[0778] A "user" refers to someone who uses the system to input records of their dreams and receives analysis results and advice.

[0779] "Dream records" refer to the specific details of dreams experienced by the user while awake, and are entered in text or audio format.

[0780] "Voice data" refers to information that a user inputs as voice and that is converted into text data by a speech recognition system.

[0781] "Character data" refers to information converted by a character recognition system from text or voice data entered by a user.

[0782] "Sentiment analysis" refers to the process of analyzing the tone and content of input data to identify the user's emotional state.

[0783] A "generative model" is a system that uses machine learning algorithms and refers to a technology that extracts themes and psychological tendencies from dream records through natural language processing.

[0784] "Feedback" refers to information provided to users based on analysis results, including psychological insights and suggestions for improvement.

[0785] "Mental health advice" refers to advice that includes customized behavioral suggestions and relaxation methods based on the user's emotional state and dream content.

[0786] A "storage device" refers to a hardware or software system used to store information.

[0787] "Visualization" refers to presenting analysis results in graph or chart format to make them easy for users to understand.

[0788] This invention is a system that allows users to input dream records in voice or text, analyzes the content, and provides psychological insights and mental health advice. The following hardware and software are used for implementation.

[0789] Users launch a dedicated application using a mobile device or tablet and input the content of their dream. In the case of voice input, voice data is acquired through the device's microphone and converted into text data using speech recognition software (e.g., speech recognition API).

[0790] The device passes the converted text data to an emotion analysis engine for tone and intonation analysis. The analysis results and the dream text data are formatted into JSON format. The device then sends this data to the server via a security protocol (e.g., HTTPS).

[0791] The server stores the received data in storage and analyzes it by running a generative model (e.g., a natural language processing model). The generative model uses machine learning algorithms to analyze dream themes and psychological tendencies. Simultaneously, an emotion analysis engine adds user emotion information.

[0792] Based on the analysis results, the server generates feedback, which is then visualized using graphs and charts. This feedback includes personalized advice for mental health, taking into account the user's current psychological state.

[0793] The device displays this feedback and advice to the user, helping them deepen the psychological insights gained through dream recording.

[0794] For example, if a user dreams of getting lost in the rain, the system analyzes this as a symbol of anxiety or a sense of loss. Based on statistical methods, the generative model prompts the user with the question, "Do you feel unsure of which direction to go right now?" The emotion analysis engine also suggests relaxation techniques, such as, "Try deep breathing or meditation to regain your composure."

[0795] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0796] Step 1:

[0797] The user launches the application and enters a record of their dream. Input can be done via voice or text. For voice input, the user speaks the dream content using the device's microphone. The device receives the voice input and stores it internally as audio data. Both input and output are audio data.

[0798] Step 2:

[0799] The device launches speech recognition software to acquire speech data and convert it into text data. Specifically, the device passes the speech data to an API and receives the converted text result. The input is speech data, and the output is text data. In this process, speech-specific tones and intonation are also used in the analysis.

[0800] Step 3:

[0801] The terminal passes the converted text data to the sentiment analysis engine, which then performs sentiment analysis. The sentiment analysis engine receives the text data as input, identifies the user's emotional state, and generates analysis results. The output is the sentiment analysis result. This process supplements the emotional information extracted from the text data.

[0802] Step 4:

[0803] The terminal integrates text data and sentiment analysis results in JSON format, and then formats the data. The formatted data is prepared for communication with the server. The input is text data and sentiment analysis results, and the output is formatted JSON data. Specifically, the data structure is converted to JSON and adapted to a secure communication protocol.

[0804] Step 5:

[0805] The terminal sends formatted JSON data to the server using a security protocol. The data being sent is already properly formatted JSON data. Through this communication process, the terminal securely transmits information to the server while preventing data leakage.

[0806] Step 6:

[0807] The server receives JSON data sent from the terminal and verifies its contents. The server checks the received data for tampering or omissions and confirms that it is valid data. The input is the received JSON data, and the output is the verified data.

[0808] Step 7:

[0809] The server saves the verified data to storage and starts the generative AI model to begin data analysis. The generative model performs natural language processing on the saved data to extract dream themes and psychological tendencies. The input is the verified data, and the output is the analysis results. Specifically, the generative model uses that data to generate insights.

[0810] Step 8:

[0811] The server generates feedback and prompts based on the analysis results of the generated AI model. The results of the sentiment analysis engine are also reflected in the feedback. The input is the analysis results, and the output is the feedback and prompts for the user. In this process, information is presented in a format that is easy for the user to understand.

[0812] Step 9:

[0813] The server creates customized mental health advice for the user based on the generated feedback and prompts. The input is the generated feedback and prompts, and the output is the customized advice. Specific examples include comforting messages and action plans that look to the distant future.

[0814] Step 10:

[0815] The terminal displays feedback, prompts, and mental health advice received from the server to the user. Through this, the user can instantly see ways to improve their psychological state in daily life. Input is data from the server, and output is a visual display to the user. In this process, the information is presented in a format that is easy for the user to understand.

[0816] (Application Example 2)

[0817] 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".

[0818] In modern life, managing the mental health of brick-and-mortar store staff is a crucial issue. However, a lack of effective means to identify the stress and anxiety staff experience at work and provide appropriate support can lead to decreased job satisfaction and productivity.

[0819] 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.

[0820] In this invention, the server includes means for acquiring emotions and events from a user in voice or text, means for processing the acquired records of emotions and events and storing them in an information aggregation device, and means for running a generative AI model to analyze the stored records of emotions and events. This enables staff at physical stores to regularly record their own emotions and experiences and receive appropriate feedback and action suggestions based on the data.

[0821] A "user" refers to an individual who uses the system to record their emotions and experiences, and to receive feedback and advice.

[0822] "Audio or text" refers to the format of information that a user provides to the system, including spoken word and written text.

[0823] "Records of emotions and events" refers to specific data about the emotions and experiences that users have felt on a daily basis.

[0824] An "information aggregation device" refers to a data storage device that stores records of acquired emotions and events for later analysis.

[0825] A "generative AI model" refers to an artificial intelligence algorithm that analyzes input information and identifies patterns.

[0826] "Feedback" refers to responses, including suggestions and evaluations, provided to the user based on the analysis results.

[0827] "Action suggestions and stress management methods" refer to specific action guidelines and methods for reducing stress that are provided to users based on the analysis results.

[0828] This invention is a system that allows staff at physical stores to record emotions and events and receive feedback and action suggestions based on that information. The system consists of a voice or text input device, an information aggregation device, a server that runs a generative AI model, and a user terminal.

[0829] Users record their emotions and events using devices such as smartphones via voice or text input. During this process, speech recognition software, such as Google Cloud Speech-to-Text, installed on the smartphone is used to convert the voice data into text. The converted data is then securely stored on an information aggregation device via the internet.

[0830] Data stored in the information aggregation device is received by a server and analyzed using a generating AI model. Natural language processing algorithms such as OpenAI's GPT-3 are used for the analysis to extract specific emotional patterns and psychological tendencies. Furthermore, feedback is generated based on the analysis results to provide a deeper understanding of the user's psychological state. Behavioral suggestions and stress management methods are also generated and provided to the user.

[0831] The feedback results are displayed visually to the user on their device. Specifically, guidance related to the analyzed emotions and events is displayed as text and graphs. This allows users to understand their own psychological state and obtain information to take appropriate actions in different situations.

[0832] For example, if a staff member at a store records their stress levels during work and the system analyzes this as "peak tension," it will then provide specific suggestions regarding relaxation techniques or adjustments to working hours.

[0833] For the generative AI model, the following prompt can be used: "A staff member reported feeling stressed during peak hours. Analyze their emotional tone and provide recommendations for stress management and relaxation techniques." This will generate appropriate feedback and provide it to the user.

[0834] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0835] Step 1:

[0836] Users record their emotions and events using their smartphones via voice or text. For voice input, the device uses Google Cloud Speech-to-Text to convert the speech to text. The input for this step is the user's voice or text, and the output is text data. Speech recognition-based data conversion takes place here.

[0837] Step 2:

[0838] The terminal transmits the converted text data to the information aggregation device using a secure communication protocol. The input to this step is the text data generated in step 1, and the output is the storage of the data in the information aggregation device. HTTPS communication is used to ensure the security of the information during data transmission.

[0839] Step 3:

[0840] The server receives text data stored in the information aggregation device and inputs it into the generating AI model. In this step, the input is data from the information aggregation device, and the output is the analysis result by the AI ​​model. Natural language processing is performed using OpenAI GPT-3 for data analysis, and emotional patterns and psychological tendencies are extracted.

[0841] Step 4:

[0842] The server generates feedback and action suggestions for the user using the analysis results of the generated AI model. The input is the analysis results obtained in step 3, and the output is feedback information. In feedback creation, data processing is performed to convert the analysis results into a format that is easy for the user to understand.

[0843] Step 5:

[0844] The terminal visually displays feedback sent from the server to the user. The input is feedback data from the server, and the output is the display of information to the user. Here, the data is provided to the user in the form of text or graphs.

[0845] 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.

[0846] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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."

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] The following is further disclosed regarding the embodiments described above.

[0867] (Claim 1)

[0868] A means of obtaining dream records from users in voice or text format,

[0869] A means of processing the acquired dream records and saving them to a database,

[0870] A means of running a generative AI model to analyze saved dream records,

[0871] A means of providing feedback to the user based on the analysis results,

[0872] A means of generating mental health advice for the user based on the analysis results,

[0873] A system that includes this.

[0874] (Claim 2)

[0875] The system according to claim 1, further comprising means for converting user-inputted speech into text.

[0876] (Claim 3)

[0877] The system according to claim 1, further comprising means for visualizing the analysis results to the user in the form of diagrams or charts.

[0878] "Example 1"

[0879] (Claim 1)

[0880] A means of obtaining information by receiving the content of dreams from the user in voice or text,

[0881] A means of processing the content of acquired dreams and storing it in an information database,

[0882] A means of running a generative AI model using natural language processing technology to analyze the content of saved dreams,

[0883] An output means that provides feedback to the user based on the analysis results,

[0884] A means of creating individual mental health advice while comparing it with past information,

[0885] A system that includes this.

[0886] (Claim 2)

[0887] The system according to claim 1, further comprising means for converting user-inputted speech into text using information processing technology.

[0888] (Claim 3)

[0889] The system according to claim 1, further comprising means for visually presenting the analysis results to the user in the form of diagrams or graphs.

[0890] "Application Example 1"

[0891] (Claim 1)

[0892] A means of obtaining dream information from users in digital format,

[0893] A means of processing the acquired dream information and storing it in memory,

[0894] A means of executing generative AI technology to analyze saved dream information,

[0895] A means of providing users with understandable feedback based on the analysis results,

[0896] A means of generating mental health advice for the user based on the analysis results,

[0897] A means of generating graphs and charts to visualize the analysis results,

[0898] A system that includes this.

[0899] (Claim 2)

[0900] The system according to claim 1, further comprising means for converting user-inputted voice information into text.

[0901] (Claim 3)

[0902] The system according to claim 1, further comprising means for analyzing dream information based on the passage of time and comparing it with past data.

[0903] "Example 2 of combining an emotion engine"

[0904] (Claim 1)

[0905] A means of obtaining a record of dreams from the user in text or audio,

[0906] A method for converting acquired audio into text data and performing emotion analysis,

[0907] A means of organizing the results of emotional analysis and dream records in a specific format and saving them to a memory device,

[0908] A means of running a generative model to analyze the records of remembered dreams,

[0909] A means of providing feedback to users based on the results of generative models and sentiment analysis,

[0910] A means of creating mental health advice for users based on feedback,

[0911] A system that includes this.

[0912] (Claim 2)

[0913] The system according to claim 1, further comprising means for visualizing and presenting the analysis results to the user.

[0914] (Claim 3)

[0915] The system according to claim 1, further comprising means for generating specific questions based on user input and facilitating analysis.

[0916] "Application example 2 when combining with an emotional engine"

[0917] (Claim 1)

[0918] Means for obtaining emotions and events from users via voice or text,

[0919] A means for processing the acquired records of emotions and events and storing them in an information aggregation device,

[0920] A means of running a generative AI model to analyze saved records of emotions and events,

[0921] A means of providing feedback to the user based on the analysis results,

[0922] A means of generating action suggestions and stress management methods for users based on analysis results,

[0923] A system that includes this.

[0924] (Claim 2)

[0925] The system according to claim 1, further comprising means for converting user-inputted speech into text.

[0926] (Claim 3)

[0927] The system according to claim 1, further comprising means for presenting the analysis results to the user in a visual format. [Explanation of Symbols]

[0928] 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 obtaining dream records from users in voice or text format, A means of processing the acquired dream records and saving them to a database, A means of running a generative AI model to analyze saved dream records, A means of providing feedback to the user based on the analysis results, A means of generating mental health advice for the user based on the analysis results, A system that includes this.

2. The system according to claim 1, further comprising means for converting user-inputted speech into text.

3. The system according to claim 1, further comprising means for visualizing the analysis results to the user in the form of diagrams or charts.

Images