system

Abstract

We provide the system. [Solution] A device that collects audio data, video data, and text data, An analysis device that performs multimodal analysis on data collected from the aforementioned device, A diagnostic device that uses the analysis results obtained by the aforementioned analysis device to diagnose applicable support systems, A notification device that notifies the user of the diagnostic results of the diagnostic device, A communication device for sharing the aforementioned diagnostic results with the local government, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the event of a disaster, it is difficult to quickly and accurately find and proceed with the support systems applicable to each victim among a large number of victims. This problem is particularly serious in the case of the elderly and when local governments are extremely busy. Conventional methods have a problem that it takes a great deal of time and effort for individual victims to determine what support systems they can receive, hindering the provision of prompt support.

Means for Solving the Problems

[0005] This invention provides a means to quickly and accurately grasp the situation of disaster victims by using a device that collects audio data, video data, and text data, and an analysis device that analyzes that data. Furthermore, by combining this with a diagnostic device that diagnoses applicable support systems based on the analysis results, it becomes possible to quickly identify the most suitable support system for each disaster victim. The diagnostic results are notified to the user and shared with local governments to enable the rapid provision of support. This reduces the burden on disaster victims and local governments and enables rapid recovery support.

[0006] A "device" is a device used to collect audio data, video data, and text data, and specifically refers to a smartphone, tablet, or personal computer.

[0007] An "analysis device" is a device that analyzes data obtained from a device in a multimodal manner, and has the function of understanding the situation of disaster victims by combining speech recognition, image processing, and natural language processing.

[0008] A "diagnostic device" is a device that identifies and diagnoses applicable support systems for disaster victims based on the analysis results obtained by an analysis device.

[0009] A "notification device" is a device that notifies the user of the diagnostic results and provides the user with information on available support systems and subsequent actions.

[0010] A "communication device" is a device used to share diagnostic results with external organizations such as local governments, and it assists in smooth information transmission and the provision of support quickly. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, when an emotion engine is combined. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0017] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] To implement this invention, a smartphone, tablet, or personal computer can be used as the device. The user uses these devices to collect audio, video, and text data related to the disaster situation. The device has the function to transmit this data to a server in real time. The server converts the received data into a multimodal analyzable format and passes it to an analysis device. This analysis device uses speech recognition, image processing, and natural language processing technologies to comprehensively analyze the situation of the disaster victims.

[0033] The results obtained from the analysis device are used as input for the diagnostic device to quickly identify applicable support systems for disaster victims. The diagnostic device diagnoses the appropriate system and generates results by comparing the results with a database of support systems.

[0034] The generated diagnostic results are communicated to the user via a notification device. The user receives this notification on their device and can then review the next steps to take and an overview of available support programs. The server also uses communication devices to share information with local governments, supporting the smooth progress of necessary procedures.

[0035] For example, when a disaster occurs in a certain area, a user can use the app to take photos of the damage to their house and describe the necessary assistance via voice. This information is sent from the device to a server and processed by an analysis device. The diagnostic device recognizes the eligibility for housing reconstruction assistance based on the analysis results and notifies the user. Based on the notification, the user can contact the local government and apply for assistance quickly.

[0036] In this way, the invention reduces the burden on disaster victims and contributes to improving the efficiency of local government operations. As a result, disaster victims can quickly access necessary support systems, making it possible to support their early recovery.

[0037] The following describes the processing flow.

[0038] Step 1:

[0039] Users operate the device to input voice, video, and text information. They use smartphones or tablets to photograph the damage or record audio explaining the situation into the microphone.

[0040] Step 2:

[0041] The device sends the entered information to the server via the app. The device compresses the digital data in real time and transfers it to the server using a secure communication protocol.

[0042] Step 3:

[0043] The server transfers the received data to the analysis device. The server then converts the data's format, preparing it for processing by the analysis device.

[0044] Step 4:

[0045] The analysis device performs multimodal analysis of the data. Audio data is converted to text using a speech recognition algorithm, and its content is analyzed through natural language processing. Video data is used to assess physical damage using image processing technology.

[0046] Step 5:

[0047] The server passes the analysis results to the diagnostic device. The diagnostic device refers to the database and identifies support programs that may be applicable to the disaster victims.

[0048] Step 6:

[0049] The diagnostic device further evaluates the applicable support programs and generates diagnostic results. The server records the information obtained from the diagnostic device in a database.

[0050] Step 7:

[0051] The server sends the diagnostic results to the terminal via a notification device. The notification device informs the user of the results and shows the necessary procedures and next steps on the interface.

[0052] Step 8:

[0053] The user reviews the diagnostic results they receive and begins the necessary procedures. They then collaborate with local governments to facilitate applications for support programs.

[0054] (Example 1)

[0055] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0056] In times of disaster, it is essential to identify and provide information on prompt and appropriate support systems for disaster victims. Currently, the process of collecting and analyzing information, and applying support systems, is complex and inefficient, potentially delaying the support that disaster victims need. To address this challenge, it is necessary to build a system that can quickly collect and analyze information, identify appropriate support systems, and promptly provide that information to disaster victims and relevant organizations.

[0057] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0058] In this invention, the server includes terminal means for collecting audio information, video information, and text information; means for transmitting the information collected from the terminal means to a central processing unit in real time; and the central processing unit includes means for converting the received information into a format that can be multimodally analyzed and passing it on to an analysis means. This makes it possible to quickly grasp the situation of disaster victims and accurately identify and provide applicable support systems during a disaster.

[0059] "Audio information" refers to information represented in digital format based on sound waveforms, and usually refers to speech, including human language.

[0060] "Visual information" refers to information represented in digital format based on the waveform of light, and includes visual image data such as still images and videos.

[0061] "Text information" refers to digital information composed of characters, and usually means information in the form of documents or messages.

[0062] "Terminal means" refers to a device capable of collecting and transmitting information, and includes portable information processing devices and personal computers.

[0063] A "central processing unit" refers to a control device that processes received information and converts it into a format appropriate for its purpose.

[0064] "Multimodal analysis" is a method for integrating and analyzing information in multiple formats, and it targets multiple data modalities, including audio, video, and text.

[0065] "Analysis means" refers to technologies for analyzing digital information and extracting features, and includes devices that use speech recognition, image analysis, and natural language processing.

[0066] "Diagnostic means" refers to a device or mechanism that identifies appropriate support measures based on analysis results and prepares for their provision.

[0067] "Information provision means" refers to a device or method for quickly notifying users of information such as diagnostic results.

[0068] "Communication means" refers to technologies used to share information with other devices or organizations, and includes network connectivity.

[0069] A "generative AI model" is an artificial intelligence technique that has the ability to understand various data formats and generate output.

[0070] To implement this invention, the user first uses an information gathering terminal. This terminal could be a portable information processing device (smartphone), a personal information terminal (tablet), or a personal computer. The user uses this terminal to collect audio, video, and text information from the disaster site. Audio information is recorded via a microphone, and video information is captured using a camera. Text information is generated through automatic speech conversion or manual input.

[0071] The terminal transmits the collected information to the central processing unit (server) in real time. To ensure security and speed, a protocol such as HTTPS is used for this communication. The server converts the received data into a format suitable for multimodal analysis and passes it to the analysis device. The analysis device uses a generative AI model to comprehensively analyze the data through speech recognition, image analysis, and natural language processing.

[0072] The information obtained through analysis is passed to a diagnostic tool. The diagnostic tool compares this information with a database of support measures to identify applicable support programs for disaster victims. This identified result is notified to the user through an information provision tool. The notification is sent to the terminal in push notification format, allowing the user to check the results immediately. Furthermore, the server shares this result with local government agencies using communication tools to facilitate the more rapid application of support programs.

[0073] Specifically, after an earthquake, users take photos of the damage to their homes with their smartphones and record voice messages stating that they need assistance with home repairs. This audio and video are sent to a server, where an analysis device assesses the extent of the damage and proposes the most suitable support program. This process is carried out by a generating AI model based on a prompt message such as, "Based on the video and audio data of the damage to your home and the support you need during the disaster, please identify the most suitable support program."

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

[0075] Step 1:

[0076] Users collect information.

[0077] Users collect audio, video, and text information from disaster sites using devices such as smartphones and tablets. Specifically, they photograph damaged buildings with cameras and record audio about the extent of the damage and the need for assistance. Camera image data and audio files are generated as input. These data are temporarily stored on the device as output.

[0078] Step 2:

[0079] The device transmits data.

[0080] The terminal transmits collected audio, video, and text data to the server in real time. Specifically, the communication module within the terminal securely transfers the data to the server using the HTTPS protocol. The input is data stored on the terminal. The output is this data reaching the server.

[0081] Step 3:

[0082] The server converts the data.

[0083] The server converts received audio, video, and text data into a format that allows for multimodal analysis. For example, it converts audio data into text and organizes video data frame by frame. The input is the transmitted raw data. The output is data formatted in a way that is easy for the analysis device to process.

[0084] Step 4:

[0085] The analysis device analyzes the data.

[0086] The analysis system on the server performs speech recognition, image analysis, and natural language processing using a generative AI model. Specifically, it converts audio data into text and identifies the extent of damage from image data. The input is converted multimodal data. The output is analysis data of the identified damage and the necessary support measures.

[0087] Step 5:

[0088] The diagnostic device identifies the support system.

[0089] The server's diagnostic device searches a database of support measures based on the analysis results and identifies support programs applicable to disaster victims. Specifically, it uses the results of a generated AI model to extract support plans that meet the suitability criteria. The input is the analysis result data. The output is information on the identified support programs.

[0090] Step 6:

[0091] The server provides information to the user.

[0092] The server notifies the user of the diagnostic results through an information provision mechanism. Specifically, it sends a push notification to the device so that the user can check the results. The input is identified support system information. The output is an information notification to the user.

[0093] Step 7:

[0094] The server shares information with local governments.

[0095] The server transmits diagnostic results to local government agencies using communication methods. Specifically, it registers the information in the local government's database, enabling relevant parties to provide support quickly. The input is the diagnostic result data. The output is the completion of information sharing with the local government.

[0096] (Application Example 1)

[0097] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0098] During disasters and emergencies, when individual safety is threatened, there is a need to quickly and accurately assess the situation and provide appropriate support systems and emergency responses. To achieve this, it is essential to collect information from multiple data sources and make integrated judgments. However, current systems are limited to fragmented information collection and analysis, making rapid response difficult.

[0099] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0100] In this invention, the server includes a terminal for collecting audio information, video information, and text information; an analysis means for analyzing the information collected from the terminal in various formats; an identification means for diagnosing applicable support systems using the analysis results obtained by the analysis means; a notification means for notifying the user of the diagnosis results of the identification means; a communication means for sharing the diagnosis results with administrative agencies; and an emergency notification means for issuing an alarm based on the dangerous situation determined by the analysis means. This makes it possible to quickly collect information and provide appropriate support systems and emergency responses even in situations where the user's safety is threatened.

[0101] A "terminal" is a device used to collect audio, video, and text information, and is a device that plays a role in acquiring information in various formats.

[0102] "Analysis means" refers to a device or system that analyzes information collected from a terminal in various formats and performs processing to determine the situation.

[0103] "Identification means" refers to a device or system that has the function of diagnosing applicable support systems based on the analysis results obtained by the analysis means.

[0104] A "notification means" is a device or system that has the function of notifying the user of the diagnostic results of the identification means, and plays the role of providing the user with the necessary information.

[0105] "Communication means" refers to a device or system that has the function of sharing diagnostic results with administrative agencies.

[0106] An "emergency notification system" is a device or system that has the function of issuing an alarm based on a dangerous situation determined by an analysis system.

[0107] To implement this invention, a mobile communication device or personal computer is used as a terminal for collecting voice, video, and text information. The user collects information in real time through this terminal and transmits it to the server. The server analyzes the received information, primarily using automatic speech recognition and image analysis techniques, and performs data analysis in various formats. Software used may include Python, OpenCV, and TENSORFLOW®. The analysis results are diagnosed by an identification means to determine applicable support systems, and the user is notified by a notification means. Furthermore, the diagnostic results are shared with administrative agencies via communication means, and an alarm is issued via an emergency notification means if necessary. This series of processes enables rapid response and information sharing, ensuring the user's safety.

[0108] For example, if a user walking at night notices something suspicious, they can alert others to the danger through voice commands or photos taken via their device. The server immediately analyzes the situation, and if danger is determined, it issues an alarm and makes an emergency call to the police and other relevant agencies. With such a system in place, users can go about their daily lives with peace of mind.

[0109] An example of a prompt to the generative AI model is, "If a user feels in danger, how would they use their smartphone's camera and audio data to immediately report it to the police?" Based on this prompt, the model can suggest appropriate procedures and countermeasures.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The user uses the device to collect audio, video, and text information from their surroundings. The device activates its camera and microphone and records this information at the times instructed by the user. Audio and image data are acquired as input and prepared to be sent for the next processing step.

[0113] Step 2:

[0114] The terminal transmits collected audio and video information to the server in real time. A data transfer protocol is used to transmit the data. Data stored on the terminal is used as input for uploading to the server.

[0115] Step 3:

[0116] The server analyzes the received audio information using an automatic speech recognition algorithm. First, it adjusts the sample rate of the audio data and removes noise. Next, it converts the audio to text using the speech recognition model and obtains the recognition result. The input is audio data, and the output is the analyzed text data.

[0117] Step 4:

[0118] The server analyzes the received video information using an image analysis algorithm. First, it preprocesses the image by removing noise and adjusting the resolution. Next, it uses an image recognition model to detect dangerous objects and situations. The input is image data, and the output is information about the analyzed objects and situations.

[0119] Step 5:

[0120] The server compiles the analysis results and sends them to the identification device to diagnose applicable support systems. The identification device refers to the analyzed text data and object information to identify the support systems required for the user. The input is the analyzed data, and the output is a proposed support system.

[0121] Step 6:

[0122] The server notifies the user of the diagnostic results using a notification mechanism. The notification is displayed through an application used on the terminal and provides the user with the necessary information. The input is the diagnostic result, and the output is the notification information for the user.

[0123] Step 7:

[0124] If necessary, the server will issue an alarm using emergency notification methods and notify relevant government and security agencies. The input is alarm information generated from the analysis results, and the output is warning information.

[0125] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0126] This invention is a system that diagnoses applicable support systems by collecting and analyzing audio, video, and text data for disaster relief. In particular, by incorporating an emotion engine that recognizes the user's emotions and uses that to help in support decisions, it is possible to provide more accurate support.

[0127] First, users use their smartphones or tablets to record the extent of damage caused by the disaster and describe their specific needs for assistance using voice. The devices have the functionality to send this data to a server, communicating in real time. The server transfers the data to an analysis device, which analyzes the collected information using relevant natural language processing and image processing algorithms. In this analysis, an emotion engine recognizes emotions from the user's voice and video, and reflects that information in the analysis results.

[0128] For example, if a disaster victim reports significant damage to their home and expresses sadness and anxiety, the emotion engine analyzes the degree of that sadness, which influences the selection of appropriate support programs. The analysis device includes this emotional state in its results and provides this information to the diagnostic device to identify applicable support programs. The diagnostic results are communicated to the user via a notification device, clearly indicating which support programs are available. Furthermore, to share information with local governments and ensure that necessary support can be provided quickly, the server sends information via a communication device and coordinates with relevant organizations.

[0129] This system enables sensitive and effective responses that take into account the feelings of disaster victims, reducing the psychological burden on users and allowing for optimal support. It also contributes to efficient responses from local governments in situations where rapid support is required.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] Users use smartphones or tablets to record the extent of the damage and provide voice descriptions of their condition and the assistance they need. The devices have the ability to capture video and record audio, and this data is treated as a series of pieces of information.

[0133] Step 2:

[0134] The terminal transmits audio, video, and text data acquired from the user to the server. The terminal formats this data appropriately and transfers it to the server in real time via a secure communication channel.

[0135] Step 3:

[0136] The server passes the received data to the analysis device. The analysis device uses natural language processing algorithms to convert the audio data into text and analyze the content of the user's speech. It also uses image processing technology to evaluate the physical state of the damage from the video data.

[0137] Step 4:

[0138] The analysis device uses an emotion engine to analyze the user's voice tone and changes in facial expressions in the video to understand the user's emotional state. This includes processes such as recognizing vocal intonation and facial muscle movements to identify emotions.

[0139] Step 5:

[0140] The server uses the analysis results provided by the analysis device to perform a diagnostic assessment of the support system. The diagnostic device compares the results with the support system information in the database and lists applicable systems while taking into account the emotional state.

[0141] Step 6:

[0142] The diagnostic results are sent from the server to the terminal via a notification device. The terminal then notifies the user of available support programs and their details, and provides guidance on the next steps to take.

[0143] Step 7:

[0144] Based on the information received by the user, the system will coordinate with local governments and apply for necessary support programs. The device will display a message containing links and contact information necessary for the procedure.

[0145] Step 8:

[0146] The server shares diagnostic results with local governments and related organizations via communication devices. This step ensures rapid information dissemination and facilitates the smooth implementation of disaster relief efforts.

[0147] (Example 2)

[0148] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0149] Conventional disaster relief systems have a problem in that they make it difficult to provide appropriate support while taking into account the feelings of the users. Furthermore, the selection of support systems and the rapid sharing of information with local governments are insufficient, often resulting in delays in appropriate responses. To solve these problems, a system is needed that accurately recognizes the feelings of disaster victims, appropriately selects support systems, and efficiently shares information.

[0150] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0151] In this invention, the server includes information processing means for collecting audio data, video data, and text data; analysis means for performing multimodal analysis of the data collected from the information processing means; and means for recognizing emotions using an AI model generated by the analysis means and integrating the analysis results. This enables accurate recognition of the emotions of disaster victims, rapid and accurate selection of applicable support systems, and prompt information sharing with local governments.

[0152] An "information processing device" is a device for collecting audio data, video data, and text data.

[0153] "Multimodal analysis" is a method that comprehensively analyzes different types of data to understand their interrelationships.

[0154] "Analysis means" refers to a method for processing collected data and extracting information that is relevant to a specific purpose.

[0155] A "generative AI model" is an artificial intelligence algorithm that learns from large datasets and is applied to natural language processing and other tasks.

[0156] "Means of recognizing emotions" refers to methods for detecting and identifying emotional states from collected data.

[0157] A "diagnostic tool" is a mechanism for identifying applicable support systems based on the analysis results.

[0158] "Notification method" refers to a method of informing the user of the diagnostic results.

[0159] "Communication methods" refer to data transmission methods used to share information within an organization.

[0160] This system is a technology developed for disaster relief, providing appropriate support systems quickly through emotion recognition. Users first use information processing devices such as smartphones or tablets to record the disaster situation around them using video and audio. For example, they can use their smartphone's camera function to photograph damage to their home and use the recording function to verbally describe the necessary support.

[0161] The terminal transmits this collected data to a server via a communication network. Wi-Fi or mobile communication networks are used for this transmission. The server processes the received data through analysis tools. The analysis utilizes natural language processing algorithms and image processing algorithms, and analyzes the user's emotions using generative AI models (for example, commonly known AI models). This enables emotion recognition from audio data and assessment of the degree of damage from video.

[0162] Based on the evaluation of emotional and video data obtained through analysis, the server uses diagnostic tools to identify applicable support systems. For selection, the AI ​​model proposes support systems using prompt messages. For example, a prompt message such as "Please propose the optimal support system based on this data" might be used.

[0163] Ultimately, information about the identified support programs will be communicated to users via notification, clearly indicating which programs are available. Furthermore, this information will be shared with local governments and relevant organizations via communication channels to expedite support. This will enable swift and accurate support for disaster victims, leading to effective relief efforts.

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] Users use smartphones or tablets as information processing devices to record disaster situations in video and audio. The inputs collected include video data and audio data indicating the support needs of disaster victims. Specifically, users film damage to their homes with their smartphones and use the recording function to record necessary support as voice messages. The output of this step is the collected multimodal data.

[0167] Step 2:

[0168] The terminal transmits the collected data described above to the server using the communication network. Video and audio data recorded by the user are used as input. The operation involves data transmission via Wi-Fi or a 4G / 5G network. The output of this step is the multimodal data delivered to the server.

[0169] Step 3:

[0170] The server passes the received data to the analysis device and begins processing the data using the analysis means. The input is the multimodal data transmitted in the previous step. The audio data is converted to text using a natural language processing algorithm, and its content is analyzed. In addition, a generative AI model is used to evaluate the voice tone and video information in order to identify the user's emotions. The output is the analysis result, which includes emotion and damage assessment information.

[0171] Step 4:

[0172] Based on the analysis results, the server uses diagnostic tools to identify applicable support programs. It receives emotion analysis data and damage assessment information as input. The server utilizes a generative AI model and selects a support program using the prompt "Please propose the optimal support program based on this data." The output is an optimized support proposal.

[0173] Step 5:

[0174] The server communicates information about the diagnosed support programs to the user via notification, clearly indicating the applicable support programs. The input is the information about the support programs identified by the server. Specifically, a push notification is sent to the terminal, allowing the user to confirm the support details. The output is an explicit notification of the support program to the user.

[0175] Step 6:

[0176] The server shares analysis results and support program information with local governments and related organizations via communication channels. Inputs are identified support program information and analysis results. Operationally, it uses APIs and secure data transfer protocols to transmit necessary information quickly and accurately. Output is information sharing among local governments and related organizations.

[0177] (Application Example 2)

[0178] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0179] In times of disaster, accurately assessing the emotions and support needs of victims is crucial for responding quickly and effectively to the diverse problems they face. However, conventional support systems have struggled to provide individualized support that takes into account the degree of emotions and mental stress. Furthermore, prioritizing support and providing rapid assistance remain challenges.

[0180] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0181] In this invention, the server includes information acquisition means for collecting audio data, video data, and text data; data analysis means for performing multimodal analysis of the data collected from the information acquisition means; and diagnostic means for identifying emotional states and diagnosing applicable support systems using the analysis results obtained by the data analysis means. This makes it possible to generate and quickly provide appropriate support plans that take into account the emotional state of each disaster victim.

[0182] "Information acquisition means" refers to devices or parts of devices that have the function of collecting audio data, video data, and text data.

[0183] "Data analysis means" refers to devices and algorithms used to analyze data collected from information acquisition means in various formats and to identify the emotional state of a user.

[0184] "Diagnostic means" refers to systems and devices used to diagnose applicable support systems and security measures for users based on analysis results obtained through data analysis means.

[0185] "Support plan generation method" refers to a system or process for creating necessary support plans according to the user's emotional state.

[0186] "Notification means" refers to devices or procedures for presenting the results of diagnostic tests and support plans to users as information.

[0187] "Data communication means" refers to the communication technologies and devices necessary to share diagnostic results with public institutions and other relevant organizations.

[0188] The system implementing this invention enhances the process of supporting disaster victims by utilizing voice, video, and text data. This system consists of information acquisition means, data analysis means, diagnostic means, support plan generation means, notification means, and data communication means.

[0189] The server first uses information acquisition means to collect audio, video, and text data from the user's smartphone or tablet. This includes microphones for audio recording and cameras for video recording. This data is transmitted to the server in real time and input into data analysis means.

[0190] The data analysis method analyzes the collected multimodal data using natural language processing and image processing algorithms. It utilizes the Google® Cloud Speech-to-Text API and Google Cloud Vision API to convert speech data to text and perform emotional analysis from video data. Furthermore, a sentiment analysis model using TensorFlow identifies the user's emotional state. Based on these results, the diagnostic tool determines appropriate support systems and security measures for the user.

[0191] The support plan generation system creates a specific support plan tailored to the user's emotional state. This includes guidance on psychological care and security measures. The generated plan is presented to the user via a notification system, such as a smartphone app or other device. Real-time notifications are provided using Firebase Cloud Messaging (FCM).

[0192] Furthermore, diagnostic results and support plans will be shared with public institutions via data communication. This will enable local governments and related organizations to take swift action. For example, if disaster victims feel anxious in evacuation shelters, they will be guided to alternative shelters and provided with psychological care.

[0193] Example of a prompt:

[0194] "Create detailed prompt messages for a system that analyzes the emotional state of users affected by a disaster, based on their voice, video, and text data, and generates the optimal support plan."

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The device collects audio, video, and text data. The user's smartphone or tablet uses its microphone and camera to capture audio and video. This data becomes the input. The device prepares to send this data to a cloud server.

[0198] Step 2:

[0199] The server passes the received data to the data analysis device. The audio data is converted to text data using the Google Cloud Speech-to-Text API. For example, an audio recording expressing a user's anxiety is converted to the text "I feel anxious about the evacuation center." The converted text data becomes the output.

[0200] Step 3:

[0201] The server analyzes the video data using the Google Cloud Vision API. This process identifies emotional expressions and situations within the video. The analysis generates output such as detecting stress levels from the user's facial expressions. This output becomes the server's input.

[0202] Step 4:

[0203] The server uses collected text and video data to perform sentiment analysis using TensorFlow. Based on the data analysis, the user's emotional state, specifically their stress level and the type of support they require, is diagnosed. This analysis result is then output.

[0204] Step 5:

[0205] Based on the diagnostic results, the server uses a support plan generation device to create an optimal support plan for the user. Considering the user's emotional state, the plan includes guidance on shelters and psychological support. The support plan is then generated as output.

[0206] Step 6:

[0207] The server uses Firebase Cloud Messaging (FCM) to send notifications of the generated support plan to the device. Based on the received data, the device displays information to the user in real time. For example, a notification screen on a smartphone might display "Information on alternative shelters."

[0208] Step 7:

[0209] The server transmits information via data communication equipment to share diagnostic results with public institutions. This allows local governments to take measures to respond quickly. The transmitted data becomes the output.

[0210] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0211] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0212] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0213] [Second Embodiment]

[0214] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0215] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0216] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0217] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0218] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0219] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0220] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0221] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0222] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0223] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0224] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0225] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0226] To implement this invention, a smartphone, tablet, or personal computer can be used as the device. The user uses these devices to collect audio, video, and text data related to the disaster situation. The device has the function to transmit this data to a server in real time. The server converts the received data into a multimodal analyzable format and passes it to an analysis device. This analysis device uses speech recognition, image processing, and natural language processing technologies to comprehensively analyze the situation of the disaster victims.

[0227] The results obtained from the analysis device are used as input for the diagnostic device to quickly identify applicable support systems for disaster victims. The diagnostic device diagnoses the appropriate system and generates results by comparing the results with a database of support systems.

[0228] The generated diagnostic results are communicated to the user via a notification device. The user receives this notification on their device and can then review the next steps to take and an overview of available support programs. The server also uses communication devices to share information with local governments, supporting the smooth progress of necessary procedures.

[0229] For example, when a disaster occurs in a certain area, a user can use the app to take photos of the damage to their house and describe the necessary assistance via voice. This information is sent from the device to a server and processed by an analysis device. The diagnostic device recognizes the eligibility for housing reconstruction assistance based on the analysis results and notifies the user. Based on the notification, the user can contact the local government and apply for assistance quickly.

[0230] In this way, the invention reduces the burden on disaster victims and contributes to improving the efficiency of local government operations. As a result, disaster victims can quickly access necessary support systems, making it possible to support their early recovery.

[0231] The following describes the processing flow.

[0232] Step 1:

[0233] Users operate the device to input voice, video, and text information. They use smartphones or tablets to photograph the damage or record audio explaining the situation into the microphone.

[0234] Step 2:

[0235] The device sends the entered information to the server via the app. The device compresses the digital data in real time and transfers it to the server using a secure communication protocol.

[0236] Step 3:

[0237] The server transfers the received data to the analysis device. The server then converts the data's format, preparing it for processing by the analysis device.

[0238] Step 4:

[0239] The analysis device performs multimodal analysis of the data. Audio data is converted to text using a speech recognition algorithm, and its content is analyzed through natural language processing. Video data is used to assess physical damage using image processing technology.

[0240] Step 5:

[0241] The server passes the analysis results to the diagnostic device. The diagnostic device refers to the database and identifies support programs that may be applicable to the disaster victims.

[0242] Step 6:

[0243] The diagnostic device further evaluates the applicable support programs and generates diagnostic results. The server records the information obtained from the diagnostic device in a database.

[0244] Step 7:

[0245] The server sends the diagnostic results to the terminal via a notification device. The notification device informs the user of the results and shows the necessary procedures and next steps on the interface.

[0246] Step 8:

[0247] The user reviews the diagnostic results they receive and begins the necessary procedures. They then collaborate with local governments to facilitate applications for support programs.

[0248] (Example 1)

[0249] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0250] In times of disaster, it is essential to identify and provide information on prompt and appropriate support systems for disaster victims. Currently, the process of collecting and analyzing information, and applying support systems, is complex and inefficient, potentially delaying the support that disaster victims need. To address this challenge, it is necessary to build a system that can quickly collect and analyze information, identify appropriate support systems, and promptly provide that information to disaster victims and relevant organizations.

[0251] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0252] In this invention, the server includes terminal means for collecting audio information, video information, and text information; means for transmitting the information collected from the terminal means to a central processing unit in real time; and the central processing unit includes means for converting the received information into a format that can be multimodally analyzed and passing it on to an analysis means. This makes it possible to quickly grasp the situation of disaster victims and accurately identify and provide applicable support systems during a disaster.

[0253] "Audio information" refers to information represented in digital format based on sound waveforms, and usually refers to speech, including human language.

[0254] "Visual information" refers to information represented in digital format based on the waveform of light, and includes visual image data such as still images and videos.

[0255] "Text information" refers to digital information composed of characters, and usually means information in the form of documents or messages.

[0256] "Terminal means" refers to a device capable of collecting and transmitting information, and includes portable information processing devices and personal computers.

[0257] A "central processing unit" refers to a control device that processes received information and converts it into a format appropriate for its purpose.

[0258] "Multimodal analysis" is a method for integrating and analyzing information in multiple formats, and it targets multiple data modalities, including audio, video, and text.

[0259] "Analysis means" refers to technologies for analyzing digital information and extracting features, and includes devices that use speech recognition, image analysis, and natural language processing.

[0260] "Diagnostic means" refers to a device or mechanism that identifies appropriate support measures based on analysis results and prepares for their provision.

[0261] "Information provision means" refers to a device or method for quickly notifying users of information such as diagnostic results.

[0262] "Communication means" refers to technologies used to share information with other devices or organizations, and includes network connectivity.

[0263] A "generative AI model" is an artificial intelligence technique that has the ability to understand various data formats and generate output.

[0264] To implement this invention, the user first uses an information gathering terminal. This terminal could be a portable information processing device (smartphone), a personal information terminal (tablet), or a personal computer. The user uses this terminal to collect audio, video, and text information from the disaster site. Audio information is recorded via a microphone, and video information is captured using a camera. Text information is generated through automatic speech conversion or manual input.

[0265] The terminal transmits the collected information to the central processing unit (server) in real time. To ensure security and speed, a protocol such as HTTPS is used for this communication. The server converts the received data into a format suitable for multimodal analysis and passes it to the analysis device. The analysis device uses a generative AI model to comprehensively analyze the data through speech recognition, image analysis, and natural language processing.

[0266] The information obtained through analysis is passed to a diagnostic tool. The diagnostic tool compares this information with a database of support measures to identify applicable support programs for disaster victims. This identified result is notified to the user through an information provision tool. The notification is sent to the terminal in push notification format, allowing the user to check the results immediately. Furthermore, the server shares this result with local government agencies using communication tools to facilitate the more rapid application of support programs.

[0267] Specifically, after an earthquake, users take photos of the damage to their homes with their smartphones and record voice messages stating that they need assistance with home repairs. This audio and video are sent to a server, where an analysis device assesses the extent of the damage and proposes the most suitable support program. This process is carried out by a generating AI model based on a prompt message such as, "Based on the video and audio data of the damage to your home and the support you need during the disaster, please identify the most suitable support program."

[0268] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0269] Step 1:

[0270] Users collect information.

[0271] Users collect audio, video, and text information from disaster sites using devices such as smartphones and tablets. Specifically, they photograph damaged buildings with cameras and record audio about the extent of the damage and the need for assistance. Camera image data and audio files are generated as input. These data are temporarily stored on the device as output.

[0272] Step 2:

[0273] The device transmits data.

[0274] The terminal transmits collected audio, video, and text data to the server in real time. Specifically, the communication module within the terminal securely transfers the data to the server using the HTTPS protocol. The input is data stored on the terminal. The output is this data reaching the server.

[0275] Step 3:

[0276] The server converts the data.

[0277] The server converts received audio, video, and text data into a format that allows for multimodal analysis. For example, it converts audio data into text and organizes video data frame by frame. The input is the transmitted raw data. The output is data formatted in a way that is easy for the analysis device to process.

[0278] Step 4:

[0279] The analysis device analyzes the data.

[0280] The analysis system on the server performs speech recognition, image analysis, and natural language processing using a generative AI model. Specifically, it converts audio data into text and identifies the extent of damage from image data. The input is converted multimodal data. The output is analysis data of the identified damage and the necessary support measures.

[0281] Step 5:

[0282] The diagnostic device identifies the support system.

[0283] The server diagnostic device searches the support measure database based on the analysis results and identifies the support systems applicable to the disaster victims. Specifically, it uses the results of the generated AI model to extract support plans that meet the eligibility criteria. As input, there is analysis result data. As output, the identified support system information is obtained.

[0284] Step 6:

[0285] The server provides information to the user.

[0286] The server notifies the user of the diagnosis result through the information providing means. Specifically, it sends a push notification to the terminal so that the user can check the result. As input, there is the identified support system information. As output, information notification to the user is performed.

[0287] Step 7:

[0288] The server shares information with the local government.

[0289] The server uses the communication means to send the diagnosis result to the local administrative agency. Specifically, it registers the information in the local government's database so that the relevant parties can provide support promptly. As input, there is the diagnosis result data. As output, information sharing with the local government is completed.

[0290] (Application Example 1)

[0291] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0292] In case of disaster or emergency, in a situation where an individual's safety is threatened, it is required to quickly and accurately judge the situation and provide appropriate support systems and emergency responses. For this purpose, it is essential to collect information from multiple data sources and make an integrated judgment. However, the current system has the problem that it only collects and analyzes information fragmentarily, making it difficult to respond quickly.

[0293] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0294] In this invention, the server includes a terminal for collecting audio information, video information, and text information; an analysis means for analyzing the information collected from the terminal in various formats; an identification means for diagnosing applicable support systems using the analysis results obtained by the analysis means; a notification means for notifying the user of the diagnosis results of the identification means; a communication means for sharing the diagnosis results with administrative agencies; and an emergency notification means for issuing an alarm based on the dangerous situation determined by the analysis means. This makes it possible to quickly collect information and provide appropriate support systems and emergency responses even in situations where the user's safety is threatened.

[0295] A "terminal" is a device used to collect audio, video, and text information, and is a device that plays a role in acquiring information in various formats.

[0296] "Analysis means" refers to a device or system that analyzes information collected from a terminal in various formats and performs processing to determine the situation.

[0297] "Identification means" refers to a device or system that has the function of diagnosing applicable support systems based on the analysis results obtained by the analysis means.

[0298] A "notification means" is a device or system that has the function of notifying the user of the diagnostic results of the identification means, and plays the role of providing the user with the necessary information.

[0299] "Communication means" refers to a device or system that has the function of sharing diagnostic results with administrative agencies.

[0300] An "emergency notification system" is a device or system that has the function of issuing an alarm based on a dangerous situation determined by an analysis system.

[0301] To implement this invention, a mobile communication device or personal computer is used as a terminal for collecting voice, video, and text information. The user collects information in real time through this terminal and transmits it to the server. The server analyzes the received information, primarily using automatic speech recognition and image analysis techniques, and performs data analysis in various formats. Software used may include Python, OpenCV, and TensorFlow. The analysis results are diagnosed by an identification means to determine applicable support systems, and the user is notified by a notification means. Furthermore, the diagnostic results are shared with administrative agencies via communication means, and an alarm is issued via an emergency notification means if necessary. This series of processes enables rapid response and information sharing, ensuring the user's safety.

[0302] For example, if a user walking at night notices something suspicious, they can alert others to the danger through voice commands or photos taken via their device. The server immediately analyzes the situation, and if danger is determined, it issues an alarm and makes an emergency call to the police and other relevant agencies. With such a system in place, users can go about their daily lives with peace of mind.

[0303] An example of a prompt to the generative AI model is, "If a user feels in danger, how would they use their smartphone's camera and audio data to immediately report it to the police?" Based on this prompt, the model can suggest appropriate procedures and countermeasures.

[0304] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0305] Step 1:

[0306] The user uses the device to collect audio, video, and text information from their surroundings. The device activates its camera and microphone and records this information at the times instructed by the user. Audio and image data are acquired as input and prepared to be sent for the next processing step.

[0307] Step 2:

[0308] The terminal transmits the collected voice information and video information to the server in real time. Here, the data is transmitted using a communication protocol for data transfer. Upload to the server is performed using the data stored in the terminal as input.

[0309] Step 3:

[0310] The server analyzes the received voice information using an automatic speech recognition algorithm. First, the sample rate of the voice data is adjusted and noise removal processing is performed. Next, it is converted into text using a speech recognition model to obtain the recognition result. The input is voice data, and the output is the analyzed text data.

[0311] Step 4:

[0312] The server analyzes the received video information using an image analysis algorithm. First, preprocessing of the image is performed, such as noise removal and resolution adjustment. Next, a dangerous object or situation is detected using an image recognition model. The input is image data, and the output is the information of the analyzed object or situation.

[0313] Step 5:

[0314] The server summarizes the analysis results and sends them to the identification means to diagnose applicable support systems. The identification means refers to the analyzed text data and object information to identify the necessary support system for the user. The input is the analyzed data, and the output is the proposal of the support system.

[0315] Step 6:

[0316] The server notifies the user of the diagnosis result using the notification means. The notification is displayed through an application used on the terminal to provide the user with the necessary information. The input is the diagnosis result, and the output is the notification information to the user.

[0317] Step 7:

[0318] If necessary, the server will issue an alarm using emergency notification methods and notify relevant government and security agencies. The input is alarm information generated from the analysis results, and the output is warning information.

[0319] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0320] This invention is a system that diagnoses applicable support systems by collecting and analyzing audio, video, and text data for disaster relief. In particular, by incorporating an emotion engine that recognizes the user's emotions and uses that to help in support decisions, it is possible to provide more accurate support.

[0321] First, users use their smartphones or tablets to record the extent of damage caused by the disaster and describe their specific needs for assistance using voice. The devices have the functionality to send this data to a server, communicating in real time. The server transfers the data to an analysis device, which analyzes the collected information using relevant natural language processing and image processing algorithms. In this analysis, an emotion engine recognizes emotions from the user's voice and video, and reflects that information in the analysis results.

[0322] For example, if a disaster victim reports significant damage to their home and expresses sadness and anxiety, the emotion engine analyzes the degree of that sadness, which influences the selection of appropriate support programs. The analysis device includes this emotional state in its results and provides this information to the diagnostic device to identify applicable support programs. The diagnostic results are communicated to the user via a notification device, clearly indicating which support programs are available. Furthermore, to share information with local governments and ensure that necessary support can be provided quickly, the server sends information via a communication device and coordinates with relevant organizations.

[0323] This system enables sensitive and effective responses that take into account the feelings of disaster victims, reducing the psychological burden on users and allowing for optimal support. It also contributes to efficient responses from local governments in situations where rapid support is required.

[0324] The following describes the processing flow.

[0325] Step 1:

[0326] Users use smartphones or tablets to record the extent of the damage and provide voice descriptions of their condition and the assistance they need. The devices have the ability to capture video and record audio, and this data is treated as a series of pieces of information.

[0327] Step 2:

[0328] The terminal transmits audio, video, and text data acquired from the user to the server. The terminal formats this data appropriately and transfers it to the server in real time via a secure communication channel.

[0329] Step 3:

[0330] The server passes the received data to the analysis device. The analysis device uses natural language processing algorithms to convert the audio data into text and analyze the content of the user's speech. It also uses image processing technology to evaluate the physical state of the damage from the video data.

[0331] Step 4:

[0332] The analysis device uses an emotion engine to analyze the user's voice tone and changes in facial expressions in the video to understand the user's emotional state. This includes processes such as recognizing vocal intonation and facial muscle movements to identify emotions.

[0333] Step 5:

[0334] The server uses the analysis results provided by the analysis device to perform a diagnostic assessment of the support system. The diagnostic device compares the results with the support system information in the database and lists applicable systems while taking into account the emotional state.

[0335] Step 6:

[0336] The diagnostic results are sent from the server to the terminal via a notification device. The terminal then notifies the user of available support programs and their details, and provides guidance on the next steps to take.

[0337] Step 7:

[0338] Based on the information received by the user, the system will coordinate with local governments and apply for necessary support programs. The device will display a message containing links and contact information necessary for the procedure.

[0339] Step 8:

[0340] The server shares diagnostic results with local governments and related organizations via communication devices. This step ensures rapid information dissemination and facilitates the smooth implementation of disaster relief efforts.

[0341] (Example 2)

[0342] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0343] Conventional disaster relief systems have a problem in that they make it difficult to provide appropriate support while taking into account the feelings of the users. Furthermore, the selection of support systems and the rapid sharing of information with local governments are insufficient, often resulting in delays in appropriate responses. To solve these problems, a system is needed that accurately recognizes the feelings of disaster victims, appropriately selects support systems, and efficiently shares information.

[0344] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0345] In this invention, the server includes information processing means for collecting audio data, video data, and text data; analysis means for performing multimodal analysis of the data collected from the information processing means; and means for recognizing emotions using an AI model generated by the analysis means and integrating the analysis results. This enables accurate recognition of the emotions of disaster victims, rapid and accurate selection of applicable support systems, and prompt information sharing with local governments.

[0346] An "information processing device" is a device for collecting audio data, video data, and text data.

[0347] "Multimodal analysis" is a method that comprehensively analyzes different types of data to understand their interrelationships.

[0348] "Analysis means" refers to a method for processing collected data and extracting information that is relevant to a specific purpose.

[0349] A "generative AI model" is an artificial intelligence algorithm that learns from large datasets and is applied to natural language processing and other tasks.

[0350] "Means of recognizing emotions" refers to methods for detecting and identifying emotional states from collected data.

[0351] A "diagnostic tool" is a mechanism for identifying applicable support systems based on the analysis results.

[0352] "Notification method" refers to a method of informing the user of the diagnostic results.

[0353] "Communication methods" refer to data transmission methods used to share information within an organization.

[0354] This system is a technology developed for disaster relief, providing appropriate support systems quickly through emotion recognition. Users first use information processing devices such as smartphones or tablets to record the disaster situation around them using video and audio. For example, they can use their smartphone's camera function to photograph damage to their home and use the recording function to verbally describe the necessary support.

[0355] The terminal transmits this collected data to a server via a communication network. Wi-Fi or mobile communication networks are used for this transmission. The server processes the received data through analysis tools. The analysis utilizes natural language processing algorithms and image processing algorithms, and analyzes the user's emotions using generative AI models (for example, commonly known AI models). This enables emotion recognition from audio data and assessment of the degree of damage from video.

[0356] Based on the evaluation of emotional and video data obtained through analysis, the server uses diagnostic tools to identify applicable support systems. For selection, the AI ​​model proposes support systems using prompt messages. For example, a prompt message such as "Please propose the optimal support system based on this data" might be used.

[0357] Ultimately, information about the identified support programs will be communicated to users via notification, clearly indicating which programs are available. Furthermore, this information will be shared with local governments and relevant organizations via communication channels to expedite support. This will enable swift and accurate support for disaster victims, leading to effective relief efforts.

[0358] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0359] Step 1:

[0360] Users use smartphones or tablets as information processing devices to record disaster situations in video and audio. The inputs collected include video data and audio data indicating the support needs of disaster victims. Specifically, users film damage to their homes with their smartphones and use the recording function to record necessary support as voice messages. The output of this step is the collected multimodal data.

[0361] Step 2:

[0362] The terminal transmits the collected data described above to the server using the communication network. Video and audio data recorded by the user are used as input. The operation involves data transmission via Wi-Fi or a 4G / 5G network. The output of this step is the multimodal data delivered to the server.

[0363] Step 3:

[0364] The server passes the received data to the analysis device and begins processing the data using the analysis means. The input is the multimodal data transmitted in the previous step. The audio data is converted to text using a natural language processing algorithm, and its content is analyzed. In addition, a generative AI model is used to evaluate the voice tone and video information in order to identify the user's emotions. The output is the analysis result, which includes emotion and damage assessment information.

[0365] Step 4:

[0366] Based on the analysis results, the server uses diagnostic tools to identify applicable support programs. It receives emotion analysis data and damage assessment information as input. The server utilizes a generative AI model and selects a support program using the prompt "Please propose the optimal support program based on this data." The output is an optimized support proposal.

[0367] Step 5:

[0368] The server communicates information about the diagnosed support programs to the user via notification, clearly indicating the applicable support programs. The input is the information about the support programs identified by the server. Specifically, a push notification is sent to the terminal, allowing the user to confirm the support details. The output is an explicit notification of the support program to the user.

[0369] Step 6:

[0370] The server shares analysis results and support program information with local governments and related organizations via communication channels. Inputs are identified support program information and analysis results. Operationally, it uses APIs and secure data transfer protocols to transmit necessary information quickly and accurately. Output is information sharing among local governments and related organizations.

[0371] (Application Example 2)

[0372] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0373] In times of disaster, accurately assessing the emotions and support needs of victims is crucial for responding quickly and effectively to the diverse problems they face. However, conventional support systems have struggled to provide individualized support that takes into account the degree of emotions and mental stress. Furthermore, prioritizing support and providing rapid assistance remain challenges.

[0374] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0375] In this invention, the server includes information acquisition means for collecting audio data, video data, and text data; data analysis means for performing multimodal analysis of the data collected from the information acquisition means; and diagnostic means for identifying emotional states and diagnosing applicable support systems using the analysis results obtained by the data analysis means. This makes it possible to generate and quickly provide appropriate support plans that take into account the emotional state of each disaster victim.

[0376] "Information acquisition means" refers to devices or parts of devices that have the function of collecting audio data, video data, and text data.

[0377] "Data analysis means" refers to devices and algorithms used to analyze data collected from information acquisition means in various formats and to identify the emotional state of a user.

[0378] "Diagnostic means" refers to systems and devices used to diagnose applicable support systems and security measures for users based on analysis results obtained through data analysis means.

[0379] "Support plan generation method" refers to a system or process for creating necessary support plans according to the user's emotional state.

[0380] "Notification means" refers to devices or procedures for presenting the results of diagnostic tests and support plans to users as information.

[0381] "Data communication means" refers to the communication technologies and devices necessary to share diagnostic results with public institutions and other relevant organizations.

[0382] The system implementing this invention enhances the process of supporting disaster victims by utilizing voice, video, and text data. This system consists of information acquisition means, data analysis means, diagnostic means, support plan generation means, notification means, and data communication means.

[0383] The server first uses information acquisition means to collect audio, video, and text data from the user's smartphone or tablet. This includes microphones for audio recording and cameras for video recording. This data is transmitted to the server in real time and input into data analysis means.

[0384] In the data analysis process, the collected multimodal data is analyzed using natural language processing and image processing algorithms. The Google Cloud Speech-to-Text API and Google Cloud Vision API are utilized to convert speech data to text and perform emotional analysis from video data. Furthermore, a sentiment analysis model using TensorFlow identifies the user's emotional state. Based on these results, the diagnostic tool determines appropriate support systems and security measures for the user.

[0385] The support plan generation system creates a specific support plan tailored to the user's emotional state. This includes guidance on psychological care and security measures. The generated plan is presented to the user via a notification system, such as a smartphone app or other device. Real-time notifications are provided using Firebase Cloud Messaging (FCM).

[0386] Furthermore, diagnostic results and support plans will be shared with public institutions via data communication. This will enable local governments and related organizations to take swift action. For example, if disaster victims feel anxious in evacuation shelters, they will be guided to alternative shelters and provided with psychological care.

[0387] Example of a prompt:

[0388] "Create detailed prompt messages for a system that analyzes the emotional state of users affected by a disaster, based on their voice, video, and text data, and generates the optimal support plan."

[0389] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0390] Step 1:

[0391] The device collects audio, video, and text data. The user's smartphone or tablet uses its microphone and camera to capture audio and video. This data becomes the input. The device prepares to send this data to a cloud server.

[0392] Step 2:

[0393] The server passes the received data to the data analysis device. The audio data is converted to text data using the Google Cloud Speech-to-Text API. For example, an audio recording expressing a user's anxiety is converted to the text "I feel anxious about the evacuation center." The converted text data becomes the output.

[0394] Step 3:

[0395] The server analyzes the video data using the Google Cloud Vision API. This process identifies emotional expressions and situations within the video. The analysis generates output such as detecting stress levels from the user's facial expressions. This output becomes the server's input.

[0396] Step 4:

[0397] The server uses collected text and video data to perform sentiment analysis using TensorFlow. Based on the data analysis, the user's emotional state, specifically their stress level and the type of support they require, is diagnosed. This analysis result is then output.

[0398] Step 5:

[0399] Based on the diagnostic results, the server uses a support plan generation device to create an optimal support plan for the user. Considering the user's emotional state, the plan includes guidance on shelters and psychological support. The support plan is then generated as output.

[0400] Step 6:

[0401] The server uses Firebase Cloud Messaging (FCM) to send notifications of the generated support plan to the device. Based on the received data, the device displays information to the user in real time. For example, a notification screen on a smartphone might display "Information on alternative shelters."

[0402] Step 7:

[0403] The server transmits information via data communication equipment to share diagnostic results with public institutions. This allows local governments to take measures to respond quickly. The transmitted data becomes the output.

[0404] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0405] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0406] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0407] [Third Embodiment]

[0408] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0409] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0410] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0411] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0412] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0413] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0414] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0415] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0416] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0417] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0418] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0419] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0420] To implement this invention, a smartphone, tablet, or personal computer can be used as the device. The user uses these devices to collect audio, video, and text data related to the disaster situation. The device has the function to transmit this data to a server in real time. The server converts the received data into a multimodal analyzable format and passes it to an analysis device. This analysis device uses speech recognition, image processing, and natural language processing technologies to comprehensively analyze the situation of the disaster victims.

[0421] The results obtained from the analysis device are used as input for the diagnostic device to quickly identify applicable support systems for disaster victims. The diagnostic device diagnoses the appropriate system and generates results by comparing the results with a database of support systems.

[0422] The generated diagnostic results are communicated to the user via a notification device. The user receives this notification on their device and can then review the next steps to take and an overview of available support programs. The server also uses communication devices to share information with local governments, supporting the smooth progress of necessary procedures.

[0423] For example, when a disaster occurs in a certain area, a user can use the app to take photos of the damage to their house and describe the necessary assistance via voice. This information is sent from the device to a server and processed by an analysis device. The diagnostic device recognizes the eligibility for housing reconstruction assistance based on the analysis results and notifies the user. Based on the notification, the user can contact the local government and apply for assistance quickly.

[0424] In this way, the invention reduces the burden on disaster victims and contributes to improving the efficiency of local government operations. As a result, disaster victims can quickly access necessary support systems, making it possible to support their early recovery.

[0425] The following describes the processing flow.

[0426] Step 1:

[0427] Users operate the device to input voice, video, and text information. They use smartphones or tablets to photograph the damage or record audio explaining the situation into the microphone.

[0428] Step 2:

[0429] The device sends the entered information to the server via the app. The device compresses the digital data in real time and transfers it to the server using a secure communication protocol.

[0430] Step 3:

[0431] The server transfers the received data to the analysis device. The server then converts the data's format, preparing it for processing by the analysis device.

[0432] Step 4:

[0433] The analysis device performs multimodal analysis of the data. Audio data is converted to text using a speech recognition algorithm, and its content is analyzed through natural language processing. Video data is used to assess physical damage using image processing technology.

[0434] Step 5:

[0435] The server passes the analysis results to the diagnostic device. The diagnostic device refers to the database and identifies support programs that may be applicable to the disaster victims.

[0436] Step 6:

[0437] The diagnostic device further evaluates the applicable support programs and generates diagnostic results. The server records the information obtained from the diagnostic device in a database.

[0438] Step 7:

[0439] The server sends the diagnostic results to the terminal via a notification device. The notification device informs the user of the results and shows the necessary procedures and next steps on the interface.

[0440] Step 8:

[0441] The user reviews the diagnostic results they receive and begins the necessary procedures. They then collaborate with local governments to facilitate applications for support programs.

[0442] (Example 1)

[0443] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0444] In times of disaster, it is essential to identify and provide information on prompt and appropriate support systems for disaster victims. Currently, the process of collecting and analyzing information, and applying support systems, is complex and inefficient, potentially delaying the support that disaster victims need. To address this challenge, it is necessary to build a system that can quickly collect and analyze information, identify appropriate support systems, and promptly provide that information to disaster victims and relevant organizations.

[0445] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0446] In this invention, the server includes terminal means for collecting audio information, video information, and text information; means for transmitting the information collected from the terminal means to a central processing unit in real time; and the central processing unit includes means for converting the received information into a format that can be multimodally analyzed and passing it on to an analysis means. This makes it possible to quickly grasp the situation of disaster victims and accurately identify and provide applicable support systems during a disaster.

[0447] "Audio information" refers to information represented in digital format based on sound waveforms, and usually refers to speech, including human language.

[0448] "Visual information" refers to information represented in digital format based on the waveform of light, and includes visual image data such as still images and videos.

[0449] "Text information" refers to digital information composed of characters, and usually means information in the form of documents or messages.

[0450] "Terminal means" refers to a device capable of collecting and transmitting information, and includes portable information processing devices and personal computers.

[0451] A "central processing unit" refers to a control device that processes received information and converts it into a format appropriate for its purpose.

[0452] "Multimodal analysis" is a method for integrating and analyzing information in multiple formats, and it targets multiple data modalities, including audio, video, and text.

[0453] "Analysis means" refers to technologies for analyzing digital information and extracting features, and includes devices that use speech recognition, image analysis, and natural language processing.

[0454] "Diagnostic means" refers to a device or mechanism that identifies appropriate support measures based on analysis results and prepares for their provision.

[0455] "Information provision means" refers to a device or method for quickly notifying users of information such as diagnostic results.

[0456] "Communication means" refers to technologies used to share information with other devices or organizations, and includes network connectivity.

[0457] A "generative AI model" is an artificial intelligence technique that has the ability to understand various data formats and generate output.

[0458] To implement this invention, the user first uses an information gathering terminal. This terminal could be a portable information processing device (smartphone), a personal information terminal (tablet), or a personal computer. The user uses this terminal to collect audio, video, and text information from the disaster site. Audio information is recorded via a microphone, and video information is captured using a camera. Text information is generated through automatic speech conversion or manual input.

[0459] The terminal transmits the collected information to the central processing unit (server) in real time. To ensure security and speed, a protocol such as HTTPS is used for this communication. The server converts the received data into a format suitable for multimodal analysis and passes it to the analysis device. The analysis device uses a generative AI model to comprehensively analyze the data through speech recognition, image analysis, and natural language processing.

[0460] The information obtained through analysis is passed to a diagnostic tool. The diagnostic tool compares this information with a database of support measures to identify applicable support programs for disaster victims. This identified result is notified to the user through an information provision tool. The notification is sent to the terminal in push notification format, allowing the user to check the results immediately. Furthermore, the server shares this result with local government agencies using communication tools to facilitate the more rapid application of support programs.

[0461] Specifically, after an earthquake, users take photos of the damage to their homes with their smartphones and record voice messages stating that they need assistance with home repairs. This audio and video are sent to a server, where an analysis device assesses the extent of the damage and proposes the most suitable support program. This process is carried out by a generating AI model based on a prompt message such as, "Based on the video and audio data of the damage to your home and the support you need during the disaster, please identify the most suitable support program."

[0462] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0463] Step 1:

[0464] Users collect information.

[0465] Users collect audio, video, and text information from disaster sites using devices such as smartphones and tablets. Specifically, they photograph damaged buildings with cameras and record audio about the extent of the damage and the need for assistance. Camera image data and audio files are generated as input. These data are temporarily stored on the device as output.

[0466] Step 2:

[0467] The device transmits data.

[0468] The terminal transmits collected audio, video, and text data to the server in real time. Specifically, the communication module within the terminal securely transfers the data to the server using the HTTPS protocol. The input is data stored on the terminal. The output is this data reaching the server.

[0469] Step 3:

[0470] The server converts the data.

[0471] The server converts received audio, video, and text data into a format that allows for multimodal analysis. For example, it converts audio data into text and organizes video data frame by frame. The input is the transmitted raw data. The output is data formatted in a way that is easy for the analysis device to process.

[0472] Step 4:

[0473] The analysis device analyzes the data.

[0474] The analysis system on the server performs speech recognition, image analysis, and natural language processing using a generative AI model. Specifically, it converts audio data into text and identifies the extent of damage from image data. The input is converted multimodal data. The output is analysis data of the identified damage and the necessary support measures.

[0475] Step 5:

[0476] The diagnostic device identifies the support system.

[0477] The server's diagnostic device searches a database of support measures based on the analysis results and identifies support programs applicable to disaster victims. Specifically, it uses the results of a generated AI model to extract support plans that meet the suitability criteria. The input is the analysis result data. The output is information on the identified support programs.

[0478] Step 6:

[0479] The server provides information to the user.

[0480] The server notifies the user of the diagnostic results through an information provision mechanism. Specifically, it sends a push notification to the device so that the user can check the results. The input is identified support system information. The output is an information notification to the user.

[0481] Step 7:

[0482] The server shares information with local governments.

[0483] The server transmits diagnostic results to local government agencies using communication methods. Specifically, it registers the information in the local government's database, enabling relevant parties to provide support quickly. The input is the diagnostic result data. The output is the completion of information sharing with the local government.

[0484] (Application Example 1)

[0485] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0486] During disasters and emergencies, when individual safety is threatened, there is a need to quickly and accurately assess the situation and provide appropriate support systems and emergency responses. To achieve this, it is essential to collect information from multiple data sources and make integrated judgments. However, current systems are limited to fragmented information collection and analysis, making rapid response difficult.

[0487] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0488] In this invention, the server includes a terminal for collecting audio information, video information, and text information; an analysis means for analyzing the information collected from the terminal in various formats; an identification means for diagnosing applicable support systems using the analysis results obtained by the analysis means; a notification means for notifying the user of the diagnosis results of the identification means; a communication means for sharing the diagnosis results with administrative agencies; and an emergency notification means for issuing an alarm based on the dangerous situation determined by the analysis means. This makes it possible to quickly collect information and provide appropriate support systems and emergency responses even in situations where the user's safety is threatened.

[0489] A "terminal" is a device used to collect audio, video, and text information, and is a device that plays a role in acquiring information in various formats.

[0490] "Analysis means" refers to a device or system that analyzes information collected from a terminal in various formats and performs processing to determine the situation.

[0491] "Identification means" refers to a device or system that has the function of diagnosing applicable support systems based on the analysis results obtained by the analysis means.

[0492] A "notification means" is a device or system that has the function of notifying the user of the diagnostic results of the identification means, and plays the role of providing the user with the necessary information.

[0493] "Communication means" refers to a device or system that has the function of sharing diagnostic results with administrative agencies.

[0494] An "emergency notification system" is a device or system that has the function of issuing an alarm based on a dangerous situation determined by an analysis system.

[0495] To implement this invention, a mobile communication device or personal computer is used as a terminal for collecting voice, video, and text information. The user collects information in real time through this terminal and transmits it to the server. The server analyzes the received information, primarily using automatic speech recognition and image analysis techniques, and performs data analysis in various formats. Software used may include Python, OpenCV, and TensorFlow. The analysis results are diagnosed by an identification means to determine applicable support systems, and the user is notified by a notification means. Furthermore, the diagnostic results are shared with administrative agencies via communication means, and an alarm is issued via an emergency notification means if necessary. This series of processes enables rapid response and information sharing, ensuring the user's safety.

[0496] For example, if a user walking at night notices something suspicious, they can alert others to the danger through voice commands or photos taken via their device. The server immediately analyzes the situation, and if danger is determined, it issues an alarm and makes an emergency call to the police and other relevant agencies. With such a system in place, users can go about their daily lives with peace of mind.

[0497] An example of a prompt to the generative AI model is, "If a user feels in danger, how would they use their smartphone's camera and audio data to immediately report it to the police?" Based on this prompt, the model can suggest appropriate procedures and countermeasures.

[0498] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0499] Step 1:

[0500] The user uses the device to collect audio, video, and text information from their surroundings. The device activates its camera and microphone and records this information at the times instructed by the user. Audio and image data are acquired as input and prepared for transmission to the next process.

[0501] Step 2:

[0502] The terminal transmits collected audio and video information to the server in real time. A data transfer protocol is used to transmit the data. Data stored on the terminal is used as input for uploading to the server.

[0503] Step 3:

[0504] The server analyzes the received audio information using an automatic speech recognition algorithm. First, it adjusts the sample rate of the audio data and removes noise. Next, it converts the audio to text using the speech recognition model and obtains the recognition result. The input is audio data, and the output is the analyzed text data.

[0505] Step 4:

[0506] The server analyzes the received video information using an image analysis algorithm. First, it preprocesses the image by removing noise and adjusting the resolution. Next, it uses an image recognition model to detect dangerous objects and situations. The input is image data, and the output is information about the analyzed objects and situations.

[0507] Step 5:

[0508] The server compiles the analysis results and sends them to the identification device to diagnose applicable support systems. The identification device refers to the analyzed text data and object information to identify the support systems required for the user. The input is the analyzed data, and the output is a proposed support system.

[0509] Step 6:

[0510] The server notifies the user of the diagnostic results using a notification mechanism. The notification is displayed through an application used on the terminal and provides the user with the necessary information. The input is the diagnostic result, and the output is the notification information for the user.

[0511] Step 7:

[0512] If necessary, the server will issue an alarm using emergency notification methods and notify relevant government and security agencies. The input is alarm information generated from the analysis results, and the output is warning information.

[0513] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0514] This invention is a system that diagnoses applicable support systems by collecting and analyzing audio, video, and text data for disaster relief. In particular, by incorporating an emotion engine that recognizes the user's emotions and uses that to help in support decisions, it is possible to provide more accurate support.

[0515] First, users use their smartphones or tablets to record the extent of damage caused by the disaster and describe their specific needs for assistance using voice. The devices have the functionality to send this data to a server, communicating in real time. The server transfers the data to an analysis device, which analyzes the collected information using relevant natural language processing and image processing algorithms. In this analysis, an emotion engine recognizes emotions from the user's voice and video, and reflects that information in the analysis results.

[0516] For example, if a disaster victim reports significant damage to their home and expresses sadness and anxiety, the emotion engine analyzes the degree of that sadness, which influences the selection of appropriate support programs. The analysis device includes this emotional state in its results and provides this information to the diagnostic device to identify applicable support programs. The diagnostic results are communicated to the user via a notification device, clearly indicating which support programs are available. Furthermore, to share information with local governments and ensure that necessary support can be provided quickly, the server sends information via a communication device and coordinates with relevant organizations.

[0517] This system enables sensitive and effective responses that take into account the feelings of disaster victims, reducing the psychological burden on users and allowing for optimal support. It also contributes to efficient responses from local governments in situations where rapid support is required.

[0518] The following describes the processing flow.

[0519] Step 1:

[0520] Users use smartphones or tablets to record the extent of the damage and provide voice descriptions of their condition and the assistance they need. The devices have the ability to capture video and record audio, and this data is treated as a series of pieces of information.

[0521] Step 2:

[0522] The terminal transmits audio, video, and text data acquired from the user to the server. The terminal formats this data appropriately and transfers it to the server in real time via a secure communication channel.

[0523] Step 3:

[0524] The server passes the received data to the analysis device. The analysis device uses natural language processing algorithms to convert the audio data into text and analyzes the content of the user's speech. It also uses image processing technology to evaluate the physical state of the damage from the video data.

[0525] Step 4:

[0526] The analysis device uses an emotion engine to analyze the user's voice tone and changes in facial expressions in the video to understand the user's emotional state. This includes processes such as recognizing vocal intonation and facial muscle movements to identify emotions.

[0527] Step 5:

[0528] The server uses the analysis results provided by the analysis device to perform a diagnostic assessment of the support system. The diagnostic device compares the results with the support system information in the database and lists applicable systems while taking into account the emotional state.

[0529] Step 6:

[0530] The diagnostic results are sent from the server to the terminal via a notification device. The terminal then notifies the user of available support programs and their details, and provides guidance on the next steps to take.

[0531] Step 7:

[0532] Based on the information received by the user, the system will coordinate with local governments and apply for necessary support programs. The device will display a message containing links and contact information necessary for the procedure.

[0533] Step 8:

[0534] The server shares diagnostic results with local governments and related organizations via communication devices. This step ensures rapid information dissemination and facilitates the smooth implementation of disaster relief efforts.

[0535] (Example 2)

[0536] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0537] Conventional disaster relief systems have a problem in that they make it difficult to provide appropriate support while taking into account the feelings of the users. Furthermore, the selection of support systems and the rapid sharing of information with local governments are insufficient, often resulting in delays in appropriate responses. To solve these problems, a system is needed that accurately recognizes the feelings of disaster victims, appropriately selects support systems, and efficiently shares information.

[0538] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0539] In this invention, the server includes information processing means for collecting audio data, video data, and text data; analysis means for performing multimodal analysis of the data collected from the information processing means; and means for recognizing emotions using an AI model generated by the analysis means and integrating the analysis results. This enables accurate recognition of the emotions of disaster victims, rapid and accurate selection of applicable support systems, and prompt information sharing with local governments.

[0540] An "information processing device" is a device for collecting audio data, video data, and text data.

[0541] "Multimodal analysis" is a method that comprehensively analyzes different types of data to understand their interrelationships.

[0542] "Analysis means" refers to a method for processing collected data and extracting information that is relevant to a specific purpose.

[0543] A "generative AI model" is an artificial intelligence algorithm that learns from large datasets and is applied to natural language processing and other tasks.

[0544] "Means of recognizing emotions" refers to methods for detecting and identifying emotional states from collected data.

[0545] A "diagnostic tool" is a mechanism for identifying applicable support systems based on the analysis results.

[0546] "Notification method" refers to a method of informing the user of the diagnostic results.

[0547] "Communication methods" refer to data transmission methods used to share information within an organization.

[0548] This system is a technology developed for disaster relief, providing appropriate support systems quickly through emotion recognition. Users first use information processing devices such as smartphones or tablets to record the disaster situation around them using video and audio. For example, they can use their smartphone's camera function to photograph damage to their home and use the recording function to verbally describe the necessary support.

[0549] The terminal transmits this collected data to a server via a communication network. Wi-Fi or mobile communication networks are used for this transmission. The server processes the received data through analysis tools. The analysis utilizes natural language processing algorithms and image processing algorithms, and analyzes the user's emotions using generative AI models (for example, commonly known AI models). This enables emotion recognition from audio data and assessment of the degree of damage from video.

[0550] Based on the evaluation of emotional and video data obtained through analysis, the server uses diagnostic tools to identify applicable support systems. For selection, the AI ​​model proposes support systems using prompt messages. For example, a prompt message such as "Please propose the optimal support system based on this data" might be used.

[0551] Ultimately, information about the identified support programs will be communicated to users via notification, clearly indicating which programs are available. Furthermore, this information will be shared with local governments and relevant organizations via communication channels to expedite support. This will enable swift and accurate support for disaster victims, leading to effective relief efforts.

[0552] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0553] Step 1:

[0554] Users use smartphones or tablets as information processing devices to record disaster situations in video and audio. The inputs collected include video data and audio data indicating the support needs of disaster victims. Specifically, users film damage to their homes with their smartphones and use the recording function to record necessary support as voice messages. The output of this step is the collected multimodal data.

[0555] Step 2:

[0556] The terminal transmits the collected data described above to the server using the communication network. Video and audio data recorded by the user are used as input. The operation involves data transmission via Wi-Fi or a 4G / 5G network. The output of this step is the multimodal data delivered to the server.

[0557] Step 3:

[0558] The server passes the received data to the analysis device and begins processing the data using the analysis means. The input is the multimodal data transmitted in the previous step. The audio data is converted to text using a natural language processing algorithm, and its content is analyzed. In addition, a generative AI model is used to evaluate the voice tone and video information in order to identify the user's emotions. The output is the analysis result, which includes emotion and damage assessment information.

[0559] Step 4:

[0560] Based on the analysis results, the server uses diagnostic tools to identify applicable support programs. It receives emotion analysis data and damage assessment information as input. The server utilizes a generative AI model and selects a support program using the prompt "Please propose the optimal support program based on this data." The output is an optimized support proposal.

[0561] Step 5:

[0562] The server communicates information about the diagnosed support programs to the user via notification, clearly indicating the applicable support programs. The input is the information about the support programs identified by the server. Specifically, a push notification is sent to the terminal, allowing the user to confirm the support details. The output is an explicit notification of the support program to the user.

[0563] Step 6:

[0564] The server shares analysis results and support program information with local governments and related organizations via communication channels. Inputs are identified support program information and analysis results. Operationally, it uses APIs and secure data transfer protocols to transmit necessary information quickly and accurately. Output is information sharing among local governments and related organizations.

[0565] (Application Example 2)

[0566] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0567] In times of disaster, accurately assessing the emotions and support needs of victims is crucial for responding quickly and effectively to the diverse problems they face. However, conventional support systems have struggled to provide individualized support that takes into account the degree of emotions and mental stress. Furthermore, prioritizing support and providing rapid assistance remain challenges.

[0568] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0569] In this invention, the server includes information acquisition means for collecting audio data, video data, and text data; data analysis means for performing multimodal analysis of the data collected from the information acquisition means; and diagnostic means for identifying emotional states and diagnosing applicable support systems using the analysis results obtained by the data analysis means. This makes it possible to generate and quickly provide appropriate support plans that take into account the emotional state of each disaster victim.

[0570] "Information acquisition means" refers to devices or parts of devices that have the function of collecting audio data, video data, and text data.

[0571] "Data analysis means" refers to devices and algorithms used to analyze data collected from information acquisition means in various formats and to identify the emotional state of a user.

[0572] "Diagnostic means" refers to systems and devices used to diagnose applicable support systems and security measures for users based on analysis results obtained through data analysis means.

[0573] "Support plan generation method" refers to a system or process for creating necessary support plans according to the user's emotional state.

[0574] "Notification means" refers to devices or procedures for presenting the results of diagnostic tests and support plans to users as information.

[0575] "Data communication means" refers to the communication technologies and devices necessary to share diagnostic results with public institutions and other relevant organizations.

[0576] The system implementing this invention enhances the process of supporting disaster victims by utilizing voice, video, and text data. This system consists of information acquisition means, data analysis means, diagnostic means, support plan generation means, notification means, and data communication means.

[0577] The server first uses information acquisition means to collect audio, video, and text data from the user's smartphone or tablet. This includes microphones for audio recording and cameras for video recording. This data is transmitted to the server in real time and input into data analysis means.

[0578] In the data analysis process, the collected multimodal data is analyzed using natural language processing and image processing algorithms. The Google Cloud Speech-to-Text API and Google Cloud Vision API are utilized to convert speech data to text and perform emotional analysis from video data. Furthermore, a sentiment analysis model using TensorFlow identifies the user's emotional state. Based on these results, the diagnostic tool determines appropriate support systems and security measures for the user.

[0579] The support plan generation system creates a specific support plan tailored to the user's emotional state. This includes guidance on psychological care and security measures. The generated plan is presented to the user via a notification system, such as a smartphone app or other device. Real-time notifications are provided using Firebase Cloud Messaging (FCM).

[0580] Furthermore, diagnostic results and support plans will be shared with public institutions via data communication. This will enable local governments and related organizations to take swift action. For example, if disaster victims feel anxious in evacuation shelters, they will be guided to alternative shelters and provided with psychological care.

[0581] Example of a prompt:

[0582] "Create detailed prompt messages for a system that analyzes the emotional state of users affected by a disaster, based on their voice, video, and text data, and generates the optimal support plan."

[0583] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0584] Step 1:

[0585] The device collects audio, video, and text data. The user's smartphone or tablet uses its microphone and camera to capture audio and video. This data becomes the input. The device prepares to send this data to a cloud server.

[0586] Step 2:

[0587] The server passes the received data to the data analysis device. The audio data is converted to text data using the Google Cloud Speech-to-Text API. For example, an audio recording expressing a user's anxiety is converted to the text "I feel anxious about the evacuation center." The converted text data becomes the output.

[0588] Step 3:

[0589] The server analyzes the video data using the Google Cloud Vision API. This process identifies emotional expressions and situations within the video. The analysis generates output such as detecting stress levels from the user's facial expressions. This output becomes the server's input.

[0590] Step 4:

[0591] The server uses collected text and video data to perform sentiment analysis using TensorFlow. Based on the data analysis, the user's emotional state, specifically their stress level and the type of support they require, is diagnosed. This analysis result is then output.

[0592] Step 5:

[0593] Based on the diagnostic results, the server uses a support plan generation device to create an optimal support plan for the user. Considering the user's emotional state, the plan includes guidance on shelters and psychological support. The support plan is then generated as output.

[0594] Step 6:

[0595] The server uses Firebase Cloud Messaging (FCM) to send notifications of the generated support plan to the device. Based on the received data, the device displays information to the user in real time. For example, a notification screen on a smartphone might display "Information on alternative shelters."

[0596] Step 7:

[0597] The server transmits information via data communication equipment to share diagnostic results with public institutions. This allows local governments to take measures to respond quickly. The transmitted data becomes the output.

[0598] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0599] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0600] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0601] [Fourth Embodiment]

[0602] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0603] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0604] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0605] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0606] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0607] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0608] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0609] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0610] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0611] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0612] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0613] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0614] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0615] To implement this invention, a smartphone, tablet, or personal computer can be used as the device. The user uses these devices to collect audio, video, and text data related to the disaster situation. The device has the function to transmit this data to a server in real time. The server converts the received data into a multimodal analyzable format and passes it to an analysis device. This analysis device uses speech recognition, image processing, and natural language processing technologies to comprehensively analyze the situation of the disaster victims.

[0616] The results obtained from the analysis device are used as input for the diagnostic device to quickly identify applicable support systems for disaster victims. The diagnostic device diagnoses the appropriate system and generates results by comparing the results with a database of support systems.

[0617] The generated diagnostic results are communicated to the user via a notification device. The user receives this notification on their device and can then review the next steps to take and an overview of available support programs. The server also uses communication devices to share information with local governments, supporting the smooth progress of necessary procedures.

[0618] For example, when a disaster occurs in a certain area, a user can use the app to take photos of the damage to their house and describe the necessary assistance via voice. This information is sent from the device to a server and processed by an analysis device. The diagnostic device recognizes the eligibility for housing reconstruction assistance based on the analysis results and notifies the user. Based on the notification, the user can contact the local government and apply for assistance quickly.

[0619] In this way, the invention reduces the burden on disaster victims and contributes to improving the efficiency of local government operations. As a result, disaster victims can quickly access necessary support systems, making it possible to support their early recovery.

[0620] The following describes the processing flow.

[0621] Step 1:

[0622] Users operate the device to input voice, video, and text information. They use smartphones or tablets to photograph the damage or record audio explaining the situation into the microphone.

[0623] Step 2:

[0624] The device sends the entered information to the server via the app. The device compresses the digital data in real time and transfers it to the server using a secure communication protocol.

[0625] Step 3:

[0626] The server transfers the received data to the analysis device. The server then converts the data's format, preparing it for processing by the analysis device.

[0627] Step 4:

[0628] The analysis device performs multimodal analysis of the data. Audio data is converted to text using a speech recognition algorithm, and its content is analyzed through natural language processing. Video data is used to assess physical damage using image processing technology.

[0629] Step 5:

[0630] The server passes the analysis results to the diagnostic device. The diagnostic device refers to the database and identifies support programs that may be applicable to the disaster victims.

[0631] Step 6:

[0632] The diagnostic device further evaluates the applicable support programs and generates diagnostic results. The server records the information obtained from the diagnostic device in a database.

[0633] Step 7:

[0634] The server sends the diagnostic results to the terminal via a notification device. The notification device informs the user of the results and shows the necessary procedures and next steps on the interface.

[0635] Step 8:

[0636] The user reviews the diagnostic results they receive and begins the necessary procedures. They then collaborate with local governments to facilitate applications for support programs.

[0637] (Example 1)

[0638] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0639] In times of disaster, it is essential to identify and provide information on prompt and appropriate support systems for disaster victims. Currently, the process of collecting and analyzing information, and applying support systems, is complex and inefficient, potentially delaying the support that disaster victims need. To address this challenge, it is necessary to build a system that can quickly collect and analyze information, identify appropriate support systems, and promptly provide that information to disaster victims and relevant organizations.

[0640] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0641] In this invention, the server includes terminal means for collecting audio information, video information, and text information; means for transmitting the information collected from the terminal means to a central processing unit in real time; and the central processing unit includes means for converting the received information into a format that can be multimodally analyzed and passing it on to an analysis means. This makes it possible to quickly grasp the situation of disaster victims and accurately identify and provide applicable support systems during a disaster.

[0642] "Audio information" refers to information represented in digital format based on sound waveforms, and usually refers to speech, including human language.

[0643] "Visual information" refers to information represented in digital format based on the waveform of light, and includes visual image data such as still images and videos.

[0644] "Text information" refers to digital information composed of characters, and usually means information in the form of documents or messages.

[0645] "Terminal means" refers to a device capable of collecting and transmitting information, and includes portable information processing devices and personal computers.

[0646] A "central processing unit" refers to a control device that processes received information and converts it into a format appropriate for its purpose.

[0647] "Multimodal analysis" is a method for integrating and analyzing information in multiple formats, and it targets multiple data modalities, including audio, video, and text.

[0648] "Analysis means" refers to technologies for analyzing digital information and extracting features, and includes devices that use speech recognition, image analysis, and natural language processing.

[0649] "Diagnostic means" refers to a device or mechanism that identifies appropriate support measures based on analysis results and prepares for their provision.

[0650] "Information provision means" refers to a device or method for quickly notifying users of information such as diagnostic results.

[0651] "Communication means" refers to technologies used to share information with other devices or organizations, and includes network connectivity.

[0652] A "generative AI model" is an artificial intelligence technique that has the ability to understand various data formats and generate output.

[0653] To implement this invention, the user first uses an information gathering terminal. This terminal could be a portable information processing device (smartphone), a personal information terminal (tablet), or a personal computer. The user uses this terminal to collect audio, video, and text information from the disaster site. Audio information is recorded via a microphone, and video information is captured using a camera. Text information is generated through automatic speech conversion or manual input.

[0654] The terminal transmits the collected information to the central processing unit (server) in real time. To ensure security and speed, a protocol such as HTTPS is used for this communication. The server converts the received data into a format suitable for multimodal analysis and passes it to the analysis device. The analysis device uses a generative AI model to comprehensively analyze the data through speech recognition, image analysis, and natural language processing.

[0655] The information obtained through analysis is passed to a diagnostic tool. The diagnostic tool compares this information with a database of support measures to identify applicable support programs for disaster victims. This identified result is notified to the user through an information provision tool. The notification is sent to the terminal in push notification format, allowing the user to check the results immediately. Furthermore, the server shares this result with local government agencies using communication tools to facilitate the more rapid application of support programs.

[0656] Specifically, after an earthquake, users take photos of the damage to their homes with their smartphones and record voice messages stating that they need assistance with home repairs. This audio and video are sent to a server, where an analysis device assesses the extent of the damage and proposes the most suitable support program. This process is carried out by a generating AI model based on a prompt message such as, "Based on the video and audio data of the damage to your home and the support you need during the disaster, please identify the most suitable support program."

[0657] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0658] Step 1:

[0659] Users collect information.

[0660] Users collect audio, video, and text information from disaster sites using devices such as smartphones and tablets. Specifically, they photograph damaged buildings with cameras and record audio about the extent of the damage and the need for assistance. Camera image data and audio files are generated as input. These data are temporarily stored on the device as output.

[0661] Step 2:

[0662] The device transmits data.

[0663] The terminal transmits collected audio, video, and text data to the server in real time. Specifically, the communication module within the terminal securely transfers data to the server using the HTTPS protocol. The input is data stored on the terminal. The output is this data reaching the server.

[0664] Step 3:

[0665] The server converts the data.

[0666] The server converts received audio, video, and text data into a format that allows for multimodal analysis. For example, it converts audio data into text and organizes video data frame by frame. The input is the transmitted raw data. The output is data formatted in a way that is easy for the analysis device to process.

[0667] Step 4:

[0668] The analysis device analyzes the data.

[0669] The analysis system on the server performs speech recognition, image analysis, and natural language processing using a generative AI model. Specifically, it converts audio data into text and identifies the extent of damage from image data. The input is converted multimodal data. The output is analysis data of the identified damage and the necessary support measures.

[0670] Step 5:

[0671] The diagnostic device identifies the support system.

[0672] The server's diagnostic device searches a database of support measures based on the analysis results and identifies support programs applicable to disaster victims. Specifically, it uses the results of a generated AI model to extract support plans that meet the suitability criteria. The input is the analysis result data. The output is information on the identified support programs.

[0673] Step 6:

[0674] The server provides information to the user.

[0675] The server notifies the user of the diagnostic results through an information provision mechanism. Specifically, it sends a push notification to the device so that the user can check the results. The input is identified support system information. The output is an information notification to the user.

[0676] Step 7:

[0677] The server shares information with local governments.

[0678] The server transmits diagnostic results to local government agencies using communication methods. Specifically, it registers the information in the local government's database, enabling relevant parties to provide support quickly. The input is the diagnostic result data. The output is the completion of information sharing with the local government.

[0679] (Application Example 1)

[0680] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0681] During disasters and emergencies, when individual safety is threatened, there is a need to quickly and accurately assess the situation and provide appropriate support systems and emergency responses. To achieve this, it is essential to collect information from multiple data sources and make integrated judgments. However, current systems are limited to fragmented information collection and analysis, making rapid response difficult.

[0682] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0683] In this invention, the server includes a terminal for collecting audio information, video information, and text information; an analysis means for analyzing the information collected from the terminal in various formats; an identification means for diagnosing applicable support systems using the analysis results obtained by the analysis means; a notification means for notifying the user of the diagnosis results of the identification means; a communication means for sharing the diagnosis results with administrative agencies; and an emergency notification means for issuing an alarm based on the dangerous situation determined by the analysis means. This makes it possible to quickly collect information and provide appropriate support systems and emergency responses even in situations where the user's safety is threatened.

[0684] A "terminal" is a device used to collect audio, video, and text information, and is a device that plays a role in acquiring information in various formats.

[0685] "Analysis means" refers to a device or system that analyzes information collected from a terminal in various formats and performs processing to determine the situation.

[0686] "Identification means" refers to a device or system that has the function of diagnosing applicable support systems based on the analysis results obtained by the analysis means.

[0687] A "notification means" is a device or system that has the function of notifying the user of the diagnostic results of the identification means, and plays the role of providing the user with the necessary information.

[0688] "Communication means" refers to a device or system that has the function of sharing diagnostic results with administrative agencies.

[0689] An "emergency notification system" is a device or system that has the function of issuing an alarm based on a dangerous situation determined by an analysis system.

[0690] To implement this invention, a mobile communication device or personal computer is used as a terminal for collecting voice, video, and text information. The user collects information in real time through this terminal and transmits it to the server. The server analyzes the received information, primarily using automatic speech recognition and image analysis techniques, and performs data analysis in various formats. Software used may include Python, OpenCV, and TensorFlow. The analysis results are diagnosed by an identification means to determine applicable support systems, and the user is notified by a notification means. Furthermore, the diagnostic results are shared with administrative agencies via communication means, and an alarm is issued via an emergency notification means if necessary. This series of processes enables rapid response and information sharing, ensuring the user's safety.

[0691] For example, if a user walking at night notices something suspicious, they can alert others to the danger through voice commands or photos taken via their device. The server immediately analyzes the situation, and if danger is determined, it issues an alarm and makes an emergency call to the police and other relevant agencies. With such a system in place, users can go about their daily lives with peace of mind.

[0692] An example of a prompt to the generative AI model is, "If a user feels in danger, how would they use their smartphone's camera and audio data to immediately report it to the police?" Based on this prompt, the model can suggest appropriate procedures and countermeasures.

[0693] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0694] Step 1:

[0695] The user uses the device to collect audio, video, and text information from their surroundings. The device activates its camera and microphone and records this information at the times instructed by the user. Audio and image data are acquired as input and prepared to be sent for the next processing step.

[0696] Step 2:

[0697] The terminal transmits collected audio and video information to the server in real time. A data transfer protocol is used to transmit the data. Data stored on the terminal is used as input for uploading to the server.

[0698] Step 3:

[0699] The server analyzes the received audio information using an automatic speech recognition algorithm. First, it adjusts the sample rate of the audio data and removes noise. Next, it converts the audio to text using the speech recognition model and obtains the recognition result. The input is audio data, and the output is the analyzed text data.

[0700] Step 4:

[0701] The server analyzes the received video information using an image analysis algorithm. First, it preprocesses the image by removing noise and adjusting the resolution. Next, it uses an image recognition model to detect dangerous objects and situations. The input is image data, and the output is information about the analyzed objects and situations.

[0702] Step 5:

[0703] The server compiles the analysis results and sends them to the identification device to diagnose applicable support systems. The identification device refers to the analyzed text data and object information to identify the support systems required for the user. The input is the analyzed data, and the output is a proposed support system.

[0704] Step 6:

[0705] The server notifies the user of the diagnostic results using a notification mechanism. The notification is displayed through an application used on the terminal and provides the user with the necessary information. The input is the diagnostic result, and the output is the notification information for the user.

[0706] Step 7:

[0707] If necessary, the server will issue an alarm using emergency notification methods and notify relevant government and security agencies. The input is alarm information generated from the analysis results, and the output is warning information.

[0708] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0709] This invention is a system that diagnoses applicable support systems by collecting and analyzing audio, video, and text data for disaster relief. In particular, by incorporating an emotion engine that recognizes the user's emotions and uses that to help in support decisions, it is possible to provide more accurate support.

[0710] First, users use their smartphones or tablets to record the extent of damage caused by the disaster and describe their specific needs for assistance using voice. The devices have the functionality to send this data to a server, communicating in real time. The server transfers the data to an analysis device, which analyzes the collected information using relevant natural language processing and image processing algorithms. In this analysis, an emotion engine recognizes emotions from the user's voice and video, and reflects that information in the analysis results.

[0711] For example, if a disaster victim reports significant damage to their home and expresses sadness and anxiety, the emotion engine analyzes the degree of that sadness, which influences the selection of appropriate support programs. The analysis device includes this emotional state in its results and provides this information to the diagnostic device to identify applicable support programs. The diagnostic results are communicated to the user via a notification device, clearly indicating which support programs are available. Furthermore, to share information with local governments and ensure that necessary support can be provided quickly, the server sends information via a communication device and coordinates with relevant organizations.

[0712] This system enables sensitive and effective responses that take into account the feelings of disaster victims, reducing the psychological burden on users and allowing for optimal support. It also contributes to efficient responses from local governments in situations where rapid support is required.

[0713] The following describes the processing flow.

[0714] Step 1:

[0715] Users use smartphones or tablets to record the extent of the damage and provide voice descriptions of their condition and the assistance they need. The devices have the ability to capture video and record audio, and this data is treated as a series of pieces of information.

[0716] Step 2:

[0717] The terminal transmits audio, video, and text data acquired from the user to the server. The terminal formats this data appropriately and transfers it to the server in real time via a secure communication channel.

[0718] Step 3:

[0719] The server passes the received data to the analysis device. The analysis device uses natural language processing algorithms to convert the audio data into text and analyzes the content of the user's speech. It also uses image processing technology to evaluate the physical state of the damage from the video data.

[0720] Step 4:

[0721] The analysis device uses an emotion engine to analyze the user's voice tone and changes in facial expressions in the video to understand the user's emotional state. This includes processes such as recognizing vocal intonation and facial muscle movements to identify emotions.

[0722] Step 5:

[0723] The server uses the analysis results provided by the analysis device to perform a diagnostic assessment of the support system. The diagnostic device compares the results with the support system information in the database and lists applicable systems while taking into account the emotional state.

[0724] Step 6:

[0725] The diagnostic results are sent from the server to the terminal via a notification device. The terminal then notifies the user of available support programs and their details, and provides guidance on the next steps to take.

[0726] Step 7:

[0727] Based on the information received by the user, the system will coordinate with local governments and apply for necessary support programs. The device will display a message containing links and contact information necessary for the procedure.

[0728] Step 8:

[0729] The server shares diagnostic results with local governments and related organizations via communication devices. This step ensures rapid information dissemination and facilitates the smooth implementation of disaster relief efforts.

[0730] (Example 2)

[0731] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0732] Conventional disaster relief systems have a problem in that they make it difficult to provide appropriate support while taking into account the feelings of the users. Furthermore, the selection of support systems and the rapid sharing of information with local governments are insufficient, often resulting in delays in appropriate responses. To solve these problems, a system is needed that accurately recognizes the feelings of disaster victims, appropriately selects support systems, and efficiently shares information.

[0733] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0734] In this invention, the server includes information processing means for collecting audio data, video data, and text data; analysis means for performing multimodal analysis of the data collected from the information processing means; and means for recognizing emotions using an AI model generated by the analysis means and integrating the analysis results. This enables accurate recognition of the emotions of disaster victims, rapid and accurate selection of applicable support systems, and prompt information sharing with local governments.

[0735] An "information processing device" is a device for collecting audio data, video data, and text data.

[0736] "Multimodal analysis" is a method that comprehensively analyzes different types of data to understand their interrelationships.

[0737] "Analysis means" refers to a method for processing collected data and extracting information that is relevant to a specific purpose.

[0738] A "generative AI model" is an artificial intelligence algorithm that learns from large datasets and is applied to natural language processing and other tasks.

[0739] "Means of recognizing emotions" refers to methods for detecting and identifying emotional states from collected data.

[0740] A "diagnostic tool" is a mechanism for identifying applicable support systems based on the analysis results.

[0741] "Notification method" refers to a method of informing the user of the diagnostic results.

[0742] "Communication methods" refer to data transmission methods used to share information within an organization.

[0743] This system is a technology developed for disaster relief, providing appropriate support systems quickly through emotion recognition. Users first use information processing devices such as smartphones or tablets to record the disaster situation around them using video and audio. For example, they can use their smartphone's camera function to photograph damage to their home and use the recording function to verbally describe the necessary support.

[0744] The terminal transmits this collected data to a server via a communication network. Wi-Fi or mobile communication networks are used for this transmission. The server processes the received data through analysis tools. The analysis utilizes natural language processing algorithms and image processing algorithms, and analyzes the user's emotions using generative AI models (for example, commonly known AI models). This enables emotion recognition from audio data and assessment of the degree of damage from video.

[0745] Based on the evaluation of emotional and video data obtained through analysis, the server uses diagnostic tools to identify applicable support systems. For selection, the AI ​​model proposes support systems using prompt messages. For example, a prompt message such as "Please propose the optimal support system based on this data" might be used.

[0746] Ultimately, information about the identified support programs will be communicated to users via notification, clearly indicating which programs are available. Furthermore, this information will be shared with local governments and relevant organizations via communication channels to expedite support. This will enable swift and accurate support for disaster victims, leading to effective relief efforts.

[0747] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0748] Step 1:

[0749] Users use smartphones or tablets as information processing devices to record disaster situations in video and audio. The inputs collected include video data and audio data indicating the support needs of disaster victims. Specifically, users film damage to their homes with their smartphones and use the recording function to record necessary support as voice messages. The output of this step is the collected multimodal data.

[0750] Step 2:

[0751] The terminal transmits the collected data described above to the server using the communication network. Video and audio data recorded by the user are used as input. The operation involves data transmission via Wi-Fi or a 4G / 5G network. The output of this step is the multimodal data delivered to the server.

[0752] Step 3:

[0753] The server passes the received data to the analysis device and begins processing the data using the analysis means. The input is the multimodal data transmitted in the previous step. The audio data is converted to text using a natural language processing algorithm, and its content is analyzed. In addition, a generative AI model is used to evaluate the voice tone and video information in order to identify the user's emotions. The output is the analysis result, which includes emotion and damage assessment information.

[0754] Step 4:

[0755] Based on the analysis results, the server uses diagnostic tools to identify applicable support programs. It receives emotion analysis data and damage assessment information as input. The server utilizes a generative AI model and selects a support program using the prompt "Please propose the optimal support program based on this data." The output is an optimized support proposal.

[0756] Step 5:

[0757] The server communicates information about the diagnosed support programs to the user via notification, clearly indicating the applicable support programs. The input is the information about the support programs identified by the server. Specifically, a push notification is sent to the terminal, allowing the user to confirm the support details. The output is an explicit notification of the support program to the user.

[0758] Step 6:

[0759] The server shares analysis results and support program information with local governments and related organizations via communication channels. Inputs are identified support program information and analysis results. Operationally, it uses APIs and secure data transfer protocols to transmit necessary information quickly and accurately. Output is information sharing among local governments and related organizations.

[0760] (Application Example 2)

[0761] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0762] In times of disaster, accurately assessing the emotions and support needs of victims is crucial for responding quickly and effectively to the diverse problems they face. However, conventional support systems have struggled to provide individualized support that takes into account the degree of emotions and mental stress. Furthermore, prioritizing support and providing rapid assistance remain challenges.

[0763] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0764] In this invention, the server includes information acquisition means for collecting audio data, video data, and text data; data analysis means for performing multimodal analysis of the data collected from the information acquisition means; and diagnostic means for identifying emotional states and diagnosing applicable support systems using the analysis results obtained by the data analysis means. This makes it possible to generate and quickly provide appropriate support plans that take into account the emotional state of each disaster victim.

[0765] "Information acquisition means" refers to devices or parts of devices that have the function of collecting audio data, video data, and text data.

[0766] "Data analysis means" refers to devices and algorithms used to analyze data collected from information acquisition means in various formats and to identify the emotional state of a user.

[0767] "Diagnostic means" refers to systems and devices used to diagnose applicable support systems and security measures for users based on analysis results obtained through data analysis means.

[0768] "Support plan generation method" refers to a system or process for creating necessary support plans according to the user's emotional state.

[0769] "Notification means" refers to devices or procedures for presenting the results of diagnostic tests and support plans to users as information.

[0770] "Data communication means" refers to the communication technologies and devices necessary to share diagnostic results with public institutions and other relevant organizations.

[0771] The system implementing this invention enhances the process of supporting disaster victims by utilizing voice, video, and text data. This system consists of information acquisition means, data analysis means, diagnostic means, support plan generation means, notification means, and data communication means.

[0772] The server first uses information acquisition means to collect audio, video, and text data from the user's smartphone or tablet. This includes microphones for audio recording and cameras for video recording. This data is transmitted to the server in real time and input into data analysis means.

[0773] In the data analysis process, the collected multimodal data is analyzed using natural language processing and image processing algorithms. The Google Cloud Speech-to-Text API and Google Cloud Vision API are utilized to convert speech data to text and perform emotional analysis from video data. Furthermore, a sentiment analysis model using TensorFlow identifies the user's emotional state. Based on these results, the diagnostic tool determines appropriate support systems and security measures for the user.

[0774] The support plan generation system creates a specific support plan tailored to the user's emotional state. This includes guidance on psychological care and security measures. The generated plan is presented to the user via a notification system, such as a smartphone app or other device. Real-time notifications are provided using Firebase Cloud Messaging (FCM).

[0775] Furthermore, diagnostic results and support plans will be shared with public institutions via data communication. This will enable local governments and related organizations to take swift action. For example, if disaster victims feel anxious in evacuation shelters, they will be guided to alternative shelters and provided with psychological care.

[0776] Example of a prompt:

[0777] "Create detailed prompt messages for a system that analyzes the emotional state of users affected by a disaster, based on their voice, video, and text data, and generates the optimal support plan."

[0778] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0779] Step 1:

[0780] The device collects audio, video, and text data. The user's smartphone or tablet uses its microphone and camera to capture audio and video. This data becomes the input. The device prepares to send this data to a cloud server.

[0781] Step 2:

[0782] The server passes the received data to the data analysis device. The audio data is converted to text data using the Google Cloud Speech-to-Text API. For example, an audio recording expressing a user's anxiety is converted to the text "I feel anxious about the evacuation center." The converted text data becomes the output.

[0783] Step 3:

[0784] The server analyzes the video data using the Google Cloud Vision API. This process identifies emotional expressions and situations within the video. The analysis generates output such as detecting stress levels from the user's facial expressions. This output becomes the server's input.

[0785] Step 4:

[0786] The server uses collected text and video data to perform sentiment analysis using TensorFlow. Based on the data analysis, the user's emotional state, specifically their stress level and the type of support they require, is diagnosed. This analysis result is then output.

[0787] Step 5:

[0788] Based on the diagnostic results, the server uses a support plan generation device to create an optimal support plan for the user. Considering the user's emotional state, the plan includes guidance on shelters and psychological support. The support plan is then generated as output.

[0789] Step 6:

[0790] The server uses Firebase Cloud Messaging (FCM) to send notifications of the generated support plan to the device. Based on the received data, the device displays information to the user in real time. For example, a notification screen on a smartphone might display "Information on alternative shelters."

[0791] Step 7:

[0792] The server transmits information via data communication equipment to share diagnostic results with public institutions. This allows local governments to take measures to respond quickly. The transmitted data becomes the output.

[0793] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0794] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0795] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0796] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0797] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0798] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0799] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0800] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0801] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0802] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0803] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0804] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0805] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0806] 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.

[0807] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0808] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using this memory.

[0809] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0810] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0811] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0812] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0813] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0814] The following is further disclosed regarding the embodiments described above.

[0815] (Claim 1)

[0816] A device that collects audio data, video data, and text data,

[0817] An analysis device that performs multimodal analysis on data collected from the aforementioned device,

[0818] A diagnostic device that uses the analysis results obtained by the aforementioned analysis device to diagnose applicable support systems,

[0819] A notification device that notifies the user of the diagnostic results of the diagnostic device,

[0820] A communication device for sharing the aforementioned diagnostic results with the local government,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, characterized in that the device is a smartphone, a tablet, or a personal computer.

[0824] (Claim 3)

[0825] The system according to claim 1, characterized in that the analysis device uses a natural language processing algorithm and an image processing algorithm.

[0826] "Example 1"

[0827] (Claim 1)

[0828] A terminal means for collecting audio information, video information, and text information,

[0829] A means for transmitting information collected from the terminal means to a central processing unit in real time,

[0830] The central processing unit includes means for converting the received information into a format that can be analyzed multimodally and passing it to the analysis means,

[0831] The aforementioned analysis means includes means for comprehensively analyzing information using speech recognition, image analysis, and natural language processing technologies.

[0832] A diagnostic means for identifying applicable support measures using the analysis results obtained by the aforementioned analysis means,

[0833] A means for notifying the user of the results of the diagnostic means through an information provision means,

[0834] A means of communication for sharing the aforementioned identification results with local administrative agencies,

[0835] A system that includes this.

[0836] (Claim 2)

[0837] The system according to claim 1, characterized in that the terminal means is a portable information processing device, a portable information terminal, or a personal computer.

[0838] (Claim 3)

[0839] The system according to claim 1, characterized in that the analysis means uses a natural language processing algorithm and an image analysis algorithm based on a generative AI model.

[0840] "Application Example 1"

[0841] (Claim 1)

[0842] A terminal that collects audio information, video information, and text information,

[0843] An analysis means for analyzing information collected from the aforementioned terminal in various formats,

[0844] An identification means for diagnosing applicable support systems using the analysis results obtained by the aforementioned analysis means,

[0845] A notification means for notifying the user of the diagnostic results of the identification means,

[0846] A means of communication for sharing the aforementioned diagnostic results with administrative agencies,

[0847] An emergency notification means that issues a warning based on the dangerous situation determined by the aforementioned analysis means,

[0848] A system that includes this.

[0849] (Claim 2)

[0850] The system according to claim 1, characterized in that the terminal is a mobile communication device, an information terminal device, or a personal computer.

[0851] (Claim 3)

[0852] The system according to claim 1, characterized in that the analysis means uses an automatic speech recognition method and an image analysis method.

[0853] "Example 2 of combining an emotion engine"

[0854] (Claim 1)

[0855] An information processing device for collecting audio data, video data, and text data,

[0856] Analysis means for performing multimodal analysis on data collected from the aforementioned information processing device,

[0857] The aforementioned analysis means recognizes emotions using a generative AI model and integrates the analysis results,

[0858] A diagnostic means for diagnosing applicable support systems based on the aforementioned integrated analysis results,

[0859] A notification means for notifying the user of the results of the diagnostic means,

[0860] A means of communication for sharing the above results with the organization,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, characterized in that the information processing device is a portable information terminal or a computer.

[0864] (Claim 3)

[0865] The system according to claim 1, characterized in that the analysis means uses a natural language processing method and an image processing method.

[0866] "Application example 2 when combining with an emotional engine"

[0867] (Claim 1)

[0868] Information acquisition means for collecting audio data, video data, and text data,

[0869] A data analysis means for performing multimodal analysis on the data collected from the aforementioned information acquisition means,

[0870] A diagnostic means that uses the analysis results obtained by the aforementioned data analysis means to identify emotional states and diagnose applicable support systems,

[0871] A support plan generation means that generates a support plan including necessary security measures and psychological care based on the aforementioned emotional state,

[0872] A notification means for presenting information on the results of the diagnostic means,

[0873] A data communication means for sharing the results of the diagnostic means with a public institution,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, characterized in that the information acquisition means is a portable information terminal or a computer device.

[0877] (Claim 3)

[0878] The system according to claim 1, characterized in that the data analysis means uses a natural language processing algorithm and an image processing algorithm. [Explanation of Symbols]

[0879] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A device that collects audio data, video data, and text data, An analysis device that performs multimodal analysis on data collected from the aforementioned device, A diagnostic device that uses the analysis results obtained by the aforementioned analysis device to diagnose applicable support systems, A notification device that notifies the user of the diagnostic results of the diagnostic device, A communication device for sharing the aforementioned diagnostic results with the local government, A system that includes this.

2. The system according to claim 1, characterized in that the device is a smartphone, a tablet, or a personal computer.

3. The system according to claim 1, characterized in that the analysis device uses a natural language processing algorithm and an image processing algorithm.

Images