System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving inputted text from victims; means for passing the received text to a generative AI engine and analyzing feelings and states of mind of the victims based on the text; means for generating optimal advice and care based on the analysis result; and means for storing a history of conversation with the victims in a database and using the history as training data for the generative AI engine.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] During disasters, many medical resources are focused on treating physical injuries and illnesses, leaving mental health care resources in short supply. Mental stress and trauma, in particular, have a devastating impact on disaster victims, necessitating early intervention. However, there are challenges with providing appropriate mental health care until medical professionals arrive on-site or when medical resources are in short supply. Furthermore, providing mental health care not only to disaster victims but also to relief workers is an important issue. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems with a system that includes a means for receiving input text from disaster victims, a means for passing the received text to a generation AI engine and analyzing the disaster victim's emotions and mental state based on the text, a means for generating optimal advice and care based on the analysis results, a means for transmitting the generated advice and care to the disaster victim's device, and a means for storing the dialogue history with the disaster victim in a database and using it as learning data for the generation AI engine. This makes it possible to provide appropriate mental care even in situations where medical resources are scarce, thereby reducing the psychological stress and trauma of disaster victims and rescuers. Furthermore, the generation AI engine learns from past dialogue history and improves the accuracy of advice and care, thereby improving the quality of long-term mental care.

[0006] A "disaster victim" is someone who has been directly or indirectly affected by a natural or man-made disaster and is experiencing psychological stress or trauma.

[0007] "Input text" refers to the text entered into the terminal by the disaster victim to express their feelings and state of mind.

[0008] The "generative AI engine" is an artificial intelligence processing system that uses natural language processing technology to analyze input text, understand the emotions and mental state of disaster victims, and generate optimal advice and care.

[0009] "Means for analysis" refers to a method or function for passing input text to a generative AI engine, analyzing its content, and identifying the psychological state of the victim.

[0010] "Advice and care" refers to specific advice and support measures generated by the generative AI engine based on the analysis results, aimed at reducing the psychological stress and trauma of disaster victims.

[0011] "Terminals" refer to devices that can connect to the Internet, such as smartphones, tablets, and computers used by disaster victims.

[0012] "Dialogue history" is a record of communication between the victim and the chatbot, and is saved as text data.

[0013] A "database" is an electronic information storage system for managing and storing dialogue history and learning data from the generative AI engine.

[0014] "Learning data" is data that the generative AI engine uses to acquire new knowledge from past interaction history and improve the quality of advice and care. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11]FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

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

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

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

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

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

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

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

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

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

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

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0036] MODE FOR CARRYING OUT THE INVENTION

[0037] The present invention is a system for providing mental care to disaster victims and relief workers in the event of a disaster. Next, a specific embodiment of the system will be described.

[0038] System Configuration

[0039] 1. On the user's device:

[0040] These are devices that can connect to the Internet, such as smartphones, tablets, and computers, used by disaster victims and relief workers.

[0041] Using this device, users can input their emotions and state of mind in text and interact with the chatbot.

[0042] 2. Server:

[0043] The central server is equipped with a generative AI engine, which is the foundation of the chatbot, and is responsible for receiving and analyzing data, generating and sending advice, and managing the database.

[0044] The server operates 24 hours a day, 365 days a year, and processes user requests in real time.

[0045] 3. Generative AI engine:

[0046] The generative AI engine uses natural language processing technology to analyze received text data and identify the psychological state of disaster victims.

[0047] The engine generates optimal advice and care based on the analysis results.

[0048] 4. Database:

[0049] The server stores the dialogue history and analysis results in a database.

[0050] The generative AI engine uses information from the database to learn and improve the accuracy of advice and care.

[0051] Program processing description

[0052] 1. User input:

[0053] Users access the chatbot from their device and enter their emotions and state of mind in text.

[0054] For example: "Lately I've been having trouble sleeping at night and I'm constantly anxious."

[0055] 2. Sending input:

[0056] The user's terminal transmits the input text data to the server.

[0057] The server passes the received text data to the generation AI engine.

[0058] 3. Data Analysis:

[0059] The generative AI engine processes text data using analytical techniques to identify the user's emotions and state of mind.

[0060] For example, emotional tags such as "anxiety," "stress," and "sleep disorders" are assigned, and scoring is performed as necessary.

[0061] 4. Advice Generation:

[0062] The generative AI engine generates appropriate advice and care based on the analysis results.

[0063] For example: "Try some relaxation techniques before sleep. Deep breathing and meditation are good options."

[0064] 5. Sending a reply:

[0065] The server transmits the generated advice to the user's terminal, and the user can confirm the advice.

[0066] 6. Recording History:

[0067] The server stores the dialogue history in a database.

[0068] The generative AI engine uses this history as learning data to improve the accuracy of future advice and care.

[0069] Specific scenarios

[0070] As an example, consider the case where a user feels anxious late at night and inputs the following into the chatbot: "I haven't been able to sleep at night recently and I'm always anxious." This input is sent from the user's device to the server, which performs text analysis using a generative AI engine. The generative AI engine extracts emotion tags such as "anxiety" and "can't sleep at night" and generates appropriate advice. The generated advice is sent from the server to the user's device, where the user receives and confirms it. The dialogue history is saved in a database by the server, and the generative AI engine uses this as learning data to help improve the accuracy of future advice.

[0071] In this way, the system of the present invention can effectively support the mental care of disaster victims and relief workers in the event of a disaster.

[0072] The processing flow will be explained below.

[0073] Step 1:

[0074] The user accesses the chatbot from their device and inputs their feelings and state of mind into the text. For example, "I haven't been able to sleep at night recently and I'm always feeling anxious."

[0075] Step 2:

[0076] The terminal sends the entered text data to the server, which temporarily stores the received text data.

[0077] Step 3:

[0078] The server passes the received text data to the generative AI engine, which then prepares the data for analysis. Specifically, it normalizes the text data as a preprocessing step and removes unnecessary spaces and special characters.

[0079] Step 4:

[0080] The generative AI engine analyzes the normalized text using natural language processing technology to identify the emotions and mental state of the victims. Specifically, it assigns emotion tags such as "anxiety," "stress," and "sleep disorders" and calculates an emotion score.

[0081] Step 5:

[0082] The generative AI engine generates optimal advice and care suggestions based on the identified emotion tags and scores. Multiple advice suggestions may be generated, and the most appropriate one is selected from them.

[0083] Step 6:

[0084] The server sends the selected advice and care to the user's device. Specifically, it formats the advice data from the generative AI engine and displays it in an appropriate format for the user.

[0085] Step 7:

[0086] The device displays the received advice and care to the user, who can then check the advice and take action if necessary.

[0087] Step 8:

[0088] The server stores the conversation history between the user and the chatbot in a database, including the input text, analysis results, advice provided, and user responses.

[0089] Step 9:

[0090] The generative AI engine periodically uses the dialogue history in the database as learning data to improve the accuracy of advice and care. The generative AI engine is retrained based on new data to improve the quality of future analysis and advice.

[0091] Example 1

[0092] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0093] Conventional mental care systems for disaster victims and relief workers have difficulty responding in real time and generating appropriate advice from large amounts of data. Furthermore, they face challenges in providing accurate care based on the psychological state of each individual user.

[0094] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0095] In this invention, the server includes means for the victim to input their emotions and mental state using a terminal, means for transmitting the input text data to the server, means for the server to pass the received text data to a generating AI engine and analyze the victim's emotions and mental state based on the text data, means for generating optimal advice and care based on the analysis results, means for transmitting the generated advice and care to the victim's terminal, and means for storing a dialogue history with the victim in a database and using it as learning data for the generating AI engine, thereby enabling appropriate mental care in real time.

[0096] "Terminals" are devices that can connect to the Internet, such as smartphones, tablets, and computers, used by disaster victims and relief workers.

[0097] A "server" is a computer device equipped with a generative AI engine that receives and analyzes data, generates and transmits advice, and manages databases.

[0098] The "generative AI engine" is an AI engine that uses natural language processing technology to analyze received text data and identify the psychological state of disaster victims.

[0099] "Text data" is sentence data that expresses emotions and mental states and is input by a user using a terminal.

[0100] "Analysis results" are the emotion tags and scoring results extracted by the generative AI engine by analyzing the text data.

[0101] "Advice" refers to instructions and suggestions for mental care for victims generated by the AI ​​engine based on the analysis results.

[0102] A "database" is a data storage system in which the server stores the interaction history with the user and the analysis results.

[0103] "Dialogue history" is data that records past interactions between a user and a generative AI engine.

[0104] "Learning data" is data that the generative AI engine uses to improve the accuracy of advice and care based on information obtained from past dialogue history, etc.

[0105] "Tagging" is the act of assigning appropriate labels to emotions and states extracted from text data by a generative AI engine.

[0106] "Scoring" is the process by which the generative AI engine gives a numerical evaluation to each emotion tag or state based on the analysis results.

[0107] MODE FOR CARRYING OUT THE INVENTION

[0108] The present invention is a system for providing mental care to disaster victims and relief workers in the event of a disaster. Next, a specific embodiment of the system will be described.

[0109] System Configuration

[0110] 1. Device:

[0111] These devices include smartphones, tablets, and computers used by disaster victims and relief workers.

[0112] The device is capable of connecting to the Internet, and users use it to input their emotions and state of mind in text and interact with the chatbot.

[0113] 2. Server:

[0114] The server is a computer device that is equipped with a generative AI engine, which is the foundation of the chatbot, and is responsible for receiving, analyzing, generating and sending advice, and managing the database.

[0115] The server operates 24 hours a day, 365 days a year, and processes user requests in real time.

[0116] 3. Generative AI engine:

[0117] The generative AI engine uses natural language processing technology to analyze received text data and identify the psychological state of disaster victims.

[0118] This engine generates optimal advice and care based on the analysis results. Specific technologies used include sentiment analysis and entity recognition.

[0119] 4. Database:

[0120] The server stores the dialogue history and analysis results in a database.

[0121] The generative AI engine uses the stored data as training data to improve the accuracy of advice and care.

[0122] Specific examples

[0123] Consider a case where a user feels anxious late at night and types, "Recently, I haven't been able to sleep at night and I'm always anxious." This input is sent from the user's device to the server. The server passes this text data to the generative AI engine, which then analyzes it. During the analysis process, emotion tags such as "anxiety," "stress," and "sleep disorder" are assigned, and a score is calculated for each tag.

[0124] Based on the analysis results, the generative AI engine generates advice such as, "Try relaxing before sleep. Deep breathing and meditation are recommended." The server sends this advice to the user's device, where the user can confirm it. The server stores the conversation history in a database, which the generative AI engine uses to improve the accuracy of future advice.

[0125] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0126] Step 1:

[0127] User input:

[0128] How it works: A user accesses a dedicated app or website using a device such as a smartphone, tablet, or computer, and enters their emotion or state of mind into a text box.

[0129] Input: Text data describing emotions and mental states. Example: "Recently, I've been having trouble sleeping at night and I'm always anxious."

[0130] Output: The input text data.

[0131] Step 2:

[0132] Sending input:

[0133] How it works: The device sends the entered text data to the server. The transmission is secure using encryption technology such as SSL.

[0134] Input: The text data entered.

[0135] Output: The text data sent to the server.

[0136] Step 3:

[0137] Data Analysis:

[0138] How it works: The server passes the received text data to the generative AI engine. The generative AI engine uses natural language processing techniques to analyze the text data. Specifically, it uses sentiment analysis and entity recognition to tag and score the text.

[0139] Input: The text data sent to the server.

[0140] Output: Emotion tags (e.g., "anxiety," "stress," "sleep disturbance") and scoring results.

[0141] Step 4:

[0142] Advice Generation:

[0143] How it works: The generative AI engine generates optimal advice and care based on the analysis results, using prompts generated by a trained AI model.

[0144] Input: sentiment tags and scoring results.

[0145] Output: Advice or care message. Example: "Try some relaxation techniques before sleep. Deep breathing and meditation are recommended."

[0146] Step 5:

[0147] Send reply:

[0148] Operation: The server sends the generated advice to the user's terminal, where the user can confirm the advice.

[0149] Input: Message of advice or care.

[0150] Output: Advice displayed on the user's terminal.

[0151] Step 6:

[0152] History Record:

[0153] How it works: The server stores the user's interaction history in a database. The generative AI engine uses this history as training data to improve the accuracy of its advice.

[0154] Input: User interaction history.

[0155] Output: Historical data stored in a database.

[0156] Through the above processing steps, the system can provide appropriate mental care in real time and accurately support the user's psychological state.

[0157] (Application example 1)

[0158] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0159] Conventional mental care systems in the event of a disaster are limited to providing care to disaster victims and often fail to provide effective measures. Furthermore, there have been no mental care systems that can also be applied to the psychological state of customers and staff in stores. Therefore, while there is a growing demand for systems that provide mental care for general customers and staff, not just in the event of a disaster, there is a lack of technology that can meet this demand. These issues need to be resolved.

[0160] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0161] In this invention, the server includes a means for receiving input text from disaster victims or customers, a means for passing the received text to a generation AI engine and analyzing the emotions and mental state of the disaster victims or customers based on the text, and a means for generating optimal advice and care based on the analysis results. This enables mental care for customers and staff not only during disasters but also in physical stores.

[0162] "Victims" refers to those who have suffered damage to their lives, property, or physical or mental health due to a disaster.

[0163] "Customer" refers to the consumer or user to whom a service or product is provided.

[0164] "Smart glasses" are Internet-connected devices worn by users to visually receive and input information.

[0165] A "robot" is a mechanical device that operates automatically according to programmed instructions to perform specific tasks.

[0166] A "generative AI engine" refers to an engine with artificial intelligence capabilities that analyzes received text data and generates appropriate advice and care.

[0167] "Text input" refers to the user inputting character data using a keyboard, voice recognition, or the like.

[0168] "Sentiment analysis" refers to the process of identifying a user's emotions and psychological state from input text data.

[0169] "Advice generation" refers to generating information regarding advice and care for the user based on the analyzed emotions and psychological state.

[0170] "Dialogue history" refers to data that records interactions between a user and a system.

[0171] A "database" refers to an electronic information system for efficiently storing, managing, and searching information.

[0172] "Learning data for the generative AI engine" refers to data such as past dialogue history that the generative AI engine uses to improve the accuracy of its analysis and advice generation.

[0173] This invention is a system that provides mental care to disaster victims, customers, and store staff. Specifically, it uses smart glasses and a robot to analyze emotions and psychological states from text input using a generative AI engine, and generates appropriate advice to provide mental care.

[0174] System Configuration

[0175] 1. On the user's device:

[0176] These devices, such as smart glasses and robots, are used by customers and staff. These devices can connect to the internet and can input emotions and psychological states through text input and voice recognition.

[0177] 2. Server:

[0178] The server is equipped with a generative AI engine, which is the foundation of the chatbot, and receives and analyzes data, generates and sends advice, manages the database, etc. The server operates 24 hours a day, 365 days a year, and processes user requests in real time.

[0179] 3. Generative AI engine:

[0180] The generative AI engine uses natural language processing technology to analyze received text data and identify the user's psychological state. This engine generates optimal advice and care based on the analysis results.

[0181] 4. Database:

[0182] The server stores the dialogue history and analysis results in a database, and the generative AI engine uses the information in this database to learn and improve the accuracy of advice and care.

[0183] Program processing description

[0184] The server receives text data entered by the user via smart glasses or a robot and passes it to the generative AI engine. The generative AI engine analyzes the received text data and identifies the user's emotions and state of mind. This analysis process uses natural language processing technology, including emotion tagging and scoring. Based on the analysis results, optimal advice and care is generated. This generated advice is sent from the server to the user via the smart glasses or robot. The server also stores the dialogue history in a database and uses it as learning data for the generative AI engine.

[0185] Specific example explanation

[0186] For example, suppose a user types "I've been busy at work lately and I'm tired" into the smart glasses. This input data is instantly sent to the server, where the generative AI engine analyzes the text. The generative AI engine assigns emotion tags such as "fatigue" and "stress" and generates appropriate advice such as "To relax, we recommend deep breathing and light stretching. It is also effective to take a short break and hydrate." This advice is then sent from the server to the user via the smart glasses.

[0187] Example prompt sentence:

[0188] "Work has been busy lately and I'm feeling tired. How can I relax?"

[0189] In this way, the system can provide effective mental care to victims, customers, and staff.

[0190] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0191] Step 1: The user inputs text about their emotions or state of mind via smart glasses or a robot. Specifically, the user inputs data in the form of, "I've been busy at work lately and I'm tired." This input data is executed through the interface of the smart glasses or robot.

[0192] Step 2: The device sends the received text data to the server. The entered text data is transferred to the server via the network, and the server prepares to pass this data to the generation AI engine.

[0193] Step 3: The server passes the received text data to the generation AI engine. The server sends a request to the generation AI engine to analyze the text data, and provides the input text to the analysis engine.

[0194] Step 4: The generative AI engine analyzes the text data. The generative AI engine uses natural language processing technology to analyze the text data and perform emotion tagging and scoring. For example, emotion tags such as "fatigue" and "stress" can be assigned, and the state can be expressed numerically.

[0195] Step 5: The generative AI engine generates optimal advice and care based on the analysis results. Based on the analyzed emotion tags and scores, the generative AI engine generates advice such as, "To relax, we recommend deep breathing and light stretching. Taking a short break and drinking plenty of water is also effective."

[0196] Step 6: The server sends the generated advice to the user's device. The server then sends the advice received from the generating AI engine back to the smart glasses or robot. This process uses network communication.

[0197] Step 7: The device presents the advice to the user. The smart glasses or robot presents the sent advice or care to the user visually or audibly.

[0198] Step 8: The server saves the conversation history with the victim or customer in a database. The server saves the content of this conversation as conversation history and uses it as learning data for future generative AI engines.

[0199] Step 9: The generative AI engine uses the training data to learn new data. Using the dialogue history data stored on the server, the generative AI engine learns new data to improve the accuracy of analysis and advice generation.

[0200] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0201] MODE FOR CARRYING OUT THE INVENTION

[0202] This invention is a system that provides mental care to disaster victims and relief workers. By combining a generative AI engine with an emotion engine that recognizes the user's emotions, this system can provide more accurate emotion analysis and advice. Next, a specific embodiment of this system will be described.

[0203] System Configuration

[0204] 1. On the user's device:

[0205] These are devices that can connect to the Internet, such as smartphones, tablets, and computers, used by disaster victims and relief workers.

[0206] Using this device, users can input their emotions and state of mind in text and interact with a chatbot that uses an emotion engine and a generative AI engine.

[0207] 2. Server:

[0208] The central server is equipped with a generative AI engine and emotion engine that form the basis of the chatbot, and is responsible for receiving and analyzing data, recognizing emotions, generating and sending advice, and managing the database.

[0209] The server operates 24 hours a day, 365 days a year, and processes user requests in real time.

[0210] 3. Generative AI engine:

[0211] The generative AI engine uses natural language processing technology to identify the psychological state of disaster victims based on the received text data and the emotion analysis results from the emotion engine.

[0212] The engine generates optimal advice and care based on the analysis results.

[0213] 4. Emotion Engine:

[0214] The emotion engine analyzes the user's real-time facial expressions and voice data in addition to the text entered by the user to recognize emotions.

[0215] This allows for a more accurate determination of the user's emotional state.

[0216] 5. Database:

[0217] The server stores the dialogue history and analysis results in a database.

[0218] The generative AI engine and emotion engine use information from the database to learn and improve the accuracy of advice and care.

[0219] Program processing description

[0220] 1. User input:

[0221] Users access the chatbot from their device and enter their emotions and state of mind in text.

[0222] For example: "Lately I've been having trouble sleeping at night and I'm constantly anxious."

[0223] 2. Real-time emotion recognition:

[0224] The user's device uses a camera and microphone to transmit the user's facial expressions and voice to the emotion engine in real time.

[0225] 3. Input and emotional data transmission:

[0226] The terminal transmits the input text data and real-time emotion data to the server.

[0227] 4. Data Analysis and Emotion Recognition:

[0228] The text data and emotion data received by the server are passed to the generative AI engine and emotion engine, respectively.

[0229] The emotion engine analyzes facial and voice data to identify emotional states.

[0230] The generative AI engine analyzes text data and performs emotion tagging and scoring.

[0231] 5. Advice Generation:

[0232] The generative AI engine integrates the emotion analysis results obtained from the emotion engine with the text analysis results to generate more accurate advice and care.

[0233] For example: "Try some relaxation techniques before sleep. Deep breathing and meditation are good options."

[0234] 6. Sending a Reply:

[0235] The server transmits the generated advice to the user's terminal.

[0236] The user checks the advice and attempts to take action if necessary.

[0237] 7. Recording History:

[0238] The server stores the dialogue history and emotion analysis results in a database.

[0239] The generative AI engine and emotion engine use this history as learning data to improve the accuracy of future advice and care.

[0240] Specific scenarios

[0241] As an example, consider the case where a user feels anxious late at night and inputs the following into the chatbot: "I haven't been able to sleep at night recently and I'm always anxious." At the same time as this input, the user's device uses its camera and microphone to capture the user's facial expression and voice data, which it then sends to the server. The server uses a generative AI engine to perform text analysis and an emotion engine to perform emotion analysis from the facial expression and voice data. The generative AI engine compiles the analysis results of the emotion engine, generates optimal advice, and sends it back to the user. This dialogue history and emotion analysis results are saved in a database and will be used as future learning data.

[0242] In this way, the system of the present invention can provide more accurate support for the mental care of disaster victims and relief workers in the event of a disaster by combining it with an emotion engine.

[0243] The processing flow will be explained below.

[0244] Step 1:

[0245] The user accesses the chatbot from their device and inputs their feelings and state of mind into the text. For example, "I haven't been able to sleep at night recently and I'm always feeling anxious."

[0246] Step 2:

[0247] The user's device sends the input text data to the server, and at the same time, the device's camera and microphone are used to transmit the user's facial expressions and voice to the emotion engine in real time.

[0248] Step 3:

[0249] The server passes the received text data to the generative AI engine, which then normalizes the data as preprocessing. Normalization refers to the process of removing unnecessary spaces and special characters.

[0250] Step 4:

[0251] The server analyzes the user's facial expressions and voice data transmitted in real time to the emotion engine, which then identifies the user's emotional state from this data.

[0252] Step 5:

[0253] The emotion engine generates analysis results and sends them to the generative AI engine, which receives the analysis results from the emotion engine and integrates them with the analysis results of the text data.

[0254] Step 6:

[0255] The generative AI engine tags and scores the user's psychological state based on the integrated data, resulting in emotional tags such as "anxiety," "stress," and "sleep disorders."

[0256] Step 7:

[0257] The generative AI engine generates advice and care suggestions based on the identified emotion tags and scores. Multiple advice suggestions may be generated, and the most appropriate one will be selected.

[0258] Step 8:

[0259] The server sends the selected advice to the user's terminal, where the advice is formatted in a user-friendly format.

[0260] Step 9:

[0261] The device displays the received advice and care to the user, who can then check the advice and try it out if necessary.

[0262] Step 10:

[0263] The server stores the conversation history between the user and the chatbot in a database, including input text, analysis results, advice provided, and sentiment analysis results.

[0264] Step 11:

[0265] The generative AI engine and emotion engine use the dialogue history from the database as learning data and retrain to improve the accuracy of future advice and care.

[0266] Example 2

[0267] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0268] Conventional technologies lack systems that can quickly alleviate the mental stress of disaster victims and relief workers and provide effective mental care. Conventional systems lack the ability to accurately analyze the user's emotional state and provide optimal advice based on that analysis. As a result, it is difficult for disaster victims to receive the psychological support they need in real time, and this can lead to continued mental exhaustion.

[0269] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0270] In this invention, the server includes means for receiving input text from the disaster victim, means for passing the received text to a generation AI engine and analyzing the disaster victim's emotions and state of mind using the text and the emotion engine, means for the generation AI engine to integrate the analysis results of the emotion engine to generate optimal advice and care, means for transmitting the generated advice and care to the disaster victim's device, means for storing the dialogue history with the disaster victim and the emotion analysis results in a database and using them as learning data for the generation AI engine, means for the emotion engine to analyze the user's facial expression and voice data in real time, and means for the device to acquire the user's facial expression and voice data and transmit it to the server. This makes it possible to analyze the disaster victim's emotional state with high accuracy and provide appropriate advice in real time.

[0271] A "victim" is an individual who has suffered physical or psychological harm as a result of a natural disaster or emergency.

[0272] "Input text" is character information that a user sends to the system through a terminal.

[0273] The "generative AI engine" is an artificial intelligence engine that uses input text data to analyze the psychological state and emotions of disaster victims and generate optimal advice and care.

[0274] The "emotion engine" is an engine that analyzes the user's facial expressions and voice data to identify their emotional state in real time.

[0275] "Terminals" are devices that can connect to the Internet, such as smartphones, tablets, and computers, used by disaster victims and relief workers.

[0276] The "server" is a central system that incorporates the generative AI engine and emotion engine and receives data, analyzes it, generates advice, sends it, and manages the database.

[0277] A "database" is a storage device within the system that stores data such as dialogue history and emotion analysis results collected by the system and makes them reusable.

[0278] "Dialogue history" is a record of interactions between the victim and the system.

[0279] "Emotion analysis results" are information on the emotional state obtained by analyzing the user's facial expressions and voice data using the emotion engine.

[0280] "Advice" refers to specific suggestions and advice provided by the generative AI engine based on the psychological state and emotional analysis of the victim.

[0281] "Care" refers to support measures and means to reduce the mental and psychological burden on disaster victims.

[0282] "Real-time" refers to a method of processing and analyzing data immediately at the moment it is generated.

[0283] "Analysis" is the process of breaking down and analyzing input data and emotional data to identify meanings and states.

[0284] "Learning data" refers to data on past dialogue history and emotion analysis results that the generative AI engine uses to improve the accuracy of advice and care.

[0285] "Scoring" is the process of assigning a numerical rating to the analyzed emotions and psychological states.

[0286] "Tagging" is the process of assigning labels to emotions or psychological states based on the analysis results.

[0287] This invention is a system that provides mental care to disaster victims and relief workers during disasters. By combining a generative AI engine with an emotion engine that recognizes the user's emotions, this system can provide more accurate emotion analysis and advice. A specific embodiment of this system is described below.

[0288] System Configuration

[0289] 1. User's device

[0290] In the event of a disaster, users can use devices with internet connectivity, such as smartphones, tablets, and PCs, to input their emotions and mental state in text and interact with a chatbot that utilizes an emotion engine and a generative AI engine.

[0291] 2. Server

[0292] The server is equipped with a generative AI engine and an emotion engine, and receives and analyzes data, recognizes emotions, generates and transmits advice, and manages the database. This central server operates 24 hours a day, 365 days a year, and processes user requests in real time.

[0293] 3. Generative AI Engine

[0294] The generative AI engine uses natural language processing technology to identify the psychological state of disaster victims based on the received text data and the emotion analysis results from the emotion engine. This engine generates optimal advice and care based on the analysis results.

[0295] 4. Emotion Engine

[0296] The emotion engine recognizes emotions by analyzing the user's real-time facial expressions and voice data in addition to the user's input text, making it possible to more accurately identify the user's emotional state.

[0297] 5. Database

[0298] The server stores the dialogue history and analysis results in a database. The generative AI engine and emotion engine use the information in the database to learn and improve the accuracy of advice and care.

[0299] Program processing description

[0300] A user accesses the chatbot using a device and inputs their feelings and mental state in text format. For example, the user might input, "I haven't been able to sleep at night recently and I'm always anxious." At the same time as this input, the device uses a camera and microphone to capture the user's facial expressions and voice data, which are then sent to the server.

[0301] The server passes the received text data and emotion data to the generative AI engine and emotion engine. The emotion engine analyzes facial expressions and voice data to identify the emotional state. The generative AI engine analyzes the text data, performs emotion tagging and scoring, and integrates these results to generate optimal advice and care information. For example, advice such as "Try relaxing before sleep. Deep breathing and meditation are recommended" is provided.

[0302] The generated advice is sent from the server to the user's device, where the user receives it. The server also stores the dialogue history and emotion analysis results in a database and uses them as learning data for future use. This improves the accuracy of the generative AI engine and emotion engine, allowing for more personalized advice to be provided.

[0303] Specific scenarios

[0304] A user feels anxious late at night, so they input to the chatbot, "I haven't been able to sleep at night recently and I'm always anxious." At the same time as this input, the user's device uses its camera and microphone to capture facial expression and voice data, which it then sends to the server. The server then uses a generative AI engine to perform text analysis, and an emotion engine to perform emotion analysis from the facial expression and voice data. The generative AI engine compiles the analysis results of the emotion engine, generates optimal advice, and sends it back to the user. This dialogue history and emotion analysis results are saved in a database and used as learning data for future use.

[0305] Prompt Sentence Examples

[0306] By inputting information into the generative AI model such as "The user has input that 'I haven't been able to sleep at night recently and I'm always anxious.' Please advise me on appropriate ways to relax in response to this," more accurate advice can be generated. In this way, by combining an emotion engine, the system of the present invention can provide more accurate support for the mental care of disaster victims and relief workers in the event of a disaster.

[0307] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0308] Program processing flow

[0309] Step 1: User Input

[0310] A user accesses the chatbot using a device and inputs their feelings and state of mind in text format. For example, a user might input, "I haven't been able to sleep at night recently, and I'm always anxious."

[0311] Input: Text data that indicates the user's emotions and state of mind

[0312] Output: The input text data generated in the terminal

[0313] Step 2: Real-time emotion recognition

[0314] The device uses a camera and microphone to capture the user's facial expressions and voice data in real time and transmits it to the emotion engine.

[0315] Specific operations: Facial recognition is performed using the camera and audio data is collected using the microphone.

[0316] Input: Real-time facial expression and voice data of the user

[0317] Output: Facial and speech data sent to the emotion engine

[0318] Step 3: Sending input and emotion data

[0319] The device sends the input text data and real-time emotion data to the server, where the data is encrypted.

[0320] Specific operation: The terminal packetizes the data, encrypts it, and sends it to the server.

[0321] Input: Text data, facial expression data, audio data

[0322] Output: Text data and emotion data received by the server

[0323] Step 4: Data analysis and emotion recognition

[0324] The server passes the received text data and emotion data to the generative AI engine and emotion engine, respectively. The emotion engine analyzes facial expression data and voice data to identify the user's emotional state. The generative AI engine analyzes the text data and performs emotion tagging and scoring.

[0325] Specific operations: The emotion engine runs facial recognition algorithms and performs voice analysis. The generative AI engine performs natural language processing.

[0326] Input: Text data, facial expression data, audio data

[0327] Output: Sentiment analysis results (sentiment tags, scoring)

[0328] Step 5: Advice Generation

[0329] The generative AI engine combines the emotion analysis results from the emotion engine with the text analysis results to generate optimal advice and care information. For example, it might generate specific advice such as, "Try relaxing before sleep. Deep breathing and meditation are recommended."

[0330] Specific operation: The generative AI engine references past dialogue history and emotional data to generate optimal advice.

[0331] Input: Sentiment analysis results, text analysis results

[0332] Output: Advice and care information

[0333] Step 6: Send your reply

[0334] The server sends the generated advice to the user's terminal, and the user receives the advice and tries it out as necessary.

[0335] Specific operation: The server packets the advice and sends it back to the device.

[0336] Input: Advice and care information

[0337] Output: Advice and care information received by the device

[0338] Step 7: Recording History

[0339] The server stores the dialogue history and emotion analysis results in a database and uses them as learning data for the future.

[0340] Specific operation: A database engine runs on the server, recording dialogue history and sentiment analysis results.

[0341] Input: Dialogue history, emotion analysis results

[0342] Output: Historical data recorded in a database

[0343] This will realize a system that can analyze the user's emotional state with high accuracy and provide appropriate advice in real time.

[0344] (Application example 2)

[0345] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0346] Conventional mental care systems have primarily analyzed only the text input of disaster victims to understand their emotional state. However, this approach does not take into account other emotional expressions, such as facial expressions and voice, resulting in insufficient accuracy in mental care. Furthermore, there are an increasing number of situations where immediate responses to stress and mental health issues are required for store and corporate employees. A system that can integrate such diverse emotional data and provide highly accurate mental care is needed.

[0347] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0348] In this invention, the server includes means for receiving input text, facial expression data, and voice data from the disaster victim, means for passing the received text, facial expression data, and voice data to a generation AI engine and an emotion engine and analyzing the emotions and mental state of the disaster victim based on the data, means for generating optimal advice and care based on the analysis results, means for sending the generated advice and care to the disaster victim's device, and means for saving a dialogue history with the disaster victim in a database and using it as learning data for the generation AI engine and the emotion engine. This makes it possible to integrate the text, facial expression, and voice data to perform highly accurate emotion analysis and provide appropriate advice, such as relaxation techniques, in real time.

[0349] "Victims" are people who have suffered physical and mental harm as a result of a disaster or emergency.

[0350] "Facial expression data" refers to data about a user's facial expressions collected in real time using a camera or other device.

[0351] "Voice data" refers to data about a user's vocalizations collected in real time using a microphone or other audio collection device.

[0352] The "generative AI engine" is an engine that uses natural language processing technology to analyze received text and emotional data, identify the user's psychological state, and generate appropriate advice.

[0353] The "emotion engine" is an engine that analyzes the user's input text, facial expression data, and voice data to recognize the user's emotional state.

[0354] The "analysis results" are the results of identifying the emotions and psychological state of the victims output by the generative AI engine and emotion engine.

[0355] "Advice or care" refers to specific courses of action, relaxation techniques, or assistance provided to improve the psychological state of a survivor.

[0356] The "database" is a data storage system that is stored on a server and that manages the dialogue history with disaster victims and the results of emotion analysis.

[0357] "Dialogue history" is a record of all past dialogues between the victim and the system.

[0358] "Learning data" refers to data that the generative AI engine and emotion engine use to improve analysis accuracy by incorporating it as new data.

[0359] This invention is a system for providing mental care to disaster victims and store employees. The system utilizes a generative AI engine and an emotion engine to generate highly accurate emotion analysis and advice.

[0360] System Configuration

[0361] User's device

[0362] The user's device is a device that can connect to the Internet, such as a smartphone, tablet, or PC. The device is equipped with a camera and microphone, and can collect facial expression and voice data in real time. The user uses the device to input their own emotions and mental state in text, and interacts with a chatbot that uses an emotion engine and a generative AI engine.

[0363] server

[0364] The central server is equipped with a generative AI engine and an emotion engine, and is responsible for receiving and analyzing data, recognizing emotions, generating and sending advice, and managing the database. The server operates 24 hours a day, 365 days a year, and processes user requests in real time. The generative AI engine uses natural language processing technology, while the emotion engine uses technology to recognize emotions from facial expressions and voice.

[0365] Generative AI Engine

[0366] The generative AI engine is an engine that identifies the user's psychological state based on the received text data and the emotion analysis results from the emotion engine.The generative AI engine generates optimal advice and care based on the analysis results.

[0367] Emotion Engine

[0368] The emotion engine analyzes the user's input text as well as facial expression and voice data to recognize emotions, enabling more accurate identification of the user's emotional state.

[0369] Database

[0370] The server stores the dialogue history and analysis results in a database. The generative AI engine and emotion engine use the information in the database to learn and improve the accuracy of advice and care.

[0371] Program processing description

[0372] 1. The user accesses the chatbot from their device and inputs their emotions and state of mind in text. For example, they might say, "I haven't been able to sleep at night recently, and I'm always feeling anxious." At the same time, the device's camera and microphone collect facial expression and voice data in real time.

[0373] 2. The device sends the collected text data, facial expression data, and voice data to the server.

[0374] 3. The server passes the received text data and emotion data to the generative AI engine and emotion engine, respectively. The emotion engine analyzes the facial expression and voice data to identify the emotional state. The generative AI engine analyzes the text data and performs emotion tagging and scoring.

[0375] 4. The generative AI engine combines the emotion analysis results from the emotion engine with the text analysis results to generate advice and care appropriate to the patient's mental state. For example, advice such as "Try relaxing before sleep. Deep breathing and meditation are recommended."

[0376] 5. The server sends the generated advice to the user's device, where the user can review the advice and take action if necessary.

[0377] 6. The server stores the dialogue history and emotion analysis results in a database. The generative AI engine and emotion engine use this history as training data to improve the accuracy of future advice and care.

[0378] Specific examples

[0379] For example, an employee working at a physical store might type into a smartphone application, "Recently, dealing with customers has been so stressful that it feels like I'm suffocating." At the same time, the smartphone's camera captures the employee's facial expression and the microphone collects their voice tone. The collected data is sent to a server, and the emotion engine recognizes the emotional state of "high stress." The generative AI engine then generates advice, such as "Take a short break and try some deep breathing and light stretching," which is displayed on the user's smartphone.

[0380] Prompt Sentence Examples

[0381] Enter your feelings or state of mind: "Lately I've been having trouble sleeping at night and I'm constantly anxious."

[0382] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0383] Step 1:

[0384] The user accesses the application on the device and inputs their feelings and mental state in text. For example, they might input, "I haven't been able to sleep at night recently and I'm always anxious." Based on this input, input text data is generated. At the same time, facial expression data and voice data are collected using the device's camera and microphone. The device temporarily stores this text, facial expression, and voice data.

[0385] Step 2:

[0386] The device sends the collected text data, facial expression data, and voice data to a server, which receives the data via the Internet. Input data is entered as text, facial expression data is sent as an image file, and voice data is sent as a voice file to the server.

[0387] Step 3:

[0388] The server passes the received text data to the generative AI engine, and the facial expression and voice data to the emotion engine. The generative AI engine uses natural language processing technology to analyze the text data and perform emotion tagging and scoring. The emotion engine analyzes the facial expression and voice data to identify the user's emotional state. These analyses are performed by algorithmic data processing and data calculation.

[0389] Step 4:

[0390] The generative AI engine integrates the emotion analysis results obtained from the emotion engine with the text analysis results. Based on this, the server generates optimal advice and care suited to the user's psychological state. For example, the generated advice might be, "Try relaxing before sleep. Deep breathing and meditation are recommended." This generated advice is data output from the generative AI engine.

[0391] Step 5:

[0392] The server sends the generated advice to the user's terminal, which then formats the received advice into an appropriate format for display to the user. The user can then review the advice and execute it as needed.

[0393] Step 6:

[0394] The server stores the dialogue history and emotion analysis results in a database. This data is used as training data for the generative AI engine and emotion engine. This history learning is expected to improve the accuracy of future emotion analysis and advice. The database is updated and the algorithm is strengthened in this step.

[0395] The above is the specific flow of operations according to the processing steps of this system.

[0396] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0397] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0398] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0399] [Second embodiment]

[0400] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0401] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0402] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0403] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0404] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0405] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0406] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0407] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0408] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0409] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0410] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0411] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0412] MODE FOR CARRYING OUT THE INVENTION

[0413] The present invention is a system for providing mental care to disaster victims and relief workers in the event of a disaster. Next, a specific embodiment of the system will be described.

[0414] System Configuration

[0415] 1. On the user's device:

[0416] These are devices that can connect to the Internet, such as smartphones, tablets, and computers, used by disaster victims and relief workers.

[0417] Using this device, users can input their emotions and state of mind in text and interact with the chatbot.

[0418] 2. Server:

[0419] The central server is equipped with a generative AI engine, which is the foundation of the chatbot, and is responsible for receiving and analyzing data, generating and sending advice, and managing the database.

[0420] The server operates 24 hours a day, 365 days a year, and processes user requests in real time.

[0421] 3. Generative AI engine:

[0422] The generative AI engine uses natural language processing technology to analyze received text data and identify the psychological state of disaster victims.

[0423] The engine generates optimal advice and care based on the analysis results.

[0424] 4. Database:

[0425] The server stores the dialogue history and analysis results in a database.

[0426] The generative AI engine uses information from the database to learn and improve the accuracy of advice and care.

[0427] Program processing description

[0428] 1. User input:

[0429] Users access the chatbot from their device and enter their emotions and state of mind in text.

[0430] For example: "Lately I've been having trouble sleeping at night and I'm constantly anxious."

[0431] 2. Sending input:

[0432] The user's terminal transmits the input text data to the server.

[0433] The server passes the received text data to the generation AI engine.

[0434] 3. Data Analysis:

[0435] The generative AI engine processes text data using analytical techniques to identify the user's emotions and state of mind.

[0436] For example, emotional tags such as "anxiety," "stress," and "sleep disorders" are assigned, and scoring is performed as necessary.

[0437] 4. Advice Generation:

[0438] The generative AI engine generates appropriate advice and care based on the analysis results.

[0439] For example: "Try some relaxation techniques before sleep. Deep breathing and meditation are good options."

[0440] 5. Sending a reply:

[0441] The server transmits the generated advice to the user's terminal, and the user can confirm the advice.

[0442] 6. Recording History:

[0443] The server stores the dialogue history in a database.

[0444] The generative AI engine uses this history as learning data to improve the accuracy of future advice and care.

[0445] Specific scenarios

[0446] As an example, consider the case where a user feels anxious late at night and inputs the following into the chatbot: "I haven't been able to sleep at night recently and I'm always anxious." This input is sent from the user's device to the server, which performs text analysis using a generative AI engine. The generative AI engine extracts emotion tags such as "anxiety" and "can't sleep at night" and generates appropriate advice. The generated advice is sent from the server to the user's device, where the user receives and confirms it. The dialogue history is saved in a database by the server, and the generative AI engine uses this as learning data to help improve the accuracy of future advice.

[0447] In this way, the system of the present invention can effectively support the mental care of disaster victims and relief workers in the event of a disaster.

[0448] The processing flow will be explained below.

[0449] Step 1:

[0450] The user accesses the chatbot from their device and inputs their feelings and state of mind into the text. For example, "I haven't been able to sleep at night recently and I'm always feeling anxious."

[0451] Step 2:

[0452] The terminal sends the entered text data to the server, which temporarily stores the received text data.

[0453] Step 3:

[0454] The server passes the received text data to the generative AI engine, which then prepares the data for analysis. Specifically, it normalizes the text data as a preprocessing step and removes unnecessary spaces and special characters.

[0455] Step 4:

[0456] The generative AI engine analyzes the normalized text using natural language processing technology to identify the emotions and mental state of the victims. Specifically, it assigns emotion tags such as "anxiety," "stress," and "sleep disorders" and calculates an emotion score.

[0457] Step 5:

[0458] The generative AI engine generates optimal advice and care suggestions based on the identified emotion tags and scores. Multiple advice suggestions may be generated, and the most appropriate one is selected from them.

[0459] Step 6:

[0460] The server sends the selected advice and care to the user's device. Specifically, it formats the advice data from the generative AI engine and displays it in an appropriate format for the user.

[0461] Step 7:

[0462] The device displays the received advice and care to the user, who can then check the advice and take action if necessary.

[0463] Step 8:

[0464] The server stores the conversation history between the user and the chatbot in a database, including the input text, analysis results, advice provided, and user responses.

[0465] Step 9:

[0466] The generative AI engine periodically uses the dialogue history in the database as learning data to improve the accuracy of advice and care. The generative AI engine is retrained based on new data to improve the quality of future analysis and advice.

[0467] Example 1

[0468] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0469] Conventional mental care systems for disaster victims and relief workers have difficulty responding in real time and generating appropriate advice from large amounts of data. Furthermore, they face challenges in providing accurate care based on the psychological state of each individual user.

[0470] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0471] In this invention, the server includes means for the victim to input their emotions and mental state using a terminal, means for transmitting the input text data to the server, means for the server to pass the received text data to a generating AI engine and analyze the victim's emotions and mental state based on the text data, means for generating optimal advice and care based on the analysis results, means for transmitting the generated advice and care to the victim's terminal, and means for storing a dialogue history with the victim in a database and using it as learning data for the generating AI engine, thereby enabling appropriate mental care in real time.

[0472] "Terminals" are devices that can connect to the Internet, such as smartphones, tablets, and computers, used by disaster victims and relief workers.

[0473] A "server" is a computer device equipped with a generative AI engine that receives and analyzes data, generates and transmits advice, and manages databases.

[0474] The "generative AI engine" is an AI engine that uses natural language processing technology to analyze received text data and identify the psychological state of disaster victims.

[0475] "Text data" is sentence data that expresses emotions and mental states and is input by a user using a terminal.

[0476] "Analysis results" are the emotion tags and scoring results extracted by the generative AI engine by analyzing the text data.

[0477] "Advice" refers to instructions and suggestions for mental care for victims generated by the AI ​​engine based on the analysis results.

[0478] A "database" is a data storage system in which the server stores the interaction history with the user and the analysis results.

[0479] "Dialogue history" is data that records past interactions between a user and a generative AI engine.

[0480] "Learning data" is data that the generative AI engine uses to improve the accuracy of advice and care based on information obtained from past dialogue history, etc.

[0481] "Tagging" is the act of assigning appropriate labels to emotions and states extracted from text data by a generative AI engine.

[0482] "Scoring" is the process by which the generative AI engine gives a numerical evaluation to each emotion tag or state based on the analysis results.

[0483] MODE FOR CARRYING OUT THE INVENTION

[0484] The present invention is a system for providing mental care to disaster victims and relief workers in the event of a disaster. Next, a specific embodiment of the system will be described.

[0485] System Configuration

[0486] 1. Device:

[0487] These devices include smartphones, tablets, and computers used by disaster victims and relief workers.

[0488] The device is capable of connecting to the Internet, and users use it to input their emotions and state of mind in text and interact with the chatbot.

[0489] 2. Server:

[0490] The server is a computer device that is equipped with a generative AI engine, which is the foundation of the chatbot, and is responsible for receiving, analyzing, generating and sending advice, and managing the database.

[0491] The server operates 24 hours a day, 365 days a year, and processes user requests in real time.

[0492] 3. Generative AI engine:

[0493] The generative AI engine uses natural language processing technology to analyze received text data and identify the psychological state of disaster victims.

[0494] This engine generates optimal advice and care based on the analysis results. Specific technologies used include sentiment analysis and entity recognition.

[0495] 4. Database:

[0496] The server stores the dialogue history and analysis results in a database.

[0497] The generative AI engine uses the stored data as training data to improve the accuracy of advice and care.

[0498] Specific examples

[0499] Consider a case where a user feels anxious late at night and types, "Recently, I haven't been able to sleep at night and I'm always anxious." This input is sent from the user's device to the server. The server passes this text data to the generative AI engine, which then analyzes it. During the analysis process, emotion tags such as "anxiety," "stress," and "sleep disorder" are assigned, and a score is calculated for each tag.

[0500] Based on the analysis results, the generative AI engine generates advice such as, "Try relaxing before sleep. Deep breathing and meditation are recommended." The server sends this advice to the user's device, where the user can confirm it. The server stores the conversation history in a database, which the generative AI engine uses to improve the accuracy of future advice.

[0501] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0502] Step 1:

[0503] User input:

[0504] How it works: A user accesses a dedicated app or website using a device such as a smartphone, tablet, or computer, and enters their emotion or state of mind into a text box.

[0505] Input: Text data describing emotions and mental states. Example: "Recently, I've been having trouble sleeping at night and I'm always anxious."

[0506] Output: The input text data.

[0507] Step 2:

[0508] Sending input:

[0509] How it works: The device sends the entered text data to the server. The transmission is secure using encryption technology such as SSL.

[0510] Input: The text data entered.

[0511] Output: The text data sent to the server.

[0512] Step 3:

[0513] Data Analysis:

[0514] How it works: The server passes the received text data to the generative AI engine. The generative AI engine uses natural language processing techniques to analyze the text data. Specifically, it uses sentiment analysis and entity recognition to tag and score the text.

[0515] Input: The text data sent to the server.

[0516] Output: Emotion tags (e.g., "anxiety," "stress," "sleep disturbance") and scoring results.

[0517] Step 4:

[0518] Advice Generation:

[0519] How it works: The generative AI engine generates optimal advice and care based on the analysis results, using prompts generated by a trained AI model.

[0520] Input: sentiment tags and scoring results.

[0521] Output: Advice or care message. Example: "Try some relaxation techniques before sleep. Deep breathing and meditation are recommended."

[0522] Step 5:

[0523] Send reply:

[0524] Operation: The server sends the generated advice to the user's terminal, where the user can confirm the advice.

[0525] Input: Message of advice or care.

[0526] Output: Advice displayed on the user's terminal.

[0527] Step 6:

[0528] History Record:

[0529] How it works: The server stores the user's interaction history in a database. The generative AI engine uses this history as training data to improve the accuracy of its advice.

[0530] Input: User interaction history.

[0531] Output: Historical data stored in a database.

[0532] Through the above processing steps, the system can provide appropriate mental care in real time and accurately support the user's psychological state.

[0533] (Application example 1)

[0534] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0535] Conventional mental care systems in the event of a disaster are limited to providing care to disaster victims and often fail to provide effective measures. Furthermore, there have been no mental care systems that can also be applied to the psychological state of customers and staff in stores. Therefore, while there is a growing demand for systems that provide mental care for general customers and staff, not just in the event of a disaster, there is a lack of technology that can meet this demand. These issues need to be resolved.

[0536] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0537] In this invention, the server includes a means for receiving input text from disaster victims or customers, a means for passing the received text to a generation AI engine and analyzing the emotions and mental state of the disaster victims or customers based on the text, and a means for generating optimal advice and care based on the analysis results. This enables mental care for customers and staff not only during disasters but also in physical stores.

[0538] "Victims" refers to those who have suffered damage to their lives, property, or physical or mental health due to a disaster.

[0539] "Customer" refers to the consumer or user to whom a service or product is provided.

[0540] "Smart glasses" are Internet-connected devices worn by users to visually receive and input information.

[0541] A "robot" is a mechanical device that operates automatically according to programmed instructions to perform specific tasks.

[0542] A "generative AI engine" refers to an engine with artificial intelligence capabilities that analyzes received text data and generates appropriate advice and care.

[0543] "Text input" refers to the user inputting character data using a keyboard, voice recognition, or the like.

[0544] "Sentiment analysis" refers to the process of identifying a user's emotions and psychological state from input text data.

[0545] "Advice generation" refers to generating information regarding advice and care for the user based on the analyzed emotions and psychological state.

[0546] "Dialogue history" refers to data that records interactions between a user and a system.

[0547] A "database" refers to an electronic information system for efficiently storing, managing, and searching information.

[0548] "Learning data for the generative AI engine" refers to data such as past dialogue history that the generative AI engine uses to improve the accuracy of its analysis and advice generation.

[0549] This invention is a system that provides mental care to disaster victims, customers, and store staff. Specifically, it uses smart glasses and a robot to analyze emotions and psychological states from text input using a generative AI engine, and generates appropriate advice to provide mental care.

[0550] System Configuration

[0551] 1. On the user's device:

[0552] These devices, such as smart glasses and robots, are used by customers and staff. These devices can connect to the internet and can input emotions and psychological states through text input and voice recognition.

[0553] 2. Server:

[0554] The server is equipped with a generative AI engine, which is the foundation of the chatbot, and receives and analyzes data, generates and sends advice, manages the database, etc. The server operates 24 hours a day, 365 days a year, and processes user requests in real time.

[0555] 3. Generative AI engine:

[0556] The generative AI engine uses natural language processing technology to analyze received text data and identify the user's psychological state. This engine generates optimal advice and care based on the analysis results.

[0557] 4. Database:

[0558] The server stores the dialogue history and analysis results in a database, and the generative AI engine uses the information in this database to learn and improve the accuracy of advice and care.

[0559] Program processing description

[0560] The server receives text data entered by the user via smart glasses or a robot and passes it to the generative AI engine. The generative AI engine analyzes the received text data and identifies the user's emotions and state of mind. This analysis process uses natural language processing technology, including emotion tagging and scoring. Based on the analysis results, optimal advice and care is generated. This generated advice is sent from the server to the user via the smart glasses or robot. The server also stores the dialogue history in a database and uses it as learning data for the generative AI engine.

[0561] Specific example explanation

[0562] For example, suppose a user types "I've been busy at work lately and I'm tired" into the smart glasses. This input data is instantly sent to the server, where the generative AI engine analyzes the text. The generative AI engine assigns emotion tags such as "fatigue" and "stress" and generates appropriate advice such as "To relax, we recommend deep breathing and light stretching. It is also effective to take a short break and hydrate." This advice is then sent from the server to the user via the smart glasses.

[0563] Example prompt sentence:

[0564] "Work has been busy lately and I'm feeling tired. How can I relax?"

[0565] In this way, the system can provide effective mental care to victims, customers, and staff.

[0566] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0567] Step 1: The user inputs text about their emotions or state of mind via smart glasses or a robot. Specifically, the user inputs data in the form of, "I've been busy at work lately and I'm tired." This input data is executed through the interface of the smart glasses or robot.

[0568] Step 2: The device sends the received text data to the server. The entered text data is transferred to the server via the network, and the server prepares to pass this data to the generation AI engine.

[0569] Step 3: The server passes the received text data to the generation AI engine. The server sends a request to the generation AI engine to analyze the text data, and provides the input text to the analysis engine.

[0570] Step 4: The generative AI engine analyzes the text data. The generative AI engine uses natural language processing technology to analyze the text data and perform emotion tagging and scoring. For example, emotion tags such as "fatigue" and "stress" can be assigned, and the state can be expressed numerically.

[0571] Step 5: The generative AI engine generates optimal advice and care based on the analysis results. Based on the analyzed emotion tags and scores, the generative AI engine generates advice such as, "To relax, we recommend deep breathing and light stretching. Taking a short break and drinking plenty of water is also effective."

[0572] Step 6: The server sends the generated advice to the user's device. The server then sends the advice received from the generating AI engine back to the smart glasses or robot. This process uses network communication.

[0573] Step 7: The device presents the advice to the user. The smart glasses or robot presents the sent advice or care to the user visually or audibly.

[0574] Step 8: The server saves the conversation history with the victim or customer in a database. The server saves the content of this conversation as conversation history and uses it as learning data for future generative AI engines.

[0575] Step 9: The generative AI engine uses the training data to learn new data. Using the dialogue history data stored on the server, the generative AI engine learns new data to improve the accuracy of analysis and advice generation.

[0576] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0577] MODE FOR CARRYING OUT THE INVENTION

[0578] This invention is a system that provides mental care to disaster victims and relief workers. By combining a generative AI engine with an emotion engine that recognizes the user's emotions, this system can provide more accurate emotion analysis and advice. Next, a specific embodiment of this system will be described.

[0579] System Configuration

[0580] 1. On the user's device:

[0581] These are devices that can connect to the Internet, such as smartphones, tablets, and computers, used by disaster victims and relief workers.

[0582] Using this device, users can input their emotions and state of mind in text and interact with a chatbot that uses an emotion engine and a generative AI engine.

[0583] 2. Server:

[0584] The central server is equipped with a generative AI engine and emotion engine that form the basis of the chatbot, and is responsible for receiving and analyzing data, recognizing emotions, generating and sending advice, and managing the database.

[0585] The server operates 24 hours a day, 365 days a year, and processes user requests in real time.

[0586] 3. Generative AI engine:

[0587] The generative AI engine uses natural language processing technology to identify the psychological state of disaster victims based on the received text data and the emotion analysis results from the emotion engine.

[0588] The engine generates optimal advice and care based on the analysis results.

[0589] 4. Emotion Engine:

[0590] The emotion engine analyzes the user's real-time facial expressions and voice data in addition to the text entered by the user to recognize emotions.

[0591] This allows for a more accurate determination of the user's emotional state.

[0592] 5. Database:

[0593] The server stores the dialogue history and analysis results in a database.

[0594] The generative AI engine and emotion engine use information from the database to learn and improve the accuracy of advice and care.

[0595] Program processing description

[0596] 1. User input:

[0597] Users access the chatbot from their device and enter their emotions and state of mind in text.

[0598] For example: "Lately I've been having trouble sleeping at night and I'm constantly anxious."

[0599] 2. Real-time emotion recognition:

[0600] The user's device uses a camera and microphone to transmit the user's facial expressions and voice to the emotion engine in real time.

[0601] 3. Input and emotional data transmission:

[0602] The terminal transmits the input text data and real-time emotion data to the server.

[0603] 4. Data Analysis and Emotion Recognition:

[0604] The text data and emotion data received by the server are passed to the generative AI engine and emotion engine, respectively.

[0605] The emotion engine analyzes facial and voice data to identify emotional states.

[0606] The generative AI engine analyzes text data and performs emotion tagging and scoring.

[0607] 5. Advice Generation:

[0608] The generative AI engine integrates the emotion analysis results obtained from the emotion engine with the text analysis results to generate more accurate advice and care.

[0609] For example: "Try some relaxation techniques before sleep. Deep breathing and meditation are good options."

[0610] 6. Sending a Reply:

[0611] The server transmits the generated advice to the user's terminal.

[0612] The user checks the advice and attempts to take action if necessary.

[0613] 7. Recording History:

[0614] The server stores the dialogue history and emotion analysis results in a database.

[0615] The generative AI engine and emotion engine use this history as learning data to improve the accuracy of future advice and care.

[0616] Specific scenarios

[0617] As an example, consider the case where a user feels anxious late at night and inputs the following into the chatbot: "I haven't been able to sleep at night recently and I'm always anxious." At the same time as this input, the user's device uses its camera and microphone to capture the user's facial expression and voice data, which it then sends to the server. The server uses a generative AI engine to perform text analysis and an emotion engine to perform emotion analysis from the facial expression and voice data. The generative AI engine compiles the analysis results of the emotion engine, generates optimal advice, and sends it back to the user. This dialogue history and emotion analysis results are saved in a database and will be used as future learning data.

[0618] In this way, the system of the present invention can provide more accurate support for the mental care of disaster victims and relief workers in the event of a disaster by combining it with an emotion engine.

[0619] The processing flow will be explained below.

[0620] Step 1:

[0621] The user accesses the chatbot from their device and inputs their feelings and state of mind into the text. For example, "I haven't been able to sleep at night recently and I'm always feeling anxious."

[0622] Step 2:

[0623] The user's device sends the input text data to the server, and at the same time, the device's camera and microphone are used to transmit the user's facial expressions and voice to the emotion engine in real time.

[0624] Step 3:

[0625] The server passes the received text data to the generative AI engine, which then normalizes the data as preprocessing. Normalization refers to the process of removing unnecessary spaces and special characters.

[0626] Step 4:

[0627] The server analyzes the user's facial expressions and voice data transmitted in real time to the emotion engine, which then identifies the user's emotional state from this data.

[0628] Step 5:

[0629] The emotion engine generates analysis results and sends them to the generative AI engine, which receives the analysis results from the emotion engine and integrates them with the analysis results of the text data.

[0630] Step 6:

[0631] The generative AI engine tags and scores the user's psychological state based on the integrated data, resulting in emotional tags such as "anxiety," "stress," and "sleep disorders."

[0632] Step 7:

[0633] The generative AI engine generates advice and care suggestions based on the identified emotion tags and scores. Multiple advice suggestions may be generated, and the most appropriate one will be selected.

[0634] Step 8:

[0635] The server sends the selected advice to the user's terminal, where the advice is formatted in a user-friendly format.

[0636] Step 9:

[0637] The device displays the received advice and care to the user, who can then check the advice and try it out if necessary.

[0638] Step 10:

[0639] The server stores the conversation history between the user and the chatbot in a database, including input text, analysis results, advice provided, and sentiment analysis results.

[0640] Step 11:

[0641] The generative AI engine and emotion engine use the dialogue history from the database as learning data and retrain to improve the accuracy of future advice and care.

[0642] Example 2

[0643] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0644] Conventional technologies lack systems that can quickly alleviate the mental stress of disaster victims and relief workers and provide effective mental care. Conventional systems lack the ability to accurately analyze the user's emotional state and provide optimal advice based on that analysis. As a result, it is difficult for disaster victims to receive the psychological support they need in real time, and this can lead to continued mental exhaustion.

[0645] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0646] In this invention, the server includes means for receiving input text from the disaster victim, means for passing the received text to a generation AI engine and analyzing the disaster victim's emotions and state of mind using the text and the emotion engine, means for the generation AI engine to integrate the analysis results of the emotion engine to generate optimal advice and care, means for transmitting the generated advice and care to the disaster victim's device, means for storing the dialogue history with the disaster victim and the emotion analysis results in a database and using them as learning data for the generation AI engine, means for the emotion engine to analyze the user's facial expression and voice data in real time, and means for the device to acquire the user's facial expression and voice data and transmit it to the server. This makes it possible to analyze the disaster victim's emotional state with high accuracy and provide appropriate advice in real time.

[0647] A "victim" is an individual who has suffered physical or psychological harm as a result of a natural disaster or emergency.

[0648] "Input text" is character information that a user sends to the system through a terminal.

[0649] The "generative AI engine" is an artificial intelligence engine that uses input text data to analyze the psychological state and emotions of disaster victims and generate optimal advice and care.

[0650] The "emotion engine" is an engine that analyzes the user's facial expressions and voice data to identify their emotional state in real time.

[0651] "Terminals" are devices that can connect to the Internet, such as smartphones, tablets, and computers, used by disaster victims and relief workers.

[0652] The "server" is a central system that incorporates the generative AI engine and emotion engine and receives data, analyzes it, generates advice, sends it, and manages the database.

[0653] A "database" is a storage device within the system that stores data such as dialogue history and emotion analysis results collected by the system and makes them reusable.

[0654] "Dialogue history" is a record of interactions between the victim and the system.

[0655] "Emotion analysis results" are information on the emotional state obtained by analyzing the user's facial expressions and voice data using the emotion engine.

[0656] "Advice" refers to specific suggestions and advice provided by the generative AI engine based on the psychological state and emotional analysis of the victim.

[0657] "Care" refers to support measures and means to reduce the mental and psychological burden on disaster victims.

[0658] "Real-time" refers to a method of processing and analyzing data immediately at the moment it is generated.

[0659] "Analysis" is the process of breaking down and analyzing input data and emotional data to identify meanings and states.

[0660] "Learning data" refers to data on past dialogue history and emotion analysis results that the generative AI engine uses to improve the accuracy of advice and care.

[0661] "Scoring" is the process of assigning a numerical rating to the analyzed emotions and psychological states.

[0662] "Tagging" is the process of assigning labels to emotions or psychological states based on the analysis results.

[0663] This invention is a system that provides mental care to disaster victims and relief workers during disasters. By combining a generative AI engine with an emotion engine that recognizes the user's emotions, this system can provide more accurate emotion analysis and advice. A specific embodiment of this system is described below.

[0664] System Configuration

[0665] 1. User's device

[0666] In the event of a disaster, users can use devices with internet connectivity, such as smartphones, tablets, and PCs, to input their emotions and mental state in text and interact with a chatbot that utilizes an emotion engine and a generative AI engine.

[0667] 2. Server

[0668] The server is equipped with a generative AI engine and an emotion engine, and receives and analyzes data, recognizes emotions, generates and transmits advice, and manages the database. This central server operates 24 hours a day, 365 days a year, and processes user requests in real time.

[0669] 3. Generative AI Engine

[0670] The generative AI engine uses natural language processing technology to identify the psychological state of disaster victims based on the received text data and the emotion analysis results from the emotion engine. This engine generates optimal advice and care based on the analysis results.

[0671] 4. Emotion Engine

[0672] The emotion engine recognizes emotions by analyzing the user's real-time facial expressions and voice data in addition to the user's input text, making it possible to more accurately identify the user's emotional state.

[0673] 5. Database

[0674] The server stores the dialogue history and analysis results in a database. The generative AI engine and emotion engine use the information in the database to learn and improve the accuracy of advice and care.

[0675] Program processing description

[0676] A user accesses the chatbot using a device and inputs their feelings and mental state in text format. For example, the user might input, "I haven't been able to sleep at night recently and I'm always anxious." At the same time as this input, the device uses a camera and microphone to capture the user's facial expressions and voice data, which are then sent to the server.

[0677] The server passes the received text data and emotion data to the generative AI engine and emotion engine. The emotion engine analyzes facial expressions and voice data to identify the emotional state. The generative AI engine analyzes the text data, performs emotion tagging and scoring, and integrates these results to generate optimal advice and care information. For example, advice such as "Try relaxing before sleep. Deep breathing and meditation are recommended" is provided.

[0678] The generated advice is sent from the server to the user's device, where the user receives it. The server also stores the dialogue history and emotion analysis results in a database and uses them as learning data for future use. This improves the accuracy of the generative AI engine and emotion engine, allowing for more personalized advice to be provided.

[0679] Specific scenarios

[0680] A user feels anxious late at night, so they input to the chatbot, "I haven't been able to sleep at night recently and I'm always anxious." At the same time as this input, the user's device uses its camera and microphone to capture facial expression and voice data, which it then sends to the server. The server then uses a generative AI engine to perform text analysis, and an emotion engine to perform emotion analysis from the facial expression and voice data. The generative AI engine compiles the analysis results of the emotion engine, generates optimal advice, and sends it back to the user. This dialogue history and emotion analysis results are saved in a database and used as learning data for future use.

[0681] Prompt Sentence Examples

[0682] By inputting information into the generative AI model such as "The user has input that 'I haven't been able to sleep at night recently and I'm always anxious.' Please advise me on appropriate ways to relax in response to this," more accurate advice can be generated. In this way, by combining an emotion engine, the system of the present invention can provide more accurate support for the mental care of disaster victims and relief workers in the event of a disaster.

[0683] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0684] Program processing flow

[0685] Step 1: User Input

[0686] A user accesses the chatbot using a device and inputs their feelings and state of mind in text format. For example, a user might input, "I haven't been able to sleep at night recently, and I'm always anxious."

[0687] Input: Text data that indicates the user's emotions and state of mind

[0688] Output: The input text data generated in the terminal

[0689] Step 2: Real-time emotion recognition

[0690] The device uses a camera and microphone to capture the user's facial expressions and voice data in real time and transmits it to the emotion engine.

[0691] Specific operations: Facial recognition is performed using the camera and audio data is collected using the microphone.

[0692] Input: Real-time facial expression and voice data of the user

[0693] Output: Facial and speech data sent to the emotion engine

[0694] Step 3: Sending input and emotion data

[0695] The device sends the input text data and real-time emotion data to the server, where the data is encrypted.

[0696] Specific operation: The terminal packetizes the data, encrypts it, and sends it to the server.

[0697] Input: Text data, facial expression data, audio data

[0698] Output: Text data and emotion data received by the server

[0699] Step 4: Data analysis and emotion recognition

[0700] The server passes the received text data and emotion data to the generative AI engine and emotion engine, respectively. The emotion engine analyzes facial expression data and voice data to identify the user's emotional state. The generative AI engine analyzes the text data and performs emotion tagging and scoring.

[0701] Specific operations: The emotion engine runs facial recognition algorithms and performs voice analysis. The generative AI engine performs natural language processing.

[0702] Input: Text data, facial expression data, audio data

[0703] Output: Sentiment analysis results (sentiment tags, scoring)

[0704] Step 5: Advice Generation

[0705] The generative AI engine combines the emotion analysis results from the emotion engine with the text analysis results to generate optimal advice and care information. For example, it might generate specific advice such as, "Try relaxing before sleep. Deep breathing and meditation are recommended."

[0706] Specific operation: The generative AI engine references past dialogue history and emotional data to generate optimal advice.

[0707] Input: Sentiment analysis results, text analysis results

[0708] Output: Advice and care information

[0709] Step 6: Send your reply

[0710] The server sends the generated advice to the user's terminal, and the user receives the advice and tries it out as necessary.

[0711] Specific operation: The server packets the advice and sends it back to the device.

[0712] Input: Advice and care information

[0713] Output: Advice and care information received by the device

[0714] Step 7: Recording History

[0715] The server stores the dialogue history and emotion analysis results in a database and uses them as learning data for the future.

[0716] Specific operation: A database engine runs on the server, recording dialogue history and sentiment analysis results.

[0717] Input: Dialogue history, emotion analysis results

[0718] Output: Historical data recorded in a database

[0719] This will realize a system that can analyze the user's emotional state with high accuracy and provide appropriate advice in real time.

[0720] (Application example 2)

[0721] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0722] Conventional mental care systems have primarily analyzed only the text input of disaster victims to understand their emotional state. However, this approach does not take into account other emotional expressions, such as facial expressions and voice, resulting in insufficient accuracy in mental care. Furthermore, there are an increasing number of situations where immediate responses to stress and mental health issues are required for store and corporate employees. A system that can integrate such diverse emotional data and provide highly accurate mental care is needed.

[0723] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0724] In this invention, the server includes means for receiving input text, facial expression data, and voice data from the disaster victim, means for passing the received text, facial expression data, and voice data to a generation AI engine and an emotion engine and analyzing the emotions and mental state of the disaster victim based on the data, means for generating optimal advice and care based on the analysis results, means for sending the generated advice and care to the disaster victim's device, and means for saving a dialogue history with the disaster victim in a database and using it as learning data for the generation AI engine and the emotion engine. This makes it possible to integrate the text, facial expression, and voice data to perform highly accurate emotion analysis and provide appropriate advice, such as relaxation techniques, in real time.

[0725] "Victims" are people who have suffered physical and mental harm as a result of a disaster or emergency.

[0726] "Facial expression data" refers to data about a user's facial expressions collected in real time using a camera or other device.

[0727] "Voice data" refers to data about a user's vocalizations collected in real time using a microphone or other audio collection device.

[0728] The "generative AI engine" is an engine that uses natural language processing technology to analyze received text and emotional data, identify the user's psychological state, and generate appropriate advice.

[0729] The "emotion engine" is an engine that analyzes the user's input text, facial expression data, and voice data to recognize the user's emotional state.

[0730] The "analysis results" are the results of identifying the emotions and psychological state of the victims output by the generative AI engine and emotion engine.

[0731] "Advice or care" refers to specific courses of action, relaxation techniques, or assistance provided to improve the psychological state of a survivor.

[0732] The "database" is a data storage system that is stored on a server and that manages the dialogue history with disaster victims and the results of emotion analysis.

[0733] "Dialogue history" is a record of all past dialogues between the victim and the system.

[0734] "Learning data" refers to data that the generative AI engine and emotion engine use to improve analysis accuracy by incorporating it as new data.

[0735] This invention is a system for providing mental care to disaster victims and store employees. The system utilizes a generative AI engine and an emotion engine to generate highly accurate emotion analysis and advice.

[0736] System Configuration

[0737] User's device

[0738] The user's device is a device that can connect to the Internet, such as a smartphone, tablet, or PC. The device is equipped with a camera and microphone, and can collect facial expression and voice data in real time. The user uses the device to input their own emotions and mental state in text, and interacts with a chatbot that uses an emotion engine and a generative AI engine.

[0739] server

[0740] The central server is equipped with a generative AI engine and an emotion engine, and is responsible for receiving and analyzing data, recognizing emotions, generating and sending advice, and managing the database. The server operates 24 hours a day, 365 days a year, and processes user requests in real time. The generative AI engine uses natural language processing technology, while the emotion engine uses technology to recognize emotions from facial expressions and voice.

[0741] Generative AI Engine

[0742] The generative AI engine is an engine that identifies the user's psychological state based on the received text data and the emotion analysis results from the emotion engine.The generative AI engine generates optimal advice and care based on the analysis results.

[0743] Emotion Engine

[0744] The emotion engine analyzes the user's input text as well as facial expression and voice data to recognize emotions, enabling more accurate identification of the user's emotional state.

[0745] Database

[0746] The server stores the dialogue history and analysis results in a database. The generative AI engine and emotion engine use the information in the database to learn and improve the accuracy of advice and care.

[0747] Program processing description

[0748] 1. The user accesses the chatbot from their device and inputs their emotions and state of mind in text. For example, they might say, "I haven't been able to sleep at night recently, and I'm always feeling anxious." At the same time, the device's camera and microphone collect facial expression and voice data in real time.

[0749] 2. The device sends the collected text data, facial expression data, and voice data to the server.

[0750] 3. The server passes the received text data and emotion data to the generative AI engine and emotion engine, respectively. The emotion engine analyzes the facial expression and voice data to identify the emotional state. The generative AI engine analyzes the text data and performs emotion tagging and scoring.

[0751] 4. The generative AI engine combines the emotion analysis results from the emotion engine with the text analysis results to generate advice and care appropriate to the patient's mental state. For example, advice such as "Try relaxing before sleep. Deep breathing and meditation are recommended."

[0752] 5. The server sends the generated advice to the user's device, where the user can review the advice and take action if necessary.

[0753] 6. The server stores the dialogue history and emotion analysis results in a database. The generative AI engine and emotion engine use this history as training data to improve the accuracy of future advice and care.

[0754] Specific examples

[0755] For example, an employee working at a physical store might type into a smartphone application, "Recently, dealing with customers has been so stressful that it feels like I'm suffocating." At the same time, the smartphone's camera captures the employee's facial expression and the microphone collects their voice tone. The collected data is sent to a server, and the emotion engine recognizes the emotional state of "high stress." The generative AI engine then generates advice, such as "Take a short break and try some deep breathing and light stretching," which is displayed on the user's smartphone.

[0756] Prompt Sentence Examples

[0757] Enter your feelings or state of mind: "Lately I've been having trouble sleeping at night and I'm constantly anxious."

[0758] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0759] Step 1:

[0760] The user accesses the application on the device and inputs their feelings and mental state in text. For example, they might input, "I haven't been able to sleep at night recently and I'm always anxious." Based on this input, input text data is generated. At the same time, facial expression data and voice data are collected using the device's camera and microphone. The device temporarily stores this text, facial expression, and voice data.

[0761] Step 2:

[0762] The device sends the collected text data, facial expression data, and voice data to a server, which receives the data via the Internet. Input data is entered as text, facial expression data is sent as an image file, and voice data is sent as a voice file to the server.

[0763] Step 3:

[0764] The server passes the received text data to the generative AI engine, and the facial expression and voice data to the emotion engine. The generative AI engine uses natural language processing technology to analyze the text data and perform emotion tagging and scoring. The emotion engine analyzes the facial expression and voice data to identify the user's emotional state. These analyses are performed by algorithmic data processing and data calculation.

[0765] Step 4:

[0766] The generative AI engine integrates the emotion analysis results obtained from the emotion engine with the text analysis results. Based on this, the server generates optimal advice and care suited to the user's psychological state. For example, the generated advice might be, "Try relaxing before sleep. Deep breathing and meditation are recommended." This generated advice is data output from the generative AI engine.

[0767] Step 5:

[0768] The server sends the generated advice to the user's terminal, which then formats the received advice into an appropriate format for display to the user. The user can then review the advice and execute it as needed.

[0769] Step 6:

[0770] The server stores the dialogue history and emotion analysis results in a database. This data is used as training data for the generative AI engine and emotion engine. This history learning is expected to improve the accuracy of future emotion analysis and advice. The database is updated and the algorithm is strengthened in this step.

[0771] The above is the specific flow of operations according to the processing steps of this system.

[0772] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0773] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0774] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0775] [Third embodiment]

[0776] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0777] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0778] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0779] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0780] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0781] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0782] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0783] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0784] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0785] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0786] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0787] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0788] MODE FOR CARRYING OUT THE INVENTION

[0789] The present invention is a system for providing mental care to disaster victims and relief workers in the event of a disaster. Next, a specific embodiment of the system will be described.

[0790] System Configuration

[0791] 1. On the user's device:

[0792] These are devices that can connect to the Internet, such as smartphones, tablets, and computers, used by disaster victims and relief workers.

[0793] Using this device, users can input their emotions and state of mind in text and interact with the chatbot.

[0794] 2. Server:

[0795] The central server is equipped with a generative AI engine, which is the foundation of the chatbot, and is responsible for receiving and analyzing data, generating and sending advice, and managing the database.

[0796] The server operates 24 hours a day, 365 days a year, and processes user requests in real time.

[0797] 3. Generative AI engine:

[0798] The generative AI engine uses natural language processing technology to analyze received text data and identify the psychological state of disaster victims.

[0799] The engine generates optimal advice and care based on the analysis results.

[0800] 4. Database:

[0801] The server stores the dialogue history and analysis results in a database.

[0802] The generative AI engine uses information from the database to learn and improve the accuracy of advice and care.

[0803] Program processing description

[0804] 1. User input:

[0805] Users access the chatbot from their device and enter their emotions and state of mind in text.

[0806] For example: "Lately I've been having trouble sleeping at night and I'm constantly anxious."

[0807] 2. Sending input:

[0808] The user's terminal transmits the input text data to the server.

[0809] The server passes the received text data to the generation AI engine.

[0810] 3. Data Analysis:

[0811] The generative AI engine processes text data using analytical techniques to identify the user's emotions and state of mind.

[0812] For example, emotional tags such as "anxiety," "stress," and "sleep disorders" are assigned, and scoring is performed as necessary.

[0813] 4. Advice Generation:

[0814] The generative AI engine generates appropriate advice and care based on the analysis results.

[0815] For example: "Try some relaxation techniques before sleep. Deep breathing and meditation are good options."

[0816] 5. Sending a reply:

[0817] The server transmits the generated advice to the user's terminal, and the user can confirm the advice.

[0818] 6. Recording History:

[0819] The server stores the dialogue history in a database.

[0820] The generative AI engine uses this history as learning data to improve the accuracy of future advice and care.

[0821] Specific scenarios

[0822] As an example, consider the case where a user feels anxious late at night and inputs the following into the chatbot: "I haven't been able to sleep at night recently and I'm always anxious." This input is sent from the user's device to the server, which performs text analysis using a generative AI engine. The generative AI engine extracts emotion tags such as "anxiety" and "can't sleep at night" and generates appropriate advice. The generated advice is sent from the server to the user's device, where the user receives and confirms it. The dialogue history is saved in a database by the server, and the generative AI engine uses this as learning data to help improve the accuracy of future advice.

[0823] In this way, the system of the present invention can effectively support the mental care of disaster victims and relief workers in the event of a disaster.

[0824] The processing flow will be explained below.

[0825] Step 1:

[0826] The user accesses the chatbot from their device and inputs their feelings and state of mind into the text. For example, "I haven't been able to sleep at night recently and I'm always feeling anxious."

[0827] Step 2:

[0828] The terminal sends the entered text data to the server, which temporarily stores the received text data.

[0829] Step 3:

[0830] The server passes the received text data to the generative AI engine, which then prepares the data for analysis. Specifically, it normalizes the text data as a preprocessing step and removes unnecessary spaces and special characters.

[0831] Step 4:

[0832] The generative AI engine analyzes the normalized text using natural language processing technology to identify the emotions and mental state of the victims. Specifically, it assigns emotion tags such as "anxiety," "stress," and "sleep disorders" and calculates an emotion score.

[0833] Step 5:

[0834] The generative AI engine generates optimal advice and care suggestions based on the identified emotion tags and scores. Multiple advice suggestions may be generated, and the most appropriate one is selected from them.

[0835] Step 6:

[0836] The server sends the selected advice and care to the user's device. Specifically, it formats the advice data from the generative AI engine and displays it in an appropriate format for the user.

[0837] Step 7:

[0838] The device displays the received advice and care to the user, who can then check the advice and take action if necessary.

[0839] Step 8:

[0840] The server stores the conversation history between the user and the chatbot in a database, including the input text, analysis results, advice provided, and user responses.

[0841] Step 9:

[0842] The generative AI engine periodically uses the dialogue history in the database as learning data to improve the accuracy of advice and care. The generative AI engine is retrained based on new data to improve the quality of future analysis and advice.

[0843] Example 1

[0844] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0845] Conventional mental care systems for disaster victims and relief workers have difficulty responding in real time and generating appropriate advice from large amounts of data. Furthermore, they face challenges in providing accurate care based on the psychological state of each individual user.

[0846] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0847] In this invention, the server includes means for the victim to input their emotions and mental state using a terminal, means for transmitting the input text data to the server, means for the server to pass the received text data to a generating AI engine and analyze the victim's emotions and mental state based on the text data, means for generating optimal advice and care based on the analysis results, means for transmitting the generated advice and care to the victim's terminal, and means for storing a dialogue history with the victim in a database and using it as learning data for the generating AI engine, thereby enabling appropriate mental care in real time.

[0848] "Terminals" are devices that can connect to the Internet, such as smartphones, tablets, and computers, used by disaster victims and relief workers.

[0849] A "server" is a computer device equipped with a generative AI engine that receives and analyzes data, generates and transmits advice, and manages databases.

[0850] The "generative AI engine" is an AI engine that uses natural language processing technology to analyze received text data and identify the psychological state of disaster victims.

[0851] "Text data" is sentence data that expresses emotions and mental states and is input by a user using a terminal.

[0852] "Analysis results" are the emotion tags and scoring results extracted by the generative AI engine by analyzing the text data.

[0853] "Advice" refers to instructions and suggestions for mental care for victims generated by the AI ​​engine based on the analysis results.

[0854] A "database" is a data storage system in which the server stores the interaction history with the user and the analysis results.

[0855] "Dialogue history" is data that records past interactions between a user and a generative AI engine.

[0856] "Learning data" is data that the generative AI engine uses to improve the accuracy of advice and care based on information obtained from past dialogue history, etc.

[0857] "Tagging" is the act of assigning appropriate labels to emotions and states extracted from text data by a generative AI engine.

[0858] "Scoring" is the process by which the generative AI engine gives a numerical evaluation to each emotion tag or state based on the analysis results.

[0859] MODE FOR CARRYING OUT THE INVENTION

[0860] The present invention is a system for providing mental care to disaster victims and relief workers in the event of a disaster. Next, a specific embodiment of the system will be described.

[0861] System Configuration

[0862] 1. Device:

[0863] These devices include smartphones, tablets, and computers used by disaster victims and relief workers.

[0864] The device is capable of connecting to the Internet, and users use it to input their emotions and state of mind in text and interact with the chatbot.

[0865] 2. Server:

[0866] The server is a computer device that is equipped with a generative AI engine, which is the foundation of the chatbot, and is responsible for receiving, analyzing, generating and sending advice, and managing the database.

[0867] The server operates 24 hours a day, 365 days a year, and processes user requests in real time.

[0868] 3. Generative AI engine:

[0869] The generative AI engine uses natural language processing technology to analyze received text data and identify the psychological state of disaster victims.

[0870] This engine generates optimal advice and care based on the analysis results. Specific technologies used include sentiment analysis and entity recognition.

[0871] 4. Database:

[0872] The server stores the dialogue history and analysis results in a database.

[0873] The generative AI engine uses the stored data as training data to improve the accuracy of advice and care.

[0874] Specific examples

[0875] Consider a case where a user feels anxious late at night and types, "Recently, I haven't been able to sleep at night and I'm always anxious." This input is sent from the user's device to the server. The server passes this text data to the generative AI engine, which then analyzes it. During the analysis process, emotion tags such as "anxiety," "stress," and "sleep disorder" are assigned, and a score is calculated for each tag.

[0876] Based on the analysis results, the generative AI engine generates advice such as, "Try relaxing before sleep. Deep breathing and meditation are recommended." The server sends this advice to the user's device, where the user can confirm it. The server stores the conversation history in a database, which the generative AI engine uses to improve the accuracy of future advice.

[0877] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0878] Step 1:

[0879] User input:

[0880] How it works: A user accesses a dedicated app or website using a device such as a smartphone, tablet, or computer, and enters their emotion or state of mind into a text box.

[0881] Input: Text data describing emotions and mental states. Example: "Recently, I've been having trouble sleeping at night and I'm always anxious."

[0882] Output: The input text data.

[0883] Step 2:

[0884] Sending input:

[0885] How it works: The device sends the entered text data to the server. The transmission is secure using encryption technology such as SSL.

[0886] Input: The text data entered.

[0887] Output: The text data sent to the server.

[0888] Step 3:

[0889] Data Analysis:

[0890] How it works: The server passes the received text data to the generative AI engine. The generative AI engine uses natural language processing techniques to analyze the text data. Specifically, it uses sentiment analysis and entity recognition to tag and score the text.

[0891] Input: The text data sent to the server.

[0892] Output: Emotion tags (e.g., "anxiety," "stress," "sleep disturbance") and scoring results.

[0893] Step 4:

[0894] Advice Generation:

[0895] How it works: The generative AI engine generates optimal advice and care based on the analysis results, using prompts generated by a trained AI model.

[0896] Input: sentiment tags and scoring results.

[0897] Output: Advice or care message. Example: "Try some relaxation techniques before sleep. Deep breathing and meditation are recommended."

[0898] Step 5:

[0899] Send reply:

[0900] Operation: The server sends the generated advice to the user's terminal, where the user can confirm the advice.

[0901] Input: Message of advice or care.

[0902] Output: Advice displayed on the user's terminal.

[0903] Step 6:

[0904] History Record:

[0905] How it works: The server stores the user's interaction history in a database. The generative AI engine uses this history as training data to improve the accuracy of its advice.

[0906] Input: User interaction history.

[0907] Output: Historical data stored in a database.

[0908] Through the above processing steps, the system can provide appropriate mental care in real time and accurately support the user's psychological state.

[0909] (Application example 1)

[0910] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0911] Conventional mental care systems in the event of a disaster are limited to providing care to disaster victims and often fail to provide effective measures. Furthermore, there have been no mental care systems that can also be applied to the psychological state of customers and staff in stores. Therefore, while there is a growing demand for systems that provide mental care for general customers and staff, not just in the event of a disaster, there is a lack of technology that can meet this demand. These issues need to be resolved.

[0912] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0913] In this invention, the server includes a means for receiving input text from disaster victims or customers, a means for passing the received text to a generation AI engine and analyzing the emotions and mental state of the disaster victims or customers based on the text, and a means for generating optimal advice and care based on the analysis results. This enables mental care for customers and staff not only during disasters but also in physical stores.

[0914] "Victims" refers to those who have suffered damage to their lives, property, or physical or mental health due to a disaster.

[0915] "Customer" refers to the consumer or user to whom a service or product is provided.

[0916] "Smart glasses" are Internet-connected devices worn by users to visually receive and input information.

[0917] A "robot" is a mechanical device that operates automatically according to programmed instructions to perform specific tasks.

[0918] A "generative AI engine" refers to an engine with artificial intelligence capabilities that analyzes received text data and generates appropriate advice and care.

[0919] "Text input" refers to the user inputting character data using a keyboard, voice recognition, or the like.

[0920] "Sentiment analysis" refers to the process of identifying a user's emotions and psychological state from input text data.

[0921] "Advice generation" refers to generating information regarding advice and care for the user based on the analyzed emotions and psychological state.

[0922] "Dialogue history" refers to data that records interactions between a user and a system.

[0923] A "database" refers to an electronic information system for efficiently storing, managing, and searching information.

[0924] "Learning data for the generative AI engine" refers to data such as past dialogue history that the generative AI engine uses to improve the accuracy of its analysis and advice generation.

[0925] This invention is a system that provides mental care to disaster victims, customers, and store staff. Specifically, it uses smart glasses and a robot to analyze emotions and psychological states from text input using a generative AI engine, and generates appropriate advice to provide mental care.

[0926] System Configuration

[0927] 1. On the user's device:

[0928] These devices, such as smart glasses and robots, are used by customers and staff. These devices can connect to the internet and can input emotions and psychological states through text input and voice recognition.

[0929] 2. Server:

[0930] The server is equipped with a generative AI engine, which is the foundation of the chatbot, and receives and analyzes data, generates and sends advice, manages the database, etc. The server operates 24 hours a day, 365 days a year, and processes user requests in real time.

[0931] 3. Generative AI engine:

[0932] The generative AI engine uses natural language processing technology to analyze received text data and identify the user's psychological state. This engine generates optimal advice and care based on the analysis results.

[0933] 4. Database:

[0934] The server stores the dialogue history and analysis results in a database, and the generative AI engine uses the information in this database to learn and improve the accuracy of advice and care.

[0935] Program processing description

[0936] The server receives text data entered by the user via smart glasses or a robot and passes it to the generative AI engine. The generative AI engine analyzes the received text data and identifies the user's emotions and state of mind. This analysis process uses natural language processing technology, including emotion tagging and scoring. Based on the analysis results, optimal advice and care is generated. This generated advice is sent from the server to the user via the smart glasses or robot. The server also stores the dialogue history in a database and uses it as learning data for the generative AI engine.

[0937] Specific example explanation

[0938] For example, suppose a user types "I've been busy at work lately and I'm tired" into the smart glasses. This input data is instantly sent to the server, where the generative AI engine analyzes the text. The generative AI engine assigns emotion tags such as "fatigue" and "stress" and generates appropriate advice such as "To relax, we recommend deep breathing and light stretching. It is also effective to take a short break and hydrate." This advice is then sent from the server to the user via the smart glasses.

[0939] Example prompt sentence:

[0940] "Work has been busy lately and I'm feeling tired. How can I relax?"

[0941] In this way, the system can provide effective mental care to victims, customers, and staff.

[0942] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0943] Step 1: The user inputs text about their emotions or state of mind via smart glasses or a robot. Specifically, the user inputs data in the form of, "I've been busy at work lately and I'm tired." This input data is executed through the interface of the smart glasses or robot.

[0944] Step 2: The device sends the received text data to the server. The entered text data is transferred to the server via the network, and the server prepares to pass this data to the generation AI engine.

[0945] Step 3: The server passes the received text data to the generation AI engine. The server sends a request to the generation AI engine to analyze the text data, and provides the input text to the analysis engine.

[0946] Step 4: The generative AI engine analyzes the text data. The generative AI engine uses natural language processing technology to analyze the text data and perform emotion tagging and scoring. For example, emotion tags such as "fatigue" and "stress" can be assigned, and the state can be expressed numerically.

[0947] Step 5: The generative AI engine generates optimal advice and care based on the analysis results. Based on the analyzed emotion tags and scores, the generative AI engine generates advice such as, "To relax, we recommend deep breathing and light stretching. Taking a short break and drinking plenty of water is also effective."

[0948] Step 6: The server sends the generated advice to the user's device. The server then sends the advice received from the generating AI engine back to the smart glasses or robot. This process uses network communication.

[0949] Step 7: The device presents the advice to the user. The smart glasses or robot presents the sent advice or care to the user visually or audibly.

[0950] Step 8: The server saves the conversation history with the victim or customer in a database. The server saves the content of this conversation as conversation history and uses it as learning data for future generative AI engines.

[0951] Step 9: The generative AI engine uses the training data to learn new data. Using the dialogue history data stored on the server, the generative AI engine learns new data to improve the accuracy of analysis and advice generation.

[0952] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0953] MODE FOR CARRYING OUT THE INVENTION

[0954] This invention is a system that provides mental care to disaster victims and relief workers. By combining a generative AI engine with an emotion engine that recognizes the user's emotions, this system can provide more accurate emotion analysis and advice. Next, a specific embodiment of this system will be described.

[0955] System Configuration

[0956] 1. On the user's device:

[0957] These are devices that can connect to the Internet, such as smartphones, tablets, and computers, used by disaster victims and relief workers.

[0958] Using this device, users can input their emotions and state of mind in text and interact with a chatbot that uses an emotion engine and a generative AI engine.

[0959] 2. Server:

[0960] The central server is equipped with a generative AI engine and emotion engine that form the basis of the chatbot, and is responsible for receiving and analyzing data, recognizing emotions, generating and sending advice, and managing the database.

[0961] The server operates 24 hours a day, 365 days a year, and processes user requests in real time.

[0962] 3. Generative AI engine:

[0963] The generative AI engine uses natural language processing technology to identify the psychological state of disaster victims based on the received text data and the emotion analysis results from the emotion engine.

[0964] The engine generates optimal advice and care based on the analysis results.

[0965] 4. Emotion Engine:

[0966] The emotion engine analyzes the user's real-time facial expressions and voice data in addition to the text entered by the user to recognize emotions.

[0967] This allows for a more accurate determination of the user's emotional state.

[0968] 5. Database:

[0969] The server stores the dialogue history and analysis results in a database.

[0970] The generative AI engine and emotion engine use information from the database to learn and improve the accuracy of advice and care.

[0971] Program processing description

[0972] 1. User input:

[0973] Users access the chatbot from their device and enter their emotions and state of mind in text.

[0974] For example: "Lately I've been having trouble sleeping at night and I'm constantly anxious."

[0975] 2. Real-time emotion recognition:

[0976] The user's device uses a camera and microphone to transmit the user's facial expressions and voice to the emotion engine in real time.

[0977] 3. Input and emotional data transmission:

[0978] The terminal transmits the input text data and real-time emotion data to the server.

[0979] 4. Data Analysis and Emotion Recognition:

[0980] The text data and emotion data received by the server are passed to the generative AI engine and emotion engine, respectively.

[0981] The emotion engine analyzes facial and voice data to identify emotional states.

[0982] The generative AI engine analyzes text data and performs emotion tagging and scoring.

[0983] 5. Advice Generation:

[0984] The generative AI engine integrates the emotion analysis results obtained from the emotion engine with the text analysis results to generate more accurate advice and care.

[0985] For example: "Try some relaxation techniques before sleep. Deep breathing and meditation are good options."

[0986] 6. Sending a Reply:

[0987] The server transmits the generated advice to the user's terminal.

[0988] The user checks the advice and attempts to take action if necessary.

[0989] 7. Recording History:

[0990] The server stores the dialogue history and emotion analysis results in a database.

[0991] The generative AI engine and emotion engine use this history as learning data to improve the accuracy of future advice and care.

[0992] Specific scenarios

[0993] As an example, consider the case where a user feels anxious late at night and inputs the following into the chatbot: "I haven't been able to sleep at night recently and I'm always anxious." At the same time as this input, the user's device uses its camera and microphone to capture the user's facial expression and voice data, which it then sends to the server. The server uses a generative AI engine to perform text analysis and an emotion engine to perform emotion analysis from the facial expression and voice data. The generative AI engine compiles the analysis results of the emotion engine, generates optimal advice, and sends it back to the user. This dialogue history and emotion analysis results are saved in a database and will be used as future learning data.

[0994] In this way, the system of the present invention can provide more accurate support for the mental care of disaster victims and relief workers in the event of a disaster by combining it with an emotion engine.

[0995] The processing flow will be explained below.

[0996] Step 1:

[0997] The user accesses the chatbot from their device and inputs their feelings and state of mind into the text. For example, "I haven't been able to sleep at night recently and I'm always feeling anxious."

[0998] Step 2:

[0999] The user's device sends the input text data to the server, and at the same time, the device's camera and microphone are used to transmit the user's facial expressions and voice to the emotion engine in real time.

[1000] Step 3:

[1001] The server passes the received text data to the generative AI engine, which then normalizes the data as preprocessing. Normalization refers to the process of removing unnecessary spaces and special characters.

[1002] Step 4:

[1003] The server analyzes the user's facial expressions and voice data transmitted in real time to the emotion engine, which then identifies the user's emotional state from this data.

[1004] Step 5:

[1005] The emotion engine generates analysis results and sends them to the generative AI engine, which receives the analysis results from the emotion engine and integrates them with the analysis results of the text data.

[1006] Step 6:

[1007] The generative AI engine tags and scores the user's psychological state based on the integrated data, resulting in emotional tags such as "anxiety," "stress," and "sleep disorders."

[1008] Step 7:

[1009] The generative AI engine generates advice and care suggestions based on the identified emotion tags and scores. Multiple advice suggestions may be generated, and the most appropriate one will be selected.

[1010] Step 8:

[1011] The server sends the selected advice to the user's terminal, where the advice is formatted in a user-friendly format.

[1012] Step 9:

[1013] The device displays the received advice and care to the user, who can then check the advice and try it out if necessary.

[1014] Step 10:

[1015] The server stores the conversation history between the user and the chatbot in a database, including input text, analysis results, advice provided, and sentiment analysis results.

[1016] Step 11:

[1017] The generative AI engine and emotion engine use the dialogue history from the database as learning data and retrain to improve the accuracy of future advice and care.

[1018] Example 2

[1019] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1020] Conventional technologies lack systems that can quickly alleviate the mental stress of disaster victims and relief workers and provide effective mental care. Conventional systems lack the ability to accurately analyze the user's emotional state and provide optimal advice based on that analysis. As a result, it is difficult for disaster victims to receive the psychological support they need in real time, and this can lead to continued mental exhaustion.

[1021] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1022] In this invention, the server includes means for receiving input text from the disaster victim, means for passing the received text to a generation AI engine and analyzing the disaster victim's emotions and state of mind using the text and the emotion engine, means for the generation AI engine to integrate the analysis results of the emotion engine to generate optimal advice and care, means for transmitting the generated advice and care to the disaster victim's device, means for storing the dialogue history with the disaster victim and the emotion analysis results in a database and using them as learning data for the generation AI engine, means for the emotion engine to analyze the user's facial expression and voice data in real time, and means for the device to acquire the user's facial expression and voice data and transmit it to the server. This makes it possible to analyze the disaster victim's emotional state with high accuracy and provide appropriate advice in real time.

[1023] A "victim" is an individual who has suffered physical or psychological harm as a result of a natural disaster or emergency.

[1024] "Input text" is character information that a user sends to the system through a terminal.

[1025] The "generative AI engine" is an artificial intelligence engine that uses input text data to analyze the psychological state and emotions of disaster victims and generate optimal advice and care.

[1026] The "emotion engine" is an engine that analyzes the user's facial expressions and voice data to identify their emotional state in real time.

[1027] "Terminals" are devices that can connect to the Internet, such as smartphones, tablets, and computers, used by disaster victims and relief workers.

[1028] The "server" is a central system that incorporates the generative AI engine and emotion engine and receives data, analyzes it, generates advice, sends it, and manages the database.

[1029] A "database" is a storage device within the system that stores data such as dialogue history and emotion analysis results collected by the system and makes them reusable.

[1030] "Dialogue history" is a record of interactions between the victim and the system.

[1031] "Emotion analysis results" are information on the emotional state obtained by analyzing the user's facial expressions and voice data using the emotion engine.

[1032] "Advice" refers to specific suggestions and advice provided by the generative AI engine based on the psychological state and emotional analysis of the victim.

[1033] "Care" refers to support measures and means to reduce the mental and psychological burden on disaster victims.

[1034] "Real-time" refers to a method of processing and analyzing data immediately at the moment it is generated.

[1035] "Analysis" is the process of breaking down and analyzing input data and emotional data to identify meanings and states.

[1036] "Learning data" refers to data on past dialogue history and emotion analysis results that the generative AI engine uses to improve the accuracy of advice and care.

[1037] "Scoring" is the process of assigning a numerical rating to the analyzed emotions and psychological states.

[1038] "Tagging" is the process of assigning labels to emotions or psychological states based on the analysis results.

[1039] This invention is a system that provides mental care to disaster victims and relief workers during disasters. By combining a generative AI engine with an emotion engine that recognizes the user's emotions, this system can provide more accurate emotion analysis and advice. A specific embodiment of this system is described below.

[1040] System Configuration

[1041] 1. User's device

[1042] In the event of a disaster, users can use devices with internet connectivity, such as smartphones, tablets, and PCs, to input their emotions and mental state in text and interact with a chatbot that utilizes an emotion engine and a generative AI engine.

[1043] 2. Server

[1044] The server is equipped with a generative AI engine and an emotion engine, and receives and analyzes data, recognizes emotions, generates and transmits advice, and manages the database. This central server operates 24 hours a day, 365 days a year, and processes user requests in real time.

[1045] 3. Generative AI Engine

[1046] The generative AI engine uses natural language processing technology to identify the psychological state of disaster victims based on the received text data and the emotion analysis results from the emotion engine. This engine generates optimal advice and care based on the analysis results.

[1047] 4. Emotion Engine

[1048] The emotion engine recognizes emotions by analyzing the user's real-time facial expressions and voice data in addition to the user's input text, making it possible to more accurately identify the user's emotional state.

[1049] 5. Database

[1050] The server stores the dialogue history and analysis results in a database. The generative AI engine and emotion engine use the information in the database to learn and improve the accuracy of advice and care.

[1051] Program processing description

[1052] A user accesses the chatbot using a device and inputs their feelings and mental state in text format. For example, the user might input, "I haven't been able to sleep at night recently and I'm always anxious." At the same time as this input, the device uses a camera and microphone to capture the user's facial expressions and voice data, which are then sent to the server.

[1053] The server passes the received text data and emotion data to the generative AI engine and emotion engine. The emotion engine analyzes facial expressions and voice data to identify the emotional state. The generative AI engine analyzes the text data, performs emotion tagging and scoring, and integrates these results to generate optimal advice and care information. For example, advice such as "Try relaxing before sleep. Deep breathing and meditation are recommended" is provided.

[1054] The generated advice is sent from the server to the user's device, where the user receives it. The server also stores the dialogue history and emotion analysis results in a database and uses them as learning data for future use. This improves the accuracy of the generative AI engine and emotion engine, allowing for more personalized advice to be provided.

[1055] Specific scenarios

[1056] A user feels anxious late at night, so they input to the chatbot, "I haven't been able to sleep at night recently and I'm always anxious." At the same time as this input, the user's device uses its camera and microphone to capture facial expression and voice data, which it then sends to the server. The server then uses a generative AI engine to perform text analysis, and an emotion engine to perform emotion analysis from the facial expression and voice data. The generative AI engine compiles the analysis results of the emotion engine, generates optimal advice, and sends it back to the user. This dialogue history and emotion analysis results are saved in a database and used as learning data for future use.

[1057] Prompt Sentence Examples

[1058] By inputting information into the generative AI model such as "The user has input that 'I haven't been able to sleep at night recently and I'm always anxious.' Please advise me on appropriate ways to relax in response to this," more accurate advice can be generated. In this way, by combining an emotion engine, the system of the present invention can provide more accurate support for the mental care of disaster victims and relief workers in the event of a disaster.

[1059] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1060] Program processing flow

[1061] Step 1: User Input

[1062] A user accesses the chatbot using a device and inputs their feelings and state of mind in text format. For example, a user might input, "I haven't been able to sleep at night recently, and I'm always anxious."

[1063] Input: Text data that indicates the user's emotions and state of mind

[1064] Output: The input text data generated in the terminal

[1065] Step 2: Real-time emotion recognition

[1066] The device uses a camera and microphone to capture the user's facial expressions and voice data in real time and transmits it to the emotion engine.

[1067] Specific operations: Facial recognition is performed using the camera and audio data is collected using the microphone.

[1068] Input: Real-time facial expression and voice data of the user

[1069] Output: Facial and speech data sent to the emotion engine

[1070] Step 3: Sending input and emotion data

[1071] The device sends the input text data and real-time emotion data to the server, where the data is encrypted.

[1072] Specific operation: The terminal packetizes the data, encrypts it, and sends it to the server.

[1073] Input: Text data, facial expression data, audio data

[1074] Output: Text data and emotion data received by the server

[1075] Step 4: Data analysis and emotion recognition

[1076] The server passes the received text data and emotion data to the generative AI engine and emotion engine, respectively. The emotion engine analyzes facial expression data and voice data to identify the user's emotional state. The generative AI engine analyzes the text data and performs emotion tagging and scoring.

[1077] Specific operations: The emotion engine runs facial recognition algorithms and performs voice analysis. The generative AI engine performs natural language processing.

[1078] Input: Text data, facial expression data, audio data

[1079] Output: Sentiment analysis results (sentiment tags, scoring)

[1080] Step 5: Advice Generation

[1081] The generative AI engine combines the emotion analysis results from the emotion engine with the text analysis results to generate optimal advice and care information. For example, it might generate specific advice such as, "Try relaxing before sleep. Deep breathing and meditation are recommended."

[1082] Specific operation: The generative AI engine references past dialogue history and emotional data to generate optimal advice.

[1083] Input: Sentiment analysis results, text analysis results

[1084] Output: Advice and care information

[1085] Step 6: Send your reply

[1086] The server sends the generated advice to the user's terminal, and the user receives the advice and tries it out as necessary.

[1087] Specific operation: The server packets the advice and sends it back to the device.

[1088] Input: Advice and care information

[1089] Output: Advice and care information received by the device

[1090] Step 7: Recording History

[1091] The server stores the dialogue history and emotion analysis results in a database and uses them as learning data for the future.

[1092] Specific operation: A database engine runs on the server, recording dialogue history and sentiment analysis results.

[1093] Input: Dialogue history, emotion analysis results

[1094] Output: Historical data recorded in a database

[1095] This will realize a system that can analyze the user's emotional state with high accuracy and provide appropriate advice in real time.

[1096] (Application example 2)

[1097] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1098] Conventional mental care systems have primarily analyzed only the text input of disaster victims to understand their emotional state. However, this approach does not take into account other emotional expressions, such as facial expressions and voice, resulting in insufficient accuracy in mental care. Furthermore, there are an increasing number of situations where immediate responses to stress and mental health issues are required for store and corporate employees. A system that can integrate such diverse emotional data and provide highly accurate mental care is needed.

[1099] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1100] In this invention, the server includes means for receiving input text, facial expression data, and voice data from the disaster victim, means for passing the received text, facial expression data, and voice data to a generation AI engine and an emotion engine and analyzing the emotions and mental state of the disaster victim based on the data, means for generating optimal advice and care based on the analysis results, means for sending the generated advice and care to the disaster victim's device, and means for saving a dialogue history with the disaster victim in a database and using it as learning data for the generation AI engine and the emotion engine. This makes it possible to integrate the text, facial expression, and voice data to perform highly accurate emotion analysis and provide appropriate advice, such as relaxation techniques, in real time.

[1101] "Victims" are people who have suffered physical and mental harm as a result of a disaster or emergency.

[1102] "Facial expression data" refers to data about a user's facial expressions collected in real time using a camera or other device.

[1103] "Voice data" refers to data about a user's vocalizations collected in real time using a microphone or other audio collection device.

[1104] The "generative AI engine" is an engine that uses natural language processing technology to analyze received text and emotional data, identify the user's psychological state, and generate appropriate advice.

[1105] The "emotion engine" is an engine that analyzes the user's input text, facial expression data, and voice data to recognize the user's emotional state.

[1106] The "analysis results" are the results of identifying the emotions and psychological state of the victims output by the generative AI engine and emotion engine.

[1107] "Advice or care" refers to specific courses of action, relaxation techniques, or assistance provided to improve the psychological state of a survivor.

[1108] The "database" is a data storage system that is stored on a server and that manages the dialogue history with disaster victims and the results of emotion analysis.

[1109] "Dialogue history" is a record of all past dialogues between the victim and the system.

[1110] "Learning data" refers to data that the generative AI engine and emotion engine use to improve analysis accuracy by incorporating it as new data.

[1111] This invention is a system for providing mental care to disaster victims and store employees. The system utilizes a generative AI engine and an emotion engine to generate highly accurate emotion analysis and advice.

[1112] System Configuration

[1113] User's device

[1114] The user's device is a device that can connect to the Internet, such as a smartphone, tablet, or PC. The device is equipped with a camera and microphone, and can collect facial expression and voice data in real time. The user uses the device to input their own emotions and mental state in text, and interacts with a chatbot that uses an emotion engine and a generative AI engine.

[1115] server

[1116] The central server is equipped with a generative AI engine and an emotion engine, and is responsible for receiving and analyzing data, recognizing emotions, generating and sending advice, and managing the database. The server operates 24 hours a day, 365 days a year, and processes user requests in real time. The generative AI engine uses natural language processing technology, while the emotion engine uses technology to recognize emotions from facial expressions and voice.

[1117] Generative AI Engine

[1118] The generative AI engine is an engine that identifies the user's psychological state based on the received text data and the emotion analysis results from the emotion engine.The generative AI engine generates optimal advice and care based on the analysis results.

[1119] Emotion Engine

[1120] The emotion engine analyzes the user's input text as well as facial expression and voice data to recognize emotions, enabling more accurate identification of the user's emotional state.

[1121] Database

[1122] The server stores the dialogue history and analysis results in a database. The generative AI engine and emotion engine use the information in the database to learn and improve the accuracy of advice and care.

[1123] Program processing description

[1124] 1. The user accesses the chatbot from their device and inputs their emotions and state of mind in text. For example, they might say, "I haven't been able to sleep at night recently, and I'm always feeling anxious." At the same time, the device's camera and microphone collect facial expression and voice data in real time.

[1125] 2. The device sends the collected text data, facial expression data, and voice data to the server.

[1126] 3. The server passes the received text data and emotion data to the generative AI engine and emotion engine, respectively. The emotion engine analyzes the facial expression and voice data to identify the emotional state. The generative AI engine analyzes the text data and performs emotion tagging and scoring.

[1127] 4. The generative AI engine combines the emotion analysis results from the emotion engine with the text analysis results to generate advice and care appropriate to the patient's mental state. For example, advice such as "Try relaxing before sleep. Deep breathing and meditation are recommended."

[1128] 5. The server sends the generated advice to the user's device, where the user can review the advice and take action if necessary.

[1129] 6. The server stores the dialogue history and emotion analysis results in a database. The generative AI engine and emotion engine use this history as training data to improve the accuracy of future advice and care.

[1130] Specific examples

[1131] For example, an employee working at a physical store might type into a smartphone application, "Recently, dealing with customers has been so stressful that it feels like I'm suffocating." At the same time, the smartphone's camera captures the employee's facial expression and the microphone collects their voice tone. The collected data is sent to a server, and the emotion engine recognizes the emotional state of "high stress." The generative AI engine then generates advice, such as "Take a short break and try some deep breathing and light stretching," which is displayed on the user's smartphone.

[1132] Prompt Sentence Examples

[1133] Enter your feelings or state of mind: "Lately I've been having trouble sleeping at night and I'm constantly anxious."

[1134] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1135] Step 1:

[1136] The user accesses the application on the device and inputs their feelings and mental state in text. For example, they might input, "I haven't been able to sleep at night recently and I'm always anxious." Based on this input, input text data is generated. At the same time, facial expression data and voice data are collected using the device's camera and microphone. The device temporarily stores this text, facial expression, and voice data.

[1137] Step 2:

[1138] The device sends the collected text data, facial expression data, and voice data to a server, which receives the data via the Internet. Input data is entered as text, facial expression data is sent as an image file, and voice data is sent as a voice file to the server.

[1139] Step 3:

[1140] The server passes the received text data to the generative AI engine, and the facial expression and voice data to the emotion engine. The generative AI engine uses natural language processing technology to analyze the text data and perform emotion tagging and scoring. The emotion engine analyzes the facial expression and voice data to identify the user's emotional state. These analyses are performed by algorithmic data processing and data calculation.

[1141] Step 4:

[1142] The generative AI engine integrates the emotion analysis results obtained from the emotion engine with the text analysis results. Based on this, the server generates optimal advice and care suited to the user's psychological state. For example, the generated advice might be, "Try relaxing before sleep. Deep breathing and meditation are recommended." This generated advice is data output from the generative AI engine.

[1143] Step 5:

[1144] The server sends the generated advice to the user's terminal, which then formats the received advice into an appropriate format for display to the user. The user can then review the advice and execute it as needed.

[1145] Step 6:

[1146] The server stores the dialogue history and emotion analysis results in a database. This data is used as training data for the generative AI engine and emotion engine. This history learning is expected to improve the accuracy of future emotion analysis and advice. The database is updated and the algorithm is strengthened in this step.

[1147] The above is the specific flow of operations according to the processing steps of this system.

[1148] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1149] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1150] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1151] [Fourth embodiment]

[1152] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1153] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1154] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1155] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1156] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1158] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1159] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1160] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1161] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1163] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1164] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1165] MODE FOR CARRYING OUT THE INVENTION

[1166] The present invention is a system for providing mental care to disaster victims and relief workers in the event of a disaster. Next, a specific embodiment of the system will be described.

[1167] System Configuration

[1168] 1. On the user's device:

[1169] These are devices that can connect to the Internet, such as smartphones, tablets, and computers, used by disaster victims and relief workers.

[1170] Using this device, users can input their emotions and state of mind in text and interact with the chatbot.

[1171] 2. Server:

[1172] The central server is equipped with a generative AI engine, which is the foundation of the chatbot, and is responsible for receiving and analyzing data, generating and sending advice, and managing the database.

[1173] The server operates 24 hours a day, 365 days a year, and processes user requests in real time.

[1174] 3. Generative AI engine:

[1175] The generative AI engine uses natural language processing technology to analyze received text data and identify the psychological state of disaster victims.

[1176] The engine generates optimal advice and care based on the analysis results.

[1177] 4. Database:

[1178] The server stores the dialogue history and analysis results in a database.

[1179] The generative AI engine uses information from the database to learn and improve the accuracy of advice and care.

[1180] Program processing description

[1181] 1. User input:

[1182] Users access the chatbot from their device and enter their emotions and state of mind in text.

[1183] For example: "Lately I've been having trouble sleeping at night and I'm constantly anxious."

[1184] 2. Sending input:

[1185] The user's terminal transmits the input text data to the server.

[1186] The server passes the received text data to the generation AI engine.

[1187] 3. Data Analysis:

[1188] The generative AI engine processes text data using analytical techniques to identify the user's emotions and state of mind.

[1189] For example, emotional tags such as "anxiety," "stress," and "sleep disorders" are assigned, and scoring is performed as necessary.

[1190] 4. Advice Generation:

[1191] The generative AI engine generates appropriate advice and care based on the analysis results.

[1192] For example: "Try some relaxation techniques before sleep. Deep breathing and meditation are good options."

[1193] 5. Sending a reply:

[1194] The server transmits the generated advice to the user's terminal, and the user can confirm the advice.

[1195] 6. Recording History:

[1196] The server stores the dialogue history in a database.

[1197] The generative AI engine uses this history as learning data to improve the accuracy of future advice and care.

[1198] Specific scenarios

[1199] As an example, consider the case where a user feels anxious late at night and inputs the following into the chatbot: "I haven't been able to sleep at night recently and I'm always anxious." This input is sent from the user's device to the server, which performs text analysis using a generative AI engine. The generative AI engine extracts emotion tags such as "anxiety" and "can't sleep at night" and generates appropriate advice. The generated advice is sent from the server to the user's device, where the user receives and confirms it. The dialogue history is saved in a database by the server, and the generative AI engine uses this as learning data to help improve the accuracy of future advice.

[1200] In this way, the system of the present invention can effectively support the mental care of disaster victims and relief workers in the event of a disaster.

[1201] The processing flow will be explained below.

[1202] Step 1:

[1203] The user accesses the chatbot from their device and inputs their feelings and state of mind into the text. For example, "I haven't been able to sleep at night recently and I'm always feeling anxious."

[1204] Step 2:

[1205] The terminal sends the entered text data to the server, which temporarily stores the received text data.

[1206] Step 3:

[1207] The server passes the received text data to the generative AI engine, which then prepares the data for analysis. Specifically, it normalizes the text data as a preprocessing step and removes unnecessary spaces and special characters.

[1208] Step 4:

[1209] The generative AI engine analyzes the normalized text using natural language processing technology to identify the emotions and mental state of the victims. Specifically, it assigns emotion tags such as "anxiety," "stress," and "sleep disorders" and calculates an emotion score.

[1210] Step 5:

[1211] The generative AI engine generates optimal advice and care suggestions based on the identified emotion tags and scores. Multiple advice suggestions may be generated, and the most appropriate one is selected from them.

[1212] Step 6:

[1213] The server sends the selected advice and care to the user's device. Specifically, it formats the advice data from the generative AI engine and displays it in an appropriate format for the user.

[1214] Step 7:

[1215] The device displays the received advice and care to the user, who can then check the advice and take action if necessary.

[1216] Step 8:

[1217] The server stores the conversation history between the user and the chatbot in a database, including the input text, analysis results, advice provided, and user responses.

[1218] Step 9:

[1219] The generative AI engine periodically uses the dialogue history in the database as learning data to improve the accuracy of advice and care. The generative AI engine is retrained based on new data to improve the quality of future analysis and advice.

[1220] Example 1

[1221] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1222] Conventional mental care systems for disaster victims and relief workers have difficulty responding in real time and generating appropriate advice from large amounts of data. Furthermore, they face challenges in providing accurate care based on the psychological state of each individual user.

[1223] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1224] In this invention, the server includes means for the victim to input their emotions and mental state using a terminal, means for transmitting the input text data to the server, means for the server to pass the received text data to a generating AI engine and analyze the victim's emotions and mental state based on the text data, means for generating optimal advice and care based on the analysis results, means for transmitting the generated advice and care to the victim's terminal, and means for storing a dialogue history with the victim in a database and using it as learning data for the generating AI engine, thereby enabling appropriate mental care in real time.

[1225] "Terminals" are devices that can connect to the Internet, such as smartphones, tablets, and computers, used by disaster victims and relief workers.

[1226] A "server" is a computer device equipped with a generative AI engine that receives and analyzes data, generates and transmits advice, and manages databases.

[1227] The "generative AI engine" is an AI engine that uses natural language processing technology to analyze received text data and identify the psychological state of disaster victims.

[1228] "Text data" is sentence data that expresses emotions and mental states and is input by a user using a terminal.

[1229] "Analysis results" are the emotion tags and scoring results extracted by the generative AI engine by analyzing the text data.

[1230] "Advice" refers to instructions and suggestions for mental care for victims generated by the AI ​​engine based on the analysis results.

[1231] A "database" is a data storage system in which the server stores the interaction history with the user and the analysis results.

[1232] "Dialogue history" is data that records past interactions between a user and a generative AI engine.

[1233] "Learning data" is data that the generative AI engine uses to improve the accuracy of advice and care based on information obtained from past dialogue history, etc.

[1234] "Tagging" is the act of assigning appropriate labels to emotions and states extracted from text data by a generative AI engine.

[1235] "Scoring" is the process by which the generative AI engine gives a numerical evaluation to each emotion tag or state based on the analysis results.

[1236] MODE FOR CARRYING OUT THE INVENTION

[1237] The present invention is a system for providing mental care to disaster victims and relief workers in the event of a disaster. Next, a specific embodiment of the system will be described.

[1238] System Configuration

[1239] 1. Device:

[1240] These devices include smartphones, tablets, and computers used by disaster victims and relief workers.

[1241] The device is capable of connecting to the Internet, and users use it to input their emotions and state of mind in text and interact with the chatbot.

[1242] 2. Server:

[1243] The server is a computer device that is equipped with a generative AI engine, which is the foundation of the chatbot, and is responsible for receiving, analyzing, generating and sending advice, and managing the database.

[1244] The server operates 24 hours a day, 365 days a year, and processes user requests in real time.

[1245] 3. Generative AI engine:

[1246] The generative AI engine uses natural language processing technology to analyze received text data and identify the psychological state of disaster victims.

[1247] This engine generates optimal advice and care based on the analysis results. Specific technologies used include sentiment analysis and entity recognition.

[1248] 4. Database:

[1249] The server stores the dialogue history and analysis results in a database.

[1250] The generative AI engine uses the stored data as training data to improve the accuracy of advice and care.

[1251] Specific examples

[1252] Consider a case where a user feels anxious late at night and types, "Recently, I haven't been able to sleep at night and I'm always anxious." This input is sent from the user's device to the server. The server passes this text data to the generative AI engine, which then analyzes it. During the analysis process, emotion tags such as "anxiety," "stress," and "sleep disorder" are assigned, and a score is calculated for each tag.

[1253] Based on the analysis results, the generative AI engine generates advice such as, "Try relaxing before sleep. Deep breathing and meditation are recommended." The server sends this advice to the user's device, where the user can confirm it. The server stores the conversation history in a database, which the generative AI engine uses to improve the accuracy of future advice.

[1254] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1255] Step 1:

[1256] User input:

[1257] How it works: A user accesses a dedicated app or website using a device such as a smartphone, tablet, or computer, and enters their emotion or state of mind into a text box.

[1258] Input: Text data describing emotions and mental states. Example: "Recently, I've been having trouble sleeping at night and I'm always anxious."

[1259] Output: The input text data.

[1260] Step 2:

[1261] Sending input:

[1262] How it works: The device sends the entered text data to the server. The transmission is secure using encryption technology such as SSL.

[1263] Input: The text data entered.

[1264] Output: The text data sent to the server.

[1265] Step 3:

[1266] Data Analysis:

[1267] How it works: The server passes the received text data to the generative AI engine. The generative AI engine uses natural language processing techniques to analyze the text data. Specifically, it uses sentiment analysis and entity recognition to tag and score the text.

[1268] Input: The text data sent to the server.

[1269] Output: Emotion tags (e.g., "anxiety," "stress," "sleep disturbance") and scoring results.

[1270] Step 4:

[1271] Advice Generation:

[1272] How it works: The generative AI engine generates optimal advice and care based on the analysis results, using prompts generated by a trained AI model.

[1273] Input: sentiment tags and scoring results.

[1274] Output: Advice or care message. Example: "Try some relaxation techniques before sleep. Deep breathing and meditation are recommended."

[1275] Step 5:

[1276] Send reply:

[1277] Operation: The server sends the generated advice to the user's terminal, where the user can confirm the advice.

[1278] Input: Message of advice or care.

[1279] Output: Advice displayed on the user's terminal.

[1280] Step 6:

[1281] History Record:

[1282] How it works: The server stores the user's interaction history in a database. The generative AI engine uses this history as training data to improve the accuracy of its advice.

[1283] Input: User interaction history.

[1284] Output: Historical data stored in a database.

[1285] Through the above processing steps, the system can provide appropriate mental care in real time and accurately support the user's psychological state.

[1286] (Application example 1)

[1287] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1288] Conventional mental care systems in the event of a disaster are limited to providing care to disaster victims and often fail to provide effective measures. Furthermore, there have been no mental care systems that can also be applied to the psychological state of customers and staff in stores. Therefore, while there is a growing demand for systems that provide mental care for general customers and staff, not just in the event of a disaster, there is a lack of technology that can meet this demand. These issues need to be resolved.

[1289] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1290] In this invention, the server includes a means for receiving input text from disaster victims or customers, a means for passing the received text to a generation AI engine and analyzing the emotions and mental state of the disaster victims or customers based on the text, and a means for generating optimal advice and care based on the analysis results. This enables mental care for customers and staff not only during disasters but also in physical stores.

[1291] "Victims" refers to those who have suffered damage to their lives, property, or physical or mental health due to a disaster.

[1292] "Customer" refers to the consumer or user to whom a service or product is provided.

[1293] "Smart glasses" are Internet-connected devices worn by users to visually receive and input information.

[1294] A "robot" is a mechanical device that operates automatically according to programmed instructions to perform specific tasks.

[1295] A "generative AI engine" refers to an engine with artificial intelligence capabilities that analyzes received text data and generates appropriate advice and care.

[1296] "Text input" refers to the user inputting character data using a keyboard, voice recognition, or the like.

[1297] "Sentiment analysis" refers to the process of identifying a user's emotions and psychological state from input text data.

[1298] "Advice generation" refers to generating information regarding advice and care for the user based on the analyzed emotions and psychological state.

[1299] "Dialogue history" refers to data that records interactions between a user and a system.

[1300] A "database" refers to an electronic information system for efficiently storing, managing, and searching information.

[1301] "Learning data for the generative AI engine" refers to data such as past dialogue history that the generative AI engine uses to improve the accuracy of its analysis and advice generation.

[1302] This invention is a system that provides mental care to disaster victims, customers, and store staff. Specifically, it uses smart glasses and a robot to analyze emotions and psychological states from text input using a generative AI engine, and generates appropriate advice to provide mental care.

[1303] System Configuration

[1304] 1. On the user's device:

[1305] These devices, such as smart glasses and robots, are used by customers and staff. These devices can connect to the internet and can input emotions and psychological states through text input and voice recognition.

[1306] 2. Server:

[1307] The server is equipped with a generative AI engine, which is the foundation of the chatbot, and receives and analyzes data, generates and sends advice, manages the database, etc. The server operates 24 hours a day, 365 days a year, and processes user requests in real time.

[1308] 3. Generative AI engine:

[1309] The generative AI engine uses natural language processing technology to analyze received text data and identify the user's psychological state. This engine generates optimal advice and care based on the analysis results.

[1310] 4. Database:

[1311] The server stores the dialogue history and analysis results in a database, and the generative AI engine uses the information in this database to learn and improve the accuracy of advice and care.

[1312] Program processing description

[1313] The server receives text data entered by the user via smart glasses or a robot and passes it to the generative AI engine. The generative AI engine analyzes the received text data and identifies the user's emotions and state of mind. This analysis process uses natural language processing technology, including emotion tagging and scoring. Based on the analysis results, optimal advice and care is generated. This generated advice is sent from the server to the user via the smart glasses or robot. The server also stores the dialogue history in a database and uses it as learning data for the generative AI engine.

[1314] Specific example explanation

[1315] For example, suppose a user types "I've been busy at work lately and I'm tired" into the smart glasses. This input data is instantly sent to the server, where the generative AI engine analyzes the text. The generative AI engine assigns emotion tags such as "fatigue" and "stress" and generates appropriate advice such as "To relax, we recommend deep breathing and light stretching. It is also effective to take a short break and hydrate." This advice is then sent from the server to the user via the smart glasses.

[1316] Example prompt sentence:

[1317] "Work has been busy lately and I'm feeling tired. How can I relax?"

[1318] In this way, the system can provide effective mental care to victims, customers, and staff.

[1319] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1320] Step 1: The user inputs text about their emotions or state of mind via smart glasses or a robot. Specifically, the user inputs data in the form of, "I've been busy at work lately and I'm tired." This input data is executed through the interface of the smart glasses or robot.

[1321] Step 2: The device sends the received text data to the server. The entered text data is transferred to the server via the network, and the server prepares to pass this data to the generation AI engine.

[1322] Step 3: The server passes the received text data to the generation AI engine. The server sends a request to the generation AI engine to analyze the text data, and provides the input text to the analysis engine.

[1323] Step 4: The generative AI engine analyzes the text data. The generative AI engine uses natural language processing technology to analyze the text data and perform emotion tagging and scoring. For example, emotion tags such as "fatigue" and "stress" can be assigned, and the state can be expressed numerically.

[1324] Step 5: The generative AI engine generates optimal advice and care based on the analysis results. Based on the analyzed emotion tags and scores, the generative AI engine generates advice such as, "To relax, we recommend deep breathing and light stretching. Taking a short break and drinking plenty of water is also effective."

[1325] Step 6: The server sends the generated advice to the user's device. The server then sends the advice received from the generating AI engine back to the smart glasses or robot. This process uses network communication.

[1326] Step 7: The device presents the advice to the user. The smart glasses or robot presents the sent advice or care to the user visually or audibly.

[1327] Step 8: The server saves the conversation history with the victim or customer in a database. The server saves the content of this conversation as conversation history and uses it as learning data for future generative AI engines.

[1328] Step 9: The generative AI engine uses the training data to learn new data. Using the dialogue history data stored on the server, the generative AI engine learns new data to improve the accuracy of analysis and advice generation.

[1329] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1330] MODE FOR CARRYING OUT THE INVENTION

[1331] This invention is a system that provides mental care to disaster victims and relief workers. By combining a generative AI engine with an emotion engine that recognizes the user's emotions, this system can provide more accurate emotion analysis and advice. Next, a specific embodiment of this system will be described.

[1332] System Configuration

[1333] 1. On the user's device:

[1334] These are devices that can connect to the Internet, such as smartphones, tablets, and computers, used by disaster victims and relief workers.

[1335] Using this device, users can input their emotions and state of mind in text and interact with a chatbot that uses an emotion engine and a generative AI engine.

[1336] 2. Server:

[1337] The central server is equipped with a generative AI engine and emotion engine that form the basis of the chatbot, and is responsible for receiving and analyzing data, recognizing emotions, generating and sending advice, and managing the database.

[1338] The server operates 24 hours a day, 365 days a year, and processes user requests in real time.

[1339] 3. Generative AI engine:

[1340] The generative AI engine uses natural language processing technology to identify the psychological state of disaster victims based on the received text data and the emotion analysis results from the emotion engine.

[1341] The engine generates optimal advice and care based on the analysis results.

[1342] 4. Emotion Engine:

[1343] The emotion engine analyzes the user's real-time facial expressions and voice data in addition to the text entered by the user to recognize emotions.

[1344] This allows for a more accurate determination of the user's emotional state.

[1345] 5. Database:

[1346] The server stores the dialogue history and analysis results in a database.

[1347] The generative AI engine and emotion engine use information from the database to learn and improve the accuracy of advice and care.

[1348] Program processing description

[1349] 1. User input:

[1350] Users access the chatbot from their device and enter their emotions and state of mind in text.

[1351] For example: "Lately I've been having trouble sleeping at night and I'm constantly anxious."

[1352] 2. Real-time emotion recognition:

[1353] The user's device uses a camera and microphone to transmit the user's facial expressions and voice to the emotion engine in real time.

[1354] 3. Input and emotional data transmission:

[1355] The terminal transmits the input text data and real-time emotion data to the server.

[1356] 4. Data Analysis and Emotion Recognition:

[1357] The text data and emotion data received by the server are passed to the generative AI engine and emotion engine, respectively.

[1358] The emotion engine analyzes facial and voice data to identify emotional states.

[1359] The generative AI engine analyzes text data and performs emotion tagging and scoring.

[1360] 5. Advice Generation:

[1361] The generative AI engine integrates the emotion analysis results obtained from the emotion engine with the text analysis results to generate more accurate advice and care.

[1362] For example: "Try some relaxation techniques before sleep. Deep breathing and meditation are good options."

[1363] 6. Sending a Reply:

[1364] The server transmits the generated advice to the user's terminal.

[1365] The user checks the advice and attempts to take action if necessary.

[1366] 7. Recording History:

[1367] The server stores the dialogue history and emotion analysis results in a database.

[1368] The generative AI engine and emotion engine use this history as learning data to improve the accuracy of future advice and care.

[1369] Specific scenarios

[1370] As an example, consider the case where a user feels anxious late at night and inputs the following into the chatbot: "I haven't been able to sleep at night recently and I'm always anxious." At the same time as this input, the user's device uses its camera and microphone to capture the user's facial expression and voice data, which it then sends to the server. The server uses a generative AI engine to perform text analysis and an emotion engine to perform emotion analysis from the facial expression and voice data. The generative AI engine compiles the analysis results of the emotion engine, generates optimal advice, and sends it back to the user. This dialogue history and emotion analysis results are saved in a database and will be used as future learning data.

[1371] In this way, the system of the present invention can provide more accurate support for the mental care of disaster victims and relief workers in the event of a disaster by combining it with an emotion engine.

[1372] The processing flow will be explained below.

[1373] Step 1:

[1374] The user accesses the chatbot from their device and inputs their feelings and state of mind into the text. For example, "I haven't been able to sleep at night recently and I'm always feeling anxious."

[1375] Step 2:

[1376] The user's device sends the input text data to the server, and at the same time, the device's camera and microphone are used to transmit the user's facial expressions and voice to the emotion engine in real time.

[1377] Step 3:

[1378] The server passes the received text data to the generative AI engine, which then normalizes the data as preprocessing. Normalization refers to the process of removing unnecessary spaces and special characters.

[1379] Step 4:

[1380] The server analyzes the user's facial expressions and voice data transmitted in real time to the emotion engine, which then identifies the user's emotional state from this data.

[1381] Step 5:

[1382] The emotion engine generates analysis results and sends them to the generative AI engine, which receives the analysis results from the emotion engine and integrates them with the analysis results of the text data.

[1383] Step 6:

[1384] The generative AI engine tags and scores the user's psychological state based on the integrated data, resulting in emotional tags such as "anxiety," "stress," and "sleep disorders."

[1385] Step 7:

[1386] The generative AI engine generates advice and care suggestions based on the identified emotion tags and scores. Multiple advice suggestions may be generated, and the most appropriate one will be selected.

[1387] Step 8:

[1388] The server sends the selected advice to the user's terminal, where the advice is formatted in a user-friendly format.

[1389] Step 9:

[1390] The device displays the received advice and care to the user, who can then check the advice and try it out if necessary.

[1391] Step 10:

[1392] The server stores the conversation history between the user and the chatbot in a database, including input text, analysis results, advice provided, and sentiment analysis results.

[1393] Step 11:

[1394] The generative AI engine and emotion engine use the dialogue history from the database as learning data and retrain to improve the accuracy of future advice and care.

[1395] Example 2

[1396] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1397] Conventional technologies lack systems that can quickly alleviate the mental stress of disaster victims and relief workers and provide effective mental care. Conventional systems lack the ability to accurately analyze the user's emotional state and provide optimal advice based on that analysis. As a result, it is difficult for disaster victims to receive the psychological support they need in real time, and this can lead to continued mental exhaustion.

[1398] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1399] In this invention, the server includes means for receiving input text from the disaster victim, means for passing the received text to a generation AI engine and analyzing the disaster victim's emotions and state of mind using the text and the emotion engine, means for the generation AI engine to integrate the analysis results of the emotion engine to generate optimal advice and care, means for transmitting the generated advice and care to the disaster victim's device, means for storing the dialogue history with the disaster victim and the emotion analysis results in a database and using them as learning data for the generation AI engine, means for the emotion engine to analyze the user's facial expression and voice data in real time, and means for the device to acquire the user's facial expression and voice data and transmit it to the server. This makes it possible to analyze the disaster victim's emotional state with high accuracy and provide appropriate advice in real time.

[1400] A "victim" is an individual who has suffered physical or psychological harm as a result of a natural disaster or emergency.

[1401] "Input text" is character information that a user sends to the system through a terminal.

[1402] The "generative AI engine" is an artificial intelligence engine that uses input text data to analyze the psychological state and emotions of disaster victims and generate optimal advice and care.

[1403] The "emotion engine" is an engine that analyzes the user's facial expressions and voice data to identify their emotional state in real time.

[1404] "Terminals" are devices that can connect to the Internet, such as smartphones, tablets, and computers, used by disaster victims and relief workers.

[1405] The "server" is a central system that incorporates the generative AI engine and emotion engine and receives data, analyzes it, generates advice, sends it, and manages the database.

[1406] A "database" is a storage device within the system that stores data such as dialogue history and emotion analysis results collected by the system and makes them reusable.

[1407] "Dialogue history" is a record of interactions between the victim and the system.

[1408] "Emotion analysis results" are information on the emotional state obtained by analyzing the user's facial expressions and voice data using the emotion engine.

[1409] "Advice" refers to specific suggestions and advice provided by the generative AI engine based on the psychological state and emotional analysis of the victim.

[1410] "Care" refers to support measures and means to reduce the mental and psychological burden on disaster victims.

[1411] "Real-time" refers to a method of processing and analyzing data immediately at the moment it is generated.

[1412] "Analysis" is the process of breaking down and analyzing input data and emotional data to identify meanings and states.

[1413] "Learning data" refers to data on past dialogue history and emotion analysis results that the generative AI engine uses to improve the accuracy of advice and care.

[1414] "Scoring" is the process of assigning a numerical rating to the analyzed emotions and psychological states.

[1415] "Tagging" is the process of assigning labels to emotions or psychological states based on the analysis results.

[1416] This invention is a system that provides mental care to disaster victims and relief workers during disasters. By combining a generative AI engine with an emotion engine that recognizes the user's emotions, this system can provide more accurate emotion analysis and advice. A specific embodiment of this system is described below.

[1417] System Configuration

[1418] 1. User's device

[1419] In the event of a disaster, users can use devices with internet connectivity, such as smartphones, tablets, and PCs, to input their emotions and mental state in text and interact with a chatbot that utilizes an emotion engine and a generative AI engine.

[1420] 2. Server

[1421] The server is equipped with a generative AI engine and an emotion engine, and receives and analyzes data, recognizes emotions, generates and transmits advice, and manages the database. This central server operates 24 hours a day, 365 days a year, and processes user requests in real time.

[1422] 3. Generative AI Engine

[1423] The generative AI engine uses natural language processing technology to identify the psychological state of disaster victims based on the received text data and the emotion analysis results from the emotion engine. This engine generates optimal advice and care based on the analysis results.

[1424] 4. Emotion Engine

[1425] The emotion engine recognizes emotions by analyzing the user's real-time facial expressions and voice data in addition to the user's input text, making it possible to more accurately identify the user's emotional state.

[1426] 5. Database

[1427] The server stores the dialogue history and analysis results in a database. The generative AI engine and emotion engine use the information in the database to learn and improve the accuracy of advice and care.

[1428] Program processing description

[1429] A user accesses the chatbot using a device and inputs their feelings and mental state in text format. For example, the user might input, "I haven't been able to sleep at night recently and I'm always anxious." At the same time as this input, the device uses a camera and microphone to capture the user's facial expressions and voice data, which are then sent to the server.

[1430] The server passes the received text data and emotion data to the generative AI engine and emotion engine. The emotion engine analyzes facial expressions and voice data to identify the emotional state. The generative AI engine analyzes the text data, performs emotion tagging and scoring, and integrates these results to generate optimal advice and care information. For example, advice such as "Try relaxing before sleep. Deep breathing and meditation are recommended" is provided.

[1431] The generated advice is sent from the server to the user's device, where the user receives it. The server also stores the dialogue history and emotion analysis results in a database and uses them as learning data for future use. This improves the accuracy of the generative AI engine and emotion engine, allowing for more personalized advice to be provided.

[1432] Specific scenarios

[1433] A user feels anxious late at night, so they input to the chatbot, "I haven't been able to sleep at night recently and I'm always anxious." At the same time as this input, the user's device uses its camera and microphone to capture facial expression and voice data, which it then sends to the server. The server then uses a generative AI engine to perform text analysis, and an emotion engine to perform emotion analysis from the facial expression and voice data. The generative AI engine compiles the analysis results of the emotion engine, generates optimal advice, and sends it back to the user. This dialogue history and emotion analysis results are saved in a database and used as learning data for future use.

[1434] Prompt Sentence Examples

[1435] By inputting information into the generative AI model such as "The user has input that 'I haven't been able to sleep at night recently and I'm always anxious.' Please advise me on appropriate ways to relax in response to this," more accurate advice can be generated. In this way, by combining an emotion engine, the system of the present invention can provide more accurate support for the mental care of disaster victims and relief workers in the event of a disaster.

[1436] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1437] Program processing flow

[1438] Step 1: User Input

[1439] A user accesses the chatbot using a device and inputs their feelings and state of mind in text format. For example, a user might input, "I haven't been able to sleep at night recently, and I'm always anxious."

[1440] Input: Text data that indicates the user's emotions and state of mind

[1441] Output: The input text data generated in the terminal

[1442] Step 2: Real-time emotion recognition

[1443] The device uses a camera and microphone to capture the user's facial expressions and voice data in real time and transmits it to the emotion engine.

[1444] Specific operations: Facial recognition is performed using the camera and audio data is collected using the microphone.

[1445] Input: Real-time facial expression and voice data of the user

[1446] Output: Facial and speech data sent to the emotion engine

[1447] Step 3: Sending input and emotion data

[1448] The device sends the input text data and real-time emotion data to the server, where the data is encrypted.

[1449] Specific operation: The terminal packetizes the data, encrypts it, and sends it to the server.

[1450] Input: Text data, facial expression data, audio data

[1451] Output: Text data and emotion data received by the server

[1452] Step 4: Data analysis and emotion recognition

[1453] The server passes the received text data and emotion data to the generative AI engine and emotion engine, respectively. The emotion engine analyzes facial expression data and voice data to identify the user's emotional state. The generative AI engine analyzes the text data and performs emotion tagging and scoring.

[1454] Specific operations: The emotion engine runs facial recognition algorithms and performs voice analysis. The generative AI engine performs natural language processing.

[1455] Input: Text data, facial expression data, audio data

[1456] Output: Sentiment analysis results (sentiment tags, scoring)

[1457] Step 5: Advice Generation

[1458] The generative AI engine combines the emotion analysis results from the emotion engine with the text analysis results to generate optimal advice and care information. For example, it might generate specific advice such as, "Try relaxing before sleep. Deep breathing and meditation are recommended."

[1459] Specific operation: The generative AI engine references past dialogue history and emotional data to generate optimal advice.

[1460] Input: Sentiment analysis results, text analysis results

[1461] Output: Advice and care information

[1462] Step 6: Send your reply

[1463] The server sends the generated advice to the user's terminal, and the user receives the advice and tries it out as necessary.

[1464] Specific operation: The server packets the advice and sends it back to the device.

[1465] Input: Advice and care information

[1466] Output: Advice and care information received by the device

[1467] Step 7: Recording History

[1468] The server stores the dialogue history and emotion analysis results in a database and uses them as learning data for the future.

[1469] Specific operation: A database engine runs on the server, recording dialogue history and sentiment analysis results.

[1470] Input: Dialogue history, emotion analysis results

[1471] Output: Historical data recorded in a database

[1472] This will realize a system that can analyze the user's emotional state with high accuracy and provide appropriate advice in real time.

[1473] (Application example 2)

[1474] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1475] Conventional mental care systems have primarily analyzed only the text input of disaster victims to understand their emotional state. However, this approach does not take into account other emotional expressions, such as facial expressions and voice, resulting in insufficient accuracy in mental care. Furthermore, there are an increasing number of situations where immediate responses to stress and mental health issues are required for store and corporate employees. A system that can integrate such diverse emotional data and provide highly accurate mental care is needed.

[1476] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1477] In this invention, the server includes means for receiving input text, facial expression data, and voice data from the disaster victim, means for passing the received text, facial expression data, and voice data to a generation AI engine and an emotion engine and analyzing the emotions and mental state of the disaster victim based on the data, means for generating optimal advice and care based on the analysis results, means for sending the generated advice and care to the disaster victim's device, and means for saving a dialogue history with the disaster victim in a database and using it as learning data for the generation AI engine and the emotion engine. This makes it possible to integrate the text, facial expression, and voice data to perform highly accurate emotion analysis and provide appropriate advice, such as relaxation techniques, in real time.

[1478] "Victims" are people who have suffered physical and mental harm as a result of a disaster or emergency.

[1479] "Facial expression data" refers to data about a user's facial expressions collected in real time using a camera or other device.

[1480] "Voice data" refers to data about a user's vocalizations collected in real time using a microphone or other audio collection device.

[1481] The "generative AI engine" is an engine that uses natural language processing technology to analyze received text and emotional data, identify the user's psychological state, and generate appropriate advice.

[1482] The "emotion engine" is an engine that analyzes the user's input text, facial expression data, and voice data to recognize the user's emotional state.

[1483] The "analysis results" are the results of identifying the emotions and psychological state of the victims output by the generative AI engine and emotion engine.

[1484] "Advice or care" refers to specific courses of action, relaxation techniques, or assistance provided to improve the psychological state of a survivor.

[1485] The "database" is a data storage system that is stored on a server and that manages the dialogue history with disaster victims and the results of emotion analysis.

[1486] "Dialogue history" is a record of all past dialogues between the victim and the system.

[1487] "Learning data" refers to data that the generative AI engine and emotion engine use to improve analysis accuracy by incorporating it as new data.

[1488] This invention is a system for providing mental care to disaster victims and store employees. The system utilizes a generative AI engine and an emotion engine to generate highly accurate emotion analysis and advice.

[1489] System Configuration

[1490] User's device

[1491] The user's device is a device that can connect to the Internet, such as a smartphone, tablet, or PC. The device is equipped with a camera and microphone, and can collect facial expression and voice data in real time. The user uses the device to input their own emotions and mental state in text, and interacts with a chatbot that uses an emotion engine and a generative AI engine.

[1492] server

[1493] The central server is equipped with a generative AI engine and an emotion engine, and is responsible for receiving and analyzing data, recognizing emotions, generating and sending advice, and managing the database. The server operates 24 hours a day, 365 days a year, and processes user requests in real time. The generative AI engine uses natural language processing technology, while the emotion engine uses technology to recognize emotions from facial expressions and voice.

[1494] Generative AI Engine

[1495] The generative AI engine is an engine that identifies the user's psychological state based on the received text data and the emotion analysis results from the emotion engine.The generative AI engine generates optimal advice and care based on the analysis results.

[1496] Emotion Engine

[1497] The emotion engine analyzes the user's input text as well as facial expression and voice data to recognize emotions, enabling more accurate identification of the user's emotional state.

[1498] Database

[1499] The server stores the dialogue history and analysis results in a database. The generative AI engine and emotion engine use the information in the database to learn and improve the accuracy of advice and care.

[1500] Program processing description

[1501] 1. The user accesses the chatbot from their device and inputs their emotions and state of mind in text. For example, they might say, "I haven't been able to sleep at night recently, and I'm always feeling anxious." At the same time, the device's camera and microphone collect facial expression and voice data in real time.

[1502] 2. The device sends the collected text data, facial expression data, and voice data to the server.

[1503] 3. The server passes the received text data and emotion data to the generative AI engine and emotion engine, respectively. The emotion engine analyzes the facial expression and voice data to identify the emotional state. The generative AI engine analyzes the text data and performs emotion tagging and scoring.

[1504] 4. The generative AI engine combines the emotion analysis results from the emotion engine with the text analysis results to generate advice and care appropriate to the patient's mental state. For example, advice such as "Try relaxing before sleep. Deep breathing and meditation are recommended."

[1505] 5. The server sends the generated advice to the user's device, where the user can review the advice and take action if necessary.

[1506] 6. The server stores the dialogue history and emotion analysis results in a database. The generative AI engine and emotion engine use this history as training data to improve the accuracy of future advice and care.

[1507] Specific examples

[1508] For example, an employee working at a physical store might type into a smartphone application, "Recently, dealing with customers has been so stressful that it feels like I'm suffocating." At the same time, the smartphone's camera captures the employee's facial expression and the microphone collects their voice tone. The collected data is sent to a server, and the emotion engine recognizes the emotional state of "high stress." The generative AI engine then generates advice, such as "Take a short break and try some deep breathing and light stretching," which is displayed on the user's smartphone.

[1509] Prompt Sentence Examples

[1510] Enter your feelings or state of mind: "Lately I've been having trouble sleeping at night and I'm constantly anxious."

[1511] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1512] Step 1:

[1513] The user accesses the application on the device and inputs their feelings and mental state in text. For example, they might input, "I haven't been able to sleep at night recently and I'm always anxious." Based on this input, input text data is generated. At the same time, facial expression data and voice data are collected using the device's camera and microphone. The device temporarily stores this text, facial expression, and voice data.

[1514] Step 2:

[1515] The device sends the collected text data, facial expression data, and voice data to a server, which receives the data via the Internet. Input data is entered as text, facial expression data is sent as an image file, and voice data is sent as a voice file to the server.

[1516] Step 3:

[1517] The server passes the received text data to the generative AI engine, and the facial expression and voice data to the emotion engine. The generative AI engine uses natural language processing technology to analyze the text data and perform emotion tagging and scoring. The emotion engine analyzes the facial expression and voice data to identify the user's emotional state. These analyses are performed by algorithmic data processing and data calculation.

[1518] Step 4:

[1519] The generative AI engine integrates the emotion analysis results obtained from the emotion engine with the text analysis results. Based on this, the server generates optimal advice and care suited to the user's psychological state. For example, the generated advice might be, "Try relaxing before sleep. Deep breathing and meditation are recommended." This generated advice is data output from the generative AI engine.

[1520] Step 5:

[1521] The server sends the generated advice to the user's terminal, which then formats the received advice into an appropriate format for display to the user. The user can then review the advice and execute it as needed.

[1522] Step 6:

[1523] The server stores the dialogue history and emotion analysis results in a database. This data is used as training data for the generative AI engine and emotion engine. This history learning is expected to improve the accuracy of future emotion analysis and advice. The database is updated and the algorithm is strengthened in this step.

[1524] The above is the specific flow of operations according to the processing steps of this system.

[1525] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1526] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1527] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1528] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1529] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1530] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1531] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1532] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1533] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1534] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1535] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1536] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1537] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1538] 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.

[1539] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1540] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1541] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1542] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1543] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1544] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1545] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1546] The following is further disclosed regarding the above embodiment.

[1547] (Claim 1)

[1548] means for receiving input text from the victim;

[1549] A means of passing the received text to a generative AI engine and analyzing the emotions and mental state of the victim based on the text;

[1550] A means of generating optimal advice and care based on the analysis results;

[1551] A means for transmitting the generated advice and care to the victim's device;

[1552] A means to store the conversation history with the disaster victims in a database and use it as learning data for the generation AI engine;

[1553] A system including:

[1554] (Claim 2)

[1555] The system according to claim 1, wherein the psychological state of the victim is classified by tagging and scoring the analysis results.

[1556] (Claim 3)

[1557] The system of claim 1, wherein the generative AI engine uses past dialogue history to learn new data and improve the accuracy of advice and care.

[1558] "Example 1"

[1559] (Claim 1)

[1560] A means for victims to input their emotions and state of mind using a terminal;

[1561] means for transmitting input text data to a server;

[1562] The server passes the received text data to a generation AI engine, and analyzes the emotions and mental state of the victims based on the text data.

[1563] A means of generating optimal advice and care based on the analysis results;

[1564] A means for transmitting the generated advice and care to the victim's device;

[1565] A means to store the conversation history with the disaster victims in a database and use it as learning data for the generation AI engine;

[1566] A system including:

[1567] (Claim 2)

[1568] The system of claim 1, wherein the analysis results are tagged and scored to classify the psychological state of the victim.

[1569] (Claim 3)

[1570] The system of claim 1, wherein the generative AI engine uses past dialogue history to learn new data and improve the accuracy of advice and care.

[1571] "Application Example 1"

[1572] (Claim 1)

[1573] means for receiving input text from a victim or customer;

[1574] A means for passing the received text to a generative AI engine and analyzing the emotions and state of mind of the victim or customer based on the text;

[1575] A means of generating optimal advice and care based on the analysis results;

[1576] a means for transmitting the generated advice or care to a victim or customer device;

[1577] A means of storing the conversation history with disaster victims or customers in a database and using it as learning data for the generation AI engine;

[1578] A means for providing mental care to customers and staff using smart glasses or robots;

[1579] A system including:

[1580] (Claim 2)

[1581] The system according to claim 1, wherein the psychological state of the victim or customer is classified by tagging and scoring the analysis results.

[1582] (Claim 3)

[1583] The system of claim 1, wherein the generative AI engine uses past dialogue history to learn new data and improve the accuracy of advice and care.

[1584] "Example 2: Combining Emotion Engines"

[1585] (Claim 1)

[1586] means for receiving input text from the victim;

[1587] A means for passing the received text to a generation AI engine and analyzing the emotions and mental state of the victim using the text and emotion engine;

[1588] The generative AI engine integrates the analysis results of the emotion engine to generate optimal advice and care,

[1589] A means for transmitting the generated advice and care to the victim's device;

[1590] A means to store the conversation history with the disaster victims and the results of emotion analysis in a database and use it as learning data for the generative AI engine.

[1591] The emotion engine analyzes the user's facial expressions and voice data in real time,

[1592] A means for the terminal to acquire facial expression and voice data of the user and transmit the data to a server;

[1593] A system including:

[1594] (Claim 2)

[1595] The system according to claim 1, wherein the psychological state of the victim is classified by tagging and scoring the analysis results.

[1596] (Claim 3)

[1597] The system of claim 1, wherein the generative AI engine uses past dialogue history and emotion analysis results to learn new data and improve the accuracy of advice and care.

[1598] "Application example 2 when combining emotion engines"

[1599] (Claim 1)

[1600] means for receiving input text, facial expression data, and voice data from a disaster victim;

[1601] A means for passing the received text, facial expression data, and voice data to a generation AI engine and emotion engine, and analyzing the emotions and mental state of the victim based on the data;

[1602] A means of generating optimal advice and care based on the analysis results;

[1603] A means for transmitting the generated advice and care to the victim's device;

[1604] A means of storing conversation history with disaster victims in a database and using it as learning data for the generative AI engine and emotion engine;

[1605] A system including:

[1606] (Claim 2)

[1607] The system according to claim 1, wherein the psychological state of the victim is classified by tagging and scoring the analysis results.

[1608] (Claim 3)

[1609] The system of claim 1, wherein the generative AI engine and emotion engine use past dialogue history to learn new data and improve the accuracy of advice and care. [Explanation of symbols]

[1610] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving input text from the victim; A means of passing the received text to a generative AI engine and analyzing the emotions and mental state of the victim based on the text; A means of generating optimal advice and care based on the analysis results; A means for transmitting the generated advice and care to the victim's device; A means to store the conversation history with the disaster victims in a database and use it as learning data for the generation AI engine; A system including:

2. The system according to claim 1 , wherein the psychological state of the victim is classified by tagging and scoring the analysis results.

3. The system of claim 1, wherein the generative AI engine uses past dialogue history to learn new data and improve the accuracy of advice and care.

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