system

Abstract

We provide the system. [Solution] A means of acquiring environmental information from a video acquisition device, A means for analyzing acquired environmental information to detect allergens floating in the air, A means for identifying detected allergen information by comparing it with existing allergen information, A means for issuing warnings tailored to individual user information based on identified allergen information, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 modern times, environmental allergens such as hay fever and house dust have a profound impact on the health and daily life of many people. Especially in outdoor and new indoor environments, it is difficult to grasp the presence of these allergens in advance and it is difficult to take appropriate measures. There is a need for a solution to improve such a situation and enable allergy patients to act with confidence.

Means for Solving the Problems

[0005] This invention provides a system that uses a video acquisition device to acquire environmental information in real time, analyzes the acquired information, and detects allergens that may be floating in the air. The detected allergens are compared with existing allergen information and identified. Subsequently, appropriate warnings are issued based on individual user information, enabling users to take quick countermeasures. Furthermore, it is possible to continuously improve the accuracy of allergen detection by utilizing user feedback.

[0006] A "video acquisition device" is a device used to electronically capture visual information from the external environment.

[0007] "Environmental information" refers to data that describes the spatial and physical conditions of a specific location or situation.

[0008] "Analysis" refers to computational methods used to identify acquired data and extract specific patterns or features.

[0009] An "allergen" is a substance that can potentially trigger an allergic reaction.

[0010] "Existing allergen information" refers to databases of allergens that have been collected or recorded in the past.

[0011] "Identification" is the process of comparing analyzed data with known information and confirming the match.

[0012] "User information" refers to individual data related to unique conditions and states, and the system customizes its response based on this information.

[0013] A "warning" is a notification that informs the user of potential dangers or situations requiring attention.

[0014] "Feedback" refers to evaluations and comments provided by users regarding the operation and results of a system.

[0015] "Improving accuracy" is a process of improvement carried out to enhance the accuracy of the results and performance of a system.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention embodies an allergen detection and warning system that utilizes the camera function of a portable information processing device such as a smartphone. This system provides users with information to quickly recognize the presence of allergens in a specific environment and take appropriate action.

[0038] The device first acquires environmental information in real time using the smartphone's camera. The video captured by the camera is pre-processed within the system and converted into a format suitable for analysis. This removes noise and improves the accuracy of the analysis. In this analysis stage, the data acquired by the device is processed using an AI agent to identify allergens such as pollen and dust floating in the air. The AI ​​agent can recognize specific allergen patterns based on a pre-trained dataset.

[0039] The allergen information identified through analysis is sent to a server. The server compares this information with an existing allergen database to confirm whether the detected substance is a specific allergen. This comparison process can reduce the rate of false alarms.

[0040] The device then generates individually customized warnings based on the allergy information previously entered by the user and the detection results. For example, if a user has a severe allergy to cedar pollen, this information can be used to issue an appropriate warning when the concentration of cedar pollen is high.

[0041] Furthermore, the system can continuously learn from user feedback and improve the accuracy of allergen detection. For example, if a user provides feedback such as "the warning is excessive" or "the warning is insufficient," the AI ​​agent will use this information to readjust the model and improve the accuracy of future warnings.

[0042] As a concrete example, when a user with hay fever is walking in a park, launching the application on their device will detect that the pollen level in the air is high and immediately issue a warning such as, "We recommend wearing a mask." In this way, the user can engage in outdoor activities with peace of mind.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The device activates the smartphone's camera and acquires real-time video of the environment. This captures the surrounding situation as visual information.

[0046] Step 2:

[0047] The device preprocesses the acquired video data, removing noise and sharpening the image. This improves the accuracy of the analysis.

[0048] Step 3:

[0049] The device uses an AI agent to analyze pre-processed video footage and identify specific allergens (e.g., pollen, dust). In this process, the AI ​​uses pre-trained data to recognize patterns in the allergens.

[0050] Step 4:

[0051] The terminal sends the analysis results to the server, where they are compared against an existing allergen database. In this step, the server compares the results against appropriate reference data to confirm whether the identified substance is a known allergen.

[0052] Step 5:

[0053] The device references the user's allergy information and generates a warning tailored to the user based on the detected allergen information. This allows for accurate warnings to be issued when the user's allergy-related risk is high.

[0054] Step 6:

[0055] Users receive warnings from their devices and take appropriate action to address allergens. They can also provide feedback on the effectiveness of the warnings.

[0056] Step 7:

[0057] Based on user feedback, the server adjusts the AI ​​agent's model to improve the accuracy of allergen detection and warnings in subsequent instances. This continuous learning process allows the system to evolve.

[0058] (Example 1)

[0059] 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."

[0060] Currently, there are limited means of detecting environmental allergens in real time using portable information processing devices and providing that information to users quickly and accurately. Furthermore, existing systems have issues with detection accuracy and the appropriateness of warnings, and their ability to learn from user feedback is insufficient. Therefore, there is a need for a new system that can quickly recognize the presence of allergens in specific environments and issue appropriate warnings to ensure user safety and comfort.

[0061] 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.

[0062] In this invention, the server includes means for acquiring environmental data from a video acquisition device, means for preprocessing the acquired environmental data and converting it into a format suitable for analysis, and means for detecting airborne allergens using an AI module with the preprocessed data. This allows users to acquire allergen information in real time using a portable information processing device and receive warnings tailored to their individual user characteristics. Furthermore, by creating prompt sentences using a generative AI model and utilizing user responses, the accuracy of allergen recognition can be further improved.

[0063] A "video acquisition device" is a hardware device that captures visual information of the environment and converts it into digital data.

[0064] "Environmental data" refers to information that describes the physical state in a specific space or situation, and includes visual information collected by video acquisition devices.

[0065] "Preprocessing" refers to the initial stages of data processing to convert it into a format that is easy to analyze, and includes processes such as noise reduction and format conversion.

[0066] An "AI module" refers to a software component that uses artificial intelligence technology to perform pattern recognition and prediction.

[0067] An "allergen" is a substance that can trigger an allergic reaction, and includes things like pollen and dust floating in the air.

[0068] "Database information" refers to a collection of data that systematically aggregates specific information and is stored in a format that can be used for comparison and analysis.

[0069] "Characteristic identification" is the process of clearly recognizing and classifying specific features and attributes based on acquired information.

[0070] "User characteristics" refer to information that indicates attributes and conditions unique to an individual user, and include information such as that person's allergies.

[0071] "Continuous learning" is an adaptive learning process in which a system improves its performance based on new data and feedback.

[0072] A "generative AI model" is a pre-trained model built for artificial intelligence to generate specific outputs based on prompts and input data.

[0073] This invention provides a system that uses a portable information processing device to detect allergens in real time and issue a warning to the user.

[0074] First, the device uses the smartphone's video acquisition device, i.e., the camera, to acquire environmental data. When a user takes this device for a walk in the park, visual information, including pollen and dust in the surroundings, is collected.

[0075] Next, the device performs preprocessing on the acquired environmental data. Specifically, this involves converting the data into a format suitable for use with the AI ​​module and removing unwanted information such as noise. This preprocessing improves the accuracy of the analysis of the acquired data.

[0076] Next, the pre-processed data is passed to the generative AI model, an AI module, for allergen detection. Based on pre-trained database information, the AI ​​module identifies and detects pollen and dust floating in the air. An example of a prompt message to the generative AI model is, "Identify the type and amount of pollen from the environmental video acquired by the camera, and provide an appropriate warning based on the pre-set allergy information."

[0077] The server compares the allergen information transmitted from the terminal with existing database information and performs characteristic identification processing to reduce false positives. This process improves prediction accuracy and ensures safety.

[0078] Finally, the device sends appropriate warnings to the user based on the identified allergen information. For example, if the user is allergic to cedar pollen, the AI ​​module will display a customized notification such as "We recommend wearing a mask" when it detects a high pollen concentration.

[0079] The system of this invention improves allergen detection capabilities by collecting feedback from users and using it for the continuous learning of the AI ​​module. For example, if a user provides feedback such as "the warnings are excessive," the terminal sends this data to the AI ​​module, and the model is readjusted based on that information to improve the accuracy of subsequent warnings.

[0080] As described above, this system provides real-time allergen detection and warnings based on individual user characteristics, supporting the safety and comfort of users' lives.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The device acquires ambient environmental data using the smartphone's camera. The input is real-time video captured by the camera. The output is this video data sent to the device's processing system. Specifically, when the user points the smartphone at the environment, the camera continuously records video.

[0084] Step 2:

[0085] The device performs preprocessing to remove noise from the acquired video. The input is raw video data. By performing noise filtering and applying color correction, it obtains a clear image in a format that can be analyzed by the AI ​​module as output. Specifically, it uses an image processing algorithm to adjust brightness and contrast and remove unnecessary background information.

[0086] Step 3:

[0087] The device feeds pre-processed data into a generating AI model to identify the characteristics of allergens. The input is pre-processed image data. Based on the trained data, the AI ​​model analyzes allergen patterns such as pollen and dust in the image and outputs identified allergen information. Specifically, the AI ​​model performs calculations to identify the type and concentration of a particular allergen.

[0088] Step 4:

[0089] The device transmits identified allergen information to the server. The input is the analysis results of an AI model. This data is sent to the server via the network, and the output allows for further matching on the server. Specifically, the device uses wireless communication to quickly upload the information to the server.

[0090] Step 5:

[0091] The server compares the received allergen information with a database to perform characteristic identification, thereby reducing false alarms. The input is allergen data sent from the terminal. The server refers to known allergen information in the database, checks if it has matching characteristics, and returns the verified allergen information as output. Specifically, the server uses a high-performance database search algorithm to perform matching quickly.

[0092] Step 6:

[0093] The device generates a warning for the user based on verified allergen information and the user's allergy information. The input consists of verified information from the server and the user's pre-registered allergy information. The device outputs an appropriate warning, such as "We recommend wearing a mask." Specifically, the device displays the alert on the screen and also adds an audio notification to alert the user.

[0094] Step 7:

[0095] The user provides feedback on the warnings displayed by the device. The input consists of opinions and comments submitted by the user on the feedback screen. The device receives this feedback and adds it to a dataset used for continuous learning of the AI ​​model. Specifically, the user interacts with the feedback form and enters specific comments such as "the warning is excessive."

[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] In indoor environments, there is a need to rapidly and accurately detect airborne allergens and provide information to enable appropriate responses for individual users. However, current systems make immediate response difficult, and there is a lack of means to improve allergen detection accuracy by incorporating user feedback.

[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 means for acquiring environmental data, means for analyzing the data to identify allergens, and means for issuing warnings and countermeasures to the user based on the analysis results. This makes it possible to quickly detect allergens in a space via a home automated device and propose appropriate responses for each user.

[0101] A "video acquisition device" is a device that has the function of acquiring visual data of the environment in real time.

[0102] "Environmental data" refers to data represented as digital information indicating the composition and condition of the air.

[0103] An "allergen" is a substance that can potentially cause an allergic reaction in the human body and can be found floating in the air.

[0104] A "stored allergen database" is an information infrastructure that contains known allergen information and is used to compare detected allergen information.

[0105] "User-specific information" refers to information related to each user's allergies and preferences.

[0106] A "household automatic device" is an electronically controlled device used within a home that is capable of operating autonomously.

[0107] A "learning function that improves accuracy" is a program that utilizes user feedback to improve the accuracy of predicting and judging actions.

[0108] A "user recommendation" is a measure that instructs users on allergen-related countermeasures and precautions.

[0109] The system for carrying out this invention includes a video acquisition device mounted on a home automated device, an AI agent for environmental data analysis, an allergen database, and a database for managing user-specific information. A server integrates these, identifies airborne allergens in real time, and provides appropriate instructions to the user.

[0110] The server acquires environmental data from home automated devices using high-resolution cameras. This data is first denoised and its resolution adjusted, and then analyzed by an AI agent. During the analysis, the data is compared with pre-trained allergen patterns and matched against a stored allergen database. Based on the analysis results, the server sends optimized warnings and countermeasures to the user based on their individual user profile.

[0111] As a concrete example, when an automated device in the home detects peanut allergens in the kitchen, the server analyzes this information and notifies the user with a warning such as, "An ingredient that may contain peanuts has been detected. Please be careful." Furthermore, user feedback allows the AI ​​agent to improve detection accuracy in the future.

[0112] An example of a prompt for a generating AI model is: "Generate a program that suggests appropriate actions to the user using the latest allergen information." This prompt causes the AI ​​model to generate a program that provides the user with the best possible course of action.

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

[0114] Step 1:

[0115] The terminal acquires environmental data using a camera mounted on a home automated device. The visual data acquired by the camera is used as input, noise reduction and resolution adjustment are performed, and the data is converted into a format suitable for analysis. The output is pre-processed environmental data.

[0116] Step 2:

[0117] The server sends pre-processed environmental data to the AI ​​agent. Based on the input data, the AI ​​agent analyzes allergen patterns. This analysis compares the data with a pre-trained allergen dataset to identify airborne allergens. The output is the identified allergen information.

[0118] Step 3:

[0119] The server compares the identified allergen information with the stored allergen database. The input is allergen information from the AI ​​agent, and the accuracy of the allergen is verified through database matching. The output is the matched allergen data.

[0120] Step 4:

[0121] The server compares the verified allergen data with the user's allergy profile and generates individually optimized warnings. The input consists of the user's individual information and allergen data, and the server creates warning messages based on this information. The output is the generated warning message.

[0122] Step 5:

[0123] The server notifies the user of the generated warning message. The user receives this warning via their terminal and obtains guidance for taking safety measures. The output is the warning message received by the user.

[0124] Step 6:

[0125] The user takes action based on the provided warnings and provides the results as feedback to the server. The input is user feedback, and based on this information, the AI ​​agent readjusts the model and learns to improve accuracy in subsequent uses. The output is the improved allergen identification model.

[0126] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0127] This invention provides a system for a portable information processing device, such as a smartphone equipped with an image acquisition device, that simultaneously recognizes allergens and the user's emotions, and provides appropriate warnings and countermeasures. The purpose of this system is to support the user in continuing their activities safely and comfortably.

[0128] First, the device uses its camera to acquire information about the surrounding environment in real time. The video data is preprocessed to make it less noisy and suitable for analysis. Based on this, the AI ​​agent identifies airborne allergens and sends that information to the server. The server then checks the reliability of this information by comparing it with an existing database.

[0129] Simultaneously, the emotion engine analyzes the user's facial expressions and voice tone obtained from the camera and microphone. It identifies each emotional state (e.g., stress, anxiety, calmness) and comprehensively evaluates this information in combination with the user's allergy information.

[0130] Based on this information, the device sends user-optimized warning messages and advice. For example, if the allergen concentration is high and the user is feeling stressed, a message such as "A high allergen concentration has been detected. Take a deep breath and calm down before putting on a mask" will be displayed. In this way, notifications that take the user's emotional state into consideration help them take more appropriate action.

[0131] As a concrete example, suppose a user is in an office when this system detects an allergen and simultaneously senses slight anxiety from the user's facial expression. Based on this information, the terminal notifies the user with a message such as, "Allergen levels have risen slightly. We recommend closing the windows and taking a short break to relax." This system allows the user to accurately understand the situation and take appropriate action.

[0132] Furthermore, user feedback is analyzed and incorporated into future detections and notifications. This allows the system's accuracy to continuously improve, further enhancing user confidence.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] The device activates the smartphone's camera and acquires real-time video data of the surrounding environment. This allows detailed environmental information to be captured by the device.

[0136] Step 2:

[0137] The terminal preprocesses the acquired video data, removing image noise and converting it into a format suitable for analysis. This preprocessing improves the accuracy of the analysis.

[0138] Step 3:

[0139] The device inputs pre-processed video data into an AI agent, which analyzes and identifies allergens floating in the air. The AI ​​agent uses a trained model to identify patterns in the allergens.

[0140] Step 4:

[0141] The terminal sends the allergen detection results to the server, which compares them with an existing allergen database to verify accuracy and consistency. This comparison ensures the reliability of the detection.

[0142] Step 5:

[0143] The device activates an emotion engine based on information obtained from the camera and microphone, analyzing the user's facial expressions and voice tone to determine their current emotional state. This includes emotions such as stress, anxiety, and calmness.

[0144] Step 6:

[0145] The device combines allergen identification information with analysis results from an emotion engine to generate personalized warning messages and countermeasures tailored to the user. During this process, the messages are adjusted to take into account the user's emotional state.

[0146] Step 7:

[0147] Users receive notifications from their devices and take the instructed actions. They also have a function to provide feedback on the content and effectiveness of the notifications. This feedback helps improve the system in the future.

[0148] Step 8:

[0149] The server analyzes user feedback and adaptively updates the AI ​​agent's model to improve the accuracy of allergen detection and sentiment analysis in subsequent uses. Through this continuous learning, the system continues to evolve.

[0150] (Example 2)

[0151] 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".

[0152] In modern society, many people suffer from allergies, and their health is threatened by allergens lurking in their living environment. In addition, the impact of allergen exposure on emotions cannot be ignored. However, conventional systems have found it difficult to monitor both allergen detection and emotions in real time and take appropriate action accordingly. Therefore, the present invention aims to integrate allergen detection and emotion analysis to provide users with appropriate warnings and countermeasures.

[0153] 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.

[0154] In this invention, the server includes means for acquiring ambient environmental information from a video acquisition device, means for preprocessing the acquired environmental information to prepare it for analysis, means for analyzing the preprocessed environmental information to detect airborne allergens, means for analyzing audio and video data to identify individual emotional states, means for comparing the detected allergen information with existing information to identify and confirm its reliability, means for issuing warnings tailored to individual user information based on the identified allergen information and emotional information, and means for collecting user feedback and using it to improve the accuracy of the system. This enables real-time management of both allergens and emotions, allowing for appropriate countermeasures and user support.

[0155] A "video acquisition device" is a device used to collect visual information about the environment, such as a camera.

[0156] "Environmental information" refers to data about the surrounding physical and atmospheric conditions, particularly information acquired as video or audio.

[0157] "Preprocessing" refers to the initial stage of data manipulation performed to prepare acquired data for analysis and processing.

[0158] An "allergen" is a substance that causes an allergic reaction in an individual and can be suspended in the air.

[0159] "Emotional state" refers to an individual's psychological and emotional condition, and is judged from things like facial expressions and tone of voice.

[0160] "Reliability" refers to the accuracy and consistency of data, and is a factor that guarantees the quality of system decisions and outputs.

[0161] A "warning" is information intended to alert users to potential dangers or risks, and is provided as a notification to the user.

[0162] "Feedback" refers to reactions and opinions obtained from users, and the information used to improve and adapt the system.

[0163] The embodiment for carrying out this invention is configured based on the respective roles of terminal, server, and user. First, the terminal uses a portable information processing device such as a smartphone to acquire environmental information through a camera. This information includes airborne allergens and user facial expression data. The terminal uses software libraries such as OpenCV and TENSORFLOW® to preprocess the acquired video information, such as by removing noise, to prepare it for analysis.

[0164] During the analysis, the device uses an AI model to identify allergens from video data and transmits this information to the server in real time. Simultaneously, the device uses voice recognition and facial expression analysis technologies to evaluate the user's emotional state based on data input from the camera and microphone.

[0165] The server receives allergen information sent from the terminal and compares it with existing information in the database to verify the reliability of the detection results. The server uses the Django framework to process the information efficiently.

[0166] Users take appropriate action based on information sent from the system. For example, if the system determines that the allergen concentration is high and the user is under stress, the device will display a message such as, "High allergen concentration detected. Please take a deep breath and then put on a mask." Such notifications are optimized according to the user's emotional state.

[0167] Furthermore, users provide feedback on the system's suggestions. This feedback is analyzed on the server and used to improve the accuracy of future analyses. Specific examples of prompts include, "Identify the types of allergens in the air and report their concentrations," and "Evaluate the user's emotions through voice and facial expression analysis." This allows users to continue living safely and comfortably with a strong sense of security.

[0168] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0169] Step 1:

[0170] The terminal activates the camera, which is a video acquisition device, and acquires environmental information in real time. The input is video data, which is then preprocessed to remove noise and adjust the resolution. Specifically, it uses the OpenCV library to perform filtering and obtains video data that can be analyzed as output.

[0171] Step 2:

[0172] The device analyzes pre-processed video data using an AI model to identify allergens in the air. The input is clean video data, and the output is information on the type and concentration of the identified allergens. Specifically, a machine learning model utilizing TensorFlow identifies the characteristics of the allergens and outputs the data.

[0173] Step 3:

[0174] The device simultaneously acquires user facial expression and voice data from its camera and microphone. This data is processed by an emotion analysis engine to identify the user's emotional state. The input is voice and facial expression data, and the output is the individual emotional state. Specifically, it uses speech recognition technology to analyze the tone of voice and facial expression analysis to evaluate emotions.

[0175] Step 4:

[0176] The server receives allergen information sent from the terminal and verifies its reliability by comparing it with an existing database. The input is allergen information, and the output is the reliability verification result. In this process, the information is processed using the Python Django framework to obtain the verification result.

[0177] Step 5:

[0178] The device generates a user-optimized warning message based on detected allergen information and emotional state. The input is confirmed allergen information and emotional data, and the output is a user-directed warning message. Specifically, it uses a generative AI model to create a message in natural language and notifies the user.

[0179] Step 6:

[0180] The user checks notifications from their device and takes appropriate action based on the instructions. They send feedback to their device, which the server collects and uses to improve the accuracy of future analyses. The input is user feedback, and the output is training data for improving analysis accuracy. Specifically, the user inputs their thoughts and suggestions for improvement regarding the system, and the server stores this information in a database.

[0181] (Application Example 2)

[0182] 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".

[0183] In recent years, the increase in airborne allergens in urban environments has caused health problems for many people. Furthermore, the lack of appropriate responses based on users' emotional states can compromise the safety and comfort of individual users. To address this, there is a need to develop a system that analyzes the environment and emotions in real time and supports optimal actions.

[0184] 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.

[0185] In this invention, the server includes means for acquiring environmental data from a video acquisition device, means for analyzing the acquired environmental data to detect airborne substances, means for identifying the detected substance information by comparing it with existing data, means for analyzing the user's emotional state and providing appropriate countermeasures, and means for providing advice to maintain the user's safety within the plant environment. This makes it possible to analyze the environment and the user's emotional state in real time and provide individually appropriate behavioral guidelines.

[0186] A "video acquisition device" is a device used to acquire ambient environmental data in image or video format.

[0187] "Environmental data" refers to information about external conditions such as substances present in the air, temperature, humidity, and light.

[0188] "Matter" refers to elements that exist in the air or environment, including solids, liquids, gases, and other chemical components.

[0189] "Existing data" refers to a collection of information that has been collected and recorded in the past, and serves as a reference material used for comparison and analysis with newly acquired data.

[0190] "User information" refers to information related to an individual user, including their health status and allergy sensitivities.

[0191] "Emotional state" refers to the user's internal psychological state and reflects specific emotions such as stress, anxiety, and relief.

[0192] "Countermeasures" refer to specific action plans or measures proposed to the user in accordance with the identified situation.

[0193] "Advice" refers to suggesting appropriate means or actions to the user based on the situation.

[0194] This invention is designed to realize a system that detects airborne allergens and the user's emotional state in real time within the user's daily living environment and takes appropriate countermeasures. The server utilizes video acquisition devices such as cameras and microphones embedded in smartphones and robots to acquire ambient environmental data. This environmental data is processed using OpenCV, and allergen analysis is performed using TensorFlow.

[0195] Furthermore, to analyze the user's emotional state, video and audio data are analyzed using Stanford NLP and Google's GCP Natural Language API to identify emotional states such as stress and anxiety. Based on this information, the server generates notifications tailored to the user's individual needs and provides optimal countermeasures in voice and text.

[0196] As a concrete example, while a user is at home, the robot could provide real-time advice such as, "The allergen concentration in your room is currently high. We recommend you turn on the air purifier and take a short break." An example of a prompt used in this case would be, "What kind of support should the robot provide when the user is feeling stressed?"

[0197] This system is expected to improve the quality of life by enabling users to take appropriate actions in response to their environment and their own emotional state.

[0198] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0199] Step 1:

[0200] The device uses a camera and microphone to acquire ambient environmental data and audio data. This input data includes information about objects in the air as images and the tone of the user's voice as audio. The device processes this data through a noise reduction filter to make it easier to analyze.

[0201] Step 2:

[0202] The server processes environmental data sent from the terminal using the OpenCV library to identify airborne substances. It performs pattern recognition on the input image data to detect allergens, and then classifies the results using TensorFlow. The output is a list of identified allergens.

[0203] Step 3:

[0204] The device analyzes voice data and captured facial expression data. Using Stanford NLP and the GCP Natural Language API, it executes emotion recognition algorithms to identify the user's emotional state. Using the analyzed voice tone and facial expression data as input, the user's emotional state (e.g., stress, anxiety, relief) is obtained as output.

[0205] Step 4:

[0206] The server matches allergens and emotional states against an existing database to generate a warning message tailored to the user. Input includes a list of identified allergens and the user's emotional state. The generated message, along with suggested countermeasures, is sent to the user's device.

[0207] Step 5:

[0208] The user's device notifies the user of received messages and suggests specific actions. For example, the device might display advice such as, "We recommend turning on the air purifier and taking a short break to relax," either verbally or in text. The input is a message received from the server, which is then transmitted to the user in an appropriate format.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] [Second Embodiment]

[0213] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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".

[0225] This invention embodies an allergen detection and warning system that utilizes the camera function of a portable information processing device such as a smartphone. This system provides users with information to quickly recognize the presence of allergens in a specific environment and take appropriate action.

[0226] The device first acquires environmental information in real time using the smartphone's camera. The video captured by the camera is pre-processed within the system and converted into a format suitable for analysis. This removes noise and improves the accuracy of the analysis. In this analysis stage, the data acquired by the device is processed using an AI agent to identify allergens such as pollen and dust floating in the air. The AI ​​agent can recognize specific allergen patterns based on a pre-trained dataset.

[0227] The allergen information identified through analysis is sent to a server. The server compares this information with an existing allergen database to confirm whether the detected substance is a specific allergen. This comparison process can reduce the rate of false alarms.

[0228] The device then generates individually customized warnings based on the allergy information previously entered by the user and the detection results. For example, if a user has a severe allergy to cedar pollen, this information can be used to issue an appropriate warning when the concentration of cedar pollen is high.

[0229] Furthermore, the system can continuously learn from user feedback and improve the accuracy of allergen detection. For example, if a user provides feedback such as "the warning is excessive" or "the warning is insufficient," the AI ​​agent will use this information to readjust the model and improve the accuracy of future warnings.

[0230] As a concrete example, when a user with hay fever is walking in a park, launching the application on their device will detect that the pollen level in the air is high and immediately issue a warning such as, "We recommend wearing a mask." In this way, the user can engage in outdoor activities with peace of mind.

[0231] The following describes the processing flow.

[0232] Step 1:

[0233] The device activates the smartphone's camera and acquires real-time video of the environment. This captures the surrounding situation as visual information.

[0234] Step 2:

[0235] The device preprocesses the acquired video data, removing noise and sharpening the image. This improves the accuracy of the analysis.

[0236] Step 3:

[0237] The device uses an AI agent to analyze pre-processed video footage and identify specific allergens (e.g., pollen, dust). In this process, the AI ​​uses pre-trained data to recognize patterns in the allergens.

[0238] Step 4:

[0239] The terminal sends the analysis results to the server, where they are compared against an existing allergen database. In this step, the server compares the results against appropriate reference data to confirm whether the identified substance is a known allergen.

[0240] Step 5:

[0241] The device references the user's allergy information and generates a warning tailored to the user based on the detected allergen information. This allows for accurate warnings to be issued when the user's allergy-related risk is high.

[0242] Step 6:

[0243] Users receive warnings from their devices and take appropriate action to address allergens. They can also provide feedback on the effectiveness of the warnings.

[0244] Step 7:

[0245] Based on user feedback, the server adjusts the AI ​​agent's model to improve the accuracy of allergen detection and warnings in subsequent instances. This continuous learning process allows the system to evolve.

[0246] (Example 1)

[0247] 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."

[0248] Currently, there are limited means of detecting environmental allergens in real time using portable information processing devices and providing that information to users quickly and accurately. Furthermore, existing systems have issues with detection accuracy and the appropriateness of warnings, and their ability to learn from user feedback is insufficient. Therefore, there is a need for a new system that can quickly recognize the presence of allergens in specific environments and issue appropriate warnings to ensure user safety and comfort.

[0249] 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.

[0250] In this invention, the server includes means for acquiring environmental data from a video acquisition device, means for preprocessing the acquired environmental data and converting it into a format suitable for analysis, and means for detecting airborne allergens using an AI module with the preprocessed data. This allows users to acquire allergen information in real time using a portable information processing device and receive warnings tailored to their individual user characteristics. Furthermore, by creating prompt sentences using a generative AI model and utilizing user responses, the accuracy of allergen recognition can be further improved.

[0251] A "video acquisition device" is a hardware device that captures visual information of the environment and converts it into digital data.

[0252] "Environmental data" refers to information that describes the physical state in a specific space or situation, and includes visual information collected by video acquisition devices.

[0253] "Preprocessing" refers to the initial stages of data processing to convert it into a format that is easy to analyze, and includes processes such as noise reduction and format conversion.

[0254] An "AI module" refers to a software component that uses artificial intelligence technology to perform pattern recognition and prediction.

[0255] An "allergen" is a substance that can trigger an allergic reaction, and includes things like pollen and dust floating in the air.

[0256] "Database information" refers to a collection of data that systematically aggregates specific information and is stored in a format that can be used for comparison and analysis.

[0257] "Characteristic identification" is the process of clearly recognizing and classifying specific features and attributes based on acquired information.

[0258] "User characteristics" refer to information that indicates attributes and conditions unique to an individual user, and include information such as that person's allergies.

[0259] "Continuous learning" is an adaptive learning process in which a system improves its performance based on new data and feedback.

[0260] A "generative AI model" is a pre-trained model built for artificial intelligence to generate specific outputs based on prompts and input data.

[0261] This invention provides a system that uses a portable information processing device to detect allergens in real time and issue a warning to the user.

[0262] First, the device uses the smartphone's video acquisition device, i.e., the camera, to acquire environmental data. When a user takes this device for a walk in the park, visual information, including pollen and dust in the surroundings, is collected.

[0263] Next, the device performs preprocessing on the acquired environmental data. Specifically, this involves converting the data into a format suitable for use with the AI ​​module and removing unwanted information such as noise. This preprocessing improves the accuracy of the analysis of the acquired data.

[0264] Next, the pre-processed data is passed to the generative AI model, an AI module, for allergen detection. Based on pre-trained database information, the AI ​​module identifies and detects pollen and dust floating in the air. An example of a prompt message to the generative AI model is, "Identify the type and amount of pollen from the environmental video acquired by the camera, and provide an appropriate warning based on the pre-set allergy information."

[0265] The server compares the allergen information transmitted from the terminal with existing database information and performs characteristic identification processing to reduce false positives. This process improves prediction accuracy and ensures safety.

[0266] Finally, the device sends appropriate warnings to the user based on the identified allergen information. For example, if the user is allergic to cedar pollen, the AI ​​module will display a customized notification such as "We recommend wearing a mask" when it detects a high pollen concentration.

[0267] The system of this invention improves allergen detection capabilities by collecting feedback from users and using it for the continuous learning of the AI ​​module. For example, if a user provides feedback such as "the warnings are excessive," the terminal sends this data to the AI ​​module, and the model is readjusted based on that information to improve the accuracy of subsequent warnings.

[0268] As described above, this system provides real-time allergen detection and warnings based on individual user characteristics, supporting the safety and comfort of users' lives.

[0269] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0270] Step 1:

[0271] The device acquires ambient environmental data using the smartphone's camera. The input is real-time video captured by the camera. The output is this video data sent to the device's processing system. Specifically, when the user points the smartphone at the environment, the camera continuously records video.

[0272] Step 2:

[0273] The device performs preprocessing to remove noise from the acquired video. The input is raw video data. By performing noise filtering and applying color correction, it obtains a clear image in a format that can be analyzed by the AI ​​module as output. Specifically, it uses an image processing algorithm to adjust brightness and contrast and remove unnecessary background information.

[0274] Step 3:

[0275] The device feeds pre-processed data into a generating AI model to identify the characteristics of allergens. The input is pre-processed image data. Based on the trained data, the AI ​​model analyzes allergen patterns such as pollen and dust in the image and outputs identified allergen information. Specifically, the AI ​​model performs calculations to identify the type and concentration of a particular allergen.

[0276] Step 4:

[0277] The terminal sends the specified allergen information to the server. The input is the analysis result of the AI model. This data is sent to the server via the network, enabling further verification on the server as the output. As a specific operation, the terminal uses wireless communication to quickly upload the information to the server.

[0278] Step 5:

[0279] The server compares the received allergen information with the database to perform characteristic identification for reducing false alarms. The input is the allergen data sent from the terminal. The server refers to the known allergen information in the database, checks if there are matching characteristics, and returns the verified allergen information as the output. As a specific operation, the server uses a high-performance database search algorithm to quickly perform the verification.

[0280] Step 6:

[0281] Based on the verified allergen information and the user's allergy information, the terminal generates a warning for the user. The input is the verified information from the server and the user's pre-registered allergy information. The terminal displays an appropriate warning as the output, such as "It is recommended to wear a mask." As a specific operation, the terminal displays an alert on the screen and also adds an audio notification to prompt the user's attention.

[0282] Step 7:

[0283] The user provides feedback on the content of the warning shown by the terminal. The input is the opinions and comments provided by the user on the feedback screen. The terminal receives this and adds it to the dataset for the continuous learning of the AI model as the output. As a specific operation, the user operates the feedback form and enters specific remarks such as "The warning is excessive."

[0284] (Application Example 1)

[0285] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0286] In an indoor environment, there is a demand to quickly and accurately detect allergens present in the air and provide information for taking appropriate measures for individual users. However, in the current system, it is difficult to respond immediately, and there is a lack of means to improve allergen detection accuracy by incorporating user feedback.

[0287] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0288] In this invention, the server includes means for acquiring environmental data, means for analyzing data to identify allergens, and means for transmitting warnings and countermeasures to users based on the analysis results. As a result, it becomes possible to quickly detect allergens in a space via a home automation device and propose appropriate responses for each user.

[0289] The "video acquisition device" is a device having a function of acquiring visual data of the environment in real time.

[0290] The "environmental data" is data represented as digital information indicating the components and state in the air.

[0291] An "allergen" is a substance that may cause an allergic reaction in the human body and may float in the air.

[0292] The "stored allergen database" is an information base storing known allergen information and for comparing the detected allergen information.

[0293] The "user-specific information" is information related to the allergic conditions and preferences of individual users.

[0294] A "household automatic device" is an electronically controlled device used within a home that is capable of operating autonomously.

[0295] A "learning function that improves accuracy" is a program that utilizes user feedback to improve the accuracy of predicting and judging actions.

[0296] A "user recommendation" is a measure that instructs users on allergen-related countermeasures and precautions.

[0297] The system for carrying out this invention includes a video acquisition device mounted on a home automated device, an AI agent for environmental data analysis, an allergen database, and a database for managing user-specific information. A server integrates these, identifies airborne allergens in real time, and provides appropriate instructions to the user.

[0298] The server acquires environmental data from home automated devices using high-resolution cameras. This data is first denoised and its resolution adjusted, and then analyzed by an AI agent. During the analysis, the data is compared with pre-trained allergen patterns and matched against a stored allergen database. Based on the analysis results, the server sends optimized warnings and countermeasures to the user based on their individual user profile.

[0299] As a concrete example, when an automated device in the home detects peanut allergens in the kitchen, the server analyzes this information and notifies the user with a warning such as, "An ingredient that may contain peanuts has been detected. Please be careful." Furthermore, user feedback allows the AI ​​agent to improve detection accuracy in the future.

[0300] An example of a prompt for a generating AI model is: "Generate a program that suggests appropriate actions to the user using the latest allergen information." This prompt causes the AI ​​model to generate a program that provides the user with the best possible course of action.

[0301] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0302] Step 1:

[0303] The terminal acquires environmental data using a camera mounted on a household automatic device. Using the visual data acquired by the camera as input, noise removal and resolution adjustment are performed, and the data is converted into a data format suitable for analysis. The output is the preprocessed environmental data.

[0304] Step 2:

[0305] The server sends the preprocessed environmental data to the AI agent. Based on the input data, the AI agent analyzes the allergen pattern. In this analysis, a comparison is made with a pre-learned allergen dataset to identify allergens in the air. The output is the identified allergen information.

[0306] Step 3:

[0307] The server compares the identified allergen information with the stored allergen database. The input is the allergen information from the AI agent, and the accuracy of the allergen is confirmed by database collation. The output is the collated allergen data.

[0308] Step 4:

[0309] The server compares the collated allergen data with the user's allergy profile and generates an individually optimized warning. The input is the user's individual information and allergen data, and a warning message is created based on that information. The output is the generated warning message.

[0310] Step 5:

[0311] The server notifies the user of the generated warning message. The user receives this warning via their terminal and obtains guidance for taking safety measures. The output is the warning message received by the user.

[0312] Step 6:

[0313] The user takes action based on the provided warnings and provides the results as feedback to the server. The input is user feedback, and based on this information, the AI ​​agent readjusts the model and learns to improve accuracy in subsequent uses. The output is the improved allergen identification model.

[0314] 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.

[0315] This invention provides a system for a portable information processing device, such as a smartphone equipped with an image acquisition device, that simultaneously recognizes allergens and the user's emotions, and provides appropriate warnings and countermeasures. The purpose of this system is to support the user in continuing their activities safely and comfortably.

[0316] First, the device uses its camera to acquire information about the surrounding environment in real time. The video data is preprocessed to make it less noisy and suitable for analysis. Based on this, the AI ​​agent identifies airborne allergens and sends that information to the server. The server then checks the reliability of this information by comparing it with an existing database.

[0317] Simultaneously, the emotion engine analyzes the user's facial expressions and voice tone obtained from the camera and microphone. It identifies each emotional state (e.g., stress, anxiety, calmness) and comprehensively evaluates this information in combination with the user's allergy information.

[0318] Based on this information, the device sends user-optimized warning messages and advice. For example, if the allergen concentration is high and the user is feeling stressed, a message such as "A high allergen concentration has been detected. Take a deep breath and calm down before putting on a mask" will be displayed. In this way, notifications that take the user's emotional state into consideration help them take more appropriate action.

[0319] As a concrete example, suppose a user is in an office when this system detects an allergen and simultaneously senses slight anxiety from the user's facial expression. Based on this information, the terminal notifies the user with a message such as, "Allergen levels have risen slightly. We recommend closing the windows and taking a short break to relax." This system allows the user to accurately understand the situation and take appropriate action.

[0320] Furthermore, user feedback is analyzed and incorporated into future detections and notifications. This allows the system's accuracy to continuously improve, further enhancing user confidence.

[0321] The following describes the processing flow.

[0322] Step 1:

[0323] The device activates the smartphone's camera and acquires real-time video data of the surrounding environment. This allows detailed environmental information to be captured by the device.

[0324] Step 2:

[0325] The terminal preprocesses the acquired video data, removing image noise and converting it into a format suitable for analysis. This preprocessing improves the accuracy of the analysis.

[0326] Step 3:

[0327] The device inputs pre-processed video data into an AI agent, which analyzes and identifies allergens floating in the air. The AI ​​agent uses a trained model to identify patterns in the allergens.

[0328] Step 4:

[0329] The terminal sends the allergen detection results to the server, which compares them with an existing allergen database to verify accuracy and consistency. This comparison ensures the reliability of the detection.

[0330] Step 5:

[0331] The device activates an emotion engine based on information obtained from the camera and microphone, analyzing the user's facial expressions and voice tone to determine their current emotional state. This includes emotions such as stress, anxiety, and calmness.

[0332] Step 6:

[0333] The device combines allergen identification information with analysis results from an emotion engine to generate personalized warning messages and countermeasures tailored to the user. During this process, the messages are adjusted to take into account the user's emotional state.

[0334] Step 7:

[0335] Users receive notifications from their devices and take the instructed actions. They also have a function to provide feedback on the content and effectiveness of the notifications. This feedback helps improve the system in the future.

[0336] Step 8:

[0337] The server analyzes user feedback and adaptively updates the AI ​​agent's model to improve the accuracy of allergen detection and sentiment analysis in subsequent uses. Through this continuous learning, the system continues to evolve.

[0338] (Example 2)

[0339] 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".

[0340] In modern society, many people suffer from allergies, and their health is threatened by allergens lurking in their living environment. In addition, the impact of allergen exposure on emotions cannot be ignored. However, conventional systems have found it difficult to monitor both allergen detection and emotions in real time and take appropriate action accordingly. Therefore, the present invention aims to integrate allergen detection and emotion analysis to provide users with appropriate warnings and countermeasures.

[0341] 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.

[0342] In this invention, the server includes means for acquiring ambient environmental information from a video acquisition device, means for preprocessing the acquired environmental information to prepare it for analysis, means for analyzing the preprocessed environmental information to detect airborne allergens, means for analyzing audio and video data to identify individual emotional states, means for comparing the detected allergen information with existing information to identify and confirm its reliability, means for issuing warnings tailored to individual user information based on the identified allergen information and emotional information, and means for collecting user feedback and using it to improve the accuracy of the system. This enables real-time management of both allergens and emotions, allowing for appropriate countermeasures and user support.

[0343] A "video acquisition device" is a device used to collect visual information about the environment, such as a camera.

[0344] "Environmental information" refers to data about the surrounding physical and atmospheric conditions, particularly information acquired as video or audio.

[0345] "Preprocessing" refers to the initial stage of data manipulation performed to prepare acquired data for analysis and processing.

[0346] An "allergen" is a substance that causes an allergic reaction in an individual and can be suspended in the air.

[0347] "Emotional state" refers to an individual's psychological and emotional condition, and is judged from things like facial expressions and tone of voice.

[0348] "Reliability" refers to the accuracy and consistency of data, and is a factor that guarantees the quality of system decisions and outputs.

[0349] A "warning" is information intended to alert users to potential dangers or risks, and is provided as a notification to the user.

[0350] "Feedback" refers to reactions and opinions obtained from users, and the information used to improve and adapt the system.

[0351] The embodiment for carrying out this invention is configured based on the respective roles of terminal, server, and user. First, the terminal uses a portable information processing device such as a smartphone to acquire environmental information through a camera. This information includes airborne allergens and user facial expression data. The terminal uses software libraries such as OpenCV and TensorFlow to preprocess the acquired video information, such as by removing noise, to prepare it for analysis.

[0352] During the analysis, the device uses an AI model to identify allergens from video data and transmits this information to the server in real time. Simultaneously, the device uses voice recognition and facial expression analysis technologies to evaluate the user's emotional state based on data input from the camera and microphone.

[0353] The server receives allergen information sent from the terminal and compares it with existing information in the database to verify the reliability of the detection results. The server uses the Django framework to process the information efficiently.

[0354] Users take appropriate action based on information sent from the system. For example, if the system determines that the allergen concentration is high and the user is under stress, the device will display a message such as, "High allergen concentration detected. Please take a deep breath and then put on a mask." Such notifications are optimized according to the user's emotional state.

[0355] Furthermore, users provide feedback on the system's suggestions. This feedback is analyzed on the server and used to improve the accuracy of future analyses. Specific examples of prompts include, "Identify the types of allergens in the air and report their concentrations," and "Evaluate the user's emotions through voice and facial expression analysis." This allows users to continue living safely and comfortably with a strong sense of security.

[0356] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0357] Step 1:

[0358] The terminal activates the camera, which is a video acquisition device, and acquires environmental information in real time. The input is video data, which is then preprocessed to remove noise and adjust the resolution. Specifically, it uses the OpenCV library to perform filtering and obtains video data that can be analyzed as output.

[0359] Step 2:

[0360] The device analyzes pre-processed video data using an AI model to identify allergens in the air. The input is clean video data, and the output is information on the type and concentration of the identified allergens. Specifically, a machine learning model utilizing TensorFlow identifies the characteristics of the allergens and outputs the data.

[0361] Step 3:

[0362] The device simultaneously acquires user facial expression and voice data from its camera and microphone. This data is processed by an emotion analysis engine to identify the user's emotional state. The input is voice and facial expression data, and the output is the individual emotional state. Specifically, it uses speech recognition technology to analyze the tone of voice and facial expression analysis to evaluate emotions.

[0363] Step 4:

[0364] The server receives allergen information sent from the terminal and verifies its reliability by comparing it with an existing database. The input is allergen information, and the output is the reliability verification result. In this process, the information is processed using the Python Django framework to obtain the verification result.

[0365] Step 5:

[0366] The device generates a user-optimized warning message based on detected allergen information and emotional state. The input is confirmed allergen information and emotional data, and the output is a user-directed warning message. Specifically, it uses a generative AI model to create a message in natural language and notifies the user.

[0367] Step 6:

[0368] The user checks notifications from their device and takes appropriate action based on the instructions. They send feedback to their device, which the server collects and uses to improve the accuracy of future analyses. The input is user feedback, and the output is training data for improving analysis accuracy. Specifically, the user inputs their thoughts and suggestions for improvement regarding the system, and the server stores this information in a database.

[0369] (Application Example 2)

[0370] 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."

[0371] In recent years, the increase in airborne allergens in urban environments has caused health problems for many people. Furthermore, the lack of appropriate responses based on users' emotional states can compromise the safety and comfort of individual users. To address this, there is a need to develop a system that analyzes the environment and emotions in real time and supports optimal actions.

[0372] 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.

[0373] In this invention, the server includes means for acquiring environmental data from a video acquisition device, means for analyzing the acquired environmental data to detect airborne substances, means for identifying the detected substance information by comparing it with existing data, means for analyzing the user's emotional state and providing appropriate countermeasures, and means for providing advice to maintain the user's safety within the plant environment. This makes it possible to analyze the environment and the user's emotional state in real time and provide individually appropriate behavioral guidelines.

[0374] A "video acquisition device" is a device used to acquire ambient environmental data in image or video format.

[0375] "Environmental data" refers to information about external conditions such as substances present in the air, temperature, humidity, and light.

[0376] "Matter" refers to elements that exist in the air or environment, including solids, liquids, gases, and other chemical components.

[0377] "Existing data" refers to a collection of information that has been collected and recorded in the past, and serves as a reference material used for comparison and analysis with newly acquired data.

[0378] "User information" refers to information related to an individual user, including their health status and allergy sensitivities.

[0379] "Emotional state" refers to the user's internal psychological state and reflects specific emotions such as stress, anxiety, and relief.

[0380] "Countermeasures" refer to specific action plans or measures proposed to the user in accordance with the identified situation.

[0381] "Advice" refers to suggesting appropriate means or actions to the user based on the situation.

[0382] This invention is designed to realize a system that detects airborne allergens and the user's emotional state in real time within the user's daily living environment and takes appropriate countermeasures. The server utilizes video acquisition devices such as cameras and microphones embedded in smartphones and robots to acquire ambient environmental data. This environmental data is processed using OpenCV, and allergen analysis is performed using TensorFlow.

[0383] Furthermore, to analyze the user's emotional state, video and audio data are analyzed using Stanford NLP and Google's GCP Natural Language API to identify emotional states such as stress and anxiety. Based on this information, the server generates notifications tailored to the user's individual needs and provides optimal countermeasures in voice and text.

[0384] As a concrete example, while a user is at home, the robot could provide real-time advice such as, "The allergen concentration in your room is currently high. We recommend you turn on the air purifier and take a short break." An example of a prompt used in this case would be, "What kind of support should the robot provide when the user is feeling stressed?"

[0385] This system is expected to improve the quality of life by enabling users to take appropriate actions in response to their environment and their own emotional state.

[0386] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0387] Step 1:

[0388] The device uses a camera and microphone to acquire ambient environmental data and audio data. This input data includes information about objects in the air as images and the tone of the user's voice as audio. The device processes this data through a noise reduction filter to make it easier to analyze.

[0389] Step 2:

[0390] The server processes environmental data sent from the terminal using the OpenCV library to identify airborne substances. It performs pattern recognition on the input image data to detect allergens, and then classifies the results using TensorFlow. The output is a list of identified allergens.

[0391] Step 3:

[0392] The device analyzes voice data and captured facial expression data. Using Stanford NLP and the GCP Natural Language API, it executes emotion recognition algorithms to identify the user's emotional state. Using the analyzed voice tone and facial expression data as input, the user's emotional state (e.g., stress, anxiety, relief) is obtained as output.

[0393] Step 4:

[0394] The server matches allergens and emotional states against an existing database to generate a warning message tailored to the user. Input includes a list of identified allergens and the user's emotional state. The generated message, along with suggested countermeasures, is sent to the user's device.

[0395] Step 5:

[0396] The user's device notifies the user of received messages and suggests specific actions. For example, the device might display advice such as, "We recommend turning on the air purifier and taking a short break to relax," either verbally or in text. The input is a message received from the server, which is then transmitted to the user in an appropriate format.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] [Third Embodiment]

[0401] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0402] 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.

[0403] 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).

[0404] 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.

[0405] 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.

[0406] 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).

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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".

[0413] This invention embodies an allergen detection and warning system that utilizes the camera function of a portable information processing device such as a smartphone. This system provides users with information to quickly recognize the presence of allergens in a specific environment and take appropriate action.

[0414] The device first acquires environmental information in real time using the smartphone's camera. The video captured by the camera is pre-processed within the system and converted into a format suitable for analysis. This removes noise and improves the accuracy of the analysis. In this analysis stage, the data acquired by the device is processed using an AI agent to identify allergens such as pollen and dust floating in the air. The AI ​​agent can recognize specific allergen patterns based on a pre-trained dataset.

[0415] The allergen information identified through analysis is sent to a server. The server compares this information with an existing allergen database to confirm whether the detected substance is a specific allergen. This comparison process can reduce the rate of false alarms.

[0416] The device then generates individually customized warnings based on the allergy information previously entered by the user and the detection results. For example, if a user has a severe allergy to cedar pollen, this information can be used to issue an appropriate warning when the concentration of cedar pollen is high.

[0417] Furthermore, the system can continuously learn from user feedback and improve the accuracy of allergen detection. For example, if a user provides feedback such as "the warning is excessive" or "the warning is insufficient," the AI ​​agent will use this information to readjust the model and improve the accuracy of future warnings.

[0418] As a concrete example, when a user with hay fever is walking in a park, launching the application on their device will detect that the pollen level in the air is high and immediately issue a warning such as, "We recommend wearing a mask." In this way, the user can engage in outdoor activities with peace of mind.

[0419] The following describes the processing flow.

[0420] Step 1:

[0421] The device activates the smartphone's camera and acquires real-time video of the environment. This captures the surrounding situation as visual information.

[0422] Step 2:

[0423] The device preprocesses the acquired video data, removing noise and sharpening the image. This improves the accuracy of the analysis.

[0424] Step 3:

[0425] The device uses an AI agent to analyze pre-processed video footage and identify specific allergens (e.g., pollen, dust). In this process, the AI ​​uses pre-trained data to recognize patterns in the allergens.

[0426] Step 4:

[0427] The terminal sends the analysis results to the server, where they are compared against an existing allergen database. In this step, the server compares the results against appropriate reference data to confirm whether the identified substance is a known allergen.

[0428] Step 5:

[0429] The device references the user's allergy information and generates a warning tailored to the user based on the detected allergen information. This allows for accurate warnings to be issued when the user's allergy-related risk is high.

[0430] Step 6:

[0431] Users receive warnings from their devices and take appropriate action to address allergens. They can also provide feedback on the effectiveness of the warnings.

[0432] Step 7:

[0433] Based on user feedback, the server adjusts the AI ​​agent's model to improve the accuracy of allergen detection and warnings in subsequent instances. This continuous learning process allows the system to evolve.

[0434] (Example 1)

[0435] 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."

[0436] Currently, there are limited means of detecting environmental allergens in real time using portable information processing devices and providing that information to users quickly and accurately. Furthermore, existing systems have issues with detection accuracy and the appropriateness of warnings, and their ability to learn from user feedback is insufficient. Therefore, there is a need for a new system that can quickly recognize the presence of allergens in specific environments and issue appropriate warnings to ensure user safety and comfort.

[0437] 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.

[0438] In this invention, the server includes means for acquiring environmental data from a video acquisition device, means for preprocessing the acquired environmental data and converting it into a format suitable for analysis, and means for detecting airborne allergens using an AI module with the preprocessed data. This allows users to acquire allergen information in real time using a portable information processing device and receive warnings tailored to their individual user characteristics. Furthermore, by creating prompt sentences using a generative AI model and utilizing user responses, the accuracy of allergen recognition can be further improved.

[0439] A "video acquisition device" is a hardware device that captures visual information of the environment and converts it into digital data.

[0440] "Environmental data" refers to information that describes the physical state in a specific space or situation, and includes visual information collected by video acquisition devices.

[0441] "Preprocessing" refers to the initial stages of data processing to convert it into a format that is easy to analyze, and includes processes such as noise reduction and format conversion.

[0442] An "AI module" refers to a software component that uses artificial intelligence technology to perform pattern recognition and prediction.

[0443] An "allergen" is a substance that can trigger an allergic reaction, and includes things like pollen and dust floating in the air.

[0444] "Database information" refers to a collection of data that systematically aggregates specific information and is stored in a format that can be used for comparison and analysis.

[0445] "Characteristic identification" is the process of clearly recognizing and classifying specific features and attributes based on acquired information.

[0446] "User characteristics" refer to information that indicates attributes and conditions unique to an individual user, and include information such as that person's allergies.

[0447] "Continuous learning" is an adaptive learning process in which a system improves its performance based on new data and feedback.

[0448] A "generative AI model" is a pre-trained model built for artificial intelligence to generate specific outputs based on prompts and input data.

[0449] This invention provides a system that uses a portable information processing device to detect allergens in real time and issue a warning to the user.

[0450] First, the device uses the smartphone's video acquisition device, i.e., the camera, to acquire environmental data. When a user takes this device for a walk in the park, visual information, including pollen and dust in the surroundings, is collected.

[0451] Next, the device performs preprocessing on the acquired environmental data. Specifically, this involves converting the data into a format suitable for use with the AI ​​module and removing unwanted information such as noise. This preprocessing improves the accuracy of the analysis of the acquired data.

[0452] Next, the pre-processed data is passed to the generative AI model, an AI module, for allergen detection. Based on pre-trained database information, the AI ​​module identifies and detects pollen and dust floating in the air. An example of a prompt message to the generative AI model is, "Identify the type and amount of pollen from the environmental video acquired by the camera, and provide an appropriate warning based on the pre-set allergy information."

[0453] The server compares the allergen information transmitted from the terminal with existing database information and performs characteristic identification processing to reduce false positives. This process improves prediction accuracy and ensures safety.

[0454] Finally, the device sends appropriate warnings to the user based on the identified allergen information. For example, if the user is allergic to cedar pollen, the AI ​​module will display a customized notification such as "We recommend wearing a mask" when it detects a high pollen concentration.

[0455] The system of this invention improves allergen detection capabilities by collecting feedback from users and using it for the continuous learning of the AI ​​module. For example, if a user provides feedback such as "the warnings are excessive," the terminal sends this data to the AI ​​module, and the model is readjusted based on that information to improve the accuracy of subsequent warnings.

[0456] As described above, this system provides real-time allergen detection and warnings based on individual user characteristics, supporting the safety and comfort of users' lives.

[0457] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0458] Step 1:

[0459] The device acquires ambient environmental data using the smartphone's camera. The input is real-time video captured by the camera. The output is this video data sent to the device's processing system. Specifically, when the user points the smartphone at the environment, the camera continuously records video.

[0460] Step 2:

[0461] The device performs preprocessing to remove noise from the acquired video. The input is raw video data. By performing noise filtering and applying color correction, it obtains a clear image in a format that can be analyzed by the AI ​​module as output. Specifically, it uses an image processing algorithm to adjust brightness and contrast and remove unnecessary background information.

[0462] Step 3:

[0463] The device feeds pre-processed data into a generating AI model to identify the characteristics of allergens. The input is pre-processed image data. Based on the trained data, the AI ​​model analyzes allergen patterns such as pollen and dust in the image and outputs identified allergen information. Specifically, the AI ​​model performs calculations to identify the type and concentration of a particular allergen.

[0464] Step 4:

[0465] The device transmits identified allergen information to the server. The input is the analysis results of an AI model. This data is sent to the server via the network, and the output allows for further matching on the server. Specifically, the device uses wireless communication to quickly upload the information to the server.

[0466] Step 5:

[0467] The server compares the received allergen information with a database to perform characteristic identification, thereby reducing false alarms. The input is allergen data sent from the terminal. The server refers to known allergen information in the database, checks if it has matching characteristics, and returns the verified allergen information as output. Specifically, the server uses a high-performance database search algorithm to perform matching quickly.

[0468] Step 6:

[0469] The device generates a warning for the user based on verified allergen information and the user's allergy information. The input consists of verified information from the server and the user's pre-registered allergy information. The device outputs an appropriate warning, such as "We recommend wearing a mask." Specifically, the device displays the alert on the screen and also adds an audio notification to alert the user.

[0470] Step 7:

[0471] The user provides feedback on the warnings displayed by the device. The input consists of opinions and comments submitted by the user on the feedback screen. The device receives this feedback and adds it to a dataset used for continuous learning of the AI ​​model. Specifically, the user interacts with the feedback form and enters specific comments such as "the warning is excessive."

[0472] (Application Example 1)

[0473] 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."

[0474] In indoor environments, there is a need to rapidly and accurately detect airborne allergens and provide information to enable appropriate responses for individual users. However, current systems make immediate response difficult, and there is a lack of means to improve allergen detection accuracy by incorporating user feedback.

[0475] 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.

[0476] In this invention, the server includes means for acquiring environmental data, means for analyzing the data to identify allergens, and means for issuing warnings and countermeasures to the user based on the analysis results. This makes it possible to quickly detect allergens in a space via a home automated device and propose appropriate responses for each user.

[0477] A "video acquisition device" is a device that has the function of acquiring visual data of the environment in real time.

[0478] "Environmental data" refers to data represented as digital information indicating the composition and condition of the air.

[0479] An "allergen" is a substance that can potentially cause an allergic reaction in the human body and can be found floating in the air.

[0480] A "stored allergen database" is an information infrastructure that contains known allergen information and is used to compare detected allergen information.

[0481] "User-specific information" refers to information related to each user's allergies and preferences.

[0482] A "household automatic device" is an electronically controlled device used within a home that is capable of operating autonomously.

[0483] A "learning function that improves accuracy" is a program that utilizes user feedback to improve the accuracy of predicting and judging actions.

[0484] A "user recommendation" is a measure that instructs users on allergen-related countermeasures and precautions.

[0485] The system for carrying out this invention includes a video acquisition device mounted on a home automated device, an AI agent for environmental data analysis, an allergen database, and a database for managing user-specific information. A server integrates these, identifies airborne allergens in real time, and provides appropriate instructions to the user.

[0486] The server acquires environmental data from home automated devices using high-resolution cameras. This data is first denoised and its resolution adjusted, and then analyzed by an AI agent. During the analysis, the data is compared with pre-trained allergen patterns and matched against a stored allergen database. Based on the analysis results, the server sends optimized warnings and countermeasures to the user based on their individual user profile.

[0487] As a concrete example, when an automated device in the home detects peanut allergens in the kitchen, the server analyzes this information and notifies the user with a warning such as, "An ingredient that may contain peanuts has been detected. Please be careful." Furthermore, user feedback allows the AI ​​agent to improve detection accuracy in the future.

[0488] An example of a prompt for a generating AI model is: "Generate a program that suggests appropriate actions to the user using the latest allergen information." This prompt causes the AI ​​model to generate a program that provides the user with the best possible course of action.

[0489] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0490] Step 1:

[0491] The terminal acquires environmental data using a camera mounted on a home automated device. The visual data acquired by the camera is used as input, noise reduction and resolution adjustment are performed, and the data is converted into a format suitable for analysis. The output is pre-processed environmental data.

[0492] Step 2:

[0493] The server sends pre-processed environmental data to the AI ​​agent. Based on the input data, the AI ​​agent analyzes allergen patterns. This analysis compares the data with a pre-trained allergen dataset to identify airborne allergens. The output is the identified allergen information.

[0494] Step 3:

[0495] The server compares the identified allergen information with the stored allergen database. The input is allergen information from the AI ​​agent, and the accuracy of the allergen is verified through database matching. The output is the matched allergen data.

[0496] Step 4:

[0497] The server compares the verified allergen data with the user's allergy profile and generates individually optimized warnings. The input consists of the user's individual information and allergen data, and the server creates warning messages based on this information. The output is the generated warning message.

[0498] Step 5:

[0499] The server notifies the user of the generated warning message. The user receives this warning via their terminal and obtains guidance for taking safety measures. The output is the warning message received by the user.

[0500] Step 6:

[0501] The user takes action based on the provided warnings and provides the results as feedback to the server. The input is user feedback, and based on this information, the AI ​​agent readjusts the model and learns to improve accuracy in subsequent uses. The output is the improved allergen identification model.

[0502] 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.

[0503] This invention provides a system for a portable information processing device, such as a smartphone equipped with an image acquisition device, that simultaneously recognizes allergens and the user's emotions, and provides appropriate warnings and countermeasures. The purpose of this system is to support the user in continuing their activities safely and comfortably.

[0504] First, the device uses its camera to acquire information about the surrounding environment in real time. The video data is preprocessed to make it less noisy and suitable for analysis. Based on this, the AI ​​agent identifies airborne allergens and sends that information to the server. The server then checks the reliability of this information by comparing it with an existing database.

[0505] Simultaneously, the emotion engine analyzes the user's facial expressions and voice tone obtained from the camera and microphone. It identifies each emotional state (e.g., stress, anxiety, calmness) and comprehensively evaluates this information in combination with the user's allergy information.

[0506] Based on this information, the device sends user-optimized warning messages and advice. For example, if the allergen concentration is high and the user is feeling stressed, a message such as "A high allergen concentration has been detected. Take a deep breath and calm down before putting on a mask" will be displayed. In this way, notifications that take the user's emotional state into consideration help them take more appropriate action.

[0507] As a concrete example, suppose a user is in an office when this system detects an allergen and simultaneously senses slight anxiety from the user's facial expression. Based on this information, the terminal notifies the user with a message such as, "Allergen levels have risen slightly. We recommend closing the windows and taking a short break to relax." This system allows the user to accurately understand the situation and take appropriate action.

[0508] Furthermore, user feedback is analyzed and incorporated into future detections and notifications. This allows the system's accuracy to continuously improve, further enhancing user confidence.

[0509] The following describes the processing flow.

[0510] Step 1:

[0511] The device activates the smartphone's camera and acquires real-time video data of the surrounding environment. This allows detailed environmental information to be captured by the device.

[0512] Step 2:

[0513] The terminal preprocesses the acquired video data, removing image noise and converting it into a format suitable for analysis. This preprocessing improves the accuracy of the analysis.

[0514] Step 3:

[0515] The device inputs pre-processed video data into an AI agent, which analyzes and identifies allergens floating in the air. The AI ​​agent uses a trained model to identify patterns in the allergens.

[0516] Step 4:

[0517] The terminal sends the allergen detection results to the server, which compares them with an existing allergen database to verify accuracy and consistency. This comparison ensures the reliability of the detection.

[0518] Step 5:

[0519] The device activates an emotion engine based on information obtained from the camera and microphone, analyzing the user's facial expressions and voice tone to determine their current emotional state. This includes emotions such as stress, anxiety, and calmness.

[0520] Step 6:

[0521] The device combines allergen identification information with analysis results from an emotion engine to generate personalized warning messages and countermeasures tailored to the user. During this process, the messages are adjusted to take into account the user's emotional state.

[0522] Step 7:

[0523] Users receive notifications from their devices and take the instructed actions. They also have a function to provide feedback on the content and effectiveness of the notifications. This feedback helps improve the system in the future.

[0524] Step 8:

[0525] The server analyzes user feedback and adaptively updates the AI ​​agent's model to improve the accuracy of allergen detection and sentiment analysis in subsequent uses. Through this continuous learning, the system continues to evolve.

[0526] (Example 2)

[0527] 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."

[0528] In modern society, many people suffer from allergies, and their health is threatened by allergens lurking in their living environment. In addition, the impact of allergen exposure on emotions cannot be ignored. However, conventional systems have found it difficult to monitor both allergen detection and emotions in real time and take appropriate action accordingly. Therefore, the present invention aims to integrate allergen detection and emotion analysis to provide users with appropriate warnings and countermeasures.

[0529] 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.

[0530] In this invention, the server includes means for acquiring ambient environmental information from a video acquisition device, means for preprocessing the acquired environmental information to prepare it for analysis, means for analyzing the preprocessed environmental information to detect airborne allergens, means for analyzing audio and video data to identify individual emotional states, means for comparing the detected allergen information with existing information to identify and confirm its reliability, means for issuing warnings tailored to individual user information based on the identified allergen information and emotional information, and means for collecting user feedback and using it to improve the accuracy of the system. This enables real-time management of both allergens and emotions, allowing for appropriate countermeasures and user support.

[0531] A "video acquisition device" is a device used to collect visual information about the environment, such as a camera.

[0532] "Environmental information" refers to data about the surrounding physical and atmospheric conditions, particularly information acquired as video or audio.

[0533] "Preprocessing" refers to the initial stage of data manipulation performed to prepare acquired data for analysis and processing.

[0534] An "allergen" is a substance that causes an allergic reaction in an individual and can be suspended in the air.

[0535] "Emotional state" refers to an individual's psychological and emotional condition, and is judged from things like facial expressions and tone of voice.

[0536] "Reliability" refers to the accuracy and consistency of data, and is a factor that guarantees the quality of system decisions and outputs.

[0537] A "warning" is information intended to alert users to potential dangers or risks, and is provided as a notification to the user.

[0538] "Feedback" refers to reactions and opinions obtained from users, and the information used to improve and adapt the system.

[0539] The embodiment for carrying out this invention is configured based on the respective roles of terminal, server, and user. First, the terminal uses a portable information processing device such as a smartphone to acquire environmental information through a camera. This information includes airborne allergens and user facial expression data. The terminal uses software libraries such as OpenCV and TensorFlow to preprocess the acquired video information, such as by removing noise, to prepare it for analysis.

[0540] During the analysis, the device uses an AI model to identify allergens from video data and transmits this information to the server in real time. Simultaneously, the device uses voice recognition and facial expression analysis technologies to evaluate the user's emotional state based on data input from the camera and microphone.

[0541] The server receives allergen information sent from the terminal and compares it with existing information in the database to verify the reliability of the detection results. The server uses the Django framework to process the information efficiently.

[0542] Users take appropriate action based on information sent from the system. For example, if the system determines that the allergen concentration is high and the user is under stress, the device will display a message such as, "High allergen concentration detected. Please take a deep breath and then put on a mask." Such notifications are optimized according to the user's emotional state.

[0543] Furthermore, users provide feedback on the system's suggestions. This feedback is analyzed on the server and used to improve the accuracy of future analyses. Specific examples of prompts include, "Identify the types of allergens in the air and report their concentrations," and "Evaluate the user's emotions through voice and facial expression analysis." This allows users to continue living safely and comfortably with a strong sense of security.

[0544] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0545] Step 1:

[0546] The terminal activates the camera, which is a video acquisition device, and acquires environmental information in real time. The input is video data, which is then preprocessed to remove noise and adjust the resolution. Specifically, it uses the OpenCV library to perform filtering and obtains video data that can be analyzed as output.

[0547] Step 2:

[0548] The device analyzes pre-processed video data using an AI model to identify allergens in the air. The input is clean video data, and the output is information on the type and concentration of the identified allergens. Specifically, a machine learning model utilizing TensorFlow identifies the characteristics of the allergens and outputs the data.

[0549] Step 3:

[0550] The device simultaneously acquires user facial expression and voice data from its camera and microphone. This data is processed by an emotion analysis engine to identify the user's emotional state. The input is voice and facial expression data, and the output is the individual emotional state. Specifically, it uses speech recognition technology to analyze the tone of voice and facial expression analysis to evaluate emotions.

[0551] Step 4:

[0552] The server receives allergen information sent from the terminal and verifies its reliability by comparing it with an existing database. The input is allergen information, and the output is the reliability verification result. In this process, the information is processed using the Python Django framework to obtain the verification result.

[0553] Step 5:

[0554] The device generates a user-optimized warning message based on detected allergen information and emotional state. The input is confirmed allergen information and emotional data, and the output is a user-directed warning message. Specifically, it uses a generative AI model to create a message in natural language and notifies the user.

[0555] Step 6:

[0556] The user checks notifications from their device and takes appropriate action based on the instructions. They send feedback to their device, which the server collects and uses to improve the accuracy of future analyses. The input is user feedback, and the output is training data for improving analysis accuracy. Specifically, the user inputs their thoughts and suggestions for improvement regarding the system, and the server stores this information in a database.

[0557] (Application Example 2)

[0558] 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."

[0559] In recent years, the increase in airborne allergens in urban environments has caused health problems for many people. Furthermore, the lack of appropriate responses based on users' emotional states can compromise the safety and comfort of individual users. To address this, there is a need to develop a system that analyzes the environment and emotions in real time and supports optimal actions.

[0560] 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.

[0561] In this invention, the server includes means for acquiring environmental data from a video acquisition device, means for analyzing the acquired environmental data to detect airborne substances, means for identifying the detected substance information by comparing it with existing data, means for analyzing the user's emotional state and providing appropriate countermeasures, and means for providing advice to maintain the user's safety within the plant environment. This makes it possible to analyze the environment and the user's emotional state in real time and provide individually appropriate behavioral guidelines.

[0562] A "video acquisition device" is a device used to acquire ambient environmental data in image or video format.

[0563] "Environmental data" refers to information about external conditions such as substances present in the air, temperature, humidity, and light.

[0564] "Matter" refers to elements that exist in the air or environment, including solids, liquids, gases, and other chemical components.

[0565] "Existing data" refers to a collection of information that has been collected and recorded in the past, and serves as a reference material used for comparison and analysis with newly acquired data.

[0566] "User information" refers to information related to an individual user, including their health status and allergy sensitivities.

[0567] "Emotional state" refers to the user's internal psychological state and reflects specific emotions such as stress, anxiety, and relief.

[0568] "Countermeasures" refer to specific action plans or measures proposed to the user in accordance with the identified situation.

[0569] "Advice" refers to suggesting appropriate means or actions to the user based on the situation.

[0570] This invention is designed to realize a system that detects airborne allergens and the user's emotional state in real time within the user's daily living environment and takes appropriate countermeasures. The server utilizes video acquisition devices such as cameras and microphones embedded in smartphones and robots to acquire ambient environmental data. This environmental data is processed using OpenCV, and allergen analysis is performed using TensorFlow.

[0571] Furthermore, to analyze the user's emotional state, video and audio data are analyzed using Stanford NLP and Google's GCP Natural Language API to identify emotional states such as stress and anxiety. Based on this information, the server generates notifications tailored to the user's individual needs and provides optimal countermeasures in voice and text.

[0572] As a concrete example, while a user is at home, the robot could provide real-time advice such as, "The allergen concentration in your room is currently high. We recommend you turn on the air purifier and take a short break." An example of a prompt used in this case would be, "What kind of support should the robot provide when the user is feeling stressed?"

[0573] This system is expected to improve the quality of life by enabling users to take appropriate actions in response to their environment and their own emotional state.

[0574] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0575] Step 1:

[0576] The device uses a camera and microphone to acquire ambient environmental data and audio data. This input data includes information about objects in the air as images and the tone of the user's voice as audio. The device processes this data through a noise reduction filter to make it easier to analyze.

[0577] Step 2:

[0578] The server processes environmental data sent from the terminal using the OpenCV library to identify airborne substances. It performs pattern recognition on the input image data to detect allergens, and then classifies the results using TensorFlow. The output is a list of identified allergens.

[0579] Step 3:

[0580] The device analyzes voice data and captured facial expression data. Using Stanford NLP and the GCP Natural Language API, it executes emotion recognition algorithms to identify the user's emotional state. Using the analyzed voice tone and facial expression data as input, the user's emotional state (e.g., stress, anxiety, relief) is obtained as output.

[0581] Step 4:

[0582] The server matches allergens and emotional states against an existing database to generate a warning message tailored to the user. Input includes a list of identified allergens and the user's emotional state. The generated message, along with suggested countermeasures, is sent to the user's device.

[0583] Step 5:

[0584] The user's device notifies the user of received messages and suggests specific actions. For example, the device might display advice such as, "We recommend turning on the air purifier and taking a short break to relax," either verbally or in text. The input is a message received from the server, which is then transmitted to the user in an appropriate format.

[0585] 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.

[0586] 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.

[0587] 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.

[0588] [Fourth Embodiment]

[0589] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0590] 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.

[0591] 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).

[0592] 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.

[0593] 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.

[0594] 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).

[0595] 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.

[0596] 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.

[0597] 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.

[0598] 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.

[0599] 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.

[0600] 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.

[0601] 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".

[0602] This invention embodies an allergen detection and warning system that utilizes the camera function of a portable information processing device such as a smartphone. This system provides users with information to quickly recognize the presence of allergens in a specific environment and take appropriate action.

[0603] The device first acquires environmental information in real time using the smartphone's camera. The video captured by the camera is pre-processed within the system and converted into a format suitable for analysis. This removes noise and improves the accuracy of the analysis. In this analysis stage, the data acquired by the device is processed using an AI agent to identify allergens such as pollen and dust floating in the air. The AI ​​agent can recognize specific allergen patterns based on a pre-trained dataset.

[0604] The allergen information identified through analysis is sent to a server. The server compares this information with an existing allergen database to confirm whether the detected substance is a specific allergen. This comparison process can reduce the rate of false alarms.

[0605] The device then generates individually customized warnings based on the allergy information previously entered by the user and the detection results. For example, if a user has a severe allergy to cedar pollen, this information can be used to issue an appropriate warning when the concentration of cedar pollen is high.

[0606] Furthermore, the system can continuously learn from user feedback and improve the accuracy of allergen detection. For example, if a user provides feedback such as "the warning is excessive" or "the warning is insufficient," the AI ​​agent will use this information to readjust the model and improve the accuracy of future warnings.

[0607] As a concrete example, when a user with hay fever is walking in a park, launching the application on their device will detect that the pollen level in the air is high and immediately issue a warning such as, "We recommend wearing a mask." In this way, the user can engage in outdoor activities with peace of mind.

[0608] The following describes the processing flow.

[0609] Step 1:

[0610] The device activates the smartphone's camera and acquires real-time video of the environment. This captures the surrounding situation as visual information.

[0611] Step 2:

[0612] The device preprocesses the acquired video data, removing noise and sharpening the image. This improves the accuracy of the analysis.

[0613] Step 3:

[0614] The device uses an AI agent to analyze pre-processed video footage and identify specific allergens (e.g., pollen, dust). In this process, the AI ​​uses pre-trained data to recognize patterns in the allergens.

[0615] Step 4:

[0616] The terminal sends the analysis results to the server, where they are compared against an existing allergen database. In this step, the server compares the results against appropriate reference data to confirm whether the identified substance is a known allergen.

[0617] Step 5:

[0618] The device references the user's allergy information and generates a warning tailored to the user based on the detected allergen information. This allows for accurate warnings to be issued when the user's allergy-related risk is high.

[0619] Step 6:

[0620] Users receive warnings from their devices and take appropriate action to address allergens. They can also provide feedback on the effectiveness of the warnings.

[0621] Step 7:

[0622] Based on user feedback, the server adjusts the AI ​​agent's model to improve the accuracy of allergen detection and warnings in subsequent instances. This continuous learning process allows the system to evolve.

[0623] (Example 1)

[0624] 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".

[0625] Currently, there are limited means of detecting environmental allergens in real time using portable information processing devices and providing that information to users quickly and accurately. Furthermore, existing systems have issues with detection accuracy and the appropriateness of warnings, and their ability to learn from user feedback is insufficient. Therefore, there is a need for a new system that can quickly recognize the presence of allergens in specific environments and issue appropriate warnings to ensure user safety and comfort.

[0626] 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.

[0627] In this invention, the server includes means for acquiring environmental data from a video acquisition device, means for preprocessing the acquired environmental data and converting it into a format suitable for analysis, and means for detecting airborne allergens using an AI module with the preprocessed data. This allows users to acquire allergen information in real time using a portable information processing device and receive warnings tailored to their individual user characteristics. Furthermore, by creating prompt sentences using a generative AI model and utilizing user responses, the accuracy of allergen recognition can be further improved.

[0628] A "video acquisition device" is a hardware device that captures visual information of the environment and converts it into digital data.

[0629] "Environmental data" refers to information that describes the physical state in a specific space or situation, and includes visual information collected by video acquisition devices.

[0630] "Preprocessing" refers to the initial stages of data processing to convert it into a format that is easy to analyze, and includes processes such as noise reduction and format conversion.

[0631] An "AI module" refers to a software component that uses artificial intelligence technology to perform pattern recognition and prediction.

[0632] An "allergen" is a substance that can trigger an allergic reaction, and includes things like pollen and dust floating in the air.

[0633] "Database information" refers to a collection of data that systematically aggregates specific information and is stored in a format that can be used for comparison and analysis.

[0634] "Characteristic identification" is the process of clearly recognizing and classifying specific features and attributes based on acquired information.

[0635] "User characteristics" refer to information that indicates attributes and conditions unique to an individual user, and include information such as that person's allergies.

[0636] "Continuous learning" is an adaptive learning process in which a system improves its performance based on new data and feedback.

[0637] A "generative AI model" is a pre-trained model built for artificial intelligence to generate specific outputs based on prompts and input data.

[0638] This invention provides a system that uses a portable information processing device to detect allergens in real time and issue a warning to the user.

[0639] First, the device uses the smartphone's video acquisition device, i.e., the camera, to acquire environmental data. When a user takes this device for a walk in the park, visual information, including pollen and dust in the surroundings, is collected.

[0640] Next, the device performs preprocessing on the acquired environmental data. Specifically, this involves converting the data into a format suitable for use with the AI ​​module and removing unwanted information such as noise. This preprocessing improves the accuracy of the analysis of the acquired data.

[0641] Next, the pre-processed data is passed to the generative AI model, an AI module, for allergen detection. Based on pre-trained database information, the AI ​​module identifies and detects pollen and dust floating in the air. An example of a prompt message to the generative AI model is, "Identify the type and amount of pollen from the environmental video acquired by the camera, and provide an appropriate warning based on the pre-set allergy information."

[0642] The server compares the allergen information transmitted from the terminal with existing database information and performs characteristic identification processing to reduce false positives. This process improves prediction accuracy and ensures safety.

[0643] Finally, the device sends appropriate warnings to the user based on the identified allergen information. For example, if the user is allergic to cedar pollen, the AI ​​module will display a customized notification such as "We recommend wearing a mask" when it detects a high pollen concentration.

[0644] The system of this invention improves allergen detection capabilities by collecting feedback from users and using it for the continuous learning of the AI ​​module. For example, if a user provides feedback such as "the warnings are excessive," the terminal sends this data to the AI ​​module, and the model is readjusted based on that information to improve the accuracy of subsequent warnings.

[0645] As described above, this system provides real-time allergen detection and warnings based on individual user characteristics, supporting the safety and comfort of users' lives.

[0646] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0647] Step 1:

[0648] The device acquires ambient environmental data using the smartphone's camera. The input is real-time video captured by the camera. The output is this video data sent to the device's processing system. Specifically, when the user points the smartphone at the environment, the camera continuously records video.

[0649] Step 2:

[0650] The device performs preprocessing to remove noise from the acquired video. The input is raw video data. By performing noise filtering and applying color correction, it obtains a clear image in a format that can be analyzed by the AI ​​module as output. Specifically, it uses an image processing algorithm to adjust brightness and contrast and remove unnecessary background information.

[0651] Step 3:

[0652] The device feeds pre-processed data into a generating AI model to identify the characteristics of allergens. The input is pre-processed image data. Based on the trained data, the AI ​​model analyzes allergen patterns such as pollen and dust in the image and outputs identified allergen information. Specifically, the AI ​​model performs calculations to identify the type and concentration of a particular allergen.

[0653] Step 4:

[0654] The device transmits identified allergen information to the server. The input is the analysis results of an AI model. This data is sent to the server via the network, and the output allows for further matching on the server. Specifically, the device uses wireless communication to quickly upload the information to the server.

[0655] Step 5:

[0656] The server compares the received allergen information with a database to perform characteristic identification, thereby reducing false alarms. The input is allergen data sent from the terminal. The server refers to known allergen information in the database, checks if it has matching characteristics, and returns the verified allergen information as output. Specifically, the server uses a high-performance database search algorithm to perform matching quickly.

[0657] Step 6:

[0658] The device generates a warning for the user based on verified allergen information and the user's allergy information. The input consists of verified information from the server and the user's pre-registered allergy information. The device outputs an appropriate warning, such as "We recommend wearing a mask." Specifically, the device displays the alert on the screen and also adds an audio notification to alert the user.

[0659] Step 7:

[0660] The user provides feedback on the warnings displayed by the device. The input consists of opinions and comments submitted by the user on the feedback screen. The device receives this feedback and adds it to a dataset used for continuous learning of the AI ​​model. Specifically, the user interacts with the feedback form and enters specific comments such as "the warning is excessive."

[0661] (Application Example 1)

[0662] 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".

[0663] In indoor environments, there is a need to rapidly and accurately detect airborne allergens and provide information to enable appropriate responses for individual users. However, current systems make immediate response difficult, and there is a lack of means to improve allergen detection accuracy by incorporating user feedback.

[0664] 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.

[0665] In this invention, the server includes means for acquiring environmental data, means for analyzing the data to identify allergens, and means for issuing warnings and countermeasures to the user based on the analysis results. This makes it possible to quickly detect allergens in a space via a home automated device and propose appropriate responses for each user.

[0666] A "video acquisition device" is a device that has the function of acquiring visual data of the environment in real time.

[0667] "Environmental data" refers to data represented as digital information indicating the composition and condition of the air.

[0668] An "allergen" is a substance that can potentially cause an allergic reaction in the human body and can be found floating in the air.

[0669] A "stored allergen database" is an information infrastructure that contains known allergen information and is used to compare detected allergen information.

[0670] "User-specific information" refers to information related to each user's allergies and preferences.

[0671] A "household automatic device" is an electronically controlled device used within a home that is capable of operating autonomously.

[0672] A "learning function that improves accuracy" is a program that utilizes user feedback to improve the accuracy of predicting and judging actions.

[0673] A "user recommendation" is a measure that instructs users on allergen-related countermeasures and precautions.

[0674] The system for carrying out this invention includes a video acquisition device mounted on a home automated device, an AI agent for environmental data analysis, an allergen database, and a database for managing user-specific information. A server integrates these, identifies airborne allergens in real time, and provides appropriate instructions to the user.

[0675] The server acquires environmental data from home automated devices using high-resolution cameras. This data is first denoised and its resolution adjusted, and then analyzed by an AI agent. During the analysis, the data is compared with pre-trained allergen patterns and matched against a stored allergen database. Based on the analysis results, the server sends optimized warnings and countermeasures to the user based on their individual user profile.

[0676] As a concrete example, when an automated device in the home detects peanut allergens in the kitchen, the server analyzes this information and notifies the user with a warning such as, "An ingredient that may contain peanuts has been detected. Please be careful." Furthermore, user feedback allows the AI ​​agent to improve detection accuracy in the future.

[0677] An example of a prompt for a generating AI model is: "Generate a program that suggests appropriate actions to the user using the latest allergen information." This prompt causes the AI ​​model to generate a program that provides the user with the best possible course of action.

[0678] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0679] Step 1:

[0680] The terminal acquires environmental data using a camera mounted on a home automated device. The visual data acquired by the camera is used as input, noise reduction and resolution adjustment are performed, and the data is converted into a format suitable for analysis. The output is pre-processed environmental data.

[0681] Step 2:

[0682] The server sends pre-processed environmental data to the AI ​​agent. Based on the input data, the AI ​​agent analyzes allergen patterns. This analysis compares the data with a pre-trained allergen dataset to identify airborne allergens. The output is the identified allergen information.

[0683] Step 3:

[0684] The server compares the identified allergen information with the stored allergen database. The input is allergen information from the AI ​​agent, and the accuracy of the allergen is verified through database matching. The output is the matched allergen data.

[0685] Step 4:

[0686] The server compares the verified allergen data with the user's allergy profile and generates individually optimized warnings. The input consists of the user's individual information and allergen data, and the server creates warning messages based on this information. The output is the generated warning message.

[0687] Step 5:

[0688] The server notifies the user of the generated warning message. The user receives this warning via their terminal and obtains guidance for taking safety measures. The output is the warning message received by the user.

[0689] Step 6:

[0690] The user takes action based on the provided warnings and provides the results as feedback to the server. The input is user feedback, and based on this information, the AI ​​agent readjusts the model and learns to improve accuracy in subsequent uses. The output is the improved allergen identification model.

[0691] 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.

[0692] This invention provides a system for a portable information processing device, such as a smartphone equipped with an image acquisition device, that simultaneously recognizes allergens and the user's emotions, and provides appropriate warnings and countermeasures. The purpose of this system is to support the user in continuing their activities safely and comfortably.

[0693] First, the device uses its camera to acquire information about the surrounding environment in real time. The video data is preprocessed to make it less noisy and suitable for analysis. Based on this, the AI ​​agent identifies airborne allergens and sends that information to the server. The server then checks the reliability of this information by comparing it with an existing database.

[0694] Simultaneously, the emotion engine analyzes the user's facial expressions and voice tone obtained from the camera and microphone. It identifies each emotional state (e.g., stress, anxiety, calmness) and comprehensively evaluates this information in combination with the user's allergy information.

[0695] Based on this information, the device sends user-optimized warning messages and advice. For example, if the allergen concentration is high and the user is feeling stressed, a message such as "A high allergen concentration has been detected. Take a deep breath and calm down before putting on a mask" will be displayed. In this way, notifications that take the user's emotional state into consideration help them take more appropriate action.

[0696] As a concrete example, suppose a user is in an office when this system detects an allergen and simultaneously senses slight anxiety from the user's facial expression. Based on this information, the terminal notifies the user with a message such as, "Allergen levels have risen slightly. We recommend closing the windows and taking a short break to relax." This system allows the user to accurately understand the situation and take appropriate action.

[0697] Furthermore, user feedback is analyzed and incorporated into future detections and notifications. This allows the system's accuracy to continuously improve, further enhancing user confidence.

[0698] The following describes the processing flow.

[0699] Step 1:

[0700] The device activates the smartphone's camera and acquires real-time video data of the surrounding environment. This allows detailed environmental information to be captured by the device.

[0701] Step 2:

[0702] The terminal preprocesses the acquired video data, removing image noise and converting it into a format suitable for analysis. This preprocessing improves the accuracy of the analysis.

[0703] Step 3:

[0704] The device inputs pre-processed video data into an AI agent, which analyzes and identifies allergens floating in the air. The AI ​​agent uses a trained model to identify patterns in the allergens.

[0705] Step 4:

[0706] The terminal sends the allergen detection results to the server, which compares them with an existing allergen database to verify accuracy and consistency. This comparison ensures the reliability of the detection.

[0707] Step 5:

[0708] The device activates an emotion engine based on information obtained from the camera and microphone, analyzing the user's facial expressions and voice tone to determine their current emotional state. This includes emotions such as stress, anxiety, and calmness.

[0709] Step 6:

[0710] The device combines allergen identification information with analysis results from an emotion engine to generate personalized warning messages and countermeasures tailored to the user. During this process, the messages are adjusted to take into account the user's emotional state.

[0711] Step 7:

[0712] Users receive notifications from their devices and take the instructed actions. They also have a function to provide feedback on the content and effectiveness of the notifications. This feedback helps improve the system in the future.

[0713] Step 8:

[0714] The server analyzes user feedback and adaptively updates the AI ​​agent's model to improve the accuracy of allergen detection and sentiment analysis in subsequent uses. Through this continuous learning, the system continues to evolve.

[0715] (Example 2)

[0716] 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".

[0717] In modern society, many people suffer from allergies, and their health is threatened by allergens lurking in their living environment. In addition, the impact of allergen exposure on emotions cannot be ignored. However, conventional systems have found it difficult to monitor both allergen detection and emotions in real time and take appropriate action accordingly. Therefore, the present invention aims to integrate allergen detection and emotion analysis to provide users with appropriate warnings and countermeasures.

[0718] 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.

[0719] In this invention, the server includes means for acquiring ambient environmental information from a video acquisition device, means for preprocessing the acquired environmental information to prepare it for analysis, means for analyzing the preprocessed environmental information to detect airborne allergens, means for analyzing audio and video data to identify individual emotional states, means for comparing the detected allergen information with existing information to identify and confirm its reliability, means for issuing warnings tailored to individual user information based on the identified allergen information and emotional information, and means for collecting user feedback and using it to improve the accuracy of the system. This enables real-time management of both allergens and emotions, allowing for appropriate countermeasures and user support.

[0720] A "video acquisition device" is a device used to collect visual information about the environment, such as a camera.

[0721] "Environmental information" refers to data about the surrounding physical and atmospheric conditions, particularly information acquired as video or audio.

[0722] "Preprocessing" refers to the initial stage of data manipulation performed to prepare acquired data for analysis and processing.

[0723] An "allergen" is a substance that causes an allergic reaction in an individual and can be suspended in the air.

[0724] "Emotional state" refers to an individual's psychological and emotional condition, and is judged from things like facial expressions and tone of voice.

[0725] "Reliability" refers to the accuracy and consistency of data, and is a factor that guarantees the quality of system decisions and outputs.

[0726] A "warning" is information intended to alert users to potential dangers or risks, and is provided as a notification to the user.

[0727] "Feedback" refers to reactions and opinions obtained from users, and the information used to improve and adapt the system.

[0728] The embodiment for carrying out this invention is configured based on the respective roles of terminal, server, and user. First, the terminal uses a portable information processing device such as a smartphone to acquire environmental information through a camera. This information includes airborne allergens and user facial expression data. The terminal uses software libraries such as OpenCV and TensorFlow to preprocess the acquired video information, such as by removing noise, to prepare it for analysis.

[0729] During the analysis, the device uses an AI model to identify allergens from video data and transmits this information to the server in real time. Simultaneously, the device uses voice recognition and facial expression analysis technologies to evaluate the user's emotional state based on data input from the camera and microphone.

[0730] The server receives allergen information sent from the terminal and compares it with existing information in the database to verify the reliability of the detection results. The server uses the Django framework to process the information efficiently.

[0731] Users take appropriate action based on information sent from the system. For example, if the system determines that the allergen concentration is high and the user is under stress, the device will display a message such as, "High allergen concentration detected. Please take a deep breath and then put on a mask." Such notifications are optimized according to the user's emotional state.

[0732] Furthermore, users provide feedback on the system's suggestions. This feedback is analyzed on the server and used to improve the accuracy of future analyses. Specific examples of prompts include, "Identify the types of allergens in the air and report their concentrations," and "Evaluate the user's emotions through voice and facial expression analysis." This allows users to continue living safely and comfortably with a strong sense of security.

[0733] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0734] Step 1:

[0735] The terminal activates the camera, which is a video acquisition device, and acquires environmental information in real time. The input is video data, which is then preprocessed to remove noise and adjust the resolution. Specifically, it uses the OpenCV library to perform filtering and obtains video data that can be analyzed as output.

[0736] Step 2:

[0737] The device analyzes pre-processed video data using an AI model to identify allergens in the air. The input is clean video data, and the output is information on the type and concentration of the identified allergens. Specifically, a machine learning model utilizing TensorFlow identifies the characteristics of the allergens and outputs the data.

[0738] Step 3:

[0739] The device simultaneously acquires user facial expression and voice data from its camera and microphone. This data is processed by an emotion analysis engine to identify the user's emotional state. The input is voice and facial expression data, and the output is the individual emotional state. Specifically, it uses speech recognition technology to analyze the tone of voice and facial expression analysis to evaluate emotions.

[0740] Step 4:

[0741] The server receives allergen information sent from the terminal and verifies its reliability by comparing it with an existing database. The input is allergen information, and the output is the reliability verification result. In this process, the information is processed using the Python Django framework to obtain the verification result.

[0742] Step 5:

[0743] The device generates a user-optimized warning message based on detected allergen information and emotional state. The input is confirmed allergen information and emotional data, and the output is a user-directed warning message. Specifically, it uses a generative AI model to create a message in natural language and notifies the user.

[0744] Step 6:

[0745] The user checks notifications from their device and takes appropriate action based on the instructions. They send feedback to their device, which the server collects and uses to improve the accuracy of future analyses. The input is user feedback, and the output is training data for improving analysis accuracy. Specifically, the user inputs their thoughts and suggestions for improvement regarding the system, and the server stores this information in a database.

[0746] (Application Example 2)

[0747] 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".

[0748] In recent years, the increase in airborne allergens in urban environments has caused health problems for many people. Furthermore, the lack of appropriate responses based on users' emotional states can compromise the safety and comfort of individual users. To address this, there is a need to develop a system that analyzes the environment and emotions in real time and supports optimal actions.

[0749] 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.

[0750] In this invention, the server includes means for acquiring environmental data from a video acquisition device, means for analyzing the acquired environmental data to detect airborne substances, means for identifying the detected substance information by comparing it with existing data, means for analyzing the user's emotional state and providing appropriate countermeasures, and means for providing advice to maintain the user's safety within the plant environment. This makes it possible to analyze the environment and the user's emotional state in real time and provide individually appropriate behavioral guidelines.

[0751] A "video acquisition device" is a device used to acquire ambient environmental data in image or video format.

[0752] "Environmental data" refers to information about external conditions such as substances present in the air, temperature, humidity, and light.

[0753] "Matter" refers to elements that exist in the air or environment, including solids, liquids, gases, and other chemical components.

[0754] "Existing data" refers to a collection of information that has been collected and recorded in the past, and serves as a reference material used for comparison and analysis with newly acquired data.

[0755] "User information" refers to information related to an individual user, including their health status and allergy sensitivities.

[0756] "Emotional state" refers to the user's internal psychological state and reflects specific emotions such as stress, anxiety, and relief.

[0757] "Countermeasures" refer to specific action plans or measures proposed to the user in accordance with the identified situation.

[0758] "Advice" refers to suggesting appropriate means or actions to the user based on the situation.

[0759] This invention is designed to realize a system that detects airborne allergens and the user's emotional state in real time within the user's daily living environment and takes appropriate countermeasures. The server utilizes video acquisition devices such as cameras and microphones embedded in smartphones and robots to acquire ambient environmental data. This environmental data is processed using OpenCV, and allergen analysis is performed using TensorFlow.

[0760] Furthermore, to analyze the user's emotional state, video and audio data are analyzed using Stanford NLP and Google's GCP Natural Language API to identify emotional states such as stress and anxiety. Based on this information, the server generates notifications tailored to the user's individual needs and provides optimal countermeasures in voice and text.

[0761] As a concrete example, while a user is at home, the robot could provide real-time advice such as, "The allergen concentration in your room is currently high. We recommend you turn on the air purifier and take a short break." An example of a prompt used in this case would be, "What kind of support should the robot provide when the user is feeling stressed?"

[0762] This system is expected to improve the quality of life by enabling users to take appropriate actions in response to their environment and their own emotional state.

[0763] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0764] Step 1:

[0765] The device uses a camera and microphone to acquire ambient environmental data and audio data. This input data includes information about objects in the air as images and the tone of the user's voice as audio. The device processes this data through a noise reduction filter to make it easier to analyze.

[0766] Step 2:

[0767] The server processes environmental data sent from the terminal using the OpenCV library to identify airborne substances. It performs pattern recognition on the input image data to detect allergens, and then classifies the results using TensorFlow. The output is a list of identified allergens.

[0768] Step 3:

[0769] The device analyzes voice data and captured facial expression data. Using Stanford NLP and the GCP Natural Language API, it executes emotion recognition algorithms to identify the user's emotional state. Using the analyzed voice tone and facial expression data as input, the user's emotional state (e.g., stress, anxiety, relief) is obtained as output.

[0770] Step 4:

[0771] The server matches allergens and emotional states against an existing database to generate a warning message tailored to the user. Input includes a list of identified allergens and the user's emotional state. The generated message, along with suggested countermeasures, is sent to the user's device.

[0772] Step 5:

[0773] The user's device notifies the user of received messages and suggests specific actions. For example, the device might display advice such as, "We recommend turning on the air purifier and taking a short break to relax," either verbally or in text. The input is a message received from the server, which is then transmitted to the user in an appropriate format.

[0774] 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.

[0775] 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.

[0776] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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."

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] The following is further disclosed regarding the embodiments described above.

[0796] (Claim 1)

[0797] A means of acquiring environmental information from a video acquisition device,

[0798] A means for analyzing acquired environmental information to detect allergens floating in the air,

[0799] A means for identifying detected allergen information by comparing it with existing allergen information,

[0800] A means for issuing warnings tailored to individual user information based on identified allergen information,

[0801] A system that includes this.

[0802] (Claim 2)

[0803] The system according to claim 1, which analyzes environmental information in real time to detect allergens.

[0804] (Claim 3)

[0805] The system according to claim 1, comprising a learning means for improving the accuracy of allergen detection based on user feedback.

[0806] "Example 1"

[0807] (Claim 1)

[0808] A means of acquiring environmental data from a video acquisition device,

[0809] A means for preprocessing acquired environmental data and converting it into a format suitable for analysis,

[0810] A means for detecting airborne allergens using an AI module with pre-processed data,

[0811] A means for comparing detected allergen information with database information to identify its characteristics,

[0812] A means for providing warnings tailored to individual user characteristics based on characteristic-identified allergen information,

[0813] A means of continuously learning to improve the recognition accuracy of the AI ​​module based on user feedback,

[0814] A system that includes this.

[0815] (Claim 2)

[0816] The system according to claim 1, which analyzes environmental data in real time to identify allergens.

[0817] (Claim 3)

[0818] The system according to claim 1, which uses a generative AI model to create prompt sentences and utilizes user responses to improve the accuracy of allergen recognition.

[0819] "Application Example 1"

[0820] (Claim 1)

[0821] A means of acquiring environmental data from video acquisition equipment,

[0822] A means of identifying airborne allergens by analyzing acquired environmental data,

[0823] A means of making a determination by comparing the identified allergen data with the stored allergen database,

[0824] A means of issuing warnings tailored to individual user information based on the determined allergen data,

[0825] It is used in household automated devices to rapidly detect allergens in the indoor environment and to propose appropriate countermeasures to the user.

[0826] A system that includes this.

[0827] (Claim 2)

[0828] The system according to claim 1, which analyzes environmental data in real time to identify allergens and performs allergen detection in the space via a household automated device.

[0829] (Claim 3)

[0830] The system according to claim 1, which includes a learning function that improves the accuracy of allergen identification based on user responses, and which links the operation of a household automated device with user recommendations.

[0831] "Example 2 of combining an emotion engine"

[0832] (Claim 1)

[0833] A means of acquiring surrounding environmental information from a video acquisition device,

[0834] A means of preprocessing acquired environmental information and preparing it for analysis,

[0835] A means for detecting airborne allergens by analyzing pre-processed environmental information,

[0836] A means for analyzing audio and video data to identify individual emotional states,

[0837] A means for identifying detected allergen information by comparing it with existing information and confirming its reliability,

[0838] A means for issuing warnings tailored to individual user information based on identified allergen information and emotional information,

[0839] A means of collecting user feedback and using it to improve the accuracy of the system,

[0840] A system that includes this.

[0841] (Claim 2)

[0842] The system according to claim 1, which analyzes environmental information and emotional information in real time to perform allergen detection and emotional evaluation.

[0843] (Claim 3)

[0844] The system according to claim 1, comprising a learning means for improving the accuracy of allergen detection and emotion analysis based on user feedback.

[0845] "Application example 2 when combining with an emotional engine"

[0846] (Claim 1)

[0847] A means of acquiring environmental data from a video acquisition device,

[0848] A means for detecting airborne substances by analyzing acquired environmental data,

[0849] A means for identifying detected substance information by comparing it with existing data,

[0850] A means of sending notifications tailored to individual user information based on identified substance information,

[0851] A means to analyze the user's emotional state and provide appropriate countermeasures for that emotional state,

[0852] Means of providing advice to maintain user safety within plant environments,

[0853] A system that includes this.

[0854] (Claim 2)

[0855] The system according to claim 1, which analyzes environmental data in real time to detect substances.

[0856] (Claim 3)

[0857] The system according to claim 1, comprising a learning means for improving the accuracy of substance detection based on user feedback. [Explanation of symbols]

[0858] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of acquiring environmental information from a video acquisition device, A means for analyzing acquired environmental information to detect allergens floating in the air, A means for identifying detected allergen information by comparing it with existing allergen information, A means for issuing warnings tailored to individual user information based on identified allergen information, A system that includes this.

2. The system according to claim 1, which analyzes environmental information in real time to detect allergens.

3. The system according to claim 1, comprising a learning means for improving the accuracy of allergen detection based on user feedback.

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