system

Abstract

We provide the system. [Solution] Means for acquiring environmental information using an acquisition device, Information processing means for analyzing environmental information from the aforementioned acquisition device, An identification means for identifying the presence of an allergen based on the results of analysis by the aforementioned information processing means, A generation means that generates a warning and a suggested response based on the allergen identified by the identification means, A notification means for notifying the user of the information generated by the generation means, A city-wide monitoring system that monitors allergens and provides residents with real-time 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 method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 society, allergens in the environment have a great impact on people's quality of life. However, systems that can easily detect the presence of allergens in the surrounding environment in real time and provide appropriate countermeasures to users are limited. In particular, there is a need for a mechanism that can always obtain allergen information regardless of location and receive customized countermeasures according to individual needs.

Means for Solving the Problems

[0005] This invention involves acquiring environmental information in real time using an acquisition device and identifying the presence of allergens by analyzing that information using a data processing means. Furthermore, it includes a generation means that generates customized warnings and countermeasures for each user based on the identified allergens, and communicates this to the user via a notification means, enabling appropriate measures to be taken immediately.

[0006] An "acquisition device" is a device used to capture information from the environment, and includes cameras, sensors, and other similar devices.

[0007] "Environmental information" refers to data about elements present in the surrounding air and space, and in particular includes information related to allergens.

[0008] "Data processing means" refers to devices or programs for analyzing environmental information obtained from acquisition devices, and performs preprocessing and analysis of the information.

[0009] "Identification means" refers to devices or programs used to determine the presence of a specific allergen from information analyzed by data processing means.

[0010] A "generation means" refers to a device or program for creating warnings and countermeasures based on information about allergens detected by specific means.

[0011] A "notification means" is a device or interface for transmitting warning and countermeasure information created by a generation means to the user.

[0012] A "generative model" is an algorithm trained on machine learning or deep learning, used to identify allergens.

[0013] "Customization" refers to the result of adjusting the content according to the user's individual information and circumstances. [Brief explanation of the drawing]

[0014] [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 a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a tagged 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.

[0018] In the following embodiments, a tagged RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. 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.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is a system that acquires information from the environment using an acquisition device attached to a terminal such as a smartphone, identifies allergens in real time by coordinating with a server, and provides this information to the user. The main operation of the system is described in detail below.

[0036] First, the device continuously collects information about the surrounding environment using the smartphone's camera and sensors. The acquired information is temporarily stored on the device and converted into a format that can be easily analyzed under specific environmental conditions.

[0037] Subsequently, this environmental information is transmitted to a server via the network. The server analyzes the received information using data processing tools and extracts the characteristics of allergens floating in the environment. Here, an AI model is used to identify the presence of allergens based on past training data. The allergen information identified through this series of processes is organized by concentration and type.

[0038] Next, the server uses a generation mechanism to create specific messages and instructions to generate warnings and suggested countermeasures tailored to the allergen. This process takes into account the user's individual information (e.g., allergy type and threshold), resulting in customized countermeasures. The generated warning information is sent to the user in real time and displayed as a notification on their smartphone screen.

[0039] For example, if a large amount of pollen is detected in a park, the device will notify the user with specific action advice, such as, "The pollen concentration is high in your current location, so we recommend you refrain from going outside or wear a mask." Based on this information, the user can then take appropriate action on the spot.

[0040] In this way, by linking acquisition devices, data processing means, identification means, generation means, and notification means, this system enables users to always be aware of the latest allergen information and take immediate safe actions.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The device activates the smartphone's camera and sensors to acquire information about the surrounding environment in real time. This information is temporarily stored as continuously captured video and digital data.

[0044] Step 2:

[0045] The terminal preprocesses the acquired video data into the required format. This preprocessing includes noise reduction, resolution adjustment, and color correction as needed. This prepares the data for subsequent analysis.

[0046] Step 3:

[0047] The terminal sends pre-processed data to the server over the network. Data compression may be performed at this stage depending on network conditions.

[0048] Step 4:

[0049] The server inputs the received data into an AI model for analysis. Here, a pre-trained deep learning algorithm is used to perform the process of detecting allergen characteristics from the video.

[0050] Step 5:

[0051] The server collects the types and concentrations of allergens identified from the AI ​​model's analysis results and records them in a database. Here, the allergens are classified and their concentrations are quantified, and the results are passed on to the next processing step.

[0052] Step 6:

[0053] Based on the identified allergen information, the server generates a warning message based on user customization information that matches the trigger conditions. The message will take into account each user's allergy type and countermeasures.

[0054] Step 7:

[0055] The device receives a warning message sent from the server and notifies the user. The notification is displayed as a pop-up on the smartphone or as an alert within the application.

[0056] Step 8:

[0057] Users can review notifications and choose actions based on the information they receive in real time. They can also provide feedback on whether the notifications were helpful.

[0058] The above is the specific processing flow for identifying allergen information in real time and notifying the user.

[0059] (Example 1)

[0060] 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."

[0061] In modern society, many people suffer health problems caused by environmental allergens, but there is a lack of systems to identify these allergens in real time and take appropriate countermeasures. Furthermore, customized responses based on individual user information are insufficient, and there is a need for a system that can provide accurate information under diverse environmental conditions.

[0062] 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.

[0063] In this invention, the server includes means for acquiring environmental information using an acquisition device, means for converting and analyzing the data format, and means for identifying the presence of allergens using a pre-trained generative model. This enables users to grasp allergen information in real time and quickly take appropriate action based on individually customized countermeasures.

[0064] An "acquisition device" is a device installed to collect environmental information, and it acquires surrounding environmental data through sensors and cameras.

[0065] "Data processing means" refers to means that analyze environmental information obtained from an acquisition device and perform data format conversion or pattern recognition as necessary.

[0066] A "generative model" is a model that learns specific patterns and characteristics based on past data and identifies the presence of allergens based on new data.

[0067] "Identification methods" refer to means of identifying allergens present in the environment and confirming their presence using analyzed and processed data.

[0068] "Generation means" refers to means of creating warning messages and countermeasures to provide to users based on identified allergen information.

[0069] A "notification method" is a means of delivering generated information to users in real time, and notifications are sent via smartphones or other communication devices.

[0070] This invention is a system that provides users with allergen information in real time, supporting them in taking safe actions on the spot. This system primarily operates through the coordinated efforts of three elements: a terminal, a server, and the user.

[0071] The device uses a smartphone as an example of an embodiment. Cameras and sensors installed in the smartphone continuously acquire information about the surrounding environment. This information includes temperature, humidity, and fine particles in the air, and is temporarily stored in the device in the initial stage. Hardware used includes high-performance camera modules and environmental sensors. The acquired data is converted into a format that is easy to analyze, such as JSON or CSV format.

[0072] The server receives this converted data via the network. The server uses data processing tools and an AI model to analyze the data. The software used here includes a generative AI model. This model searches for allergen characteristics based on past training data and identifies allergens present at the user's current location. The server uses these identification tools to identify allergens floating in the environment and extract their concentration and type.

[0073] The server then generates warnings and suggested actions for the user based on the data mentioned above. This process takes the user's individual information into account to create customized messages. The generated warnings are sent to the user's device in real time.

[0074] Users will receive this warning information through their devices. For example, if a large amount of pollen is detected in a park, the device will receive a message stating, "The pollen concentration is high in your current location, so we recommend you stay indoors or wear a mask." This allows users to quickly choose the appropriate course of action.

[0075] As a concrete example, prompt messages are generated in the format of "Investigate the pollen concentration in the park and, based on the results, suggest the most appropriate course of action for the user." This system allows users to always be aware of the latest allergen information and take safe actions.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The device collects information about its surroundings. Cameras and sensors are used to acquire data such as temperature, humidity, visual data, and airborne particle information. The input is raw data obtained from the environment, which is temporarily stored in internal memory. The output is raw environmental data for later processing.

[0079] Step 2:

[0080] The raw data acquired by the terminal is converted into a format that is easy to analyze. The input is the raw data collected in step 1. In this conversion process, the data is converted into JSON or CSV format. The output is data formatted in a format that is easy for data processing tools to recognize.

[0081] Step 3:

[0082] The data converted from the terminal is sent to the server via the network. The input is formatted environmental data. The data is sent to the server via a secure protocol using Wi-Fi or Bluetooth. The output is the environmental data received by the server.

[0083] Step 4:

[0084] The server analyzes the received environmental data. The input is the data received in step 3. The server uses data processing tools and leverages a generative AI model to extract allergen characteristics from the data. The output is information on the identified allergens.

[0085] Step 5:

[0086] The server generates a warning message based on identified allergen information. The input is information about the characteristics and concentration of the allergen. It generates a customized message considering the user's individual information. The output is the warning message sent to the user.

[0087] Step 6:

[0088] The system notifies the user of the generated warning on their device. The input is the warning message generated in step 5. The warning is displayed on the user's smartphone screen via the notification function. The output is warning information that the user can review, allowing them to take appropriate action based on it.

[0089] (Application Example 1)

[0090] 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."

[0091] In modern urban environments, the concentrations and types of allergens change constantly, posing a high risk of adverse health effects on residents. However, systems that can grasp this information in real time and prompt residents to take appropriate measures are still insufficient. In particular, it is difficult to suggest appropriate actions tailored to individual users based on their individual allergy information. Against this backdrop, there is a need to monitor allergens throughout the city and issue customized warnings to individual users.

[0092] 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.

[0093] In this invention, the server includes means for acquiring environmental information, means for processing information, means for identifying information, means for generating information, means for notifying information, and means for monitoring the city. This enables monitoring of allergens throughout the city, real-time identification of allergens based on the acquired information, and the proposal of customized warnings and safety measures tailored to individual residents.

[0094] An "acquisition device" is a device used to collect information from the environment, and includes sensors, cameras, and other similar devices.

[0095] An "information processing means" is a system that analyzes environmental information obtained from an acquisition device and performs data conversion.

[0096] "Identification means" refers to a method of detecting the presence of a specific substance or allergen based on data analyzed by information processing means.

[0097] "Generation means" refers to the process of creating warnings and countermeasures for users based on the information identified by the identification means.

[0098] A "notification method" refers to a technique for notifying users of generated information in real time, using methods such as screen displays and voice guidance.

[0099] A "city monitoring system" is a mechanism that monitors environmental information throughout a city and continuously provides useful allergen information to a wide range of residents.

[0100] A system for carrying out this invention consists of coordinating an acquisition device, data processing means, identification means, generation means, notification means, and urban monitoring means.

[0101] The server first collects environmental information from sensors and smartphone cameras via an acquisition device. This environmental information includes various environmental data such as temperature, humidity, and particulate matter concentration. Subsequently, data processing means are used to formalize this raw data and convert it into an analyzable state. Database software and data analysis tools are used for this process, specifically Apache® Kafka and Python.

[0102] Next, the server uses identification methods and leverages an AI model (e.g., a generative AI model using TENSORFLOW®) to identify the presence of allergens from the analyzed data. This process involves referencing past training data to achieve highly accurate identification.

[0103] Based on the identified allergen information, the generation system creates customized warning messages and suggested countermeasures tailored to each user's individual needs. This generation process applies a generation AI model and dynamically generates messages using JavaScript® or Python.

[0104] Finally, the generated information is sent to the user's smartphone in real time via the reporting system. This notification is delivered via the application's push notification function or SMS, allowing residents to take immediate action based on the information.

[0105] For example, if the system identifies a sudden surge in pollen levels in a particular park on a given day, it will generate a message such as, "The pollen concentration near XX Park is currently very high. Please use △△ Plaza instead," and notify the user's smartphone. An example of a prompt message would be, "Pollen levels in the city center, current time," to obtain the analysis results.

[0106] This enables allergen monitoring at the city level, providing users with a safe and healthy living environment.

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] The device collects environmental information using the smartphone's camera and various sensors as acquisition devices. The input is ambient environmental data, and the output is this data converted into an easily manageable format and stored within the device. A data formatting algorithm is used for this conversion.

[0110] Step 2:

[0111] The terminal sends organized environmental data to the server via the network. The input is formatted data stored on the terminal, and the output is the data successfully transferred to the server. HTTP or WebSocket protocols are commonly used for this transmission.

[0112] Step 3:

[0113] The server analyzes the received data using information processing tools. The input is the raw data received by the server, and the output is the analyzed data. Here, Python and R are used to perform data analysis and extract particularly abnormal data and allergen candidates.

[0114] Step 4:

[0115] The server identifies the presence of allergens based on the analyzed data using an identification method. In this step, the input is the analyzed data, and the output is the identified allergen information. Allergens are identified with high accuracy based on machine learning algorithms using a generative AI model such as TensorFlow.

[0116] Step 5:

[0117] The server generates warning messages based on allergen information identified using a generation mechanism. The input is identified allergen information, and the output is a customized warning message delivered to the user. A generation AI model is used to generate messages tailored to individual situations.

[0118] Step 6:

[0119] The server notifies the user's device in real time of a message generated as a notification method. The input is the generated warning message, and the output is the notification information displayed on the user's device. This notification is delivered using push notification technology or SMS.

[0120] Step 7:

[0121] The user takes appropriate action based on the received notification. The input is the warning message displayed on the device, and the output is the user's action choice. For example, they can take measures such as staying indoors or wearing a mask.

[0122] 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.

[0123] This invention is a system that collects environmental information using an acquisition device installed in a smartphone or similar mobile terminal, and, in cooperation with a server including an emotion engine, simultaneously analyzes allergen information and the user's emotions to provide warnings and countermeasures optimized for the user. Specific embodiments are described below.

[0124] First, the device continuously acquires information about the surrounding environment using the smartphone's camera and sensors. This information may include video and audio data, which forms the basis for detecting the presence of allergens and specific elements in the environment.

[0125] Subsequently, this environmental data is transmitted to a server. The server analyzes the received information using data processing tools and detects allergens in the environment through identification tools. In this process, a trained generative model is used to perform highly accurate classification of the characteristics of each allergen.

[0126] Furthermore, the system acquires user voice and facial expression data through the device, which is then analyzed by the server's emotion engine. The emotion engine analyzes the user's voice tone and facial microexpressions to identify the user's emotional state. This information is used to evaluate the user's stress level, level of distractibility, and other factors.

[0127] Based on identified allergens and emotional information, the server uses a generation mechanism to create customized warning messages and suggested actions. Here, the most appropriate approach is selected for the user's current emotional state, and reassuring advice is provided.

[0128] For example, if a high pollen concentration is detected and the user is also experiencing stress, the device can notify the user with a message such as, "The current pollen concentration is high, but we suggest ways to relax. Try taking deep breaths and take actions within your limits." This allows the user to choose actions that provide both mental and physical comfort at that moment.

[0129] In this way, the system improves the user's quality of life and supports a comfortable daily life through a series of processes from information gathering and analysis to customized notifications.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The device activates the smartphone's camera and sensors to continuously acquire environmental information and user voice and video data. Environmental information includes airborne allergens and lighting conditions. The user's voice and facial expressions are also captured simultaneously.

[0133] Step 2:

[0134] The terminal temporarily stores the acquired environment and user data and performs preprocessing according to each data format. This preprocessing includes denoising images, optimizing resolution, and clearing audio data.

[0135] Step 3:

[0136] The terminal sends pre-processed data to the server via the network. The data is divided into environmental data for allergen detection and user data for emotion analysis before being sent.

[0137] Step 4:

[0138] The server receives environmental data and uses data processing tools to analyze allergens. An AI model is used to effectively detect allergens in the environment and evaluate their concentrations.

[0139] Step 5:

[0140] Simultaneously, the server analyzes the user's voice and video data using an emotion engine. It evaluates the user's emotional state based on factors such as voice tone and facial microexpressions.

[0141] Step 6:

[0142] The server generates customized warning messages and suggested actions based on allergen information and the user's emotional state. This includes messages in a gentle tone that takes the user's emotional state into consideration.

[0143] Step 7:

[0144] The device verifies the warning message received from the server and immediately notifies the user. The notification is provided in the form of a pop-up message or audio alert to prompt the user to take action.

[0145] Step 8:

[0146] Users receive notifications and act upon the suggested actions. They can also use the feedback function to provide information to the system regarding the usefulness and emotional impact of the notification.

[0147] In this way, this system can comprehensively analyze the user's environment and emotions and provide optimal solutions in real time.

[0148] (Example 2)

[0149] 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".

[0150] There is a need to instantly grasp an individual's health and emotional state based on environmental information and provide personalized warnings and countermeasures. However, conventional technologies have had the problem of difficulty in efficiently collecting and analyzing surrounding information and individual emotional information, and providing appropriate feedback based on that information.

[0151] 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.

[0152] In this invention, the server includes a terminal that acquires information about its surroundings through an acquisition means, a transmission means that transmits information from the terminal, and a data processing means that analyzes the information transmitted by the transmission means. This makes it possible to provide highly responsive, personalized messages to individual users.

[0153] "Means of acquisition" refers to devices and methods for collecting information about the surrounding environment.

[0154] "Transmission means" refers to the process or method of transmitting collected data to another device or system.

[0155] "Data processing means" refers to a device or method for analyzing received information and processing it for a specific purpose.

[0156] "Generation means" refers to the process or method for creating personalized messages or suggestions based on analysis results.

[0157] "Notification means" refers to the process or method of conveying information to users.

[0158] This invention is a system that provides personalized feedback to users by collecting and analyzing environmental information. It is mainly realized by using acquisition means, transmission means, data processing means, generation means, and notification means. Specific embodiments are shown below.

[0159] The device continuously acquires information about the surrounding environment using the smartphone's camera, microphone, and various sensors. These sensor devices include, for example, light sensors, temperature sensors, and humidity sensors. The device collects this data in real time to form the basis for detecting the presence of allergens in the surroundings.

[0160] The terminal transmits the collected data to the server via an internet connection. The server analyzes the received data using data processing equipment. A pre-trained generative AI model is used for the analysis to detect and classify specific allergens. This generates highly accurate allergen information based on the data.

[0161] Furthermore, the device acquires additional data from the user, such as voice and facial expressions. This data is analyzed by the server's emotion engine to evaluate the user's emotional state. The emotion engine detects subtle changes in tone and facial expressions to identify the user's stress and relaxation levels.

[0162] Based on the analyzed allergen information and emotional data, the server generates a unique message through a generation mechanism. This message takes into account the user's current emotional state and environmental circumstances, and includes reassuring advice.

[0163] The device will notify the user of this message using a notification method. For example, if high pollen levels are detected and the user is also feeling stressed, a customized message such as, "Currently, pollen levels are high, but here are some ways to relax. Try taking deep breaths and act within your limits," will be displayed on the device.

[0164] This approach allows users to take the most appropriate action on the spot, potentially improving their quality of life.

[0165] Example of a prompt:

[0166] "We would like you to collect information on allergens currently present in the environment, evaluate the user's emotional state, and propose the most appropriate countermeasures."

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The device acquires information about its surroundings using the smartphone's camera and various sensors. This input includes video data, audio data, and temperature and humidity data. In particular, it captures visual information using the camera and senses changes in the sound environment using the microphone. The output is environmental data compiled from this collected data.

[0170] Step 2:

[0171] The terminal transmits the acquired environmental data to the server via the internet. This transmission occurs in real time, and a highly secure HTTP or HTTPS protocol is used for transmission. The output is the unprocessed environmental data received by the server.

[0172] Step 3:

[0173] The server analyzes the received environmental data using data processing tools. This analysis uses a pre-trained generative AI model to identify the presence of allergens from video and audio. The input is unanalyzed environmental data, and the output is detected allergen information. The AI ​​model classifies the data and highlights factors that match specific indicators.

[0174] Step 4:

[0175] The device acquires additional data on the user's voice and facial expressions. This input is used to identify the user's everyday stress levels and moods. The device uses dedicated sensors to acquire this data. The output is user emotion data based on voice tone and facial expressions.

[0176] Step 5:

[0177] The server uses an emotion engine to analyze the user's emotional data. The input is voice and facial expression data sent from the terminal, and the output is information that quantifies or classifies the user's emotional state. The emotion engine detects things like tone of voice and subtle facial muscle movements.

[0178] Step 6:

[0179] The server generates messages through a generation mechanism based on detected allergen information and the user's emotional state. The input is the analysis result from the previous step, and the output is an alert message and suggestion optimized for the user. Here, reassuring language is selected.

[0180] Step 7:

[0181] The device notifies the user of messages received from the server. These notifications appear as pop-ups on the smartphone screen, allowing the user to see them immediately. The output is the final message presented to the user, including specific suggestions regarding the actions the user should take.

[0182] (Application Example 2)

[0183] 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 device 14 will be referred to as the "terminal."

[0184] In care settings for the elderly and those with weakened immune systems, there is a need to prevent health effects from allergens while providing care that is tailored to the emotional state of the users. However, current methods lack systems that combine real-time allergen detection with emotional state analysis, which limits the provision of appropriate warnings and countermeasures.

[0185] 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.

[0186] In this invention, the server includes means for acquiring environmental information using an acquisition device, means for analyzing allergens and emotional states using data processing means, and means for generating customized warnings and countermeasures based on the analysis results. This makes it possible to provide caregivers with warnings and countermeasures optimized for the user in real time.

[0187] "Acquisition device" refers to a hardware device for collecting environmental information, such as the camera or sensor of a smartphone.

[0188] "Data processing means" refers to software functions that analyze acquired environmental information and evaluate the presence of allergens and the user's emotional state.

[0189] "Identification means" refers to a function for identifying allergens in the environment based on analysis results obtained through data processing.

[0190] "Generation means" refers to software that has the function of creating warnings and countermeasures based on allergens identified by specific means.

[0191] "Emotional analysis tools" refer to functions that analyze a user's emotional state and optimize the generated information based on the user's emotions.

[0192] "Notification means" refers to means of conveying information generated or optimized by the generation means to the user or caregiver.

[0193] The system that realizes this application example relies on the coordination of various hardware and software. This system collects information from the environment, analyzes emotional states, and notifies users of generated warnings and countermeasures, thereby providing optimal care for elderly and immunocompromised users in care settings.

[0194] The server is responsible for data processing and analyzes environmental information transmitted from acquisition devices. Specifically, it uses data acquired by cameras and sensors of smartphones and smart glasses. This data may include allergen information, user voice, and facial expressions. The server receives this data and performs highly accurate analysis using generative AI models such as GOOGLE TENSOR® Flow and Amazon SageMaker.

[0195] Furthermore, the server uses an emotion analysis engine to analyze the collected user's voice tone and facial microexpressions to identify the user's emotional state. This allows for an assessment of the user's stress level and level of distractibility.

[0196] Based on the analyzed information, the server generates appropriate warning messages and suggested solutions. The generated messages are optimized to suit the user's emotional state. For example, if the user has pollen allergies and is also experiencing stress, the server will generate a message such as, "The current pollen concentration is high, but we suggest ways to relax."

[0197] This generated information is transmitted in real time to caregivers via notification devices and smart glasses interfaces. This allows caregivers to provide users with the most appropriate care at any given time.

[0198] For example, if pollen levels rise sharply and elderly residents become anxious, care staff can receive instructions through the application such as, "The pollen levels in the facility are high, so please increase indoor activities and create a more relaxed atmosphere."

[0199] Example of a prompt:

[0200] "To ensure elderly residents feel safe and secure in care facilities, please generate care advice on the server based on conditions that worsen pollen allergy symptoms."

[0201] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0202] Step 1:

[0203] The device continuously acquires information about its surroundings using a camera sensor. The acquired environmental information is stored as image data. The input is a real-time video stream, and the output is image frames collected in real time.

[0204] Step 2:

[0205] The device acquires the user's voice and facial expression data. This involves capturing audio and video data through the microphone and camera and saving it as voice tone and facial expression data. The input is the user's voice stream and video, and the output is a processable audio file and video frames.

[0206] Step 3:

[0207] The terminal transmits acquired environmental information and user voice and facial expression data to the server. The input is the dataset obtained in the previous step, and the output is data packets for further processing by the server.

[0208] Step 4:

[0209] The server analyzes the received environmental information using generative AI models such as Google® TensorFlow. Data processing involves classification using machine learning algorithms based on allergen characteristics. The input is an image frame, and the output is identified allergen information.

[0210] Step 5:

[0211] The server uses an emotion analysis engine to analyze the user's voice and facial expression data. This involves voice tone analysis and facial signal processing to identify the user's emotional state. Inputs are audio and video data, and output is information about the user's emotional state.

[0212] Step 6:

[0213] The server combines allergen information and user emotional state information to generate appropriate warning messages and suggested actions. Using the generation mechanism, it creates optimal explanatory and prompt statements tailored to the situation. The input is the two output data sets mentioned earlier, and the output is the generated message or advice.

[0214] Step 7:

[0215] The server sends the generated message to the terminal via a notification system. This allows caregivers to receive this information in real time at the care site. The input is the generated warning message, and the output is the notification displayed on the user interface.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] [Second Embodiment]

[0220] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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".

[0232] This invention is a system that acquires information from the environment using an acquisition device attached to a terminal such as a smartphone, identifies allergens in real time by coordinating with a server, and provides this information to the user. The main operation of the system is described in detail below.

[0233] First, the device continuously collects information about the surrounding environment using the smartphone's camera and sensors. The acquired information is temporarily stored on the device and converted into a format that can be easily analyzed under specific environmental conditions.

[0234] Subsequently, this environmental information is transmitted to a server via the network. The server analyzes the received information using data processing tools and extracts the characteristics of allergens floating in the environment. Here, an AI model is used to identify the presence of allergens based on past training data. The allergen information identified through this series of processes is organized by concentration and type.

[0235] Next, the server uses a generation mechanism to create specific messages and instructions to generate warnings and suggested countermeasures tailored to the allergen. This process takes into account the user's individual information (e.g., allergy type and threshold), resulting in customized countermeasures. The generated warning information is sent to the user in real time and displayed as a notification on their smartphone screen.

[0236] For example, if a large amount of pollen is detected in a park, the device will notify the user with specific action advice, such as, "The pollen concentration is high in your current location, so we recommend you refrain from going outside or wear a mask." Based on this information, the user can then take appropriate action on the spot.

[0237] In this way, by linking acquisition devices, data processing means, identification means, generation means, and notification means, this system enables users to always be aware of the latest allergen information and take immediate safe actions.

[0238] The following describes the processing flow.

[0239] Step 1:

[0240] The device activates the smartphone's camera and sensors to acquire information about the surrounding environment in real time. This information is temporarily stored as continuously captured video and digital data.

[0241] Step 2:

[0242] The terminal preprocesses the acquired video data into the required format. This preprocessing includes noise reduction, resolution adjustment, and color correction as needed. This prepares the data for subsequent analysis.

[0243] Step 3:

[0244] The terminal sends pre-processed data to the server over the network. Data compression may be performed at this stage depending on network conditions.

[0245] Step 4:

[0246] The server inputs the received data into an AI model for analysis. Here, a pre-trained deep learning algorithm is used to perform the process of detecting allergen characteristics from the video.

[0247] Step 5:

[0248] The server collects the types and concentrations of allergens identified from the AI ​​model's analysis results and records them in a database. Here, the allergens are classified and their concentrations are quantified, and the results are passed on to the next processing step.

[0249] Step 6:

[0250] Based on the identified allergen information, the server generates a warning message based on user customization information that matches the trigger conditions. The message will take into account each user's allergy type and countermeasures.

[0251] Step 7:

[0252] The device receives a warning message sent from the server and notifies the user. The notification is displayed as a pop-up on the smartphone or as an alert within the application.

[0253] Step 8:

[0254] Users can review notifications and choose actions based on the information they receive in real time. They can also provide feedback on whether the notifications were helpful.

[0255] The above is the specific processing flow for identifying allergen information in real time and notifying the user.

[0256] (Example 1)

[0257] 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."

[0258] In modern society, many people suffer health problems caused by environmental allergens, but there is a lack of systems to identify these allergens in real time and take appropriate countermeasures. Furthermore, customized responses based on individual user information are insufficient, and there is a need for a system that can provide accurate information under diverse environmental conditions.

[0259] 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.

[0260] In this invention, the server includes means for acquiring environmental information using an acquisition device, means for converting and analyzing the data format, and means for identifying the presence of allergens using a pre-trained generative model. This enables users to grasp allergen information in real time and quickly take appropriate action based on individually customized countermeasures.

[0261] An "acquisition device" is a device installed to collect environmental information, and it acquires surrounding environmental data through sensors and cameras.

[0262] "Data processing means" refers to means that analyze environmental information obtained from an acquisition device and perform data format conversion or pattern recognition as necessary.

[0263] A "generative model" is a model that learns specific patterns and characteristics based on past data and identifies the presence of allergens based on new data.

[0264] "Identification methods" refer to means of identifying allergens present in the environment and confirming their presence using analyzed and processed data.

[0265] "Generation means" refers to means of creating warning messages and countermeasures to provide to users based on identified allergen information.

[0266] A "notification method" is a means of delivering generated information to users in real time, and notifications are sent via smartphones or other communication devices.

[0267] This invention is a system that provides users with allergen information in real time, supporting them in taking safe actions on the spot. This system primarily operates through the coordinated efforts of three elements: a terminal, a server, and the user.

[0268] The device uses a smartphone as an example of an embodiment. Cameras and sensors installed in the smartphone continuously acquire information about the surrounding environment. This information includes temperature, humidity, and fine particles in the air, and is temporarily stored in the device in the initial stage. Hardware used includes high-performance camera modules and environmental sensors. The acquired data is converted into a format that is easy to analyze, such as JSON or CSV format.

[0269] The server receives this converted data via the network. The server uses data processing tools and an AI model to analyze the data. The software used here includes a generative AI model. This model searches for allergen characteristics based on past training data and identifies allergens present at the user's current location. The server uses these identification tools to identify allergens floating in the environment and extract their concentration and type.

[0270] The server then generates warnings and suggested actions for the user based on the data mentioned above. This process takes the user's individual information into account to create customized messages. The generated warnings are sent to the user's device in real time.

[0271] Users will receive this warning information through their devices. For example, if a large amount of pollen is detected in a park, the device will receive a message stating, "The pollen concentration is high in your current location, so we recommend you stay indoors or wear a mask." This allows users to quickly choose the appropriate course of action.

[0272] As a concrete example, prompt messages are generated in the format of "Investigate the pollen concentration in the park and, based on the results, suggest the most appropriate course of action for the user." This system allows users to always be aware of the latest allergen information and take safe actions.

[0273] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0274] Step 1:

[0275] The device collects information about its surroundings. Cameras and sensors are used to acquire data such as temperature, humidity, visual data, and airborne particle information. The input is raw data obtained from the environment, which is temporarily stored in internal memory. The output is raw environmental data for later processing.

[0276] Step 2:

[0277] Convert the raw data obtained by the terminal into a format that is easy to analyze. The input is the raw data collected in Step 1. In this conversion process, the data is converted into JSON or CSV format. The output is the data formatted in a format that is easy for the data processing means to recognize.

[0278] Step 3:

[0279] Transmit the data converted by the terminal to the server via the network. The input is the formatted environmental data. Use Wi-Fi or Bluetooth to transmit the data to the server through a secure protocol. The output is the environmental data received by the server.

[0280] Step 4:

[0281] The server analyzes the received environmental data. The input is the data received in Step 3. The server uses data processing means and utilizes the generated AI model to extract the characteristics of allergens from the data. The output is the information of the identified allergens.

[0282] Step 5:

[0283] The server generates a warning message based on the identified allergen information. The input is the information regarding the characteristics and concentration of allergens. Consider the user's individual information and generate a customized message. The output is the warning message sent to the user.

[0284] Step 6:

[0285] Notify the user's terminal of the generated warning. The input is the warning message generated in Step 5. Display the warning on the user's smartphone screen through the notification function. The output is the warning information that the user can confirm, based on which appropriate actions can be taken.

[0286] (Application Example 1)

[0287] 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".

[0288] In a modern urban environment, the concentration and types of allergy-causing substances change moment by moment and are likely to have an adverse impact on the health of residents. However, a system that can grasp this information in real time and prompt residents to take countermeasures has not yet been sufficiently provided. In particular, it is difficult to propose appropriate actions suitable for users based on individual allergy information. Against this background, it is necessary to monitor allergens throughout the city and issue customized warnings to individual users.

[0289] 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.

[0290] In this invention, the server includes means for acquiring environmental information, information processing means, identification means, generation means, notification means, and urban monitoring means. As a result, it becomes possible to monitor allergens throughout the city, identify allergens in real time based on the acquired information, and propose customized warnings and safety measures for individual residents.

[0291] The "acquisition device" is a device for collecting information in the environment and includes sensors, cameras, etc.

[0292] The "information processing means" is a system that analyzes the environmental information obtained from the acquisition device and performs data conversion.

[0293] The "identification means" is a method for detecting the presence of specific substances or allergens based on the data analyzed by the information processing means.

[0294] "Generation means" refers to the process of creating warnings and countermeasures for users based on the information identified by the identification means.

[0295] A "notification method" refers to a technique for notifying users of generated information in real time, using methods such as screen displays and voice guidance.

[0296] A "city monitoring system" is a mechanism that monitors environmental information throughout a city and continuously provides useful allergen information to a wide range of residents.

[0297] A system for carrying out this invention consists of coordinating an acquisition device, data processing means, identification means, generation means, notification means, and urban monitoring means.

[0298] The server first collects environmental information from sensors and smartphone cameras via an acquisition device. This environmental information includes various environmental data such as temperature, humidity, and particulate matter concentration. Subsequently, data processing means are used to formalize this raw data and convert it into an analyzable state. Database software and data analysis tools are used for this process, specifically Apache Kafka and Python.

[0299] Next, the server uses identification methods and leverages an AI model (e.g., a generative AI model using TensorFlow) to identify the presence of allergens from the analyzed data. This process involves referencing past training data to achieve highly accurate identification.

[0300] Based on the identified allergen information, the generation system creates customized warning messages and suggested countermeasures tailored to each user's individual needs. This generation process applies a generation AI model and dynamically generates messages using JavaScript or Python.

[0301] Finally, the generated information is notified to the user's smartphone in real time via the notification means. This notification is carried out through the push notification function of the application or SMS, and residents can take immediate actions based on the information.

[0302] As a specific example, when it is identified that the pollen dispersal amount has increased rapidly in a certain park on a certain day, the server generates a message such as "The pollen concentration is currently very high near XX Park. Please use YY Square instead." and notifies the user's smartphone. As an example of the prompt text, "Pollen dispersal situation, urban center, current time" is input, and the analysis result is obtained.

[0303] This enables allergen monitoring at the urban level and provides a safe and healthy living environment for users.

[0304] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0305] Step 1:

[0306] The terminal uses the camera and various sensors of the smartphone as acquisition devices to collect environmental information. The input is the ambient environmental data, and the output is stored in the terminal in a state where the data is converted into a format that is easy to organize. A data formatting algorithm is used for this conversion.

[0307] Step 2:

[0308] The terminal sends the organized environmental data to the server via the network. The input is the formatted data stored on the terminal, and the output is the data successfully transferred to the server. It is common to use the HTTP protocol or WebSocket for this transmission.

[0309] Step 3:

[0310] The server analyzes the received data using information processing tools. The input is the raw data received by the server, and the output is the analyzed data. Here, Python and R are used to perform data analysis and extract particularly abnormal data and allergen candidates.

[0311] Step 4:

[0312] The server identifies the presence of allergens based on the analyzed data using an identification method. In this step, the input is the analyzed data, and the output is the identified allergen information. Allergens are identified with high accuracy based on machine learning algorithms using a generative AI model such as TensorFlow.

[0313] Step 5:

[0314] The server generates warning messages based on allergen information identified using a generation mechanism. The input is identified allergen information, and the output is a customized warning message delivered to the user. A generation AI model is used to generate messages tailored to individual situations.

[0315] Step 6:

[0316] The server notifies the user's device in real time of a message generated as a notification method. The input is the generated warning message, and the output is the notification information displayed on the user's device. This notification is delivered using push notification technology or SMS.

[0317] Step 7:

[0318] The user takes appropriate action based on the received notification. The input is the warning message displayed on the device, and the output is the user's action choice. For example, they can take measures such as staying indoors or wearing a mask.

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

[0320] This invention is a system that collects environmental information using an acquisition device installed in a smartphone or similar mobile terminal, and, in cooperation with a server including an emotion engine, simultaneously analyzes allergen information and the user's emotions to provide warnings and countermeasures optimized for the user. Specific embodiments are described below.

[0321] First, the device continuously acquires information about the surrounding environment using the smartphone's camera and sensors. This information may include video and audio data, which forms the basis for detecting the presence of allergens and specific elements in the environment.

[0322] Subsequently, this environmental data is transmitted to a server. The server analyzes the received information using data processing tools and detects allergens in the environment through identification tools. In this process, a trained generative model is used to perform highly accurate classification of the characteristics of each allergen.

[0323] Furthermore, the system acquires user voice and facial expression data through the device, which is then analyzed by the server's emotion engine. The emotion engine analyzes the user's voice tone and facial microexpressions to identify the user's emotional state. This information is used to evaluate the user's stress level, level of distractibility, and other factors.

[0324] Based on identified allergens and emotional information, the server uses a generation mechanism to create customized warning messages and suggested actions. Here, the most appropriate approach is selected for the user's current emotional state, and reassuring advice is provided.

[0325] For example, if a high pollen concentration is detected and the user is also experiencing stress, the device can notify the user with a message such as, "The current pollen concentration is high, but we suggest ways to relax. Try taking deep breaths and take actions within your limits." This allows the user to choose actions that provide both mental and physical comfort at that moment.

[0326] In this way, the system improves the user's quality of life and supports a comfortable daily life through a series of processes from information gathering and analysis to customized notifications.

[0327] The following describes the processing flow.

[0328] Step 1:

[0329] The device activates the smartphone's camera and sensors to continuously acquire environmental information and user voice and video data. Environmental information includes airborne allergens and lighting conditions. The user's voice and facial expressions are also captured simultaneously.

[0330] Step 2:

[0331] The terminal temporarily stores the acquired environment and user data and performs preprocessing according to each data format. This preprocessing includes denoising images, optimizing resolution, and clearing audio data.

[0332] Step 3:

[0333] The terminal sends pre-processed data to the server via the network. The data is divided into environmental data for allergen detection and user data for emotion analysis before being sent.

[0334] Step 4:

[0335] The server receives environmental data and uses data processing tools to analyze allergens. An AI model is used to effectively detect allergens in the environment and evaluate their concentrations.

[0336] Step 5:

[0337] Simultaneously, the server analyzes the user's voice and video data using an emotion engine. It evaluates the user's emotional state based on factors such as voice tone and facial microexpressions.

[0338] Step 6:

[0339] The server generates customized warning messages and suggested actions based on allergen information and the user's emotional state. This includes messages in a gentle tone that takes the user's emotional state into consideration.

[0340] Step 7:

[0341] The device verifies the warning message received from the server and immediately notifies the user. The notification is provided in the form of a pop-up message or audio alert to prompt the user to take action.

[0342] Step 8:

[0343] Users receive notifications and act upon the suggested actions. They can also use the feedback function to provide information to the system regarding the usefulness and emotional impact of the notification.

[0344] In this way, this system can comprehensively analyze the user's environment and emotions and provide optimal solutions in real time.

[0345] (Example 2)

[0346] 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".

[0347] There is a need to instantly grasp an individual's health and emotional state based on environmental information and provide personalized warnings and countermeasures. However, conventional technologies have had the problem of difficulty in efficiently collecting and analyzing surrounding information and individual emotional information, and providing appropriate feedback based on that information.

[0348] 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.

[0349] In this invention, the server includes a terminal that acquires information about its surroundings through an acquisition means, a transmission means that transmits information from the terminal, and a data processing means that analyzes the information transmitted by the transmission means. This makes it possible to provide highly responsive, personalized messages to individual users.

[0350] "Means of acquisition" refers to devices and methods for collecting information about the surrounding environment.

[0351] "Transmission means" refers to the process or method of transmitting collected data to another device or system.

[0352] "Data processing means" refers to a device or method for analyzing received information and processing it for a specific purpose.

[0353] "Generation means" refers to the process or method for creating personalized messages or suggestions based on analysis results.

[0354] "Notification means" refers to the process or method of conveying information to users.

[0355] This invention is a system that provides personalized feedback to users by collecting and analyzing environmental information. It is mainly realized by using acquisition means, transmission means, data processing means, generation means, and notification means. Specific embodiments are shown below.

[0356] The device continuously acquires information about the surrounding environment using the smartphone's camera, microphone, and various sensors. These sensor devices include, for example, light sensors, temperature sensors, and humidity sensors. The device collects this data in real time to form the basis for detecting the presence of allergens in the surroundings.

[0357] The terminal transmits the collected data to the server via an internet connection. The server analyzes the received data using data processing equipment. A pre-trained generative AI model is used for the analysis to detect and classify specific allergens. This generates highly accurate allergen information based on the data.

[0358] Furthermore, the device acquires additional data from the user, such as voice and facial expressions. This data is analyzed by the server's emotion engine to evaluate the user's emotional state. The emotion engine detects subtle changes in tone and facial expressions to identify the user's stress and relaxation levels.

[0359] Based on the analyzed allergen information and emotional data, the server generates a unique message through a generation mechanism. This message takes into account the user's current emotional state and environmental circumstances, and includes reassuring advice.

[0360] The device will notify the user of this message using a notification method. For example, if high pollen levels are detected and the user is also feeling stressed, a customized message such as, "Currently, pollen levels are high, but here are some ways to relax. Try taking deep breaths and act within your limits," will be displayed on the device.

[0361] This approach allows users to take the most appropriate action on the spot, potentially improving their quality of life.

[0362] Example of a prompt:

[0363] "We would like you to collect information on allergens currently present in the environment, evaluate the user's emotional state, and propose the most appropriate countermeasures."

[0364] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0365] Step 1:

[0366] The device acquires information about its surroundings using the smartphone's camera and various sensors. This input includes video data, audio data, and temperature and humidity data. In particular, it captures visual information using the camera and senses changes in the sound environment using the microphone. The output is environmental data compiled from this collected data.

[0367] Step 2:

[0368] The terminal transmits the acquired environmental data to the server via the internet. This transmission occurs in real time, and a highly secure HTTP or HTTPS protocol is used for transmission. The output is the unprocessed environmental data received by the server.

[0369] Step 3:

[0370] The server analyzes the received environmental data using data processing tools. This analysis uses a pre-trained generative AI model to identify the presence of allergens from video and audio. The input is unanalyzed environmental data, and the output is detected allergen information. The AI ​​model classifies the data and highlights factors that match specific indicators.

[0371] Step 4:

[0372] The device acquires additional data on the user's voice and facial expressions. This input is used to identify the user's everyday stress levels and moods. The device uses dedicated sensors to acquire this data. The output is user emotion data based on voice tone and facial expressions.

[0373] Step 5:

[0374] The server uses an emotion engine to analyze the user's emotional data. The input is voice and facial expression data sent from the terminal, and the output is information that quantifies or classifies the user's emotional state. The emotion engine detects things like tone of voice and subtle facial muscle movements.

[0375] Step 6:

[0376] The server generates messages through a generation mechanism based on detected allergen information and the user's emotional state. The input is the analysis result from the previous step, and the output is an alert message and suggestion optimized for the user. Here, reassuring language is selected.

[0377] Step 7:

[0378] The device notifies the user of messages received from the server. These notifications appear as pop-ups on the smartphone screen, allowing the user to see them immediately. The output is the final message presented to the user, including specific suggestions regarding the actions the user should take.

[0379] (Application Example 2)

[0380] 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."

[0381] In care settings for the elderly and those with weakened immune systems, there is a need to prevent health effects from allergens while providing care that is tailored to the emotional state of the users. However, current methods lack systems that combine real-time allergen detection with emotional state analysis, which limits the provision of appropriate warnings and countermeasures.

[0382] 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.

[0383] In this invention, the server includes means for acquiring environmental information using an acquisition device, means for analyzing allergens and emotional states using data processing means, and means for generating customized warnings and countermeasures based on the analysis results. This makes it possible to provide caregivers with warnings and countermeasures optimized for the user in real time.

[0384] "Acquisition device" refers to a hardware device for collecting environmental information, such as the camera or sensor of a smartphone.

[0385] "Data processing means" refers to software functions that analyze acquired environmental information and evaluate the presence of allergens and the user's emotional state.

[0386] "Identification means" refers to a function for identifying allergens in the environment based on analysis results obtained through data processing.

[0387] "Generation means" refers to software that has the function of creating warnings and countermeasures based on allergens identified by specific means.

[0388] "Emotional analysis tools" refer to functions that analyze a user's emotional state and optimize the generated information based on the user's emotions.

[0389] "Notification means" refers to means of conveying information generated or optimized by the generation means to the user or caregiver.

[0390] The system that realizes this application example relies on the coordination of various hardware and software. This system collects information from the environment, analyzes emotional states, and notifies users of generated warnings and countermeasures, thereby providing optimal care for elderly and immunocompromised users in care settings.

[0391] The server is responsible for data processing and analyzes environmental information transmitted from acquisition devices. Specifically, it uses data acquired by cameras and sensors of smartphones and smart glasses. This data may include allergen information, user voice, and facial expressions. The server receives this data and performs highly accurate analysis using generative AI models such as Google TensorFlow and Amazon SageMaker.

[0392] Furthermore, the server uses an emotion analysis engine to analyze the collected user's voice tone and facial microexpressions to identify the user's emotional state. This allows for an assessment of the user's stress level and level of distractibility.

[0393] Based on the analyzed information, the server generates appropriate warning messages and suggested solutions. The generated messages are optimized to suit the user's emotional state. For example, if the user has pollen allergies and is also experiencing stress, the server will generate a message such as, "The current pollen concentration is high, but we suggest ways to relax."

[0394] This generated information is transmitted in real time to caregivers via notification devices and smart glasses interfaces. This allows caregivers to provide users with the most appropriate care at any given time.

[0395] For example, if pollen levels rise sharply and elderly residents become anxious, care staff can receive instructions through the application such as, "The pollen levels in the facility are high, so please increase indoor activities and create a more relaxed atmosphere."

[0396] Example of a prompt:

[0397] "To ensure elderly residents feel safe and secure in care facilities, please generate care advice on the server based on conditions that worsen pollen allergy symptoms."

[0398] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0399] Step 1:

[0400] The device continuously acquires information about its surroundings using a camera sensor. The acquired environmental information is stored as image data. The input is a real-time video stream, and the output is image frames collected in real time.

[0401] Step 2:

[0402] The device acquires the user's voice and facial expression data. This involves capturing audio and video data through the microphone and camera and saving it as voice tone and facial expression data. The input is the user's voice stream and video, and the output is a processable audio file and video frames.

[0403] Step 3:

[0404] The terminal transmits acquired environmental information and user voice and facial expression data to the server. The input is the dataset obtained in the previous step, and the output is data packets for further processing by the server.

[0405] Step 4:

[0406] The server analyzes the received environmental information using generative AI models such as Google TensorFlow. Data processing involves classification using machine learning algorithms based on allergen characteristics. The input is an image frame, and the output is identified allergen information.

[0407] Step 5:

[0408] The server uses an emotion analysis engine to analyze the user's voice and facial expression data. This involves voice tone analysis and facial signal processing to identify the user's emotional state. Inputs are audio and video data, and output is information about the user's emotional state.

[0409] Step 6:

[0410] The server combines allergen information and user emotional state information to generate appropriate warning messages and suggested actions. Using the generation mechanism, it creates optimal explanatory and prompt statements tailored to the situation. The input is the two output data sets mentioned earlier, and the output is the generated message or advice.

[0411] Step 7:

[0412] The server sends the generated message to the terminal via a notification system. This allows caregivers to receive this information in real time at the care site. The input is the generated warning message, and the output is the notification displayed on the user interface.

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

[0414] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

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

[0416] [Third Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0429] This invention is a system that acquires information from the environment using an acquisition device attached to a terminal such as a smartphone, identifies allergens in real time by coordinating with a server, and provides this information to the user. The main operation of the system is described in detail below.

[0430] First, the device continuously collects information about the surrounding environment using the smartphone's camera and sensors. The acquired information is temporarily stored on the device and converted into a format that can be easily analyzed under specific environmental conditions.

[0431] Subsequently, this environmental information is transmitted to a server via the network. The server analyzes the received information using data processing tools and extracts the characteristics of allergens floating in the environment. Here, an AI model is used to identify the presence of allergens based on past training data. The allergen information identified through this series of processes is organized by concentration and type.

[0432] Next, the server uses a generation mechanism to create specific messages and instructions to generate warnings and suggested countermeasures tailored to the allergen. This process takes into account the user's individual information (e.g., allergy type and threshold), resulting in customized countermeasures. The generated warning information is sent to the user in real time and displayed as a notification on their smartphone screen.

[0433] For example, if a large amount of pollen is detected in a park, the device will notify the user with specific action advice, such as, "The pollen concentration is high in your current location, so we recommend you refrain from going outside or wear a mask." Based on this information, the user can then take appropriate action on the spot.

[0434] In this way, by linking acquisition devices, data processing means, identification means, generation means, and notification means, this system enables users to always be aware of the latest allergen information and take immediate safe actions.

[0435] The following describes the processing flow.

[0436] Step 1:

[0437] The device activates the smartphone's camera and sensors to acquire information about the surrounding environment in real time. This information is temporarily stored as continuously captured video and digital data.

[0438] Step 2:

[0439] The terminal preprocesses the acquired video data into the required format. This preprocessing includes noise reduction, resolution adjustment, and color correction as needed. This prepares the data for subsequent analysis.

[0440] Step 3:

[0441] The terminal sends pre-processed data to the server over the network. Data compression may be performed at this stage depending on network conditions.

[0442] Step 4:

[0443] The server inputs the received data into an AI model for analysis. Here, a pre-trained deep learning algorithm is used to perform the process of detecting allergen characteristics from the video.

[0444] Step 5:

[0445] The server collects the types and concentrations of allergens identified from the AI ​​model's analysis results and records them in a database. Here, the allergens are classified and their concentrations are quantified, and the results are passed on to the next processing step.

[0446] Step 6:

[0447] Based on the identified allergen information, the server generates a warning message based on user customization information that matches the trigger conditions. The message will take into account each user's allergy type and countermeasures.

[0448] Step 7:

[0449] The device receives a warning message sent from the server and notifies the user. The notification is displayed as a pop-up on the smartphone or as an alert within the application.

[0450] Step 8:

[0451] Users can review notifications and choose actions based on the information they receive in real time. They can also provide feedback on whether the notifications were helpful.

[0452] The above is the specific processing flow for identifying allergen information in real time and notifying the user.

[0453] (Example 1)

[0454] 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."

[0455] In modern society, many people suffer health problems caused by environmental allergens, but there is a lack of systems to identify these allergens in real time and take appropriate countermeasures. Furthermore, customized responses based on individual user information are insufficient, and there is a need for a system that can provide accurate information under diverse environmental conditions.

[0456] 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.

[0457] In this invention, the server includes means for acquiring environmental information using an acquisition device, means for converting and analyzing the data format, and means for identifying the presence of allergens using a pre-trained generative model. This enables users to grasp allergen information in real time and quickly take appropriate action based on individually customized countermeasures.

[0458] An "acquisition device" is a device installed to collect environmental information, and it acquires surrounding environmental data through sensors and cameras.

[0459] "Data processing means" refers to means that analyze environmental information obtained from an acquisition device and perform data format conversion or pattern recognition as necessary.

[0460] A "generative model" is a model that learns specific patterns and characteristics based on past data and identifies the presence of allergens based on new data.

[0461] "Identification methods" refer to means of identifying allergens present in the environment and confirming their presence using analyzed and processed data.

[0462] "Generation means" refers to means of creating warning messages and countermeasures to provide to users based on identified allergen information.

[0463] A "notification method" is a means of delivering generated information to users in real time, and notifications are sent via smartphones or other communication devices.

[0464] This invention is a system that provides users with allergen information in real time, supporting them in taking safe actions on the spot. This system primarily operates through the coordinated efforts of three elements: a terminal, a server, and the user.

[0465] The device uses a smartphone as an example of an embodiment. Cameras and sensors installed in the smartphone continuously acquire information about the surrounding environment. This information includes temperature, humidity, and fine particles in the air, and is temporarily stored in the device in the initial stage. Hardware used includes high-performance camera modules and environmental sensors. The acquired data is converted into a format that is easy to analyze, such as JSON or CSV format.

[0466] The server receives this converted data via the network. The server uses data processing tools and an AI model to analyze the data. The software used here includes a generative AI model. This model searches for allergen characteristics based on past training data and identifies allergens present at the user's current location. The server uses these identification tools to identify allergens floating in the environment and extract their concentration and type.

[0467] The server then generates warnings and suggested actions for the user based on the data mentioned above. This process takes the user's individual information into account to create customized messages. The generated warnings are sent to the user's device in real time.

[0468] Users will receive this warning information through their devices. For example, if a large amount of pollen is detected in a park, the device will receive a message stating, "The pollen concentration is high in your current location, so we recommend you stay indoors or wear a mask." This allows users to quickly choose the appropriate course of action.

[0469] As a concrete example, prompt messages are generated in the format of "Investigate the pollen concentration in the park and, based on the results, suggest the most appropriate course of action for the user." This system allows users to always be aware of the latest allergen information and take safe actions.

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

[0471] Step 1:

[0472] The device collects information about its surroundings. Cameras and sensors are used to acquire data such as temperature, humidity, visual data, and airborne particle information. The input is raw data obtained from the environment, which is temporarily stored in internal memory. The output is raw environmental data for later processing.

[0473] Step 2:

[0474] The raw data acquired by the terminal is converted into a format that is easy to analyze. The input is the raw data collected in step 1. In this conversion process, the data is converted into JSON or CSV format. The output is data formatted in a format that is easy for data processing tools to recognize.

[0475] Step 3:

[0476] The data converted from the terminal is sent to the server via the network. The input is formatted environmental data. The data is sent to the server via a secure protocol using Wi-Fi or Bluetooth. The output is the environmental data received by the server.

[0477] Step 4:

[0478] The server analyzes the received environmental data. The input is the data received in step 3. The server uses data processing tools and leverages a generative AI model to extract allergen characteristics from the data. The output is information on the identified allergens.

[0479] Step 5:

[0480] The server generates a warning message based on identified allergen information. The input is information about the characteristics and concentration of the allergen. It generates a customized message considering the user's individual information. The output is the warning message sent to the user.

[0481] Step 6:

[0482] The system notifies the user of the generated warning on their device. The input is the warning message generated in step 5. The warning is displayed on the user's smartphone screen via the notification function. The output is warning information that the user can review, allowing them to take appropriate action based on it.

[0483] (Application Example 1)

[0484] 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."

[0485] In modern urban environments, the concentrations and types of allergens change constantly, posing a high risk of adverse health effects on residents. However, systems that can grasp this information in real time and prompt residents to take appropriate measures are still insufficient. In particular, it is difficult to suggest appropriate actions tailored to individual users based on their individual allergy information. Against this backdrop, there is a need to monitor allergens throughout the city and issue customized warnings to individual users.

[0486] 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.

[0487] In this invention, the server includes means for acquiring environmental information, means for processing information, means for identifying information, means for generating information, means for notifying information, and means for monitoring the city. This enables monitoring of allergens throughout the city, real-time identification of allergens based on the acquired information, and the proposal of customized warnings and safety measures tailored to individual residents.

[0488] An "acquisition device" is a device used to collect information from the environment, and includes sensors, cameras, and other similar devices.

[0489] An "information processing means" is a system that analyzes environmental information obtained from an acquisition device and performs data conversion.

[0490] "Identification means" refers to a method of detecting the presence of a specific substance or allergen based on data analyzed by information processing means.

[0491] "Generation means" refers to the process of creating warnings and countermeasures for users based on the information identified by the identification means.

[0492] A "notification method" refers to a technique for notifying users of generated information in real time, using methods such as screen displays and voice guidance.

[0493] A "city monitoring system" is a mechanism that monitors environmental information throughout a city and continuously provides useful allergen information to a wide range of residents.

[0494] A system for carrying out this invention consists of coordinating an acquisition device, data processing means, identification means, generation means, notification means, and urban monitoring means.

[0495] The server first collects environmental information from sensors and smartphone cameras via an acquisition device. This environmental information includes various environmental data such as temperature, humidity, and particulate matter concentration. Subsequently, data processing means are used to formalize this raw data and convert it into an analyzable state. Database software and data analysis tools are used for this process, specifically Apache Kafka and Python.

[0496] Next, the server uses identification methods and leverages an AI model (e.g., a generative AI model using TensorFlow) to identify the presence of allergens from the analyzed data. This process involves referencing past training data to achieve highly accurate identification.

[0497] Based on the identified allergen information, the generation system creates customized warning messages and suggested countermeasures tailored to each user's individual needs. This generation process applies a generation AI model and dynamically generates messages using JavaScript or Python.

[0498] Finally, the generated information is sent to the user's smartphone in real time via the reporting system. This notification is delivered via the application's push notification function or SMS, allowing residents to take immediate action based on the information.

[0499] For example, if the system identifies a sudden surge in pollen levels in a particular park on a given day, it will generate a message such as, "The pollen concentration near XX Park is currently very high. Please use △△ Plaza instead," and notify the user's smartphone. An example of a prompt message would be, "Pollen levels in the city center, current time," to obtain the analysis results.

[0500] This enables allergen monitoring at the city level, providing users with a safe and healthy living environment.

[0501] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0502] Step 1:

[0503] The device collects environmental information using the smartphone's camera and various sensors as acquisition devices. The input is ambient environmental data, and the output is this data converted into an easily manageable format and stored within the device. A data formatting algorithm is used for this conversion.

[0504] Step 2:

[0505] The terminal sends organized environmental data to the server via the network. The input is formatted data stored on the terminal, and the output is the data successfully transferred to the server. HTTP or WebSocket protocols are commonly used for this transmission.

[0506] Step 3:

[0507] The server analyzes the received data using information processing tools. The input is the raw data received by the server, and the output is the analyzed data. Here, Python and R are used to perform data analysis and extract particularly abnormal data and allergen candidates.

[0508] Step 4:

[0509] The server identifies the presence of allergens based on the analyzed data using an identification method. In this step, the input is the analyzed data, and the output is the identified allergen information. Allergens are identified with high accuracy based on machine learning algorithms using a generative AI model such as TensorFlow.

[0510] Step 5:

[0511] The server generates warning messages based on allergen information identified using a generation mechanism. The input is identified allergen information, and the output is a customized warning message delivered to the user. A generation AI model is used to generate messages tailored to individual situations.

[0512] Step 6:

[0513] The server notifies the user's device in real time of a message generated as a notification method. The input is the generated warning message, and the output is the notification information displayed on the user's device. This notification is delivered using push notification technology or SMS.

[0514] Step 7:

[0515] The user takes appropriate action based on the received notification. The input is the warning message displayed on the device, and the output is the user's action choice. For example, they can take measures such as staying indoors or wearing a mask.

[0516] 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.

[0517] This invention is a system that collects environmental information using an acquisition device installed in a smartphone or similar mobile terminal, and, in cooperation with a server including an emotion engine, simultaneously analyzes allergen information and the user's emotions to provide warnings and countermeasures optimized for the user. Specific embodiments are described below.

[0518] First, the device continuously acquires information about the surrounding environment using the smartphone's camera and sensors. This information may include video and audio data, which forms the basis for detecting the presence of allergens and specific elements in the environment.

[0519] Subsequently, this environmental data is transmitted to a server. The server analyzes the received information using data processing tools and detects allergens in the environment through identification tools. In this process, a trained generative model is used to perform highly accurate classification of the characteristics of each allergen.

[0520] Furthermore, the system acquires user voice and facial expression data through the device, which is then analyzed by the server's emotion engine. The emotion engine analyzes the user's voice tone and facial microexpressions to identify the user's emotional state. This information is used to evaluate the user's stress level, level of distractibility, and other factors.

[0521] Based on identified allergens and emotional information, the server uses a generation mechanism to create customized warning messages and suggested actions. Here, the most appropriate approach is selected for the user's current emotional state, and reassuring advice is provided.

[0522] For example, if a high pollen concentration is detected and the user is also experiencing stress, the device can notify the user with a message such as, "The current pollen concentration is high, but we suggest ways to relax. Try taking deep breaths and take actions within your limits." This allows the user to choose actions that provide both mental and physical comfort at that moment.

[0523] In this way, the system improves the user's quality of life and supports a comfortable daily life through a series of processes from information gathering and analysis to customized notifications.

[0524] The following describes the processing flow.

[0525] Step 1:

[0526] The device activates the smartphone's camera and sensors to continuously acquire environmental information and user voice and video data. Environmental information includes airborne allergens and lighting conditions. The user's voice and facial expressions are also captured simultaneously.

[0527] Step 2:

[0528] The terminal temporarily stores the acquired environment and user data and performs preprocessing according to each data format. This preprocessing includes denoising images, optimizing resolution, and clearing audio data.

[0529] Step 3:

[0530] The terminal sends pre-processed data to the server via the network. The data is divided into environmental data for allergen detection and user data for emotion analysis before being sent.

[0531] Step 4:

[0532] The server receives environmental data and uses data processing tools to analyze allergens. An AI model is used to effectively detect allergens in the environment and evaluate their concentrations.

[0533] Step 5:

[0534] Simultaneously, the server analyzes the user's voice and video data using an emotion engine. It evaluates the user's emotional state based on factors such as voice tone and facial microexpressions.

[0535] Step 6:

[0536] The server generates customized warning messages and suggested actions based on allergen information and the user's emotional state. This includes messages in a gentle tone that takes the user's emotional state into consideration.

[0537] Step 7:

[0538] The device verifies the warning message received from the server and immediately notifies the user. The notification is provided in the form of a pop-up message or audio alert to prompt the user to take action.

[0539] Step 8:

[0540] Users receive notifications and act upon the suggested actions. They can also use the feedback function to provide information to the system regarding the usefulness and emotional impact of the notification.

[0541] In this way, this system can comprehensively analyze the user's environment and emotions and provide optimal solutions in real time.

[0542] (Example 2)

[0543] 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."

[0544] There is a need to instantly grasp an individual's health and emotional state based on environmental information and provide personalized warnings and countermeasures. However, conventional technologies have had the problem of difficulty in efficiently collecting and analyzing surrounding information and individual emotional information, and providing appropriate feedback based on that information.

[0545] 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.

[0546] In this invention, the server includes a terminal that acquires information about its surroundings through an acquisition means, a transmission means that transmits information from the terminal, and a data processing means that analyzes the information transmitted by the transmission means. This makes it possible to provide highly responsive, personalized messages to individual users.

[0547] "Means of acquisition" refers to devices and methods for collecting information about the surrounding environment.

[0548] "Transmission means" refers to the process or method of transmitting collected data to another device or system.

[0549] "Data processing means" refers to a device or method for analyzing received information and processing it for a specific purpose.

[0550] "Generation means" refers to the process or method for creating personalized messages or suggestions based on analysis results.

[0551] "Notification means" refers to the process or method of conveying information to users.

[0552] This invention is a system that provides personalized feedback to users by collecting and analyzing environmental information. It is mainly realized by using acquisition means, transmission means, data processing means, generation means, and notification means. Specific embodiments are shown below.

[0553] The device continuously acquires information about the surrounding environment using the smartphone's camera, microphone, and various sensors. These sensor devices include, for example, light sensors, temperature sensors, and humidity sensors. The device collects this data in real time to form the basis for detecting the presence of allergens in the surroundings.

[0554] The terminal transmits the collected data to the server via an internet connection. The server analyzes the received data using data processing equipment. A pre-trained generative AI model is used for the analysis to detect and classify specific allergens. This generates highly accurate allergen information based on the data.

[0555] Furthermore, the device acquires additional data from the user, such as voice and facial expressions. This data is analyzed by the server's emotion engine to evaluate the user's emotional state. The emotion engine detects subtle changes in tone and facial expressions to identify the user's stress and relaxation levels.

[0556] Based on the analyzed allergen information and emotional data, the server generates a unique message through a generation mechanism. This message takes into account the user's current emotional state and environmental circumstances, and includes reassuring advice.

[0557] The device will notify the user of this message using a notification method. For example, if high pollen levels are detected and the user is also feeling stressed, a customized message such as, "Currently, pollen levels are high, but here are some ways to relax. Try taking deep breaths and act within your limits," will be displayed on the device.

[0558] This approach allows users to take the most appropriate action on the spot, potentially improving their quality of life.

[0559] Example of a prompt:

[0560] "We would like you to collect information on allergens currently present in the environment, evaluate the user's emotional state, and propose the most appropriate countermeasures."

[0561] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0562] Step 1:

[0563] The device acquires information about its surroundings using the smartphone's camera and various sensors. This input includes video data, audio data, and temperature and humidity data. In particular, it captures visual information using the camera and senses changes in the sound environment using the microphone. The output is environmental data compiled from this collected data.

[0564] Step 2:

[0565] The terminal transmits the acquired environmental data to the server via the internet. This transmission occurs in real time, and a highly secure HTTP or HTTPS protocol is used for transmission. The output is the unprocessed environmental data received by the server.

[0566] Step 3:

[0567] The server analyzes the received environmental data using data processing tools. This analysis uses a pre-trained generative AI model to identify the presence of allergens from video and audio. The input is unanalyzed environmental data, and the output is detected allergen information. The AI ​​model classifies the data and highlights factors that match specific indicators.

[0568] Step 4:

[0569] The device acquires additional data on the user's voice and facial expressions. This input is used to identify the user's everyday stress levels and moods. The device uses dedicated sensors to acquire this data. The output is user emotion data based on voice tone and facial expressions.

[0570] Step 5:

[0571] The server uses an emotion engine to analyze the user's emotional data. The input is voice and facial expression data sent from the terminal, and the output is information that quantifies or classifies the user's emotional state. The emotion engine detects things like tone of voice and subtle facial muscle movements.

[0572] Step 6:

[0573] The server generates messages through a generation mechanism based on detected allergen information and the user's emotional state. The input is the analysis result from the previous step, and the output is an alert message and suggestion optimized for the user. Here, reassuring language is selected.

[0574] Step 7:

[0575] The device notifies the user of messages received from the server. These notifications appear as pop-ups on the smartphone screen, allowing the user to see them immediately. The output is the final message presented to the user, including specific suggestions regarding the actions the user should take.

[0576] (Application Example 2)

[0577] 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."

[0578] In care settings for the elderly and those with weakened immune systems, there is a need to prevent health effects from allergens while providing care that is tailored to the emotional state of the users. However, current methods lack systems that combine real-time allergen detection with emotional state analysis, which limits the provision of appropriate warnings and countermeasures.

[0579] 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.

[0580] In this invention, the server includes means for acquiring environmental information using an acquisition device, means for analyzing allergens and emotional states using data processing means, and means for generating customized warnings and countermeasures based on the analysis results. This makes it possible to provide caregivers with warnings and countermeasures optimized for the user in real time.

[0581] "Acquisition device" refers to a hardware device for collecting environmental information, such as the camera or sensor of a smartphone.

[0582] "Data processing means" refers to software functions that analyze acquired environmental information and evaluate the presence of allergens and the user's emotional state.

[0583] "Identification means" refers to a function for identifying allergens in the environment based on analysis results obtained through data processing.

[0584] "Generation means" refers to software that has the function of creating warnings and countermeasures based on allergens identified by specific means.

[0585] "Emotional analysis tools" refer to functions that analyze a user's emotional state and optimize the generated information based on the user's emotions.

[0586] "Notification means" refers to means of conveying information generated or optimized by the generation means to the user or caregiver.

[0587] The system that realizes this application example relies on the coordination of various hardware and software. This system collects information from the environment, analyzes emotional states, and notifies users of generated warnings and countermeasures, thereby providing optimal care for elderly and immunocompromised users in care settings.

[0588] The server is responsible for data processing and analyzes environmental information transmitted from acquisition devices. Specifically, it uses data acquired by cameras and sensors of smartphones and smart glasses. This data may include allergen information, user voice, and facial expressions. The server receives this data and performs highly accurate analysis using generative AI models such as Google TensorFlow and Amazon SageMaker.

[0589] Furthermore, the server uses an emotion analysis engine to analyze the collected user's voice tone and facial microexpressions to identify the user's emotional state. This allows for an assessment of the user's stress level and level of distractibility.

[0590] Based on the analyzed information, the server generates appropriate warning messages and suggested solutions. The generated messages are optimized to suit the user's emotional state. For example, if the user has pollen allergies and is also experiencing stress, the server will generate a message such as, "The current pollen concentration is high, but we suggest ways to relax."

[0591] This generated information is transmitted in real time to caregivers via notification devices and smart glasses interfaces. This allows caregivers to provide users with the most appropriate care at any given time.

[0592] For example, if pollen levels rise sharply and elderly residents become anxious, care staff can receive instructions through the application such as, "The pollen levels in the facility are high, so please increase indoor activities and create a more relaxed atmosphere."

[0593] Example of a prompt:

[0594] "To ensure elderly residents feel safe and secure in care facilities, please generate care advice on the server based on conditions that worsen pollen allergy symptoms."

[0595] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0596] Step 1:

[0597] The device continuously acquires information about its surroundings using a camera sensor. The acquired environmental information is stored as image data. The input is a real-time video stream, and the output is image frames collected in real time.

[0598] Step 2:

[0599] The device acquires the user's voice and facial expression data. This involves capturing audio and video data through the microphone and camera and saving it as voice tone and facial expression data. The input is the user's voice stream and video, and the output is a processable audio file and video frames.

[0600] Step 3:

[0601] The terminal transmits acquired environmental information and user voice and facial expression data to the server. The input is the dataset obtained in the previous step, and the output is data packets for further processing by the server.

[0602] Step 4:

[0603] The server analyzes the received environmental information using generative AI models such as Google TensorFlow. Data processing involves classification using machine learning algorithms based on allergen characteristics. The input is an image frame, and the output is identified allergen information.

[0604] Step 5:

[0605] The server uses an emotion analysis engine to analyze the user's voice and facial expression data. This involves voice tone analysis and facial signal processing to identify the user's emotional state. Inputs are audio and video data, and output is information about the user's emotional state.

[0606] Step 6:

[0607] The server combines allergen information and user emotional state information to generate appropriate warning messages and suggested actions. Using the generation mechanism, it creates optimal explanatory and prompt statements tailored to the situation. The input is the two output data sets mentioned earlier, and the output is the generated message or advice.

[0608] Step 7:

[0609] The server sends the generated message to the terminal via a notification system. This allows caregivers to receive this information in real time at the care site. The input is the generated warning message, and the output is the notification displayed on the user interface.

[0610] 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.

[0611] 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.

[0612] 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.

[0613] [Fourth Embodiment]

[0614] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0615] 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.

[0616] 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).

[0617] 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.

[0618] 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.

[0619] 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).

[0620] 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.

[0621] 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.

[0622] 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.

[0623] 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.

[0624] 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.

[0625] 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.

[0626] 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".

[0627] This invention is a system that acquires information from the environment using an acquisition device attached to a terminal such as a smartphone, identifies allergens in real time by coordinating with a server, and provides this information to the user. The main operation of the system is described in detail below.

[0628] First, the device continuously collects information about the surrounding environment using the smartphone's camera and sensors. The acquired information is temporarily stored on the device and converted into a format that can be easily analyzed under specific environmental conditions.

[0629] Subsequently, this environmental information is transmitted to a server via the network. The server analyzes the received information using data processing tools and extracts the characteristics of allergens floating in the environment. Here, an AI model is used to identify the presence of allergens based on past training data. The allergen information identified through this series of processes is organized by concentration and type.

[0630] Next, the server uses a generation mechanism to create specific messages and instructions to generate warnings and suggested countermeasures tailored to the allergen. This process takes into account the user's individual information (e.g., allergy type and threshold), resulting in customized countermeasures. The generated warning information is sent to the user in real time and displayed as a notification on their smartphone screen.

[0631] For example, if a large amount of pollen is detected in a park, the device will notify the user with specific action advice, such as, "The pollen concentration is high in your current location, so we recommend you refrain from going outside or wear a mask." Based on this information, the user can then take appropriate action on the spot.

[0632] In this way, by linking acquisition devices, data processing means, identification means, generation means, and notification means, this system enables users to always be aware of the latest allergen information and take immediate safe actions.

[0633] The following describes the processing flow.

[0634] Step 1:

[0635] The device activates the smartphone's camera and sensors to acquire information about the surrounding environment in real time. This information is temporarily stored as continuously captured video and digital data.

[0636] Step 2:

[0637] The terminal preprocesses the acquired video data into the required format. This preprocessing includes noise reduction, resolution adjustment, and color correction as needed. This prepares the data for subsequent analysis.

[0638] Step 3:

[0639] The terminal sends pre-processed data to the server over the network. Data compression may be performed at this stage depending on network conditions.

[0640] Step 4:

[0641] The server inputs the received data into an AI model for analysis. Here, a pre-trained deep learning algorithm is used to perform the process of detecting allergen characteristics from the video.

[0642] Step 5:

[0643] The server collects the types and concentrations of allergens identified from the AI ​​model's analysis results and records them in a database. Here, the allergens are classified and their concentrations are quantified, and the results are passed on to the next processing step.

[0644] Step 6:

[0645] Based on the identified allergen information, the server generates a warning message based on user customization information that matches the trigger conditions. The message will take into account each user's allergy type and countermeasures.

[0646] Step 7:

[0647] The device receives a warning message sent from the server and notifies the user. The notification is displayed as a pop-up on the smartphone or as an alert within the application.

[0648] Step 8:

[0649] Users can review notifications and choose actions based on the information they receive in real time. They can also provide feedback on whether the notifications were helpful.

[0650] The above is the specific processing flow for identifying allergen information in real time and notifying the user.

[0651] (Example 1)

[0652] 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".

[0653] In modern society, many people suffer health problems caused by environmental allergens, but there is a lack of systems to identify these allergens in real time and take appropriate countermeasures. Furthermore, customized responses based on individual user information are insufficient, and there is a need for a system that can provide accurate information under diverse environmental conditions.

[0654] 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.

[0655] In this invention, the server includes means for acquiring environmental information using an acquisition device, means for converting and analyzing the data format, and means for identifying the presence of allergens using a pre-trained generative model. This enables users to grasp allergen information in real time and quickly take appropriate action based on individually customized countermeasures.

[0656] An "acquisition device" is a device installed to collect environmental information, and it acquires surrounding environmental data through sensors and cameras.

[0657] "Data processing means" refers to means that analyze environmental information obtained from an acquisition device and perform data format conversion or pattern recognition as necessary.

[0658] A "generative model" is a model that learns specific patterns and characteristics based on past data and identifies the presence of allergens based on new data.

[0659] "Identification methods" refer to means of identifying allergens present in the environment and confirming their presence using analyzed and processed data.

[0660] "Generation means" refers to means of creating warning messages and countermeasures to provide to users based on identified allergen information.

[0661] A "notification method" is a means of delivering generated information to users in real time, and notifications are sent via smartphones or other communication devices.

[0662] This invention is a system that provides users with allergen information in real time, supporting them in taking safe actions on the spot. This system primarily operates through the coordinated efforts of three elements: a terminal, a server, and the user.

[0663] The device uses a smartphone as an example of an embodiment. Cameras and sensors installed in the smartphone continuously acquire information about the surrounding environment. This information includes temperature, humidity, and fine particles in the air, and is temporarily stored in the device in the initial stage. Hardware used includes high-performance camera modules and environmental sensors. The acquired data is converted into a format that is easy to analyze, such as JSON or CSV format.

[0664] The server receives this converted data via the network. The server uses data processing tools and an AI model to analyze the data. The software used here includes a generative AI model. This model searches for allergen characteristics based on past training data and identifies allergens present at the user's current location. The server uses these identification tools to identify allergens floating in the environment and extract their concentration and type.

[0665] The server then generates warnings and suggested actions for the user based on the data mentioned above. This process takes the user's individual information into account to create customized messages. The generated warnings are sent to the user's device in real time.

[0666] Users will receive this warning information through their devices. For example, if a large amount of pollen is detected in a park, the device will receive a message stating, "The pollen concentration is high in your current location, so we recommend you stay indoors or wear a mask." This allows users to quickly choose the appropriate course of action.

[0667] As a concrete example, prompt messages are generated in the format of "Investigate the pollen concentration in the park and, based on the results, suggest the most appropriate course of action for the user." This system allows users to always be aware of the latest allergen information and take safe actions.

[0668] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0669] Step 1:

[0670] The device collects information about its surroundings. Cameras and sensors are used to acquire data such as temperature, humidity, visual data, and airborne particle information. The input is raw data obtained from the environment, which is temporarily stored in internal memory. The output is raw environmental data for later processing.

[0671] Step 2:

[0672] The raw data acquired by the terminal is converted into a format that is easy to analyze. The input is the raw data collected in step 1. In this conversion process, the data is converted into JSON or CSV format. The output is data formatted in a format that is easy for data processing tools to recognize.

[0673] Step 3:

[0674] The data converted from the terminal is sent to the server via the network. The input is formatted environmental data. The data is sent to the server via a secure protocol using Wi-Fi or Bluetooth. The output is the environmental data received by the server.

[0675] Step 4:

[0676] The server analyzes the received environmental data. The input is the data received in step 3. The server uses data processing tools and leverages a generative AI model to extract allergen characteristics from the data. The output is information on the identified allergens.

[0677] Step 5:

[0678] The server generates a warning message based on identified allergen information. The input is information about the characteristics and concentration of the allergen. It generates a customized message considering the user's individual information. The output is the warning message sent to the user.

[0679] Step 6:

[0680] The system notifies the user of the generated warning on their device. The input is the warning message generated in step 5. The warning is displayed on the user's smartphone screen via the notification function. The output is warning information that the user can review, allowing them to take appropriate action based on it.

[0681] (Application Example 1)

[0682] 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".

[0683] In modern urban environments, the concentrations and types of allergens change constantly, posing a high risk of adverse health effects on residents. However, systems that can grasp this information in real time and prompt residents to take appropriate measures are still insufficient. In particular, it is difficult to suggest appropriate actions tailored to individual users based on their individual allergy information. Against this backdrop, there is a need to monitor allergens throughout the city and issue customized warnings to individual users.

[0684] 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.

[0685] In this invention, the server includes means for acquiring environmental information, means for processing information, means for identifying information, means for generating information, means for notifying information, and means for monitoring the city. This enables monitoring of allergens throughout the city, real-time identification of allergens based on the acquired information, and the proposal of customized warnings and safety measures tailored to individual residents.

[0686] An "acquisition device" is a device used to collect information from the environment, and includes sensors, cameras, and other similar devices.

[0687] An "information processing means" is a system that analyzes environmental information obtained from an acquisition device and performs data conversion.

[0688] "Identification means" refers to a method of detecting the presence of a specific substance or allergen based on data analyzed by information processing means.

[0689] "Generation means" refers to the process of creating warnings and countermeasures for users based on the information identified by the identification means.

[0690] A "notification method" refers to a technique for notifying users of generated information in real time, using methods such as screen displays and voice guidance.

[0691] A "city monitoring system" is a mechanism that monitors environmental information throughout a city and continuously provides useful allergen information to a wide range of residents.

[0692] A system for carrying out this invention consists of coordinating an acquisition device, data processing means, identification means, generation means, notification means, and urban monitoring means.

[0693] The server first collects environmental information from sensors and smartphone cameras via an acquisition device. This environmental information includes various environmental data such as temperature, humidity, and particulate matter concentration. Subsequently, data processing means are used to formalize this raw data and convert it into an analyzable state. Database software and data analysis tools are used for this process, specifically Apache Kafka and Python.

[0694] Next, the server uses identification methods and leverages an AI model (e.g., a generative AI model using TensorFlow) to identify the presence of allergens from the analyzed data. This process involves referencing past training data to achieve highly accurate identification.

[0695] Based on the identified allergen information, the generation system creates customized warning messages and suggested countermeasures tailored to each user's individual needs. This generation process applies a generation AI model and dynamically generates messages using JavaScript or Python.

[0696] Finally, the generated information is sent to the user's smartphone in real time via the reporting system. This notification is delivered via the application's push notification function or SMS, allowing residents to take immediate action based on the information.

[0697] For example, if the system identifies a sudden surge in pollen levels in a particular park on a given day, it will generate a message such as, "The pollen concentration near XX Park is currently very high. Please use △△ Plaza instead," and notify the user's smartphone. An example of a prompt message would be, "Pollen levels in the city center, current time," to obtain the analysis results.

[0698] This enables allergen monitoring at the city level, providing users with a safe and healthy living environment.

[0699] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0700] Step 1:

[0701] The device collects environmental information using the smartphone's camera and various sensors as acquisition devices. The input is ambient environmental data, and the output is this data converted into an easily manageable format and stored within the device. A data formatting algorithm is used for this conversion.

[0702] Step 2:

[0703] The terminal sends organized environmental data to the server via the network. The input is formatted data stored on the terminal, and the output is the data successfully transferred to the server. HTTP or WebSocket protocols are commonly used for this transmission.

[0704] Step 3:

[0705] The server analyzes the received data using information processing tools. The input is the raw data received by the server, and the output is the analyzed data. Here, Python and R are used to perform data analysis and extract particularly abnormal data and allergen candidates.

[0706] Step 4:

[0707] The server identifies the presence of allergens based on the analyzed data using an identification method. In this step, the input is the analyzed data, and the output is the identified allergen information. Allergens are identified with high accuracy based on machine learning algorithms using a generative AI model such as TensorFlow.

[0708] Step 5:

[0709] The server generates warning messages based on allergen information identified using a generation mechanism. The input is identified allergen information, and the output is a customized warning message delivered to the user. A generation AI model is used to generate messages tailored to individual situations.

[0710] Step 6:

[0711] The server notifies the user's device in real time of a message generated as a notification method. The input is the generated warning message, and the output is the notification information displayed on the user's device. This notification is delivered using push notification technology or SMS.

[0712] Step 7:

[0713] The user takes appropriate action based on the received notification. The input is the warning message displayed on the device, and the output is the user's action choice. For example, they can take measures such as staying indoors or wearing a mask.

[0714] 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.

[0715] This invention is a system that collects environmental information using an acquisition device installed in a smartphone or similar mobile terminal, and, in cooperation with a server including an emotion engine, simultaneously analyzes allergen information and the user's emotions to provide warnings and countermeasures optimized for the user. Specific embodiments are described below.

[0716] First, the device continuously acquires information about the surrounding environment using the smartphone's camera and sensors. This information may include video and audio data, which forms the basis for detecting the presence of allergens and specific elements in the environment.

[0717] Subsequently, this environmental data is transmitted to a server. The server analyzes the received information using data processing tools and detects allergens in the environment through identification tools. In this process, a trained generative model is used to perform highly accurate classification of the characteristics of each allergen.

[0718] Furthermore, the system acquires user voice and facial expression data through the device, which is then analyzed by the server's emotion engine. The emotion engine analyzes the user's voice tone and facial microexpressions to identify the user's emotional state. This information is used to evaluate the user's stress level, level of distractibility, and other factors.

[0719] Based on identified allergens and emotional information, the server uses a generation mechanism to create customized warning messages and suggested actions. Here, the most appropriate approach is selected for the user's current emotional state, and reassuring advice is provided.

[0720] For example, if a high pollen concentration is detected and the user is also experiencing stress, the device can notify the user with a message such as, "The current pollen concentration is high, but we suggest ways to relax. Try taking deep breaths and take actions within your limits." This allows the user to choose actions that provide both mental and physical comfort at that moment.

[0721] In this way, the system improves the user's quality of life and supports a comfortable daily life through a series of processes from information gathering and analysis to customized notifications.

[0722] The following describes the processing flow.

[0723] Step 1:

[0724] The device activates the smartphone's camera and sensors to continuously acquire environmental information and user voice and video data. Environmental information includes airborne allergens and lighting conditions. The user's voice and facial expressions are also captured simultaneously.

[0725] Step 2:

[0726] The terminal temporarily stores the acquired environment and user data and performs preprocessing according to each data format. This preprocessing includes denoising images, optimizing resolution, and clearing audio data.

[0727] Step 3:

[0728] The terminal sends pre-processed data to the server via the network. The data is divided into environmental data for allergen detection and user data for emotion analysis before being sent.

[0729] Step 4:

[0730] The server receives environmental data and uses data processing tools to analyze allergens. An AI model is used to effectively detect allergens in the environment and evaluate their concentrations.

[0731] Step 5:

[0732] Simultaneously, the server analyzes the user's voice and video data using an emotion engine. It evaluates the user's emotional state based on factors such as voice tone and facial microexpressions.

[0733] Step 6:

[0734] The server generates customized warning messages and suggested actions based on allergen information and the user's emotional state. This includes messages in a gentle tone that takes the user's emotional state into consideration.

[0735] Step 7:

[0736] The device verifies the warning message received from the server and immediately notifies the user. The notification is provided in the form of a pop-up message or audio alert to prompt the user to take action.

[0737] Step 8:

[0738] Users receive notifications and act upon the suggested actions. They can also use the feedback function to provide information to the system regarding the usefulness and emotional impact of the notification.

[0739] In this way, this system can comprehensively analyze the user's environment and emotions and provide optimal solutions in real time.

[0740] (Example 2)

[0741] 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".

[0742] There is a need to instantly grasp an individual's health and emotional state based on environmental information and provide personalized warnings and countermeasures. However, conventional technologies have had the problem of difficulty in efficiently collecting and analyzing surrounding information and individual emotional information, and providing appropriate feedback based on that information.

[0743] 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.

[0744] In this invention, the server includes a terminal that acquires information about its surroundings through an acquisition means, a transmission means that transmits information from the terminal, and a data processing means that analyzes the information transmitted by the transmission means. This makes it possible to provide highly responsive, personalized messages to individual users.

[0745] "Means of acquisition" refers to devices and methods for collecting information about the surrounding environment.

[0746] "Transmission means" refers to the process or method of transmitting collected data to another device or system.

[0747] "Data processing means" refers to a device or method for analyzing received information and processing it for a specific purpose.

[0748] "Generation means" refers to the process or method for creating personalized messages or suggestions based on analysis results.

[0749] "Notification means" refers to the process or method of conveying information to users.

[0750] This invention is a system that provides personalized feedback to users by collecting and analyzing environmental information. It is mainly realized by using acquisition means, transmission means, data processing means, generation means, and notification means. Specific embodiments are shown below.

[0751] The device continuously acquires information about the surrounding environment using the smartphone's camera, microphone, and various sensors. These sensor devices include, for example, light sensors, temperature sensors, and humidity sensors. The device collects this data in real time to form the basis for detecting the presence of allergens in the surroundings.

[0752] The terminal transmits the collected data to the server via an internet connection. The server analyzes the received data using data processing equipment. A pre-trained generative AI model is used for the analysis to detect and classify specific allergens. This generates highly accurate allergen information based on the data.

[0753] Furthermore, the device acquires additional data from the user, such as voice and facial expressions. This data is analyzed by the server's emotion engine to evaluate the user's emotional state. The emotion engine detects subtle changes in tone and facial expressions to identify the user's stress and relaxation levels.

[0754] Based on the analyzed allergen information and emotional data, the server generates a unique message through a generation mechanism. This message takes into account the user's current emotional state and environmental circumstances, and includes reassuring advice.

[0755] The device will notify the user of this message using a notification method. For example, if high pollen levels are detected and the user is also feeling stressed, a customized message such as, "Currently, pollen levels are high, but here are some ways to relax. Try taking deep breaths and act within your limits," will be displayed on the device.

[0756] This approach allows users to take the most appropriate action on the spot, potentially improving their quality of life.

[0757] Example of a prompt:

[0758] "We would like you to collect information on allergens currently present in the environment, evaluate the user's emotional state, and propose the most appropriate countermeasures."

[0759] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0760] Step 1:

[0761] The device acquires information about its surroundings using the smartphone's camera and various sensors. This input includes video data, audio data, and temperature and humidity data. In particular, it captures visual information using the camera and senses changes in the sound environment using the microphone. The output is environmental data compiled from this collected data.

[0762] Step 2:

[0763] The terminal transmits the acquired environmental data to the server via the internet. This transmission occurs in real time, and a highly secure HTTP or HTTPS protocol is used for transmission. The output is the unprocessed environmental data received by the server.

[0764] Step 3:

[0765] The server analyzes the received environmental data using data processing tools. This analysis uses a pre-trained generative AI model to identify the presence of allergens from video and audio. The input is unanalyzed environmental data, and the output is detected allergen information. The AI ​​model classifies the data and highlights factors that match specific indicators.

[0766] Step 4:

[0767] The device acquires additional data on the user's voice and facial expressions. This input is used to identify the user's everyday stress levels and moods. The device uses dedicated sensors to acquire this data. The output is user emotion data based on voice tone and facial expressions.

[0768] Step 5:

[0769] The server uses an emotion engine to analyze the user's emotional data. The input is voice and facial expression data sent from the terminal, and the output is information that quantifies or classifies the user's emotional state. The emotion engine detects things like tone of voice and subtle facial muscle movements.

[0770] Step 6:

[0771] The server generates messages through a generation mechanism based on detected allergen information and the user's emotional state. The input is the analysis result from the previous step, and the output is an alert message and suggestion optimized for the user. Here, reassuring language is selected.

[0772] Step 7:

[0773] The device notifies the user of messages received from the server. These notifications appear as pop-ups on the smartphone screen, allowing the user to see them immediately. The output is the final message presented to the user, including specific suggestions regarding the actions the user should take.

[0774] (Application Example 2)

[0775] 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".

[0776] In care settings for the elderly and those with weakened immune systems, there is a need to prevent health effects from allergens while providing care that is tailored to the emotional state of the users. However, current methods lack systems that combine real-time allergen detection with emotional state analysis, which limits the provision of appropriate warnings and countermeasures.

[0777] 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.

[0778] In this invention, the server includes means for acquiring environmental information using an acquisition device, means for analyzing allergens and emotional states using data processing means, and means for generating customized warnings and countermeasures based on the analysis results. This makes it possible to provide caregivers with warnings and countermeasures optimized for the user in real time.

[0779] "Acquisition device" refers to a hardware device for collecting environmental information, such as the camera or sensor of a smartphone.

[0780] "Data processing means" refers to software functions that analyze acquired environmental information and evaluate the presence of allergens and the user's emotional state.

[0781] "Identification means" refers to a function for identifying allergens in the environment based on analysis results obtained through data processing.

[0782] "Generation means" refers to software that has the function of creating warnings and countermeasures based on allergens identified by specific means.

[0783] "Emotional analysis tools" refer to functions that analyze a user's emotional state and optimize the generated information based on the user's emotions.

[0784] "Notification means" refers to means of conveying information generated or optimized by the generation means to the user or caregiver.

[0785] The system that realizes this application example relies on the coordination of various hardware and software. This system collects information from the environment, analyzes emotional states, and notifies users of generated warnings and countermeasures, thereby providing optimal care for elderly and immunocompromised users in care settings.

[0786] The server is responsible for data processing and analyzes environmental information transmitted from acquisition devices. Specifically, it uses data acquired by cameras and sensors of smartphones and smart glasses. This data may include allergen information, user voice, and facial expressions. The server receives this data and performs highly accurate analysis using generative AI models such as Google TensorFlow and Amazon SageMaker.

[0787] Furthermore, the server uses an emotion analysis engine to analyze the collected user's voice tone and facial microexpressions to identify the user's emotional state. This allows for an assessment of the user's stress level and level of distractibility.

[0788] Based on the analyzed information, the server generates appropriate warning messages and suggested solutions. The generated messages are optimized to suit the user's emotional state. For example, if the user has pollen allergies and is also experiencing stress, the server will generate a message such as, "The current pollen concentration is high, but we suggest ways to relax."

[0789] This generated information is transmitted in real time to caregivers via notification devices and smart glasses interfaces. This allows caregivers to provide users with the most appropriate care at any given time.

[0790] For example, if pollen levels rise sharply and elderly residents become anxious, care staff can receive instructions through the application such as, "The pollen levels in the facility are high, so please increase indoor activities and create a more relaxed atmosphere."

[0791] Example of a prompt:

[0792] "To ensure elderly residents feel safe and secure in care facilities, please generate care advice on the server based on conditions that worsen pollen allergy symptoms."

[0793] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0794] Step 1:

[0795] The device continuously acquires information about its surroundings using a camera sensor. The acquired environmental information is stored as image data. The input is a real-time video stream, and the output is image frames collected in real time.

[0796] Step 2:

[0797] The device acquires the user's voice and facial expression data. This involves capturing audio and video data through the microphone and camera and saving it as voice tone and facial expression data. The input is the user's voice stream and video, and the output is a processable audio file and video frames.

[0798] Step 3:

[0799] The terminal transmits acquired environmental information and user voice and facial expression data to the server. The input is the dataset obtained in the previous step, and the output is data packets for further processing by the server.

[0800] Step 4:

[0801] The server analyzes the received environmental information using generative AI models such as Google TensorFlow. Data processing involves classification using machine learning algorithms based on allergen characteristics. The input is an image frame, and the output is identified allergen information.

[0802] Step 5:

[0803] The server uses an emotion analysis engine to analyze the user's voice and facial expression data. This involves voice tone analysis and facial signal processing to identify the user's emotional state. Inputs are audio and video data, and output is information about the user's emotional state.

[0804] Step 6:

[0805] The server combines allergen information and user emotional state information to generate appropriate warning messages and suggested actions. Using the generation mechanism, it creates optimal explanatory and prompt statements tailored to the situation. The input is the two output data sets mentioned earlier, and the output is the generated message or advice.

[0806] Step 7:

[0807] The server sends the generated message to the terminal via a notification system. This allows caregivers to receive this information in real time at the care site. The input is the generated warning message, and the output is the notification displayed on the user interface.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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."

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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 as being incorporated by reference.

[0829] The following is further disclosed regarding the embodiments described above.

[0830] (Claim 1)

[0831] Means for acquiring environmental information using an acquisition device,

[0832] A data processing means for analyzing environmental information from the aforementioned acquisition device,

[0833] Identification means for identifying the presence of an allergen based on the results of analysis by the aforementioned data processing means,

[0834] A generation means that generates warnings and countermeasures based on the allergen identified by the aforementioned identification means,

[0835] A notification means for notifying the user of the information generated by the generation means,

[0836] A system that includes this.

[0837] (Claim 2)

[0838] The system according to claim 1, wherein the data processing means identifies allergens using a pre-learned generative model.

[0839] (Claim 3)

[0840] The system according to claim 1, wherein the notification means issues a customized warning based on the user's individual information.

[0841] "Example 1"

[0842] (Claim 1)

[0843] Means for acquiring environmental information using an acquisition device,

[0844] Means for analyzing environmental information from the aforementioned acquisition device, converting it into a data format, and temporarily storing it,

[0845] The means includes transmitting the converted information over a network and using data processing means to analyze the received data,

[0846] The aforementioned data processing means includes an identification means that identifies the presence of allergens based on past data using a pre-learned generative model,

[0847] A generation means that generates customized warnings and countermeasures that take into account the user's individual information based on the allergens identified by the aforementioned identification means,

[0848] A notification means that notifies the user of the generated warning information in real time and prompts them to take appropriate action,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, characterized in that the data processing means includes a step of converting the data format in order to efficiently analyze environmental information.

[0852] (Claim 3)

[0853] The system according to claim 1, characterized in that the notification means generates a prompt sentence using a generation AI model when a warning is issued.

[0854] "Application Example 1"

[0855] (Claim 1)

[0856] Means for acquiring environmental information using an acquisition device,

[0857] Information processing means for analyzing environmental information from the aforementioned acquisition device,

[0858] An identification means for identifying the presence of an allergen based on the results of analysis by the aforementioned information processing means,

[0859] A generation means that generates a warning and a suggested response based on the allergen identified by the identification means,

[0860] A notification means for notifying the user of the information generated by the generation means,

[0861] A city-wide monitoring system that monitors allergens and provides residents with real-time information,

[0862] A system that includes this.

[0863] (Claim 2)

[0864] The system according to claim 1, wherein the information processing means identifies allergens using a pre-learned generative model.

[0865] (Claim 3)

[0866] The system according to claim 1, wherein the notification means issues customized warnings based on the user's individual information and suggests safe routes within the city.

[0867] "Example 2 of combining an emotion engine"

[0868] (Claim 1)

[0869] A terminal that acquires information about its surroundings through an acquisition means,

[0870] A transmission means for transmitting information from the aforementioned terminal,

[0871] A data processing means for analyzing the information transmitted by the transmission means,

[0872] A generation means that generates an individualized message based on the factors identified by the data processing means,

[0873] A notification means that displays the message generated by the generation means to the user,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, wherein the data processing means classifies information using a pre-trained generative model.

[0877] (Claim 3)

[0878] The system according to claim 1, wherein the notification means sends a message that is appropriately customized based on the user's emotional state.

[0879] "Application example 2 when combining with an emotional engine"

[0880] (Claim 1)

[0881] Means for acquiring environmental information using an acquisition device,

[0882] A data processing means for analyzing environmental information from the aforementioned acquisition device,

[0883] Identification means for identifying the presence of an allergen based on the results of analysis by the aforementioned data processing means,

[0884] A generation means that generates warnings and countermeasures based on the allergen identified by the aforementioned identification means,

[0885] An emotion analysis means that analyzes the user's emotional state and optimizes the information generated by the generation means based on the user's emotions,

[0886] A notification means for notifying the care provider of the optimized information,

[0887] A system that includes this.

[0888] (Claim 2)

[0889] The system according to claim 1, wherein the data processing means identifies allergens using a pre-trained generative model and analyzes the user's emotional state.

[0890] (Claim 3)

[0891] The system according to claim 1, wherein the notification means issues a customized warning based on the user's individual information and emotional state. [Explanation of Symbols]

[0892] 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. Means for acquiring environmental information using an acquisition device, Information processing means for analyzing environmental information from the aforementioned acquisition device, An identification means for identifying the presence of an allergen based on the results of analysis by the aforementioned information processing means, A generation means that generates a warning and a suggested response based on the allergen identified by the identification means, A notification means for notifying the user of the information generated by the generation means, A city-wide monitoring system that monitors allergens and provides residents with real-time information, A system that includes this.

2. The system according to claim 1, wherein the information processing means identifies allergens using a pre-learned generative model.

3. The system according to claim 1, wherein the notification means issues customized warnings based on the user's individual information and suggests safe routes within the city.

Images