System

Abstract

A system is provided.SOLUTION: A system comprising means for a user to collect a biological sample, means for a terminal to convert the biological sample into a data format and transmit the data to a server, means for the server to perform preprocessing and analyze biological sample data, means for the server to perform prediction and diagnosis of a disease using a generated AI model, means for the server to analyze genetic information of an individual and identify a root of ancestry, means for the server to generate an analysis result and transmit the result to the terminal, and means for the terminal to visually display the analysis result to a user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Current medical technology requires multiple tests and diagnostic procedures to detect many diseases early. This requires a significant amount of time, cost, and burden on patients. Furthermore, these conventional methods make it difficult to quickly and accurately provide cancer risk in specific areas or preventative measures tailored to individual health conditions. In addition, analyzing ancestral roots based on a patient's genetic information requires separate, specialized testing. For these reasons, there is a need for technology that can predict the risk of a wide range of diseases and analyze ancestral roots in a single test, thereby reducing the burden on patients. [Means for solving the problem]

[0005] The present invention provides a system including: a means for a user to collect their own saliva or blood; a means for a terminal to convert the biological sample into a data format and transmit it to a server; a means for the server to preprocess the received biological sample data; a means for the server to predict disease risk and perform a diagnosis using a generative AI model; a means for the server to identify ancestral roots based on an individual's genetic information; a means for the server to generate and encrypt analysis results and transmit them to the terminal; and a means for the terminal to visually display the analysis results to the user. This system enables users to predict the risk of a wide range of diseases and analyze ancestral roots with a single test. Furthermore, the server can use the generative AI model to accurately predict cancer risk in specific areas and provide appropriate health advice to the user based on the analysis results. Furthermore, the analysis results are encrypted and transmitted to the terminal using a secure communication protocol, ensuring data security and privacy.

[0006] A "biological sample" is a biological substance, such as saliva or blood, collected from a user.

[0007] A "user" is an individual who uses the system to check their own health status.

[0008] A "terminal" is a device through which a user inputs a sample and transmits the sample data to a server.

[0009] A "server" is a central processing unit that analyzes received biological sample data and generates results.

[0010] "Preprocessing" refers to the process of performing preparatory tasks on received biological sample data, such as noise removal, normalization, and missing data completion.

[0011] A "generative AI model" is an artificial intelligence algorithm that predicts disease risk and makes diagnoses based on biological sample data.

[0012] "Risk prediction" refers to the use of generative AI models to predict future diseases and health conditions.

[0013] "Diagnosis" refers to determining the presence or risk of a particular disease based on received biological sample data.

[0014] "Genetic information" is data containing genetic characteristics such as an individual's DNA sequence.

[0015] "Ancestral roots" are the geographic and ethnic origins of an ancestor identified based on an individual's genetic information.

[0016] "Encryption" means transforming data using cryptography to protect the data as it is transmitted.

[0017] A "communications protocol" is a standard that defines the rules and procedures for sending and receiving data.

[0018] "Visual display" means displaying the analysis results on the screen of the user's terminal in an easy-to-read format. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0023] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0024] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0025] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0030] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0032] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0037] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0038] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0039] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0040] System Configuration

[0041] The present invention is a system for users to analyze their own health status and predict their risk of disease. The system comprises the following components:

[0042] 1. User Device

[0043] It is a device that allows a user to collect a biological sample of saliva or blood and transmits the data to a server.

[0044] 2. Server

[0045] It is a central processing unit that analyzes received biological sample data and performs disease risk prediction, health checkups, and ancestral root analysis.

[0046] Program processing

[0047] Sample collection and submission

[0048] 1. The user collects a saliva or blood sample using a dedicated collection kit, which is then placed in the device to prevent leakage.

[0049] 2. The terminal converts the sample data into the appropriate data format and sends it to the server. The terminal scans the sample, stores it as digital data, and then starts the transmission process to the server.

[0050] Data reception and analysis

[0051] 3. The server receives the sample data and performs preprocessing, including noise removal, data normalization, and missing data completion.

[0052] 4. The server begins analyzing the biological sample data using the generated AI model.

[0053] Input data into the model and perform parameter tuning and feature engineering.

[0054] 5. The server performs disease prediction and diagnosis, for example, calculating the risk of cancer in specific areas such as lung cancer or colon cancer.

[0055] 6. The server also performs ancestry analysis based on the individual's genetic information. By analyzing the DNA sample, it identifies individual genetic patterns and generates ancestry information based on past genetic patterns.

[0056] Generating and notifying results

[0057] 7. The server compiles the results of the analysis and generates a report for the user, which includes a disease risk score, health advice, and information about ancestry.

[0058] 8. The server encrypts the result report and sends it to the user terminal using the appropriate communication protocol.

[0059] 9. The device receives the transmitted results and visually displays them through the user interface, allowing the user to review the results and receive detailed risk assessments and health advice.

[0060] Specific examples

[0061] Example 1: Disease risk prediction using saliva samples

[0062] 1. The user collects a saliva sample using a dedicated collection kit.

[0063] 2. The user places the saliva sample on the terminal and starts the sample scan.

[0064] 3. The device sends the sample data to the server.

[0065] 4. The server receives the saliva data and performs preprocessing.

[0066] 5. The server performs analysis using the generative AI model to assess lung cancer risk.

[0067] 6. The server analyzes your ancestral roots based on your genetic information.

[0068] 7. The server generates a result report, encrypts it, and sends it to the user's terminal.

[0069] 8. The device receives the results and displays them in the user interface.

[0070] Example 2: Comprehensive health check using blood samples

[0071] 1. The user collects a blood sample using a dedicated collection kit.

[0072] 2. The user places the blood sample into the terminal.

[0073] 3. The device sends the sample data to the server.

[0074] 4. The server receives the blood data and performs preprocessing.

[0075] 5. The server performs analysis using the generated AI model to assess the risk of diabetes and cardiovascular disease.

[0076] 6. The server analyzes your ancestral roots based on your genetic information.

[0077] 7. The server generates a result report, encrypts it, and sends it to the user's terminal.

[0078] 8. The device receives the results and displays them in the user interface.

[0079] The present invention can be carried out as described above.

[0080] The processing flow will be explained below.

[0081] Step 1:

[0082] The user collects a saliva or blood sample using a special collection kit.

[0083] Action: Follow the instructions on the collection kit to collect the correct amount of sample. Seal the sample in the collection kit to prevent leakage.

[0084] Step 2:

[0085] The user sets the collected sample in the terminal.

[0086] Operation: The collected sample is placed in a dedicated device and secured in place, starting the terminal's sample scanning mode.

[0087] Step 3:

[0088] The device formats the sample data and sends it to the server.

[0089] Operation: Scans the sample, stores it as digital data, converts it into the appropriate data format, and initiates the process of sending it to the server.

[0090] Step 4:

[0091] A server receives the biological sample data.

[0092] Action: Checks and receives data sent from the terminal. Sends a message confirming receipt to the terminal.

[0093] Step 5:

[0094] The server performs preprocessing on the data received.

[0095] Actions: Performs noise removal, data normalization, and missing data imputation. Converts data into an analysis-ready format.

[0096] Step 6:

[0097] The server begins analyzing the data using the generative AI model.

[0098] How it works: Input data into a generative AI model and run analysis using machine learning algorithms, including parameter tuning and feature engineering.

[0099] Step 7:

[0100] The server predicts disease risk and diagnoses it.

[0101] How it works: Calculates the risk of certain diseases, such as lung cancer or colon cancer, based on biological sample data. Generates specific diagnostic results.

[0102] Step 8:

[0103] The server analyzes an individual's ancestral roots based on their genetic information.

[0104] How it works: Analyzes DNA data and matches genetic characteristics with historical data to identify ancestral geographic roots and genetic patterns.

[0105] Step 9:

[0106] The server generates a report of the data analysis results.

[0107] What it does: Organizes the results of each analysis and creates a report for the user, including disease risk scores, health advice, and ancestry information.

[0108] Step 10:

[0109] The server encrypts the report and sends it to the user terminal.

[0110] How it works: The generated report is encrypted and sent to the user's device using a secure communication protocol.

[0111] Step 11:

[0112] The terminal visually displays the received reports to the user.

[0113] What it does: It decodes reports sent from the server and displays them in a user interface, allowing users to review results and understand health risks and ancestry information.

[0114] Example 1

[0115] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0116] In modern society, it is important for individuals to regularly monitor their health status and take appropriate health management measures. However, conventional health checkups and disease risk assessment methods are often time-consuming, costly, and have limited access. Furthermore, ancestral root analysis requires specialized knowledge and equipment, making it difficult for average users to access. The present invention aims to provide a system that allows users to easily monitor their health status and genetic roots.

[0117] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0118] In this invention, the server includes means for preprocessing received digital data, means for analyzing biological sample data using a generative AI model, and means for assessing disease risk scores. This enables users to easily and quickly understand their own health status and disease risk, and further analyze their ancestral roots.

[0119] A "user" is an individual who collects a saliva or blood sample using a biological sample collection kit and sets it in a dedicated terminal.

[0120] A "biological sample" is a sample, such as saliva or blood, collected from a living organism and is a source of data for analyzing health status and genetic information.

[0121] A "terminal" is a device that scans biological samples collected by a user, converts them into digital data, and sends them to a server.

[0122] "Digital data" refers to information obtained from a biological sample that has been converted into a format that can be stored and transmitted electronically.

[0123] The "server" is a central processing unit that analyzes the received digital data and performs disease risk assessments and ancestral root analysis.

[0124] "Preprocessing" refers to processes such as noise removal, data normalization, and missing data completion that are performed on the data received by the server.

[0125] A "generative AI model" is an artificial intelligence model used to analyze biological sample data and perform disease risk assessment and genetic information analysis.

[0126] A "disease risk score" is a quantified indicator of the likelihood of contracting a particular disease, as assessed by a generative AI model.

[0127] "Ancestry analysis" is the process of analyzing past genetic patterns based on an individual's genetic information to identify the region and roots of one's ancestors.

[0128] A "report" is a written or digital document provided to a user that includes the results of a disease risk assessment or ancestry analysis.

[0129] "Encryption" is the process of transforming data using a specific algorithm to secure the data being transmitted.

[0130] The present invention is a system that allows users to analyze their own health status, predict disease risks, and analyze their ancestral roots. This system is composed of three elements: a user, a terminal, and a server.

[0131] First, the user uses a dedicated collection kit to collect a saliva or blood sample, which is a biological specimen. The collected sample is sealed to prevent leakage and placed in the terminal. The terminal scans the biological specimen and converts it into digital data. The terminal then transmits this digital data to a server.

[0132] The server performs preprocessing on the received digital data. This preprocessing includes noise removal, data normalization, and missing data completion. After preprocessing is complete, the data is analyzed using a generative AI model. Machine learning libraries such as TensorFlow and PyTorch can be used for the generative AI model.

[0133] As part of the analysis, the server will assess a disease risk score, calculating a numerical value for the risk of certain diseases, such as lung cancer or colon cancer. It will also perform ancestry analysis based on an individual's genetic information. By analyzing specific patterns in the DNA sample, the server can pinpoint the region and origins of an individual's ancestors based on their past genetic patterns.

[0134] The analysis results are compiled by the server and generated into a detailed report, which includes disease risk assessment, health advice, and ancestry information. The report is encrypted and sent to the device using a secure communication protocol (e.g., HTTPS).

[0135] The device interprets the received report and visually displays it to the user, who can then access detailed risk assessments, health advice, and ancestral information through a user interface.

[0136] Specific examples

[0137] Example 1: Disease risk prediction using saliva samples

[0138] 1. The user collects a saliva sample using a dedicated collection kit.

[0139] 2. The user places the saliva sample on the terminal and starts the sample scan.

[0140] 3. The device sends the sample data to the server.

[0141] 4. The server receives the saliva data and performs preprocessing.

[0142] 5. The server performs analysis using the generative AI model to assess lung cancer risk.

[0143] 6. The server analyzes your ancestral roots based on your genetic information.

[0144] 7. The server generates a result report, encrypts it, and sends it to the user's terminal.

[0145] 8. The device receives the results and displays them in the user interface.

[0146] Example 2: Comprehensive health check using blood samples

[0147] 1. The user collects a blood sample using a dedicated collection kit.

[0148] 2. The user places the blood sample into the terminal.

[0149] 3. The device sends the sample data to the server.

[0150] 4. The server receives the blood data and performs preprocessing.

[0151] 5. The server performs analysis using the generated AI model to assess the risk of diabetes and cardiovascular disease.

[0152] 6. The server analyzes your ancestral roots based on your genetic information.

[0153] 7. The server generates a result report, encrypts it, and sends it to the user's terminal.

[0154] 8. The device receives the results and displays them in the user interface.

[0155] Example prompt

[0156] Example of server prompt:

[0157] "Sample data has been received. Please perform preprocessing and analyze the biological sample data. As analysis results, please output disease risk assessment and ancestral roots information."

[0158] Example prompt for a server-side AI model:

[0159] "Given your genetic sample data, calculate risk scores for the following diseases: lung cancer, colon cancer, diabetes, and cardiovascular disease. Additionally, analyze your personal genetic information to identify your ancestral roots."

[0160] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0161] Step 1:

[0162] Sample collection

[0163] The user collects a saliva or blood sample using a dedicated collection kit, which is then sealed to prevent leakage.

[0164] Input: Dedicated collection kit, user

[0165] Output: Collected biological sample (saliva or blood)

[0166] Step 2:

[0167] Set of data

[0168] The user places the collected sample into the terminal and inserts the sample into the terminal's sample scanner.

[0169] Input: collected biological sample, terminal

[0170] Output: Set sample

[0171] Step 3:

[0172] Scanning Data

[0173] The terminal scans the sample and stores it as digital data. The scanner reads the necessary information from the biological specimen and converts it into a digital format.

[0174] Input: Set sample

[0175] Output: Digital data

[0176] Step 4:

[0177] Sending data

[0178] The device sends the digital data to the server, using a network communication protocol (e.g., HTTPS) in an encrypted format.

[0179] Input: Digital data

[0180] Output: Data sent to the server

[0181] Step 5:

[0182] Data reception and preprocessing

[0183] The server preprocesses the digital data it receives, removing noise, normalizing the data, and filling in missing data.

[0184] Input: Data sent to the server

[0185] Output: Pre-processed digital data

[0186] Step 6:

[0187] Analysis using generative AI models

[0188] The server analyzes the data using a generative AI model, performs parameter tuning and feature engineering on the data, and then uses a predictive model (e.g., using TensorFlow or PyTorch) to assess disease risk.

[0189] Input: Preprocessed digital data

[0190] Output: Analysis results (disease risk score, ancestral roots information)

[0191] Step 7:

[0192] Disease risk assessment

[0193] The server calculates the risk of a specific disease (e.g., lung cancer, colon cancer, etc.) based on the analysis results of the generated AI model, generates a risk score, and provides a numerical assessment.

[0194] Input: Analysis results

[0195] Output: Disease risk score

[0196] Step 8:

[0197] Ancestry analysis

[0198] The server analyzes ancestral roots based on genetic information, referencing specific DNA patterns and identifying regions and roots based on past genetic patterns.

[0199] Input: Analysis results

[0200] Output: Ancestor roots information

[0201] Step 9:

[0202] Report Generation

[0203] The server generates a detailed report based on your disease risk score and ancestry information, including risk assessment, health advice, and ancestry information.

[0204] Input: Disease risk score, ancestry information

[0205] Output: Generated report

[0206] Step 10:

[0207] Data encryption and transmission

[0208] The server generates reports and encrypts them and sends them to the device using a secure communication protocol (e.g., SSL / TLS).

[0209] Input: Generated report

[0210] Output: Encrypted report

[0211] Step 11:

[0212] Displaying the results

[0213] The device interprets the received report and visually displays it to the user, who can then view the analysis results and receive detailed health advice through the user interface.

[0214] Input: Encrypted report

[0215] Output: Decoded report (displayed in the user interface)

[0216] (Application example 1)

[0217] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0218] Conventional health analysis systems simply provide users with analysis results, but do not offer appropriate health promotion product recommendations or services. This makes it difficult for users to take appropriate measures based on the analysis results, making consistent health management difficult. Another issue is the existence of security risks in the process of receiving and checking the analysis results.

[0219] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0220] In this invention, the server includes: [means for generating analysis results and transmitting them to the terminal as a report including personalized health promotion product advertisements;] [means for encrypting the analysis results and transmitting them to the terminal using a secure communication protocol; and] [means for predicting cancer risk in specific locations using the generative AI model.] This allows users to use optimal health promotion products and services based on the analysis results, enabling safe and consistent health management. Furthermore, the analysis results can be received in a form with enhanced security.

[0221] A "user" is an individual who uses the system or an entity who provides a biological sample and receives the analysis results.

[0222] A "biological sample" is a biological substance such as saliva or blood collected from a user, and is input data for analyzing the health condition.

[0223] A "terminal" is a device that allows a user to convert a biological sample into a data format and transmit it to a server.

[0224] A "data format" is a biological sample that has been digitized and converted into an analyzable format.

[0225] The "server" is a central processing unit that analyzes the received biometric sample data and performs disease risk prediction, health checkups, ancestral root analysis, and advertising suggestions.

[0226] "Preprocessing" refers to processing of biological sample data, such as noise removal, data normalization, and missing data complementation.

[0227] A "generative AI model" is a machine learning model that predicts and diagnoses disease risk based on biological sample data.

[0228] "Disease prediction" is the analysis of biological sample data to assess the risk of developing a particular disease.

[0229] "Diagnosis" is the medical evaluation of a specific health condition with a prognosis for disease.

[0230] "Genetic information" refers to an individual's genetic pattern obtained from genes such as DNA.

[0231] "Ancestral roots" refers to information about past genetic patterns and lineages identified based on genetic information.

[0232] A "report" is a document that summarizes the analysis results and includes a disease risk score, health advice, ancestry, and personalized recommendations for health-promoting products and services.

[0233] "Personalized health promotion products" are products and services that promote health that are individually suggested based on the analysis results.

[0234] "Advertisements" are introductions of health promotion products and services suggested based on the user's health condition.

[0235] "Visually displaying" means displaying the analysis results and advertisements on the screen of the terminal so that the user can check them.

[0236] "Encryption" refers to converting the analysis results so that they cannot be read by third parties, and is a technology that transmits them via a secure communication protocol.

[0237] A "secure communication protocol" is a standard communication specification for securely sending and receiving data.

[0238] MODE FOR CARRYING OUT THE INVENTION

[0239] System Configuration

[0240] The present invention is a system that analyzes a user's health status, predicts disease risk, and suggests appropriate health promotion products and services. The system is composed of the following elements:

[0241] 1. User terminal: A device that allows a user to collect a biological sample and transmit the data to the server.

[0242] 2. Server: A central processing unit that analyzes the received biological sample data and performs disease risk prediction, health checkups, ancestral root analysis, and advertising suggestions.

[0243] Program processing

[0244] Sample collection and submission

[0245] The user collects a saliva or blood sample using a dedicated collection kit. The collected sample is then placed in the device to prevent leakage. The terminal converts the biological sample into an appropriate data format and sends it to the server. The terminal also scans the biological sample and stores it as digital data. The process of sending it to the server then begins.

[0246] Data reception and analysis

[0247] The server receives the sent sample data and performs preprocessing, including noise removal, data normalization, and missing data completion. The server then begins analyzing the biological sample data using the generated AI model. This allows for disease prediction and diagnosis. For example, it calculates the risk of cancer in specific areas, such as lung cancer or colon cancer. The server also performs ancestral analysis based on an individual's genetic information. By analyzing the DNA sample, individual genetic patterns are identified and ancestral information is generated based on past genetic patterns.

[0248] Generating and notifying results

[0249] The server compiles the analysis results, generates a report containing personalized health promotion product advertisements, encrypts it, and sends it to the user's device. The device visually displays the received results through a user interface. The user can review the results and obtain a detailed risk assessment, health advice, and details of the advertised products.

[0250] Hardware and software used

[0251] User terminals are general mobile devices such as smartphones and tablet terminals.

[0252] The server is a high-performance cloud server or on-premise server.

[0253] The software uses libraries for data analysis and machine learning (e.g., Python, Flask, and some AI model libraries).

[0254] A generative AI model is a machine learning model that predicts and diagnoses disease risk based on biological sample data.

[0255] We use encryption technology and secure communication protocols (e.g., SSL / TLS) to ensure the secure transmission and reception of data.

[0256] Specific examples

[0257] For example, if a user opens the app and submits a saliva sample, the following occurs:

[0258] User Action:

[0259] 1. Collect a saliva sample and enter it into the smartphone app.

[0260] 2. Follow the instructions in the app to send sample data to the server.

[0261] Server processing example:

[0262] The server receives the sample data and inputs it into the analysis model.

[0263] Generate reports of the user's health risks (e.g., lung cancer risk) and ancestry information.

[0264] Advertisement proposal example:

[0265] Based on the report, the app will display recommendations such as "recommended supplements for those at high risk of lung cancer" and "fitness programs suited to your constitution."

[0266] Prompt Sentence Examples

[0267] Recommend appropriate health promotion products and services based on the user's health risks and ancestry information. Implement a program that receives the user's saliva data, analyzes it with an AI model, and suggests advertisements based on the generated report.

[0268] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0269] Processing flow and steps

[0270] Step 1:

[0271] The user collects a biological sample.

[0272] Specific behavior:

[0273] Users collect a saliva or blood sample using a special collection kit and place it in the device to prevent leakage.

[0274] Input: User's biological sample (saliva or blood)

[0275] Output: A biological sample is placed in the device.

[0276] Step 2:

[0277] The terminal scans the biological sample and converts it into a data format.

[0278] Specific behavior:

[0279] The scanning function of the terminal is used to convert the biological sample into digital data, which is then formatted into an appropriate data format.

[0280] Input: Biological sample placed on the device

[0281] Output: Digital data converted into a data format

[0282] Step 3:

[0283] The terminal transmits the digital data to the server.

[0284] Specific behavior:

[0285] This initiates the process of sending digital data from the terminal to the server, and the data is encrypted and sent securely.

[0286] Input: Digital data converted into a data format

[0287] Output: Digital data sent to the server

[0288] Step 4:

[0289] The server receives the transmitted data and performs pre-processing.

[0290] Specific behavior:

[0291] The server performs preprocessing on the received data, such as noise removal, data normalization, and missing data completion.

[0292] Input: Digital data sent from the terminal

[0293] Output: Preprocessed data

[0294] Step 5:

[0295] The server performs data analysis using the generated AI model.

[0296] Specific behavior:

[0297] The server inputs the preprocessed data into a generative AI model, performs parameter tuning and feature engineering, and then performs analysis to predict disease risk and health status from biological samples.

[0298] Input: Preprocessed digital data

[0299] Output: Analysis results (disease risk score and health status assessment)

[0300] Step 6:

[0301] The server analyzes ancestral roots based on genetic information.

[0302] Specific behavior:

[0303] The server uses analytical models to identify genetic patterns from a user's DNA sample and analyze their ancestral roots.

[0304] Input: Preprocessed digital data and DNA information

[0305] Output: Ancestor roots analysis results

[0306] Step 7:

[0307] A server generates a report containing personalized health and wellness product promotions.

[0308] Specific behavior:

[0309] The server combines the analysis results with information such as past purchasing history and interests and preferences to create a report that includes suggestions for health-promoting products and services that are appropriate for the user.

[0310] Input: Analysis results, purchase history, interest and preference information

[0311] Output: Personalized health report and advertising suggestions

[0312] Step 8:

[0313] The server encrypts the report and sends it to the device.

[0314] Specific behavior:

[0315] The server encrypts the generated report and sends it to the terminal using a secure communication protocol (e.g., SSL / TLS).

[0316] Input: Personalized health reports and advertising suggestions

[0317] Output: Encrypted report

[0318] Step 9:

[0319] The device visually displays the analysis results and advertisements to the user.

[0320] Specific behavior:

[0321] The device decrypts the received encrypted report and visually displays it through a user interface, allowing the user to view the analysis results and details of suggested health promotion products and services.

[0322] Input: Encrypted report

[0323] Output: Analysis results and advertising suggestions displayed on the user interface

[0324] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0325] System Configuration

[0326] The present invention is a system for users to analyze their own health status and predict their risk of disease. The system comprises the following components:

[0327] 1. User Device

[0328] This device allows users to collect saliva or blood samples and transmits the data to a server. The device also has an emotion engine that can recognize the user's emotions.

[0329] 2. Server

[0330] It is a central processing unit that analyzes received biometric sample data and emotional data to predict disease risk, conduct health checks, and analyze ancestral roots.

[0331] Program processing

[0332] Sample collection and submission

[0333] 1. The user collects a saliva or blood sample using a dedicated collection kit, which is then placed in the device to prevent leakage.

[0334] 2. When the user sets the sample, they use the device camera to scan their face. The emotion engine analyzes the user's facial expressions and obtains emotion data.

[0335] 3. The device converts the sample data and emotion data into an appropriate data format and sends it to the server. The device scans the sample, saves it as digital data, and then starts the process of sending it to the server along with the emotion data.

[0336] Data reception and analysis

[0337] 4. The server receives the sample data and emotion data and performs preprocessing, including noise removal, data normalization, and missing data completion.

[0338] 5. The server begins analyzing the biometric sample data and emotional data using the generated AI model.

[0339] Data is input into the model and analysis is performed using machine learning algorithms, including parameter tuning and feature engineering.

[0340] 6. The server performs disease risk prediction and diagnosis. It performs risk assessment taking into account emotional data and calculates the risk of cancer in specific areas, such as lung cancer or colon cancer.

[0341] 7. The server also performs ancestry analysis based on an individual's genetic information. By analyzing DNA samples, it identifies individual genetic patterns and generates ancestry information based on past genetic patterns.

[0342] Generating and notifying results

[0343] 8. The server compiles the analysis results and generates a report for the user, which may include a disease risk score, health advice based on the user's emotional state, and information about their ancestry.

[0344] 9. The server encrypts the result report and sends it to the user terminal using the appropriate communication protocol.

[0345] 10. The device receives the transmitted results and visually displays them through the user interface. The user can then review the results and receive detailed risk assessments and health advice, while also receiving interactive feedback based on the user's emotional state.

[0346] Specific examples

[0347] Example 1: Disease risk prediction and sentiment analysis using saliva samples

[0348] 1. The user collects a saliva sample using a dedicated collection kit.

[0349] 2. The user places a saliva sample on the device and scans their face to obtain emotional data.

[0350] 3. The device sends the sample data and emotion data to the server.

[0351] 4. The server receives the saliva data and emotion data and performs preprocessing.

[0352] 5. The server analyzes the generated AI model and assesses the risk of lung cancer, taking emotional data into account.

[0353] 6. The server analyzes your ancestral roots based on your genetic information.

[0354] 7. The server generates a result report, encrypts it, and sends it to the user's terminal.

[0355] 8. The device receives the results and displays them on the user interface, providing health advice based on the user's emotional state.

[0356] Example 2: Comprehensive health check and sentiment analysis using blood samples

[0357] 1. The user collects a blood sample using a dedicated collection kit.

[0358] 2. The user places a blood sample into the device and scans their face to obtain emotional data.

[0359] 3. The device sends the sample data and emotion data to the server.

[0360] 4. The server receives the blood data and emotion data and performs preprocessing.

[0361] 5. The server performs analysis using the generated AI model to assess the risk of diabetes and cardiovascular disease, taking emotional data into account.

[0362] 6. The server analyzes your ancestral roots based on your genetic information.

[0363] 7. The server generates a result report, encrypts it, and sends it to the user's terminal.

[0364] 8. The device receives the results and displays them on the user interface, providing health advice based on the user's emotional state.

[0365] The present invention can be carried out as described above.

[0366] The processing flow will be explained below.

[0367] Step 1:

[0368] The user collects a saliva or blood sample using a special collection kit.

[0369] Action: Collect the appropriate amount of biological sample according to the instructions on the collection kit. Seal the sample in the collection kit to prevent leakage.

[0370] Step 2:

[0371] The user sets the collected sample in the terminal.

[0372] Operation: The collected sample is placed in a dedicated device and secured in place, starting the terminal's sample scanning mode.

[0373] Step 3:

[0374] The user uses the device's camera to scan their face and obtain emotional data.

[0375] How it works: The device's emotion engine analyzes the user's facial expressions and generates emotion data in real time.

[0376] Step 4:

[0377] The terminal formats the sample data and emotion data and sends them to the server.

[0378] Operation: Sample data is scanned and converted into digital data. At the same time, emotion data is formatted and the process of sending it to the server begins.

[0379] Step 5:

[0380] A server receives the biometric sample data and the emotion data.

[0381] Operation: Checks and receives data sent from the terminal. Sends a receipt confirmation to the terminal.

[0382] Step 6:

[0383] The server performs preprocessing.

[0384] What it does: It denoises, normalizes, and fills in missing data from biological samples. It also preprocesses emotion data and prepares it for analysis.

[0385] Step 7:

[0386] The server begins analyzing the data using the generative AI model.

[0387] How it works: Input data into a generative AI model to perform disease risk and diagnostic analysis, taking emotional data into account.

[0388] Step 8:

[0389] The server takes emotion data into account when performing disease risk prediction and diagnosis.

[0390] How it works: Combines biological sample data with emotional data to assess the risk of certain diseases, such as lung or colon cancer.

[0391] Step 9:

[0392] The server analyzes an individual's genetic information to identify their ancestral roots.

[0393] How it works: Analyzes DNA samples to identify genetic patterns, then matches them with historical data to identify your ancestral geographic roots.

[0394] Step 10:

[0395] The server generates a report of the analysis results.

[0396] What it does: Generates a detailed report with risk scores, emotion-based health advice, and ancestry information.

[0397] Step 11:

[0398] The server encrypts the report and sends it to the user terminal.

[0399] Operation: The generated report is encrypted and sent to the user's device using a secure communication protocol.

[0400] Step 12:

[0401] The terminal receives the analysis results and visually displays them to the user.

[0402] What it does: It interprets the received reports and displays them through a user interface, allowing users to view detailed risk assessments, health advice, and ancestry information, while also providing interactive feedback based on emotional state.

[0403] Example 2

[0404] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0405] Current health diagnostic systems face challenges in enabling users to comprehensively analyze their own health status and predict disease risk. Disease risk prediction requires consideration of both biomaterials and emotional state, but conventional systems are unable to integrate these factors. Providing analysis results to users quickly and conveniently is also a challenge.

[0406] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0407] In this invention, the server includes: [means for analyzing biomaterial data and emotional data using a generative AI model]; [means for predicting and diagnosing disease from the biomaterial data]; and [means for analyzing an individual's genetic information and identifying ancestry information]. This allows the user to obtain integrated analysis results of their own biomaterial and emotional state, enabling prediction and early diagnosis of health risks.

[0408] A "biological material" is a biological sample such as saliva or blood taken from a user.

[0409] "Digital data" refers to an electronic data format for analyzing, storing, and transmitting samples and emotional data by computer.

[0410] A "generative AI model" is an artificial intelligence system that uses machine learning algorithms to analyze data and make predictions and diagnoses.

[0411] An "emotion engine" is software or a device that analyzes a user's face and facial expressions to identify their emotional state.

[0412] "Preprocessing" refers to the process of removing noise from biomaterial data and emotion data, normalizing the data, and filling in missing data.

[0413] "Disease prediction and diagnosis" refers to assessing the risk of contracting a specific disease based on analyzed data and diagnosing it early.

[0414] "Genetic Information" refers to a user's genetic characteristics based on their individual genetic patterns or DNA data.

[0415] "Ancestry information" analyzes past genetic patterns based on the user's genetic information and provides information about their ancestors.

[0416] A "communications protocol" defines the procedures and rules for sending and receiving data over a network.

[0417] "Interactive feedback" refers to responses and advice provided in real time based on the user's behavior and emotional state.

[0418] System Configuration

[0419] The present invention is a system for users to analyze their own health status and predict their risk of disease. The system comprises the following components:

[0420] 1. User Device

[0421] This device allows users to collect biomaterials such as saliva or blood and transmits the data to a server. The device also has an emotion engine and is capable of recognizing the user's emotions.

[0422] 2. Server

[0423] This is a central processing unit that analyzes the received biomaterial data and emotional data, and performs disease risk prediction, health checks, and ancestral roots analysis.

[0424] Program processing

[0425] Sample collection and submission

[0426] 1. The user collects a saliva or blood sample using a dedicated collection kit, which is then placed in the device to prevent leakage.

[0427] 2. When the user sets the sample, they use the device camera to scan their face. The emotion engine analyzes the user's facial expressions and obtains emotion data.

[0428] 3. The device converts the sample data and emotion data into an appropriate data format and sends it to the server. The device scans the sample, saves it as digital data, and then starts the process of sending it to the server along with the emotion data.

[0429] Data reception and analysis

[0430] 4. The server receives the sample data and emotion data and performs preprocessing, including noise removal, data normalization, and missing data completion.

[0431] 5. The server begins analyzing the biometric sample data and emotional data using the generated AI model.

[0432] Data is input into the model and analysis is performed using machine learning algorithms, including parameter tuning and feature engineering.

[0433] 6. The server performs disease risk prediction and diagnosis. It performs risk assessment taking into account emotional data and calculates the risk of cancer in specific areas, such as lung cancer or colon cancer.

[0434] 7. The server also performs ancestry analysis based on an individual's genetic information. By analyzing DNA samples, it identifies individual genetic patterns and generates ancestry information based on past genetic patterns.

[0435] Generating and notifying results

[0436] 8. The server compiles the analysis results and generates a report for the user, which may include a disease risk score, health advice based on the user's emotional state, and information about their ancestry.

[0437] 9. The server encrypts the result report and sends it to the user terminal using the appropriate communication protocol.

[0438] 10. The device receives the transmitted results and visually displays them through the user interface. The user can then review the results and receive detailed risk assessments and health advice, while also receiving interactive feedback based on the user's emotional state.

[0439] Specific examples

[0440] Example 1: Disease risk prediction and sentiment analysis using saliva samples

[0441] 1. The user collects a saliva sample using a dedicated collection kit.

[0442] 2. The user places a saliva sample on the device and scans their face to obtain emotional data.

[0443] 3. The device sends the sample data and emotion data to the server.

[0444] 4. The server receives the data and performs preprocessing.

[0445] 5. The server begins analysis using the generated AI model, for example, to assess lung cancer risk, taking emotional data into account.

[0446] 6. The server performs ancestral roots analysis based on the genetic information.

[0447] 7. The server generates a result report, encrypts it, and sends it to the user's terminal.

[0448] 8. The device receives the results and displays them on the user interface, providing health advice based on the emotional state.

[0449] Example 2: Comprehensive health check and sentiment analysis using blood samples

[0450] 1. The user collects a blood sample using a dedicated collection kit.

[0451] 2. The user inserts a blood sample into the device and scans their face to obtain emotional data.

[0452] 3. The device sends the sample data and emotion data to the server.

[0453] 4. The server receives the data and performs preprocessing.

[0454] 5. The server begins analysis using the generated AI model, assessing, for example, the risk of diabetes or cardiovascular disease, taking emotional data into account.

[0455] 6. The server performs ancestral roots analysis based on the genetic information.

[0456] 7. The server generates a result report, encrypts it, and sends it to the user's terminal.

[0457] 8. The device receives the results and displays them on the user interface, providing health advice based on the emotional state.

[0458] In this way, the present invention can be implemented.

[0459] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0460] Step 1:

[0461] The user collects a saliva or blood sample using a special collection kit.

[0462] Input: Specialized collection kit, user's saliva or blood.

[0463] Output: sealed biomaterial sample.

[0464] How it works: The user follows the instructions on the collection kit, spitting saliva into a test tube or pricking their finger with a needle to collect blood. The sample is then sealed to prevent leakage.

[0465] Step 2:

[0466] When the user sets the sample, they scan their face using the device's camera.

[0467] Input: User's face, device camera.

[0468] Output: Emotion data.

[0469] Specific operation: The user follows the instructions displayed on the device screen, faces the camera, and remains still for a certain period of time. The emotion engine analyzes the user's facial expressions and generates emotion data.

[0470] Step 3:

[0471] The terminal converts the sample data and emotion data into an appropriate data format and transmits it to the server.

[0472] Input: Biomaterial samples, emotion data.

[0473] Output: Digitized sample data and emotion data.

[0474] How it works: The device scans the sample and stores it as digital data, which is then packaged with emotion data into a data packet and sent to a server using an encryption protocol.

[0475] Step 4:

[0476] The server receives the sample data and emotion data and performs preprocessing.

[0477] Input: Digitized sample data and emotion data.

[0478] Output: Preprocessed sample data and emotion data.

[0479] Specific operation: The server removes noise from the received data, normalizes the data, and fills in missing data, thereby maintaining data consistency and preparing it for analysis.

[0480] Step 5:

[0481] The server begins analyzing the data using the generative AI model.

[0482] Input: Preprocessed sample data and emotion data.

[0483] Output: Analysis results.

[0484] How it works: Data is fed into a generative AI model, and analysis is performed using machine learning algorithms, tuning parameters and performing feature engineering to extract meaningful features from the data.

[0485] Step 6:

[0486] The server predicts disease risk and diagnoses it.

[0487] Input: Analysis result data.

[0488] Output: Disease risk prediction and diagnostic results.

[0489] Specific operation: For example, calculate the risk of lung cancer, colon cancer, etc. from specific biomarkers and genetic information, and perform risk assessment taking emotional data into account.

[0490] Step 7:

[0491] The server analyzes an individual's ancestral roots based on their genetic information.

[0492] Input: Personal genetic information data.

[0493] Output: Ancestry information.

[0494] What it does: Identifies genetic patterns from DNA samples and matches them with historical genetic databases to generate ancestry information.

[0495] Step 8:

[0496] The server compiles the analysis results and generates a report to provide to the user.

[0497] Input: Disease risk prediction results, emotional state data, ancestry information.

[0498] Output: Results report.

[0499] What it does: Generates reports with disease risk scores, health advice based on emotional state, and ancestry information.

[0500] Step 9:

[0501] The server encrypts the results report and transmits it to the user terminal using a secure communication protocol.

[0502] Input: Results report.

[0503] Output: Encrypted results report.

[0504] Specific operation: Using encryption protocols such as SSL / TLS, the report data is protected and sent to the user terminal.

[0505] Step 10:

[0506] The terminal receives the transmitted results and visually displays them through a user interface.

[0507] Input: Encrypted results report.

[0508] Output: A visual display to the user.

[0509] How it works: The analysis results are displayed through a device application, allowing users to check risk assessments, health advice, ancestry information, etc. Interactive feedback based on emotional state is also displayed on the screen.

[0510] (Application example 2)

[0511] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0512] Conventional health management systems predict disease risk using only a user's biometric sample data, making it difficult to provide advice that takes into account the user's emotional state and stress level. Furthermore, health advice in virtual reality environments is not commonly provided, and real-time feedback is lacking. This makes it difficult for users to comprehensively understand their own health status.

[0513] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0514] In this invention, the server includes: [means for a user to collect a biological sample;] [means for a terminal to convert the biological sample into a data format and send it to the server;] [means for the server to perform preprocessing and analyze the biological sample data;] [means for the server to predict and diagnose disease using a generative AI model;] [means for the server to analyze an individual's genetic information and identify ancestral roots;] [means for the server to generate analysis results and send them to the terminal;] [means for the terminal to visually display the analysis results to the user;] [means for acquiring the user's emotional data using a camera-equipped head-mounted display; and [means for the server to analyze the emotional data and provide advice based on the user's health condition in real time.] This enables users to understand their own health condition in real time in a virtual reality environment and receive personalized health advice that takes their emotional state into consideration.

[0515] A "biological sample" is a biological component, such as a user's saliva or blood, that is collected to understand the user's health condition.

[0516] A "terminal" is a device on which a user sets a biological sample and transmits the data to a server, and is also a device that has the function of acquiring emotion data using a camera.

[0517] The "server" is a central processing unit that analyzes the received biological sample data and emotion data to predict disease risk, perform diagnosis, and analyze ancestry.

[0518] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to analyze received data and predict disease risk and diagnose disease.

[0519] "Genetic information" refers to a user's DNA data, which is used to analyze ancestral roots and disease risks.

[0520] "Emotion data" is data that indicates the emotional state of the user, and is data that is acquired by the user scanning their face with a camera.

[0521] A "head-mounted display" is a device worn by the user that displays information visually and acquires emotional data using a camera.

[0522] "Real-time" means that the process of collecting data on the user's health and emotional state, analyzing it, and providing feedback is carried out instantly.

[0523] "Personalization" means providing information and advice that is individually tailored to each user based on their health and emotional state.

[0524] The present invention provides a system for enabling a user to analyze his or her own health and emotional state, and obtain health risk predictions and personalized advice. This system is implemented in the following manner.

[0525] System Configuration

[0526] The main components of this system include:

[0527] 1. User terminal: A device where the user collects biometric samples and sends them to the server. The terminal is equipped with a camera and has the function of scanning the user's face and acquiring emotional data.

[0528] 2. Server: A central processing unit that analyzes received biometric sample data and emotional data to predict disease risk, conduct health checks, and analyze ancestral roots. Generative AI models are used for the analysis.

[0529] 3. Head-mounted display (HMD): A device worn by the user to visually check the analysis results. It also captures emotional data using a camera.

[0530] The specific hardware and software used

[0531] Hardware:

[0532] User device (smartphone or dedicated device)

[0533] Head-mounted display (HMD) with camera

[0534] Server device

[0535] software:

[0536] Sentiment analysis software (OpenCV, Keras)

[0537] Generative AI model (machine learning algorithm) on the server

[0538] Data transmission protocol (HTTP, HTTPS)

[0539] Processing flow explanation

[0540] 1. Biological sample collection and transmission:

[0541] The user collects a saliva or blood sample using a dedicated kit and inserts it into the device, which converts the biometric sample into digital data and sends it to a server. At the same time, the device's camera scans the face and captures emotional data.

[0542] 2. Data Receipt and Analysis:

[0543] The server analyzes the received biometric sample data and emotional data. It performs preprocessing, such as noise removal and data normalization, and then inputs the data into a generative AI model to predict disease risk. It also analyzes emotional data and generates advice tailored to the user's health condition.

[0544] 3. Results generation and notification:

[0545] The server compiles the analysis results, generates a report for the user, encrypts it, and sends it to the device. The device receives the report and displays it visually to the user through a head-mounted display, while also providing interactive feedback based on the user's emotional state.

[0546] Examples of specific examples and prompts

[0547] Example: A user can track their health status while shopping in a virtual store and receive real-time health advice. For example, if the system determines that the user is feeling stressed, it will suggest relaxing music or products.

[0548] Example prompt sentence:

[0549] "Design a VR application that tracks users' health status and provides real-time health advice while they shop in a virtual store. Combine sentiment analysis with biometric sample data to provide optimal personalized feedback."

[0550] The system allows users to gain a comprehensive understanding of their health status in a virtual reality environment and receive personalized health advice in real time.

[0551] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0552] Step 1:

[0553] The user collects a biological sample (saliva or blood) using a dedicated kit. The input is the user's biological sample, and the output is the collected biological sample. The specific operation is to place the biological sample in a dedicated container to prevent leakage.

[0554] Step 2:

[0555] The user places a biometric sample on the device, and the device's camera scans their face to obtain emotional data. The input is the biometric sample and the user's facial information, and the output is digital biometric sample data and emotional data. Specifically, the system scans the face with the camera and performs facial expression analysis.

[0556] Step 3:

[0557] The device converts the biometric sample data and emotion data into an appropriate data format and sends it to the server. The input is the digital biometric sample data and emotion data, and the output is the data sent to the server. Specifically, the data is converted into an appropriate format and sent to the server via an HTTP request.

[0558] Step 4:

[0559] The server receives the biometric sample data and emotion data and performs preprocessing including noise removal, data normalization, and missing data completion. The input is the biometric sample data and emotion data sent to the server, and the output is the preprocessed data. Specific operations include applying noise filtering and data normalization algorithms.

[0560] Step 5:

[0561] The server uses the generated AI model to analyze the biometric sample data and emotional data. The input is the preprocessed biometric sample data and emotional data, and the output is the analysis results. Specifically, the data is input into a model using a machine learning algorithm to perform risk prediction and diagnosis.

[0562] Step 6:

[0563] The server generates a report based on the analysis results, including the user's disease risk score and health advice, encrypts it, and sends it to the device. The input is the analysis results, and the output is an encrypted report. Specifically, the report for the user is constructed using a report generation algorithm and encrypted using SSL / TLS.

[0564] Step 7:

[0565] The terminal decrypts the received report and visually displays it through the user interface. The input is the encrypted report, and the output is the analysis result displayed on the user interface. Specifically, after decryption, the report is displayed on the GUI so that the user can check the results.

[0566] Step 8:

[0567] Based on the reports displayed on the device, interactive feedback is provided according to the user's emotional state. The input is feedback information based on emotional data, and the output is personalized advice provided to the user. Specifically, advice based on the results of emotion analysis is generated and presented in real time.

[0568] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0569] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0570] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0571] [Second embodiment]

[0572] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0573] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0574] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0575] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0576] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0577] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0578] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0579] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0580] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0581] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0582] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0583] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0584] System Configuration

[0585] The present invention is a system for users to analyze their own health status and predict their risk of disease. The system comprises the following components:

[0586] 1. User Device

[0587] It is a device that allows a user to collect a biological sample of saliva or blood and transmits the data to a server.

[0588] 2. Server

[0589] It is a central processing unit that analyzes received biological sample data and performs disease risk prediction, health checkups, and ancestral root analysis.

[0590] Program processing

[0591] Sample collection and submission

[0592] 1. The user collects a saliva or blood sample using a dedicated collection kit, which is then placed in the device to prevent leakage.

[0593] 2. The terminal converts the sample data into the appropriate data format and sends it to the server. The terminal scans the sample, stores it as digital data, and then starts the transmission process to the server.

[0594] Data reception and analysis

[0595] 3. The server receives the sample data and performs preprocessing, including noise removal, data normalization, and missing data completion.

[0596] 4. The server begins analyzing the biological sample data using the generated AI model.

[0597] Input data into the model and perform parameter tuning and feature engineering.

[0598] 5. The server performs disease prediction and diagnosis, for example, calculating the risk of cancer in specific areas such as lung cancer or colon cancer.

[0599] 6. The server also performs ancestry analysis based on the individual's genetic information. By analyzing the DNA sample, it identifies individual genetic patterns and generates ancestry information based on past genetic patterns.

[0600] Generating and notifying results

[0601] 7. The server compiles the results of the analysis and generates a report for the user, which includes a disease risk score, health advice, and information about ancestry.

[0602] 8. The server encrypts the result report and sends it to the user terminal using the appropriate communication protocol.

[0603] 9. The device receives the transmitted results and visually displays them through the user interface, allowing the user to review the results and receive detailed risk assessments and health advice.

[0604] Specific examples

[0605] Example 1: Disease risk prediction using saliva samples

[0606] 1. The user collects a saliva sample using a dedicated collection kit.

[0607] 2. The user places the saliva sample on the terminal and starts the sample scan.

[0608] 3. The device sends the sample data to the server.

[0609] 4. The server receives the saliva data and performs preprocessing.

[0610] 5. The server performs analysis using the generative AI model to assess lung cancer risk.

[0611] 6. The server analyzes your ancestral roots based on your genetic information.

[0612] 7. The server generates a result report, encrypts it, and sends it to the user's terminal.

[0613] 8. The device receives the results and displays them in the user interface.

[0614] Example 2: Comprehensive health check using blood samples

[0615] 1. The user collects a blood sample using a dedicated collection kit.

[0616] 2. The user places the blood sample into the terminal.

[0617] 3. The device sends the sample data to the server.

[0618] 4. The server receives the blood data and performs preprocessing.

[0619] 5. The server performs analysis using the generated AI model to assess the risk of diabetes and cardiovascular disease.

[0620] 6. The server analyzes your ancestral roots based on your genetic information.

[0621] 7. The server generates a result report, encrypts it, and sends it to the user's terminal.

[0622] 8. The device receives the results and displays them in the user interface.

[0623] The present invention can be carried out as described above.

[0624] The processing flow will be explained below.

[0625] Step 1:

[0626] The user collects a saliva or blood sample using a special collection kit.

[0627] Action: Follow the instructions on the collection kit to collect the correct amount of sample. Seal the sample in the collection kit to prevent leakage.

[0628] Step 2:

[0629] The user sets the collected sample in the terminal.

[0630] Operation: The collected sample is placed in a dedicated device and secured in place, starting the terminal's sample scanning mode.

[0631] Step 3:

[0632] The device formats the sample data and sends it to the server.

[0633] Operation: Scans the sample, stores it as digital data, converts it into the appropriate data format, and initiates the process of sending it to the server.

[0634] Step 4:

[0635] A server receives the biological sample data.

[0636] Action: Checks and receives data sent from the terminal. Sends a message confirming receipt to the terminal.

[0637] Step 5:

[0638] The server performs preprocessing on the data received.

[0639] Actions: Performs noise removal, data normalization, and missing data imputation. Converts data into an analysis-ready format.

[0640] Step 6:

[0641] The server begins analyzing the data using the generative AI model.

[0642] How it works: Input data into a generative AI model and run analysis using machine learning algorithms, including parameter tuning and feature engineering.

[0643] Step 7:

[0644] The server predicts disease risk and diagnoses it.

[0645] How it works: Calculates the risk of certain diseases, such as lung cancer or colon cancer, based on biological sample data. Generates specific diagnostic results.

[0646] Step 8:

[0647] The server analyzes an individual's ancestral roots based on their genetic information.

[0648] How it works: Analyzes DNA data and matches genetic characteristics with historical data to identify ancestral geographic roots and genetic patterns.

[0649] Step 9:

[0650] The server generates a report of the data analysis results.

[0651] What it does: Organizes the results of each analysis and creates a report for the user, including disease risk scores, health advice, and ancestry information.

[0652] Step 10:

[0653] The server encrypts the report and sends it to the user terminal.

[0654] How it works: The generated report is encrypted and sent to the user's device using a secure communication protocol.

[0655] Step 11:

[0656] The terminal visually displays the received reports to the user.

[0657] What it does: It decodes reports sent from the server and displays them in a user interface, allowing users to review results and understand health risks and ancestry information.

[0658] Example 1

[0659] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0660] In modern society, it is important for individuals to regularly monitor their health status and take appropriate health management measures. However, conventional health checkups and disease risk assessment methods are often time-consuming, costly, and have limited access. Furthermore, ancestral root analysis requires specialized knowledge and equipment, making it difficult for average users to access. The present invention aims to provide a system that allows users to easily monitor their health status and genetic roots.

[0661] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0662] In this invention, the server includes means for preprocessing received digital data, means for analyzing biological sample data using a generative AI model, and means for assessing disease risk scores. This enables users to easily and quickly understand their own health status and disease risk, and further analyze their ancestral roots.

[0663] A "user" is an individual who collects a saliva or blood sample using a biological sample collection kit and sets it in a dedicated terminal.

[0664] A "biological sample" is a sample, such as saliva or blood, collected from a living organism and is a source of data for analyzing health status and genetic information.

[0665] A "terminal" is a device that scans biological samples collected by a user, converts them into digital data, and sends them to a server.

[0666] "Digital data" refers to information obtained from a biological sample that has been converted into a format that can be stored and transmitted electronically.

[0667] The "server" is a central processing unit that analyzes the received digital data and performs disease risk assessments and ancestral root analysis.

[0668] "Preprocessing" refers to processes such as noise removal, data normalization, and missing data completion that are performed on the data received by the server.

[0669] A "generative AI model" is an artificial intelligence model used to analyze biological sample data and perform disease risk assessment and genetic information analysis.

[0670] A "disease risk score" is a quantified indicator of the likelihood of contracting a particular disease, as assessed by a generative AI model.

[0671] "Ancestry analysis" is the process of analyzing past genetic patterns based on an individual's genetic information to identify the region and roots of one's ancestors.

[0672] A "report" is a written or digital document provided to a user that includes the results of a disease risk assessment or ancestry analysis.

[0673] "Encryption" is the process of transforming data using a specific algorithm to secure the data being transmitted.

[0674] The present invention is a system that allows users to analyze their own health status, predict disease risks, and analyze their ancestral roots. This system is composed of three elements: a user, a terminal, and a server.

[0675] First, the user uses a dedicated collection kit to collect a saliva or blood sample, which is a biological specimen. The collected sample is sealed to prevent leakage and placed in the terminal. The terminal scans the biological specimen and converts it into digital data. The terminal then transmits this digital data to a server.

[0676] The server performs preprocessing on the received digital data. This preprocessing includes noise removal, data normalization, and missing data completion. After preprocessing is complete, the data is analyzed using a generative AI model. Machine learning libraries such as TensorFlow and PyTorch can be used for the generative AI model.

[0677] As part of the analysis, the server will assess a disease risk score, calculating a numerical value for the risk of certain diseases, such as lung cancer or colon cancer. It will also perform ancestry analysis based on an individual's genetic information. By analyzing specific patterns in the DNA sample, the server can pinpoint the region and origins of an individual's ancestors based on their past genetic patterns.

[0678] The analysis results are compiled by the server and generated into a detailed report, which includes disease risk assessment, health advice, and ancestry information. The report is encrypted and sent to the device using a secure communication protocol (e.g., HTTPS).

[0679] The device interprets the received report and visually displays it to the user, who can then access detailed risk assessments, health advice, and ancestral information through a user interface.

[0680] Specific examples

[0681] Example 1: Disease risk prediction using saliva samples

[0682] 1. The user collects a saliva sample using a dedicated collection kit.

[0683] 2. The user places the saliva sample on the terminal and starts the sample scan.

[0684] 3. The device sends the sample data to the server.

[0685] 4. The server receives the saliva data and performs preprocessing.

[0686] 5. The server performs analysis using the generative AI model to assess lung cancer risk.

[0687] 6. The server analyzes your ancestral roots based on your genetic information.

[0688] 7. The server generates a result report, encrypts it, and sends it to the user's terminal.

[0689] 8. The device receives the results and displays them in the user interface.

[0690] Example 2: Comprehensive health check using blood samples

[0691] 1. The user collects a blood sample using a dedicated collection kit.

[0692] 2. The user places the blood sample into the terminal.

[0693] 3. The device sends the sample data to the server.

[0694] 4. The server receives the blood data and performs preprocessing.

[0695] 5. The server performs analysis using the generated AI model to assess the risk of diabetes and cardiovascular disease.

[0696] 6. The server analyzes your ancestral roots based on your genetic information.

[0697] 7. The server generates a result report, encrypts it, and sends it to the user's terminal.

[0698] 8. The device receives the results and displays them in the user interface.

[0699] Example prompt

[0700] Example of server prompt:

[0701] "Sample data has been received. Please perform preprocessing and analyze the biological sample data. As analysis results, please output disease risk assessment and ancestral roots information."

[0702] Example prompt for a server-side AI model:

[0703] "Given your genetic sample data, calculate risk scores for the following diseases: lung cancer, colon cancer, diabetes, and cardiovascular disease. Additionally, analyze your personal genetic information to identify your ancestral roots."

[0704] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0705] Step 1:

[0706] Sample collection

[0707] The user collects a saliva or blood sample using a dedicated collection kit, which is then sealed to prevent leakage.

[0708] Input: Dedicated collection kit, user

[0709] Output: Collected biological sample (saliva or blood)

[0710] Step 2:

[0711] Set of data

[0712] The user places the collected sample into the terminal and inserts the sample into the terminal's sample scanner.

[0713] Input: collected biological sample, terminal

[0714] Output: Set sample

[0715] Step 3:

[0716] Scanning Data

[0717] The terminal scans the sample and stores it as digital data. The scanner reads the necessary information from the biological specimen and converts it into a digital format.

[0718] Input: Set sample

[0719] Output: Digital data

[0720] Step 4:

[0721] Sending data

[0722] The device sends the digital data to the server, using a network communication protocol (e.g., HTTPS) in an encrypted format.

[0723] Input: Digital data

[0724] Output: Data sent to the server

[0725] Step 5:

[0726] Data reception and preprocessing

[0727] The server preprocesses the digital data it receives, removing noise, normalizing the data, and filling in missing data.

[0728] Input: Data sent to the server

[0729] Output: Pre-processed digital data

[0730] Step 6:

[0731] Analysis using generative AI models

[0732] The server analyzes the data using a generative AI model, performs parameter tuning and feature engineering on the data, and then uses a predictive model (e.g., using TensorFlow or PyTorch) to assess disease risk.

[0733] Input: Preprocessed digital data

[0734] Output: Analysis results (disease risk score, ancestral roots information)

[0735] Step 7:

[0736] Disease risk assessment

[0737] The server calculates the risk of a specific disease (e.g., lung cancer, colon cancer, etc.) based on the analysis results of the generated AI model, generates a risk score, and provides a numerical assessment.

[0738] Input: Analysis results

[0739] Output: Disease risk score

[0740] Step 8:

[0741] Ancestry analysis

[0742] The server analyzes ancestral roots based on genetic information, referencing specific DNA patterns and identifying regions and roots based on past genetic patterns.

[0743] Input: Analysis results

[0744] Output: Ancestor roots information

[0745] Step 9:

[0746] Report Generation

[0747] The server generates a detailed report based on your disease risk score and ancestry information, including risk assessment, health advice, and ancestry information.

[0748] Input: Disease risk score, ancestry information

[0749] Output: Generated report

[0750] Step 10:

[0751] Data encryption and transmission

[0752] The server generates reports and encrypts them and sends them to the device using a secure communication protocol (e.g., SSL / TLS).

[0753] Input: Generated report

[0754] Output: Encrypted report

[0755] Step 11:

[0756] Displaying the results

[0757] The device interprets the received report and visually displays it to the user, who can then view the analysis results and receive detailed health advice through the user interface.

[0758] Input: Encrypted report

[0759] Output: Decoded report (displayed in the user interface)

[0760] (Application example 1)

[0761] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0762] Conventional health analysis systems simply provide users with analysis results, but do not offer appropriate health promotion product recommendations or services. This makes it difficult for users to take appropriate measures based on the analysis results, making consistent health management difficult. Another issue is the existence of security risks in the process of receiving and checking the analysis results.

[0763] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0764] In this invention, the server includes: [means for generating analysis results and transmitting them to the terminal as a report including personalized health promotion product advertisements;] [means for encrypting the analysis results and transmitting them to the terminal using a secure communication protocol; and] [means for predicting cancer risk in specific locations using the generative AI model.] This allows users to use optimal health promotion products and services based on the analysis results, enabling safe and consistent health management. Furthermore, the analysis results can be received in a form with enhanced security.

[0765] A "user" is an individual who uses the system or an entity who provides a biological sample and receives the analysis results.

[0766] A "biological sample" is a biological substance such as saliva or blood collected from a user, and is input data for analyzing the health condition.

[0767] A "terminal" is a device that allows a user to convert a biological sample into a data format and transmit it to a server.

[0768] A "data format" is a biological sample that has been digitized and converted into an analyzable format.

[0769] The "server" is a central processing unit that analyzes the received biometric sample data and performs disease risk prediction, health checkups, ancestral root analysis, and advertising suggestions.

[0770] "Preprocessing" refers to processing of biological sample data, such as noise removal, data normalization, and missing data complementation.

[0771] A "generative AI model" is a machine learning model that predicts and diagnoses disease risk based on biological sample data.

[0772] "Disease prediction" is the analysis of biological sample data to assess the risk of developing a particular disease.

[0773] "Diagnosis" is the medical evaluation of a specific health condition with a prognosis for disease.

[0774] "Genetic information" refers to an individual's genetic pattern obtained from genes such as DNA.

[0775] "Ancestral roots" refers to information about past genetic patterns and lineages identified based on genetic information.

[0776] A "report" is a document that summarizes the analysis results and includes a disease risk score, health advice, ancestry, and personalized recommendations for health-promoting products and services.

[0777] "Personalized health promotion products" are products and services that promote health that are individually suggested based on the analysis results.

[0778] "Advertisements" are introductions of health promotion products and services suggested based on the user's health condition.

[0779] "Visually displaying" means displaying the analysis results and advertisements on the screen of the terminal so that the user can check them.

[0780] "Encryption" refers to converting the analysis results so that they cannot be read by third parties, and is a technology that transmits them via a secure communication protocol.

[0781] A "secure communication protocol" is a standard communication specification for securely sending and receiving data.

[0782] MODE FOR CARRYING OUT THE INVENTION

[0783] System Configuration

[0784] The present invention is a system that analyzes a user's health status, predicts disease risk, and suggests appropriate health promotion products and services. The system is composed of the following elements:

[0785] 1. User terminal: A device that allows a user to collect a biological sample and transmit the data to the server.

[0786] 2. Server: A central processing unit that analyzes the received biological sample data and performs disease risk prediction, health checkups, ancestral root analysis, and advertising suggestions.

[0787] Program processing

[0788] Sample collection and submission

[0789] The user collects a saliva or blood sample using a dedicated collection kit. The collected sample is then placed in the device to prevent leakage. The terminal converts the biological sample into an appropriate data format and sends it to the server. The terminal also scans the biological sample and stores it as digital data. The process of sending it to the server then begins.

[0790] Data reception and analysis

[0791] The server receives the sent sample data and performs preprocessing, including noise removal, data normalization, and missing data completion. The server then begins analyzing the biological sample data using the generated AI model. This allows for disease prediction and diagnosis. For example, it calculates the risk of cancer in specific areas, such as lung cancer or colon cancer. The server also performs ancestral analysis based on an individual's genetic information. By analyzing the DNA sample, individual genetic patterns are identified and ancestral information is generated based on past genetic patterns.

[0792] Generating and notifying results

[0793] The server compiles the analysis results, generates a report containing personalized health promotion product advertisements, encrypts it, and sends it to the user's device. The device visually displays the received results through a user interface. The user can review the results and obtain a detailed risk assessment, health advice, and details of the advertised products.

[0794] Hardware and software used

[0795] User terminals are general mobile devices such as smartphones and tablet terminals.

[0796] The server is a high-performance cloud server or on-premise server.

[0797] The software uses libraries for data analysis and machine learning (e.g., Python, Flask, and some AI model libraries).

[0798] A generative AI model is a machine learning model that predicts and diagnoses disease risk based on biological sample data.

[0799] We use encryption technology and secure communication protocols (e.g., SSL / TLS) to ensure the secure transmission and reception of data.

[0800] Specific examples

[0801] For example, if a user opens the app and submits a saliva sample, the following occurs:

[0802] User Action:

[0803] 1. Collect a saliva sample and enter it into the smartphone app.

[0804] 2. Follow the instructions in the app to send sample data to the server.

[0805] Server processing example:

[0806] The server receives the sample data and inputs it into the analysis model.

[0807] Generate reports of the user's health risks (e.g., lung cancer risk) and ancestry information.

[0808] Advertisement proposal example:

[0809] Based on the report, the app will display recommendations such as "recommended supplements for those at high risk of lung cancer" and "fitness programs suited to your constitution."

[0810] Prompt Sentence Examples

[0811] Recommend appropriate health promotion products and services based on the user's health risks and ancestry information. Implement a program that receives the user's saliva data, analyzes it with an AI model, and suggests advertisements based on the generated report.

[0812] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0813] Processing flow and steps

[0814] Step 1:

[0815] The user collects a biological sample.

[0816] Specific behavior:

[0817] Users collect a saliva or blood sample using a special collection kit and place it in the device to prevent leakage.

[0818] Input: User's biological sample (saliva or blood)

[0819] Output: A biological sample is placed in the device.

[0820] Step 2:

[0821] The terminal scans the biological sample and converts it into a data format.

[0822] Specific behavior:

[0823] The scanning function of the terminal is used to convert the biological sample into digital data, which is then formatted into an appropriate data format.

[0824] Input: Biological sample placed on the device

[0825] Output: Digital data converted into a data format

[0826] Step 3:

[0827] The terminal transmits the digital data to the server.

[0828] Specific behavior:

[0829] This initiates the process of sending digital data from the terminal to the server, and the data is encrypted and sent securely.

[0830] Input: Digital data converted into a data format

[0831] Output: Digital data sent to the server

[0832] Step 4:

[0833] The server receives the transmitted data and performs pre-processing.

[0834] Specific behavior:

[0835] The server performs preprocessing on the received data, such as noise removal, data normalization, and missing data completion.

[0836] Input: Digital data sent from the terminal

[0837] Output: Preprocessed data

[0838] Step 5:

[0839] The server performs data analysis using the generated AI model.

[0840] Specific behavior:

[0841] The server inputs the preprocessed data into a generative AI model, performs parameter tuning and feature engineering, and then performs analysis to predict disease risk and health status from biological samples.

[0842] Input: Preprocessed digital data

[0843] Output: Analysis results (disease risk score and health status assessment)

[0844] Step 6:

[0845] The server analyzes ancestral roots based on genetic information.

[0846] Specific behavior:

[0847] The server uses analytical models to identify genetic patterns from a user's DNA sample and analyze their ancestral roots.

[0848] Input: Preprocessed digital data and DNA information

[0849] Output: Ancestor roots analysis results

[0850] Step 7:

[0851] A server generates a report containing personalized health and wellness product promotions.

[0852] Specific behavior:

[0853] The server combines the analysis results with information such as past purchasing history and interests and preferences to create a report that includes suggestions for health-promoting products and services that are appropriate for the user.

[0854] Input: Analysis results, purchase history, interest and preference information

[0855] Output: Personalized health report and advertising suggestions

[0856] Step 8:

[0857] The server encrypts the report and sends it to the device.

[0858] Specific behavior:

[0859] The server encrypts the generated report and sends it to the terminal using a secure communication protocol (e.g., SSL / TLS).

[0860] Input: Personalized health reports and advertising suggestions

[0861] Output: Encrypted report

[0862] Step 9:

[0863] The device visually displays the analysis results and advertisements to the user.

[0864] Specific behavior:

[0865] The device decrypts the received encrypted report and visually displays it through a user interface, allowing the user to view the analysis results and details of suggested health promotion products and services.

[0866] Input: Encrypted report

[0867] Output: Analysis results and advertising suggestions displayed on the user interface

[0868] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0869] System Configuration

[0870] The present invention is a system for users to analyze their own health status and predict their risk of disease. The system comprises the following components:

[0871] 1. User Device

[0872] This device allows users to collect saliva or blood samples and transmits the data to a server. The device also has an emotion engine that can recognize the user's emotions.

[0873] 2. Server

[0874] It is a central processing unit that analyzes received biometric sample data and emotional data to predict disease risk, conduct health checks, and analyze ancestral roots.

[0875] Program processing

[0876] Sample collection and submission

[0877] 1. The user collects a saliva or blood sample using a dedicated collection kit, which is then placed in the device to prevent leakage.

[0878] 2. When the user sets the sample, they use the device camera to scan their face. The emotion engine analyzes the user's facial expressions and obtains emotion data.

[0879] 3. The device converts the sample data and emotion data into an appropriate data format and sends it to the server. The device scans the sample, saves it as digital data, and then starts the process of sending it to the server along with the emotion data.

[0880] Data reception and analysis

[0881] 4. The server receives the sample data and emotion data and performs preprocessing, including noise removal, data normalization, and missing data completion.

[0882] 5. The server begins analyzing the biometric sample data and emotional data using the generated AI model.

[0883] Data is input into the model and analysis is performed using machine learning algorithms, including parameter tuning and feature engineering.

[0884] 6. The server performs disease risk prediction and diagnosis. It performs risk assessment taking into account emotional data and calculates the risk of cancer in specific areas, such as lung cancer or colon cancer.

[0885] 7. The server also performs ancestry analysis based on an individual's genetic information. By analyzing DNA samples, it identifies individual genetic patterns and generates ancestry information based on past genetic patterns.

[0886] Generating and notifying results

[0887] 8. The server compiles the analysis results and generates a report for the user, which may include a disease risk score, health advice based on the user's emotional state, and information about their ancestry.

[0888] 9. The server encrypts the result report and sends it to the user terminal using the appropriate communication protocol.

[0889] 10. The device receives the transmitted results and visually displays them through the user interface. The user can then review the results and receive detailed risk assessments and health advice, while also receiving interactive feedback based on the user's emotional state.

[0890] Specific examples

[0891] Example 1: Disease risk prediction and sentiment analysis using saliva samples

[0892] 1. The user collects a saliva sample using a dedicated collection kit.

[0893] 2. The user places a saliva sample on the device and scans their face to obtain emotional data.

[0894] 3. The device sends the sample data and emotion data to the server.

[0895] 4. The server receives the saliva data and emotion data and performs preprocessing.

[0896] 5. The server analyzes the generated AI model and assesses the risk of lung cancer, taking emotional data into account.

[0897] 6. The server analyzes your ancestral roots based on your genetic information.

[0898] 7. The server generates a result report, encrypts it, and sends it to the user's terminal.

[0899] 8. The device receives the results and displays them on the user interface, providing health advice based on the user's emotional state.

[0900] Example 2: Comprehensive health check and sentiment analysis using blood samples

[0901] 1. The user collects a blood sample using a dedicated collection kit.

[0902] 2. The user places a blood sample into the device and scans their face to obtain emotional data.

[0903] 3. The device sends the sample data and emotion data to the server.

[0904] 4. The server receives the blood data and emotion data and performs preprocessing.

[0905] 5. The server performs analysis using the generated AI model to assess the risk of diabetes and cardiovascular disease, taking emotional data into account.

[0906] 6. The server analyzes your ancestral roots based on your genetic information.

[0907] 7. The server generates a result report, encrypts it, and sends it to the user's terminal.

[0908] 8. The device receives the results and displays them on the user interface, providing health advice based on the user's emotional state.

[0909] The present invention can be carried out as described above.

[0910] The processing flow will be explained below.

[0911] Step 1:

[0912] The user collects a saliva or blood sample using a special collection kit.

[0913] Action: Collect the appropriate amount of biological sample according to the instructions on the collection kit. Seal the sample in the collection kit to prevent leakage.

[0914] Step 2:

[0915] The user sets the collected sample in the terminal.

[0916] Operation: The collected sample is placed in a dedicated device and secured in place, starting the terminal's sample scanning mode.

[0917] Step 3:

[0918] The user uses the device's camera to scan their face and obtain emotional data.

[0919] How it works: The device's emotion engine analyzes the user's facial expressions and generates emotion data in real time.

[0920] Step 4:

[0921] The terminal formats the sample data and emotion data and sends them to the server.

[0922] Operation: Sample data is scanned and converted into digital data. At the same time, emotion data is formatted and the process of sending it to the server begins.

[0923] Step 5:

[0924] A server receives the biometric sample data and the emotion data.

[0925] Operation: Checks and receives data sent from the terminal. Sends a receipt confirmation to the terminal.

[0926] Step 6:

[0927] The server performs preprocessing.

[0928] What it does: It denoises, normalizes, and fills in missing data from biological samples. It also preprocesses emotion data and prepares it for analysis.

[0929] Step 7:

[0930] The server begins analyzing the data using the generative AI model.

[0931] How it works: Input data into a generative AI model to perform disease risk and diagnostic analysis, taking emotional data into account.

[0932] Step 8:

[0933] The server takes emotion data into account when performing disease risk prediction and diagnosis.

[0934] How it works: Combines biological sample data with emotional data to assess the risk of certain diseases, such as lung or colon cancer.

[0935] Step 9:

[0936] The server analyzes an individual's genetic information to identify their ancestral roots.

[0937] How it works: Analyzes DNA samples to identify genetic patterns, then matches them with historical data to identify your ancestral geographic roots.

[0938] Step 10:

[0939] The server generates a report of the analysis results.

[0940] What it does: Generates a detailed report with risk scores, emotion-based health advice, and ancestry information.

[0941] Step 11:

[0942] The server encrypts the report and sends it to the user terminal.

[0943] Operation: The generated report is encrypted and sent to the user's device using a secure communication protocol.

[0944] Step 12:

[0945] The terminal receives the analysis results and visually displays them to the user.

[0946] What it does: It interprets the received reports and displays them through a user interface, allowing users to view detailed risk assessments, health advice, and ancestry information, while also providing interactive feedback based on emotional state.

[0947] Example 2

[0948] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0949] Current health diagnostic systems face challenges in enabling users to comprehensively analyze their own health status and predict disease risk. Disease risk prediction requires consideration of both biomaterials and emotional state, but conventional systems are unable to integrate these factors. Providing analysis results to users quickly and conveniently is also a challenge.

[0950] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0951] In this invention, the server includes: [means for analyzing biomaterial data and emotional data using a generative AI model]; [means for predicting and diagnosing disease from the biomaterial data]; and [means for analyzing an individual's genetic information and identifying ancestry information]. This allows the user to obtain integrated analysis results of their own biomaterial and emotional state, enabling prediction and early diagnosis of health risks.

[0952] A "biological material" is a biological sample such as saliva or blood taken from a user.

[0953] "Digital data" refers to an electronic data format for analyzing, storing, and transmitting samples and emotional data by computer.

[0954] A "generative AI model" is an artificial intelligence system that uses machine learning algorithms to analyze data and make predictions and diagnoses.

[0955] An "emotion engine" is software or a device that analyzes a user's face and facial expressions to identify their emotional state.

[0956] "Preprocessing" refers to the process of removing noise from biomaterial data and emotion data, normalizing the data, and filling in missing data.

[0957] "Disease prediction and diagnosis" refers to assessing the risk of contracting a specific disease based on analyzed data and diagnosing it early.

[0958] "Genetic Information" refers to a user's genetic characteristics based on their individual genetic patterns or DNA data.

[0959] "Ancestry information" analyzes past genetic patterns based on the user's genetic information and provides information about their ancestors.

[0960] A "communications protocol" defines the procedures and rules for sending and receiving data over a network.

[0961] "Interactive feedback" refers to responses and advice provided in real time based on the user's behavior and emotional state.

[0962] System Configuration

[0963] The present invention is a system for users to analyze their own health status and predict their risk of disease. The system comprises the following components:

[0964] 1. User Device

[0965] This device allows users to collect biomaterials such as saliva or blood and transmits the data to a server. The device also has an emotion engine and is capable of recognizing the user's emotions.

[0966] 2. Server

[0967] This is a central processing unit that analyzes the received biomaterial data and emotional data, and performs disease risk prediction, health checks, and ancestral roots analysis.

[0968] Program processing

[0969] Sample collection and submission

[0970] 1. The user collects a saliva or blood sample using a dedicated collection kit, which is then placed in the device to prevent leakage.

[0971] 2. When the user sets the sample, they use the device camera to scan their face. The emotion engine analyzes the user's facial expressions and obtains emotion data.

[0972] 3. The device converts the sample data and emotion data into an appropriate data format and sends it to the server. The device scans the sample, saves it as digital data, and then starts the process of sending it to the server along with the emotion data.

[0973] Data reception and analysis

[0974] 4. The server receives the sample data and emotion data and performs preprocessing, including noise removal, data normalization, and missing data completion.

[0975] 5. The server begins analyzing the biometric sample data and emotional data using the generated AI model.

[0976] Data is input into the model and analysis is performed using machine learning algorithms, including parameter tuning and feature engineering.

[0977] 6. The server performs disease risk prediction and diagnosis. It performs risk assessment taking into account emotional data and calculates the risk of cancer in specific areas, such as lung cancer or colon cancer.

[0978] 7. The server also performs ancestry analysis based on an individual's genetic information. By analyzing DNA samples, it identifies individual genetic patterns and generates ancestry information based on past genetic patterns.

[0979] Generating and notifying results

[0980] 8. The server compiles the analysis results and generates a report for the user, which may include a disease risk score, health advice based on the user's emotional state, and information about their ancestry.

[0981] 9. The server encrypts the result report and sends it to the user terminal using the appropriate communication protocol.

[0982] 10. The device receives the transmitted results and visually displays them through the user interface. The user can then review the results and receive detailed risk assessments and health advice, while also receiving interactive feedback based on the user's emotional state.

[0983] Specific examples

[0984] Example 1: Disease risk prediction and sentiment analysis using saliva samples

[0985] 1. The user collects a saliva sample using a dedicated collection kit.

[0986] 2. The user places a saliva sample on the device and scans their face to obtain emotional data.

[0987] 3. The device sends the sample data and emotion data to the server.

[0988] 4. The server receives the data and performs preprocessing.

[0989] 5. The server begins analysis using the generated AI model, for example, to assess lung cancer risk, taking emotional data into account.

[0990] 6. The server performs ancestral roots analysis based on the genetic information.

[0991] 7. The server generates a result report, encrypts it, and sends it to the user's terminal.

[0992] 8. The device receives the results and displays them on the user interface, providing health advice based on the emotional state.

[0993] Example 2: Comprehensive health check and sentiment analysis using blood samples

[0994] 1. The user collects a blood sample using a dedicated collection kit.

[0995] 2. The user inserts a blood sample into the device and scans their face to obtain emotional data.

[0996] 3. The device sends the sample data and emotion data to the server.

[0997] 4. The server receives the data and performs preprocessing.

[0998] 5. The server begins analysis using the generated AI model, assessing, for example, the risk of diabetes or cardiovascular disease, taking emotional data into account.

[0999] 6. The server performs ancestral roots analysis based on the genetic information.

[1000] 7. The server generates a result report, encrypts it, and sends it to the user's terminal.

[1001] 8. The device receives the results and displays them on the user interface, providing health advice based on the emotional state.

[1002] In this way, the present invention can be implemented.

[1003] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1004] Step 1:

[1005] The user collects a saliva or blood sample using a special collection kit.

[1006] Input: Specialized collection kit, user's saliva or blood.

[1007] Output: sealed biomaterial sample.

[1008] How it works: The user follows the instructions on the collection kit, spitting saliva into a test tube or pricking their finger with a needle to collect blood. The sample is then sealed to prevent leakage.

[1009] Step 2:

[1010] When the user sets the sample, they scan their face using the device's camera.

[1011] Input: User's face, device camera.

[1012] Output: Emotion data.

[1013] Specific operation: The user follows the instructions displayed on the device screen, faces the camera, and remains still for a certain period of time. The emotion engine analyzes the user's facial expressions and generates emotion data.

[1014] Step 3:

[1015] The terminal converts the sample data and emotion data into an appropriate data format and transmits it to the server.

[1016] Input: Biomaterial samples, emotion data.

[1017] Output: Digitized sample data and emotion data.

[1018] How it works: The device scans the sample and stores it as digital data, which is then packaged with emotion data into a data packet and sent to a server using an encryption protocol.

[1019] Step 4:

[1020] The server receives the sample data and emotion data and performs preprocessing.

[1021] Input: Digitized sample data and emotion data.

[1022] Output: Preprocessed sample data and emotion data.

[1023] Specific operation: The server removes noise from the received data, normalizes the data, and fills in missing data, thereby maintaining data consistency and preparing it for analysis.

[1024] Step 5:

[1025] The server begins analyzing the data using the generative AI model.

[1026] Input: Preprocessed sample data and emotion data.

[1027] Output: Analysis results.

[1028] How it works: Data is fed into a generative AI model, and analysis is performed using machine learning algorithms, tuning parameters and performing feature engineering to extract meaningful features from the data.

[1029] Step 6:

[1030] The server predicts disease risk and diagnoses it.

[1031] Input: Analysis result data.

[1032] Output: Disease risk prediction and diagnostic results.

[1033] Specific operation: For example, calculate the risk of lung cancer, colon cancer, etc. from specific biomarkers and genetic information, and perform risk assessment taking emotional data into account.

[1034] Step 7:

[1035] The server analyzes an individual's ancestral roots based on their genetic information.

[1036] Input: Personal genetic information data.

[1037] Output: Ancestry information.

[1038] What it does: Identifies genetic patterns from DNA samples and matches them with historical genetic databases to generate ancestry information.

[1039] Step 8:

[1040] The server compiles the analysis results and generates a report to provide to the user.

[1041] Input: Disease risk prediction results, emotional state data, ancestry information.

[1042] Output: Results report.

[1043] What it does: Generates reports with disease risk scores, health advice based on emotional state, and ancestry information.

[1044] Step 9:

[1045] The server encrypts the results report and transmits it to the user terminal using a secure communication protocol.

[1046] Input: Results report.

[1047] Output: Encrypted results report.

[1048] Specific operation: Using encryption protocols such as SSL / TLS, the report data is protected and sent to the user terminal.

[1049] Step 10:

[1050] The terminal receives the transmitted results and visually displays them through a user interface.

[1051] Input: Encrypted results report.

[1052] Output: A visual display to the user.

[1053] How it works: The analysis results are displayed through a device application, allowing users to check risk assessments, health advice, ancestry information, etc. Interactive feedback based on emotional state is also displayed on the screen.

[1054] (Application example 2)

[1055] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1056] Conventional health management systems predict disease risk using only a user's biometric sample data, making it difficult to provide advice that takes into account the user's emotional state and stress level. Furthermore, health advice in virtual reality environments is not commonly provided, and real-time feedback is lacking. This makes it difficult for users to comprehensively understand their own health status.

[1057] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1058] In this invention, the server includes: [means for a user to collect a biological sample;] [means for a terminal to convert the biological sample into a data format and send it to the server;] [means for the server to perform preprocessing and analyze the biological sample data;] [means for the server to predict and diagnose disease using a generative AI model;] [means for the server to analyze an individual's genetic information and identify ancestral roots;] [means for the server to generate analysis results and send them to the terminal;] [means for the terminal to visually display the analysis results to the user;] [means for acquiring the user's emotional data using a camera-equipped head-mounted display; and [means for the server to analyze the emotional data and provide advice based on the user's health condition in real time.] This enables users to understand their own health condition in real time in a virtual reality environment and receive personalized health advice that takes their emotional state into consideration.

[1059] A "biological sample" is a biological component, such as a user's saliva or blood, that is collected to understand the user's health condition.

[1060] A "terminal" is a device on which a user sets a biological sample and transmits the data to a server, and is also a device that has the function of acquiring emotion data using a camera.

[1061] The "server" is a central processing unit that analyzes the received biological sample data and emotion data to predict disease risk, perform diagnosis, and analyze ancestry.

[1062] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to analyze received data and predict disease risk and diagnose disease.

[1063] "Genetic information" refers to a user's DNA data, which is used to analyze ancestral roots and disease risks.

[1064] "Emotion data" is data that indicates the emotional state of the user, and is data that is acquired by the user scanning their face with a camera.

[1065] A "head-mounted display" is a device worn by the user that displays information visually and acquires emotional data using a camera.

[1066] "Real-time" means that the process of collecting data on the user's health and emotional state, analyzing it, and providing feedback is carried out instantly.

[1067] "Personalization" means providing information and advice that is individually tailored to each user based on their health and emotional state.

[1068] The present invention provides a system for enabling a user to analyze his or her own health and emotional state, and obtain health risk predictions and personalized advice. This system is implemented in the following manner.

[1069] System Configuration

[1070] The main components of this system include:

[1071] 1. User terminal: A device where the user collects biometric samples and sends them to the server. The terminal is equipped with a camera and has the function of scanning the user's face and acquiring emotional data.

[1072] 2. Server: A central processing unit that analyzes received biometric sample data and emotional data to predict disease risk, conduct health checks, and analyze ancestral roots. Generative AI models are used for the analysis.

[1073] 3. Head-mounted display (HMD): A device worn by the user to visually check the analysis results. It also captures emotional data using a camera.

[1074] The specific hardware and software used

[1075] Hardware:

[1076] User device (smartphone or dedicated device)

[1077] Head-mounted display (HMD) with camera

[1078] Server device

[1079] software:

[1080] Sentiment analysis software (OpenCV, Keras)

[1081] Generative AI model (machine learning algorithm) on the server

[1082] Data transmission protocol (HTTP, HTTPS)

[1083] Processing flow explanation

[1084] 1. Biological sample collection and transmission:

[1085] The user collects a saliva or blood sample using a dedicated kit and inserts it into the device, which converts the biometric sample into digital data and sends it to a server. At the same time, the device's camera scans the face and captures emotional data.

[1086] 2. Data Receipt and Analysis:

[1087] The server analyzes the received biometric sample data and emotional data. It performs preprocessing, such as noise removal and data normalization, and then inputs the data into a generative AI model to predict disease risk. It also analyzes emotional data and generates advice tailored to the user's health condition.

[1088] 3. Results generation and notification:

[1089] The server compiles the analysis results, generates a report for the user, encrypts it, and sends it to the device. The device receives the report and displays it visually to the user through a head-mounted display, while also providing interactive feedback based on the user's emotional state.

[1090] Examples of specific examples and prompts

[1091] Example: A user can track their health status while shopping in a virtual store and receive real-time health advice. For example, if the system determines that the user is feeling stressed, it will suggest relaxing music or products.

[1092] Example prompt sentence:

[1093] "Design a VR application that tracks users' health status and provides real-time health advice while they shop in a virtual store. Combine sentiment analysis with biometric sample data to provide optimal personalized feedback."

[1094] The system allows users to gain a comprehensive understanding of their health status in a virtual reality environment and receive personalized health advice in real time.

[1095] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1096] Step 1:

[1097] The user collects a biological sample (saliva or blood) using a dedicated kit. The input is the user's biological sample, and the output is the collected biological sample. The specific operation is to place the biological sample in a dedicated container to prevent leakage.

[1098] Step 2:

[1099] The user places a biometric sample on the device, and the device's camera scans their face to obtain emotional data. The input is the biometric sample and the user's facial information, and the output is digital biometric sample data and emotional data. Specifically, the system scans the face with the camera and performs facial expression analysis.

[1100] Step 3:

[1101] The device converts the biometric sample data and emotion data into an appropriate data format and sends it to the server. The input is the digital biometric sample data and emotion data, and the output is the data sent to the server. Specifically, the data is converted into an appropriate format and sent to the server via an HTTP request.

[1102] Step 4:

[1103] The server receives the biometric sample data and emotion data and performs preprocessing including noise removal, data normalization, and missing data completion. The input is the biometric sample data and emotion data sent to the server, and the output is the preprocessed data. Specific operations include applying noise filtering and data normalization algorithms.

[1104] Step 5:

[1105] The server uses the generated AI model to analyze the biometric sample data and emotional data. The input is the preprocessed biometric sample data and emotional data, and the output is the analysis results. Specifically, the data is input into a model using a machine learning algorithm to perform risk prediction and diagnosis.

[1106] Step 6:

[1107] The server generates a report based on the analysis results, including the user's disease risk score and health advice, encrypts it, and sends it to the device. The input is the analysis results, and the output is an encrypted report. Specifically, the report for the user is constructed using a report generation algorithm and encrypted using SSL / TLS.

[1108] Step 7:

[1109] The terminal decrypts the received report and visually displays it through the user interface. The input is the encrypted report, and the output is the analysis result displayed on the user interface. Specifically, after decryption, the report is displayed on the GUI so that the user can check the results.

[1110] Step 8:

[1111] Based on the reports displayed on the device, interactive feedback is provided according to the user's emotional state. The input is feedback information based on emotional data, and the output is personalized advice provided to the user. Specifically, advice based on the results of emotion analysis is generated and presented in real time.

[1112] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1113] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1114] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1115] [Third embodiment]

[1116] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1117] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1119] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1120] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1123] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1124] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1126] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1127] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1128] System Configuration

[1129] The present invention is a system for users to analyze their own health status and predict their risk of disease. The system comprises the following components:

[1130] 1. User Device

[1131] It is a device that allows a user to collect a biological sample of saliva or blood and transmits the data to a server.

[1132] 2. Server

[1133] It is a central processing unit that analyzes received biological sample data and performs disease risk prediction, health checkups, and ancestral root analysis.

[1134] Program processing

[1135] Sample collection and submission

[1136] 1. The user collects a saliva or blood sample using a dedicated collection kit, which is then placed in the device to prevent leakage.

[1137] 2. The terminal converts the sample data into the appropriate data format and sends it to the server. The terminal scans the sample, stores it as digital data, and then starts the transmission process to the server.

[1138] Data reception and analysis

[1139] 3. The server receives the sample data and performs preprocessing, including noise removal, data normalization, and missing data completion.

[1140] 4. The server begins analyzing the biological sample data using the generated AI model.

[1141] Input data into the model and perform parameter tuning and feature engineering.

[1142] 5. The server performs disease prediction and diagnosis, for example, calculating the risk of cancer in specific areas such as lung cancer or colon cancer.

[1143] 6. The server also performs ancestry analysis based on the individual's genetic information. By analyzing the DNA sample, it identifies individual genetic patterns and generates ancestry information based on past genetic patterns.

[1144] Generating and notifying results

[1145] 7. The server compiles the results of the analysis and generates a report for the user, which includes a disease risk score, health advice, and information about ancestry.

[1146] 8. The server encrypts the result report and sends it to the user terminal using the appropriate communication protocol.

[1147] 9. The device receives the transmitted results and visually displays them through the user interface, allowing the user to review the results and receive detailed risk assessments and health advice.

[1148] Specific examples

[1149] Example 1: Disease risk prediction using saliva samples

[1150] 1. The user collects a saliva sample using a dedicated collection kit.

[1151] 2. The user places the saliva sample on the terminal and starts the sample scan.

[1152] 3. The device sends the sample data to the server.

[1153] 4. The server receives the saliva data and performs preprocessing.

[1154] 5. The server performs analysis using the generative AI model to assess lung cancer risk.

[1155] 6. The server analyzes your ancestral roots based on your genetic information.

[1156] 7. The server generates a result report, encrypts it, and sends it to the user's terminal.

[1157] 8. The device receives the results and displays them in the user interface.

[1158] Example 2: Comprehensive health check using blood samples

[1159] 1. The user collects a blood sample using a dedicated collection kit.

[1160] 2. The user places the blood sample into the terminal.

[1161] 3. The device sends the sample data to the server.

[1162] 4. The server receives the blood data and performs preprocessing.

[1163] 5. The server performs analysis using the generated AI model to assess the risk of diabetes and cardiovascular disease.

[1164] 6. The server analyzes your ancestral roots based on your genetic information.

[1165] 7. The server generates a result report, encrypts it, and sends it to the user's terminal.

[1166] 8. The device receives the results and displays them in the user interface.

[1167] The present invention can be carried out as described above.

[1168] The processing flow will be explained below.

[1169] Step 1:

[1170] The user collects a saliva or blood sample using a special collection kit.

[1171] Action: Follow the instructions on the collection kit to collect the correct amount of sample. Seal the sample in the collection kit to prevent leakage.

[1172] Step 2:

[1173] The user sets the collected sample in the terminal.

[1174] Operation: The collected sample is placed in a dedicated device and secured in place, starting the terminal's sample scanning mode.

[1175] Step 3:

[1176] The device formats the sample data and sends it to the server.

[1177] Operation: Scans the sample, stores it as digital data, converts it into the appropriate data format, and initiates the process of sending it to the server.

[1178] Step 4:

[1179] A server receives the biological sample data.

[1180] Action: Checks and receives data sent from the terminal. Sends a message confirming receipt to the terminal.

[1181] Step 5:

[1182] The server performs preprocessing on the data received.

[1183] Actions: Performs noise removal, data normalization, and missing data imputation. Converts data into an analysis-ready format.

[1184] Step 6:

[1185] The server begins analyzing the data using the generative AI model.

[1186] How it works: Input data into a generative AI model and run analysis using machine learning algorithms, including parameter tuning and feature engineering.

[1187] Step 7:

[1188] The server predicts disease risk and diagnoses it.

[1189] How it works: Calculates the risk of certain diseases, such as lung cancer or colon cancer, based on biological sample data. Generates specific diagnostic results.

[1190] Step 8:

[1191] The server analyzes an individual's ancestral roots based on their genetic information.

[1192] How it works: Analyzes DNA data and matches genetic characteristics with historical data to identify ancestral geographic roots and genetic patterns.

[1193] Step 9:

[1194] The server generates a report of the data analysis results.

[1195] What it does: Organizes the results of each analysis and creates a report for the user, including disease risk scores, health advice, and ancestry information.

[1196] Step 10:

[1197] The server encrypts the report and sends it to the user terminal.

[1198] How it works: The generated report is encrypted and sent to the user's device using a secure communication protocol.

[1199] Step 11:

[1200] The terminal visually displays the received reports to the user.

[1201] What it does: It decodes reports sent from the server and displays them in a user interface, allowing users to review results and understand health risks and ancestry information.

[1202] Example 1

[1203] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1204] In modern society, it is important for individuals to regularly monitor their health status and take appropriate health management measures. However, conventional health checkups and disease risk assessment methods are often time-consuming, costly, and have limited access. Furthermore, ancestral root analysis requires specialized knowledge and equipment, making it difficult for average users to access. The present invention aims to provide a system that allows users to easily monitor their health status and genetic roots.

[1205] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1206] In this invention, the server includes means for preprocessing received digital data, means for analyzing biological sample data using a generative AI model, and means for assessing disease risk scores. This enables users to easily and quickly understand their own health status and disease risk, and further analyze their ancestral roots.

[1207] A "user" is an individual who collects a saliva or blood sample using a biological sample collection kit and sets it in a dedicated terminal.

[1208] A "biological sample" is a sample, such as saliva or blood, collected from a living organism and is a source of data for analyzing health status and genetic information.

[1209] A "terminal" is a device that scans biological samples collected by a user, converts them into digital data, and sends them to a server.

[1210] "Digital data" refers to information obtained from a biological sample that has been converted into a format that can be stored and transmitted electronically.

[1211] The "server" is a central processing unit that analyzes the received digital data and performs disease risk assessments and ancestral root analysis.

[1212] "Preprocessing" refers to processes such as noise removal, data normalization, and missing data completion that are performed on the data received by the server.

[1213] A "generative AI model" is an artificial intelligence model used to analyze biological sample data and perform disease risk assessment and genetic information analysis.

[1214] A "disease risk score" is a quantified indicator of the likelihood of contracting a particular disease, as assessed by a generative AI model.

[1215] "Ancestry analysis" is the process of analyzing past genetic patterns based on an individual's genetic information to identify the region and roots of one's ancestors.

[1216] A "report" is a written or digital document provided to a user that includes the results of a disease risk assessment or ancestry analysis.

[1217] "Encryption" is the process of transforming data using a specific algorithm to secure the data being transmitted.

[1218] The present invention is a system that allows users to analyze their own health status, predict disease risks, and analyze their ancestral roots. This system is composed of three elements: a user, a terminal, and a server.

[1219] First, the user uses a dedicated collection kit to collect a saliva or blood sample, which is a biological specimen. The collected sample is sealed to prevent leakage and placed in the terminal. The terminal scans the biological specimen and converts it into digital data. The terminal then transmits this digital data to a server.

[1220] The server performs preprocessing on the received digital data. This preprocessing includes noise removal, data normalization, and missing data completion. After preprocessing is complete, the data is analyzed using a generative AI model. Machine learning libraries such as TensorFlow and PyTorch can be used for the generative AI model.

[1221] As part of the analysis, the server will assess a disease risk score, calculating a numerical value for the risk of certain diseases, such as lung cancer or colon cancer. It will also perform ancestry analysis based on an individual's genetic information. By analyzing specific patterns in the DNA sample, the server can pinpoint the region and origins of an individual's ancestors based on their past genetic patterns.

[1222] The analysis results are compiled by the server and generated into a detailed report, which includes disease risk assessment, health advice, and ancestry information. The report is encrypted and sent to the device using a secure communication protocol (e.g., HTTPS).

[1223] The device interprets the received report and visually displays it to the user, who can then access detailed risk assessments, health advice, and ancestral information through a user interface.

[1224] Specific examples

[1225] Example 1: Disease risk prediction using saliva samples

[1226] 1. The user collects a saliva sample using a dedicated collection kit.

[1227] 2. The user places the saliva sample on the terminal and starts the sample scan.

[1228] 3. The device sends the sample data to the server.

[1229] 4. The server receives the saliva data and performs preprocessing.

[1230] 5. The server performs analysis using the generative AI model to assess lung cancer risk.

[1231] 6. The server analyzes your ancestral roots based on your genetic information.

[1232] 7. The server generates a result report, encrypts it, and sends it to the user's terminal.

[1233] 8. The device receives the results and displays them in the user interface.

[1234] Example 2: Comprehensive health check using blood samples

[1235] 1. The user collects a blood sample using a dedicated collection kit.

[1236] 2. The user places the blood sample into the terminal.

[1237] 3. The device sends the sample data to the server.

[1238] 4. The server receives the blood data and performs preprocessing.

[1239] 5. The server performs analysis using the generated AI model to assess the risk of diabetes and cardiovascular disease.

[1240] 6. The server analyzes your ancestral roots based on your genetic information.

[1241] 7. The server generates a result report, encrypts it, and sends it to the user's terminal.

[1242] 8. The device receives the results and displays them in the user interface.

[1243] Example prompt

[1244] Example of server prompt:

[1245] "Sample data has been received. Please perform preprocessing and analyze the biological sample data. As analysis results, please output disease risk assessment and ancestral roots information."

[1246] Example prompt for a server-side AI model:

[1247] "Given your genetic sample data, calculate risk scores for the following diseases: lung cancer, colon cancer, diabetes, and cardiovascular disease. Additionally, analyze your personal genetic information to identify your ancestral roots."

[1248] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1249] Step 1:

[1250] Sample collection

[1251] The user collects a saliva or blood sample using a dedicated collection kit, which is then sealed to prevent leakage.

[1252] Input: Dedicated collection kit, user

[1253] Output: Collected biological sample (saliva or blood)

[1254] Step 2:

[1255] Set of data

[1256] The user places the collected sample into the terminal and inserts the sample into the terminal's sample scanner.

[1257] Input: collected biological sample, terminal

[1258] Output: Set sample

[1259] Step 3:

[1260] Scanning Data

[1261] The terminal scans the sample and stores it as digital data. The scanner reads the necessary information from the biological specimen and converts it into a digital format.

[1262] Input: Set sample

[1263] Output: Digital data

[1264] Step 4:

[1265] Sending data

[1266] The device sends the digital data to the server, using a network communication protocol (e.g., HTTPS) in an encrypted format.

[1267] Input: Digital data

[1268] Output: Data sent to the server

[1269] Step 5:

[1270] Data reception and preprocessing

[1271] The server preprocesses the digital data it receives, removing noise, normalizing the data, and filling in missing data.

[1272] Input: Data sent to the server

[1273] Output: Pre-processed digital data

[1274] Step 6:

[1275] Analysis using generative AI models

[1276] The server analyzes the data using a generative AI model, performs parameter tuning and feature engineering on the data, and then uses a predictive model (e.g., using TensorFlow or PyTorch) to assess disease risk.

[1277] Input: Preprocessed digital data

[1278] Output: Analysis results (disease risk score, ancestral roots information)

[1279] Step 7:

[1280] Disease risk assessment

[1281] The server calculates the risk of a specific disease (e.g., lung cancer, colon cancer, etc.) based on the analysis results of the generated AI model, generates a risk score, and provides a numerical assessment.

[1282] Input: Analysis results

[1283] Output: Disease risk score

[1284] Step 8:

[1285] Ancestry analysis

[1286] The server analyzes ancestral roots based on genetic information, referencing specific DNA patterns and identifying regions and roots based on past genetic patterns.

[1287] Input: Analysis results

[1288] Output: Ancestor roots information

[1289] Step 9:

[1290] Report Generation

[1291] The server generates a detailed report based on your disease risk score and ancestry information, including risk assessment, health advice, and ancestry information.

[1292] Input: Disease risk score, ancestry information

[1293] Output: Generated report

[1294] Step 10:

[1295] Data encryption and transmission

[1296] The server generates reports and encrypts them and sends them to the device using a secure communication protocol (e.g., SSL / TLS).

[1297] Input: Generated report

[1298] Output: Encrypted report

[1299] Step 11:

[1300] Displaying the results

[1301] The device interprets the received report and visually displays it to the user, who can then view the analysis results and receive detailed health advice through the user interface.

[1302] Input: Encrypted report

[1303] Output: Decoded report (displayed in the user interface)

[1304] (Application example 1)

[1305] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1306] Conventional health analysis systems simply provide users with analysis results, but do not offer appropriate health promotion product recommendations or services. This makes it difficult for users to take appropriate measures based on the analysis results, making consistent health management difficult. Another issue is the existence of security risks in the process of receiving and checking the analysis results.

[1307] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1308] In this invention, the server includes: [means for generating analysis results and transmitting them to the terminal as a report including personalized health promotion product advertisements;] [means for encrypting the analysis results and transmitting them to the terminal using a secure communication protocol; and] [means for predicting cancer risk in specific locations using the generative AI model.] This allows users to use optimal health promotion products and services based on the analysis results, enabling safe and consistent health management. Furthermore, the analysis results can be received in a form with enhanced security.

[1309] A "user" is an individual who uses the system or an entity who provides a biological sample and receives the analysis results.

[1310] A "biological sample" is a biological substance such as saliva or blood collected from a user, and is input data for analyzing the health condition.

[1311] A "terminal" is a device that allows a user to convert a biological sample into a data format and transmit it to a server.

[1312] A "data format" is a biological sample that has been digitized and converted into an analyzable format.

[1313] The "server" is a central processing unit that analyzes the received biometric sample data and performs disease risk prediction, health checkups, ancestral root analysis, and advertising suggestions.

[1314] "Preprocessing" refers to processing of biological sample data, such as noise removal, data normalization, and missing data complementation.

[1315] A "generative AI model" is a machine learning model that predicts and diagnoses disease risk based on biological sample data.

[1316] "Disease prediction" is the analysis of biological sample data to assess the risk of developing a particular disease.

[1317] "Diagnosis" is the medical evaluation of a specific health condition with a prognosis for disease.

[1318] "Genetic information" refers to an individual's genetic pattern obtained from genes such as DNA.

[1319] "Ancestral roots" refers to information about past genetic patterns and lineages identified based on genetic information.

[1320] A "report" is a document that summarizes the analysis results and includes a disease risk score, health advice, ancestry, and personalized recommendations for health-promoting products and services.

[1321] "Personalized health promotion products" are products and services that promote health that are individually suggested based on the analysis results.

[1322] "Advertisements" are introductions of health promotion products and services suggested based on the user's health condition.

[1323] "Visually displaying" means displaying the analysis results and advertisements on the screen of the terminal so that the user can check them.

[1324] "Encryption" refers to converting the analysis results so that they cannot be read by third parties, and is a technology that transmits them via a secure communication protocol.

[1325] A "secure communication protocol" is a standard communication specification for securely sending and receiving data.

[1326] MODE FOR CARRYING OUT THE INVENTION

[1327] System Configuration

[1328] The present invention is a system that analyzes a user's health status, predicts disease risk, and suggests appropriate health promotion products and services. The system is composed of the following elements:

[1329] 1. User terminal: A device that allows a user to collect a biological sample and transmit the data to the server.

[1330] 2. Server: A central processing unit that analyzes the received biological sample data and performs disease risk prediction, health checkups, ancestral root analysis, and advertising suggestions.

[1331] Program processing

[1332] Sample collection and submission

[1333] The user collects a saliva or blood sample using a dedicated collection kit. The collected sample is then placed in the device to prevent leakage. The terminal converts the biological sample into an appropriate data format and sends it to the server. The terminal also scans the biological sample and stores it as digital data. The process of sending it to the server then begins.

[1334] Data reception and analysis

[1335] The server receives the sent sample data and performs preprocessing, including noise removal, data normalization, and missing data completion. The server then begins analyzing the biological sample data using the generated AI model. This allows for disease prediction and diagnosis. For example, it calculates the risk of cancer in specific areas, such as lung cancer or colon cancer. The server also performs ancestral analysis based on an individual's genetic information. By analyzing the DNA sample, individual genetic patterns are identified and ancestral information is generated based on past genetic patterns.

[1336] Generating and notifying results

[1337] The server compiles the analysis results, generates a report containing personalized health promotion product advertisements, encrypts it, and sends it to the user's device. The device visually displays the received results through a user interface. The user can review the results and obtain a detailed risk assessment, health advice, and details of the advertised products.

[1338] Hardware and software used

[1339] User terminals are general mobile devices such as smartphones and tablet terminals.

[1340] The server is a high-performance cloud server or on-premise server.

[1341] The software uses libraries for data analysis and machine learning (e.g., Python, Flask, and some AI model libraries).

[1342] A generative AI model is a machine learning model that predicts and diagnoses disease risk based on biological sample data.

[1343] We use encryption technology and secure communication protocols (e.g., SSL / TLS) to ensure the secure transmission and reception of data.

[1344] Specific examples

[1345] For example, if a user opens the app and submits a saliva sample, the following occurs:

[1346] User Action:

[1347] 1. Collect a saliva sample and enter it into the smartphone app.

[1348] 2. Follow the instructions in the app to send sample data to the server.

[1349] Server processing example:

[1350] The server receives the sample data and inputs it into the analysis model.

[1351] Generate reports of the user's health risks (e.g., lung cancer risk) and ancestry information.

[1352] Advertisement proposal example:

[1353] Based on the report, the app will display recommendations such as "recommended supplements for those at high risk of lung cancer" and "fitness programs suited to your constitution."

[1354] Prompt Sentence Examples

[1355] Recommend appropriate health promotion products and services based on the user's health risks and ancestry information. Implement a program that receives the user's saliva data, analyzes it with an AI model, and suggests advertisements based on the generated report.

[1356] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1357] Processing flow and steps

[1358] Step 1:

[1359] The user collects a biological sample.

[1360] Specific behavior:

[1361] Users collect a saliva or blood sample using a special collection kit and place it in the device to prevent leakage.

[1362] Input: User's biological sample (saliva or blood)

[1363] Output: A biological sample is placed in the device.

[1364] Step 2:

[1365] The terminal scans the biological sample and converts it into a data format.

[1366] Specific behavior:

[1367] The scanning function of the terminal is used to convert the biological sample into digital data, which is then formatted into an appropriate data format.

[1368] Input: Biological sample placed on the device

[1369] Output: Digital data converted into a data format

[1370] Step 3:

[1371] The terminal transmits the digital data to the server.

[1372] Specific behavior:

[1373] This initiates the process of sending digital data from the terminal to the server, and the data is encrypted and sent securely.

[1374] Input: Digital data converted into a data format

[1375] Output: Digital data sent to the server

[1376] Step 4:

[1377] The server receives the transmitted data and performs pre-processing.

[1378] Specific behavior:

[1379] The server performs preprocessing on the received data, such as noise removal, data normalization, and missing data completion.

[1380] Input: Digital data sent from the terminal

[1381] Output: Preprocessed data

[1382] Step 5:

[1383] The server performs data analysis using the generated AI model.

[1384] Specific behavior:

[1385] The server inputs the preprocessed data into a generative AI model, performs parameter tuning and feature engineering, and then performs analysis to predict disease risk and health status from biological samples.

[1386] Input: Preprocessed digital data

[1387] Output: Analysis results (disease risk score and health status assessment)

[1388] Step 6:

[1389] The server analyzes ancestral roots based on genetic information.

[1390] Specific behavior:

[1391] The server uses analytical models to identify genetic patterns from a user's DNA sample and analyze their ancestral roots.

[1392] Input: Preprocessed digital data and DNA information

[1393] Output: Ancestor roots analysis results

[1394] Step 7:

[1395] A server generates a report containing personalized health and wellness product promotions.

[1396] Specific behavior:

[1397] The server combines the analysis results with information such as past purchasing history and interests and preferences to create a report that includes suggestions for health-promoting products and services that are appropriate for the user.

[1398] Input: Analysis results, purchase history, interest and preference information

[1399] Output: Personalized health report and advertising suggestions

[1400] Step 8:

[1401] The server encrypts the report and sends it to the device.

[1402] Specific behavior:

[1403] The server encrypts the generated report and sends it to the terminal using a secure communication protocol (e.g., SSL / TLS).

[1404] Input: Personalized health reports and advertising suggestions

[1405] Output: Encrypted report

[1406] Step 9:

[1407] The device visually displays the analysis results and advertisements to the user.

[1408] Specific behavior:

[1409] The device decrypts the received encrypted report and visually displays it through a user interface, allowing the user to view the analysis results and details of suggested health promotion products and services.

[1410] Input: Encrypted report

[1411] Output: Analysis results and advertising suggestions displayed on the user interface

[1412] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1413] System Configuration

[1414] The present invention is a system for users to analyze their own health status and predict their risk of disease. The system comprises the following components:

[1415] 1. User Device

[1416] This device allows users to collect saliva or blood samples and transmits the data to a server. The device also has an emotion engine that can recognize the user's emotions.

[1417] 2. Server

[1418] It is a central processing unit that analyzes received biometric sample data and emotional data to predict disease risk, conduct health checks, and analyze ancestral roots.

[1419] Program processing

[1420] Sample collection and submission

[1421] 1. The user collects a saliva or blood sample using a dedicated collection kit, which is then placed in the device to prevent leakage.

[1422] 2. When the user sets the sample, they use the device camera to scan their face. The emotion engine analyzes the user's facial expressions and obtains emotion data.

[1423] 3. The device converts the sample data and emotion data into an appropriate data format and sends it to the server. The device scans the sample, saves it as digital data, and then starts the process of sending it to the server along with the emotion data.

[1424] Data reception and analysis

[1425] 4. The server receives the sample data and emotion data and performs preprocessing, including noise removal, data normalization, and missing data completion.

[1426] 5. The server begins analyzing the biometric sample data and emotional data using the generated AI model.

[1427] Data is input into the model and analysis is performed using machine learning algorithms, including parameter tuning and feature engineering.

[1428] 6. The server performs disease risk prediction and diagnosis. It performs risk assessment taking into account emotional data and calculates the risk of cancer in specific areas, such as lung cancer or colon cancer.

[1429] 7. The server also performs ancestry analysis based on an individual's genetic information. By analyzing DNA samples, it identifies individual genetic patterns and generates ancestry information based on past genetic patterns.

[1430] Generating and notifying results

[1431] 8. The server compiles the analysis results and generates a report for the user, which may include a disease risk score, health advice based on the user's emotional state, and information about their ancestry.

[1432] 9. The server encrypts the result report and sends it to the user terminal using the appropriate communication protocol.

[1433] 10. The device receives the transmitted results and visually displays them through the user interface. The user can then review the results and receive detailed risk assessments and health advice, while also receiving interactive feedback based on the user's emotional state.

[1434] Specific examples

[1435] Example 1: Disease risk prediction and sentiment analysis using saliva samples

[1436] 1. The user collects a saliva sample using a dedicated collection kit.

[1437] 2. The user places a saliva sample on the device and scans their face to obtain emotional data.

[1438] 3. The device sends the sample data and emotion data to the server.

[1439] 4. The server receives the saliva data and emotion data and performs preprocessing.

[1440] 5. The server analyzes the generated AI model and assesses the risk of lung cancer, taking emotional data into account.

[1441] 6. The server analyzes your ancestral roots based on your genetic information.

[1442] 7. The server generates a result report, encrypts it, and sends it to the user's terminal.

[1443] 8. The device receives the results and displays them on the user interface, providing health advice based on the user's emotional state.

[1444] Example 2: Comprehensive health check and sentiment analysis using blood samples

[1445] 1. The user collects a blood sample using a dedicated collection kit.

[1446] 2. The user places a blood sample into the device and scans their face to obtain emotional data.

[1447] 3. The device sends the sample data and emotion data to the server.

[1448] 4. The server receives the blood data and emotion data and performs preprocessing.

[1449] 5. The server performs analysis using the generated AI model to assess the risk of diabetes and cardiovascular disease, taking emotional data into account.

[1450] 6. The server analyzes your ancestral roots based on your genetic information.

[1451] 7. The server generates a result report, encrypts it, and sends it to the user's terminal.

[1452] 8. The device receives the results and displays them on the user interface, providing health advice based on the user's emotional state.

[1453] The present invention can be carried out as described above.

[1454] The processing flow will be explained below.

[1455] Step 1:

[1456] The user collects a saliva or blood sample using a special collection kit.

[1457] Action: Collect the appropriate amount of biological sample according to the instructions on the collection kit. Seal the sample in the collection kit to prevent leakage.

[1458] Step 2:

[1459] The user sets the collected sample in the terminal.

[1460] Operation: The collected sample is placed in a dedicated device and secured in place, starting the terminal's sample scanning mode.

[1461] Step 3:

[1462] The user uses the device's camera to scan their face and obtain emotional data.

[1463] How it works: The device's emotion engine analyzes the user's facial expressions and generates emotion data in real time.

[1464] Step 4:

[1465] The terminal formats the sample data and emotion data and sends them to the server.

[1466] Operation: Sample data is scanned and converted into digital data. At the same time, emotion data is formatted and the process of sending it to the server begins.

[1467] Step 5:

[1468] A server receives the biometric sample data and the emotion data.

[1469] Operation: Checks and receives data sent from the terminal. Sends a receipt confirmation to the terminal.

[1470] Step 6:

[1471] The server performs preprocessing.

[1472] What it does: It denoises, normalizes, and fills in missing data from biological samples. It also preprocesses emotion data and prepares it for analysis.

[1473] Step 7:

[1474] The server begins analyzing the data using the generative AI model.

[1475] How it works: Input data into a generative AI model to perform disease risk and diagnostic analysis, taking emotional data into account.

[1476] Step 8:

[1477] The server takes emotion data into account when performing disease risk prediction and diagnosis.

[1478] How it works: Combines biological sample data with emotional data to assess the risk of certain diseases, such as lung or colon cancer.

[1479] Step 9:

[1480] The server analyzes an individual's genetic information to identify their ancestral roots.

[1481] How it works: Analyzes DNA samples to identify genetic patterns, then matches them with historical data to identify your ancestral geographic roots.

[1482] Step 10:

[1483] The server generates a report of the analysis results.

[1484] What it does: Generates a detailed report with risk scores, emotion-based health advice, and ancestry information.

[1485] Step 11:

[1486] The server encrypts the report and sends it to the user terminal.

[1487] Operation: The generated report is encrypted and sent to the user's device using a secure communication protocol.

[1488] Step 12:

[1489] The terminal receives the analysis results and visually displays them to the user.

[1490] What it does: It interprets the received reports and displays them through a user interface, allowing users to view detailed risk assessments, health advice, and ancestry information, while also providing interactive feedback based on emotional state.

[1491] Example 2

[1492] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1493] Current health diagnostic systems face challenges in enabling users to comprehensively analyze their own health status and predict disease risk. Disease risk prediction requires consideration of both biomaterials and emotional state, but conventional systems are unable to integrate these factors. Providing analysis results to users quickly and conveniently is also a challenge.

[1494] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1495] In this invention, the server includes: [means for analyzing biomaterial data and emotional data using a generative AI model]; [means for predicting and diagnosing disease from the biomaterial data]; and [means for analyzing an individual's genetic information and identifying ancestry information]. This allows the user to obtain integrated analysis results of their own biomaterial and emotional state, enabling prediction and early diagnosis of health risks.

[1496] A "biological material" is a biological sample such as saliva or blood taken from a user.

[1497] "Digital data" refers to an electronic data format for analyzing, storing, and transmitting samples and emotional data by computer.

[1498] A "generative AI model" is an artificial intelligence system that uses machine learning algorithms to analyze data and make predictions and diagnoses.

[1499] An "emotion engine" is software or a device that analyzes a user's face and facial expressions to identify their emotional state.

[1500] "Preprocessing" refers to the process of removing noise from biomaterial data and emotion data, normalizing the data, and filling in missing data.

[1501] "Disease prediction and diagnosis" refers to assessing the risk of contracting a specific disease based on analyzed data and diagnosing it early.

[1502] "Genetic Information" refers to a user's genetic characteristics based on their individual genetic patterns or DNA data.

[1503] "Ancestry information" analyzes past genetic patterns based on the user's genetic information and provides information about their ancestors.

[1504] A "communications protocol" defines the procedures and rules for sending and receiving data over a network.

[1505] "Interactive feedback" refers to responses and advice provided in real time based on the user's behavior and emotional state.

[1506] System Configuration

[1507] The present invention is a system for users to analyze their own health status and predict their risk of disease. The system comprises the following components:

[1508] 1. User Device

[1509] This device allows users to collect biomaterials such as saliva or blood and transmits the data to a server. The device also has an emotion engine and is capable of recognizing the user's emotions.

[1510] 2. Server

[1511] This is a central processing unit that analyzes the received biomaterial data and emotional data, and performs disease risk prediction, health checks, and ancestral roots analysis.

[1512] Program processing

[1513] Sample collection and submission

[1514] 1. The user collects a saliva or blood sample using a dedicated collection kit, which is then placed in the device to prevent leakage.

[1515] 2. When the user sets the sample, they use the device camera to scan their face. The emotion engine analyzes the user's facial expressions and obtains emotion data.

[1516] 3. The device converts the sample data and emotion data into an appropriate data format and sends it to the server. The device scans the sample, saves it as digital data, and then starts the process of sending it to the server along with the emotion data.

[1517] Data reception and analysis

[1518] 4. The server receives the sample data and emotion data and performs preprocessing, including noise removal, data normalization, and missing data completion.

[1519] 5. The server begins analyzing the biometric sample data and emotional data using the generated AI model.

[1520] Data is input into the model and analysis is performed using machine learning algorithms, including parameter tuning and feature engineering.

[1521] 6. The server performs disease risk prediction and diagnosis. It performs risk assessment taking into account emotional data and calculates the risk of cancer in specific areas, such as lung cancer or colon cancer.

[1522] 7. The server also performs ancestry analysis based on an individual's genetic information. By analyzing DNA samples, it identifies individual genetic patterns and generates ancestry information based on past genetic patterns.

[1523] Generating and notifying results

[1524] 8. The server compiles the analysis results and generates a report for the user, which may include a disease risk score, health advice based on the user's emotional state, and information about their ancestry.

[1525] 9. The server encrypts the result report and sends it to the user terminal using the appropriate communication protocol.

[1526] 10. The device receives the transmitted results and visually displays them through the user interface. The user can then review the results and receive detailed risk assessments and health advice, while also receiving interactive feedback based on the user's emotional state.

[1527] Specific examples

[1528] Example 1: Disease risk prediction and sentiment analysis using saliva samples

[1529] 1. The user collects a saliva sample using a dedicated collection kit.

[1530] 2. The user places a saliva sample on the device and scans their face to obtain emotional data.

[1531] 3. The device sends the sample data and emotion data to the server.

[1532] 4. The server receives the data and performs preprocessing.

[1533] 5. The server begins analysis using the generated AI model, for example, to assess lung cancer risk, taking emotional data into account.

[1534] 6. The server performs ancestral roots analysis based on the genetic information.

[1535] 7. The server generates a result report, encrypts it, and sends it to the user's terminal.

[1536] 8. The device receives the results and displays them on the user interface, providing health advice based on the emotional state.

[1537] Example 2: Comprehensive health check and sentiment analysis using blood samples

[1538] 1. The user collects a blood sample using a dedicated collection kit.

[1539] 2. The user inserts a blood sample into the device and scans their face to obtain emotional data.

[1540] 3. The device sends the sample data and emotion data to the server.

[1541] 4. The server receives the data and performs preprocessing.

[1542] 5. The server begins analysis using the generated AI model, assessing, for example, the risk of diabetes or cardiovascular disease, taking emotional data into account.

[1543] 6. The server performs ancestral roots analysis based on the genetic information.

[1544] 7. The server generates a result report, encrypts it, and sends it to the user's terminal.

[1545] 8. The device receives the results and displays them on the user interface, providing health advice based on the emotional state.

[1546] In this way, the present invention can be implemented.

[1547] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1548] Step 1:

[1549] The user collects a saliva or blood sample using a special collection kit.

[1550] Input: Specialized collection kit, user's saliva or blood.

[1551] Output: sealed biomaterial sample.

[1552] How it works: The user follows the instructions on the collection kit, spitting saliva into a test tube or pricking their finger with a needle to collect blood. The sample is then sealed to prevent leakage.

[1553] Step 2:

[1554] When the user sets the sample, they scan their face using the device's camera.

[1555] Input: User's face, device camera.

[1556] Output: Emotion data.

[1557] Specific operation: The user follows the instructions displayed on the device screen, faces the camera, and remains still for a certain period of time. The emotion engine analyzes the user's facial expressions and generates emotion data.

[1558] Step 3:

[1559] The terminal converts the sample data and emotion data into an appropriate data format and transmits it to the server.

[1560] Input: Biomaterial samples, emotion data.

[1561] Output: Digitized sample data and emotion data.

[1562] How it works: The device scans the sample and stores it as digital data, which is then packaged with emotion data into a data packet and sent to a server using an encryption protocol.

[1563] Step 4:

[1564] The server receives the sample data and emotion data and performs preprocessing.

[1565] Input: Digitized sample data and emotion data.

[1566] Output: Preprocessed sample data and emotion data.

[1567] Specific operation: The server removes noise from the received data, normalizes the data, and fills in missing data, thereby maintaining data consistency and preparing it for analysis.

[1568] Step 5:

[1569] The server begins analyzing the data using the generative AI model.

[1570] Input: Preprocessed sample data and emotion data.

[1571] Output: Analysis results.

[1572] How it works: Data is fed into a generative AI model, and analysis is performed using machine learning algorithms, tuning parameters and performing feature engineering to extract meaningful features from the data.

[1573] Step 6:

[1574] The server predicts disease risk and diagnoses it.

[1575] Input: Analysis result data.

[1576] Output: Disease risk prediction and diagnostic results.

[1577] Specific operation: For example, calculate the risk of lung cancer, colon cancer, etc. from specific biomarkers and genetic information, and perform risk assessment taking emotional data into account.

[1578] Step 7:

[1579] The server analyzes an individual's ancestral roots based on their genetic information.

[1580] Input: Personal genetic information data.

[1581] Output: Ancestry information.

[1582] What it does: Identifies genetic patterns from DNA samples and matches them with historical genetic databases to generate ancestry information.

[1583] Step 8:

[1584] The server compiles the analysis results and generates a report to provide to the user.

[1585] Input: Disease risk prediction results, emotional state data, ancestry information.

[1586] Output: Results report.

[1587] What it does: Generates reports with disease risk scores, health advice based on emotional state, and ancestry information.

[1588] Step 9:

[1589] The server encrypts the results report and transmits it to the user terminal using a secure communication protocol.

[1590] Input: Results report.

[1591] Output: Encrypted results report.

[1592] Specific operation: Using encryption protocols such as SSL / TLS, the report data is protected and sent to the user terminal.

[1593] Step 10:

[1594] The terminal receives the transmitted results and visually displays them through a user interface.

[1595] Input: Encrypted results report.

[1596] Output: A visual display to the user.

[1597] How it works: The analysis results are displayed through a device application, allowing users to check risk assessments, health advice, ancestry information, etc. Interactive feedback based on emotional state is also displayed on the screen.

[1598] (Application example 2)

[1599] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1600] Conventional health management systems predict disease risk using only a user's biometric sample data, making it difficult to provide advice that takes into account the user's emotional state and stress level. Furthermore, health advice in virtual reality environments is not commonly provided, and real-time feedback is lacking. This makes it difficult for users to comprehensively understand their own health status.

[1601] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1602] In this invention, the server includes: [means for a user to collect a biological sample;] [means for a terminal to convert the biological sample into a data format and send it to the server;] [means for the server to perform preprocessing and analyze the biological sample data;] [means for the server to predict and diagnose disease using a generative AI model;] [means for the server to analyze an individual's genetic information and identify ancestral roots;] [means for the server to generate analysis results and send them to the terminal;] [means for the terminal to visually display the analysis results to the user;] [means for acquiring the user's emotional data using a camera-equipped head-mounted display; and [means for the server to analyze the emotional data and provide advice based on the user's health condition in real time.] This enables users to understand their own health condition in real time in a virtual reality environment and receive personalized health advice that takes their emotional state into consideration.

[1603] A "biological sample" is a biological component, such as a user's saliva or blood, that is collected to understand the user's health condition.

[1604] A "terminal" is a device on which a user sets a biological sample and transmits the data to a server, and is also a device that has the function of acquiring emotion data using a camera.

[1605] The "server" is a central processing unit that analyzes the received biological sample data and emotion data to predict disease risk, perform diagnosis, and analyze ancestry.

[1606] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to analyze received data and predict disease risk and diagnose disease.

[1607] "Genetic information" refers to a user's DNA data, which is used to analyze ancestral roots and disease risks.

[1608] "Emotion data" is data that indicates the emotional state of the user, and is data that is acquired by the user scanning their face with a camera.

[1609] A "head-mounted display" is a device worn by the user that displays information visually and acquires emotional data using a camera.

[1610] "Real-time" means that the process of collecting data on the user's health and emotional state, analyzing it, and providing feedback is carried out instantly.

[1611] "Personalization" means providing information and advice that is individually tailored to each user based on their health and emotional state.

[1612] The present invention provides a system for enabling a user to analyze his or her own health and emotional state, and obtain health risk predictions and personalized advice. This system is implemented in the following manner.

[1613] System Configuration

[1614] The main components of this system include:

[1615] 1. User terminal: A device where the user collects biometric samples and sends them to the server. The terminal is equipped with a camera and has the function of scanning the user's face and acquiring emotional data.

[1616] 2. Server: A central processing unit that analyzes received biometric sample data and emotional data to predict disease risk, conduct health checks, and analyze ancestral roots. Generative AI models are used for the analysis.

[1617] 3. Head-mounted display (HMD): A device worn by the user to visually check the analysis results. It also captures emotional data using a camera.

[1618] The specific hardware and software used

[1619] Hardware:

[1620] User device (smartphone or dedicated device)

[1621] Head-mounted display (HMD) with camera

[1622] Server device

[1623] software:

[1624] Sentiment analysis software (OpenCV, Keras)

[1625] Generative AI model (machine learning algorithm) on the server

[1626] Data transmission protocol (HTTP, HTTPS)

[1627] Processing flow explanation

[1628] 1. Biological sample collection and transmission:

[1629] The user collects a saliva or blood sample using a dedicated kit and inserts it into the device, which converts the biometric sample into digital data and sends it to a server. At the same time, the device's camera scans the face and captures emotional data.

[1630] 2. Data Receipt and Analysis:

[1631] The server analyzes the received biometric sample data and emotional data. It performs preprocessing, such as noise removal and data normalization, and then inputs the data into a generative AI model to predict disease risk. It also analyzes emotional data and generates advice tailored to the user's health condition.

[1632] 3. Results generation and notification:

[1633] The server compiles the analysis results, generates a report for the user, encrypts it, and sends it to the device. The device receives the report and displays it visually to the user through a head-mounted display, while also providing interactive feedback based on the user's emotional state.

[1634] Examples of specific examples and prompts

[1635] Example: A user can track their health status while shopping in a virtual store and receive real-time health advice. For example, if the system determines that the user is feeling stressed, it will suggest relaxing music or products.

[1636] Example prompt sentence:

[1637] "Design a VR application that tracks users' health status and provides real-time health advice while they shop in a virtual store. Combine sentiment analysis with biometric sample data to provide optimal personalized feedback."

[1638] The system allows users to gain a comprehensive understanding of their health status in a virtual reality environment and receive personalized health advice in real time.

[1639] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1640] Step 1:

[1641] The user collects a biological sample (saliva or blood) using a dedicated kit. The input is the user's biological sample, and the output is the collected biological sample. The specific operation is to place the biological sample in a dedicated container to prevent leakage.

[1642] Step 2:

[1643] The user places a biometric sample on the device, and the device's camera scans their face to obtain emotional data. The input is the biometric sample and the user's facial information, and the output is digital biometric sample data and emotional data. Specifically, the system scans the face with the camera and performs facial expression analysis.

[1644] Step 3:

[1645] The device converts the biometric sample data and emotion data into an appropriate data format and sends it to the server. The input is the digital biometric sample data and emotion data, and the output is the data sent to the server. Specifically, the data is converted into an appropriate format and sent to the server via an HTTP request.

[1646] Step 4:

[1647] The server receives the biometric sample data and emotion data and performs preprocessing including noise removal, data normalization, and missing data completion. The input is the biometric sample data and emotion data sent to the server, and the output is the preprocessed data. Specific operations include applying noise filtering and data normalization algorithms.

[1648] Step 5:

[1649] The server uses the generated AI model to analyze the biometric sample data and emotional data. The input is the preprocessed biometric sample data and emotional data, and the output is the analysis results. Specifically, the data is input into a model using a machine learning algorithm to perform risk prediction and diagnosis.

[1650] Step 6:

[1651] The server generates a report based on the analysis results, including the user's disease risk score and health advice, encrypts it, and sends it to the device. The input is the analysis results, and the output is an encrypted report. Specifically, the report for the user is constructed using a report generation algorithm and encrypted using SSL / TLS.

[1652] Step 7:

[1653] The terminal decrypts the received report and visually displays it through the user interface. The input is the encrypted report, and the output is the analysis result displayed on the user interface. Specifically, after decryption, the report is displayed on the GUI so that the user can check the results.

[1654] Step 8:

[1655] Based on the reports displayed on the device, interactive feedback is provided according to the user's emotional state. The input is feedback information based on emotional data, and the output is personalized advice provided to the user. Specifically, advice based on the results of emotion analysis is generated and presented in real time.

[1656] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1657] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1658] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1659] [Fourth embodiment]

[1660] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1661] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1662] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1663] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1664] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1665] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1666] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1667] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1668] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1669] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1670] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1671] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1672] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1673] System Configuration

[1674] The present invention is a system for users to analyze their own health status and predict their risk of disease. The system comprises the following components:

[1675] 1. User Device

[1676] It is a device that allows a user to collect a biological sample of saliva or blood and transmits the data to a server.

[1677] 2. Server

[1678] It is a central processing unit that analyzes received biological sample data and performs disease risk prediction, health checkups, and ancestral root analysis.

[1679] Program processing

[1680] Sample collection and submission

[1681] 1. The user collects a saliva or blood sample using a dedicated collection kit, which is then placed in the device to prevent leakage.

[1682] 2. The terminal converts the sample data into the appropriate data format and sends it to the server. The terminal scans the sample, stores it as digital data, and then starts the transmission process to the server.

[1683] Data reception and analysis

[1684] 3. The server receives the sample data and performs preprocessing, including noise removal, data normalization, and missing data completion.

[1685] 4. The server begins analyzing the biological sample data using the generated AI model.

[1686] Input data into the model and perform parameter tuning and feature engineering.

[1687] 5. The server performs disease prediction and diagnosis, for example, calculating the risk of cancer in specific areas such as lung cancer or colon cancer.

[1688] 6. The server also performs ancestry analysis based on the individual's genetic information. By analyzing the DNA sample, it identifies individual genetic patterns and generates ancestry information based on past genetic patterns.

[1689] Generating and notifying results

[1690] 7. The server compiles the results of the analysis and generates a report for the user, which includes a disease risk score, health advice, and information about ancestry.

[1691] 8. The server encrypts the result report and sends it to the user terminal using the appropriate communication protocol.

[1692] 9. The device receives the transmitted results and visually displays them through the user interface, allowing the user to review the results and receive detailed risk assessments and health advice.

[1693] Specific examples

[1694] Example 1: Disease risk prediction using saliva samples

[1695] 1. The user collects a saliva sample using a dedicated collection kit.

[1696] 2. The user places the saliva sample on the terminal and starts the sample scan.

[1697] 3. The device sends the sample data to the server.

[1698] 4. The server receives the saliva data and performs preprocessing.

[1699] 5. The server performs analysis using the generative AI model to assess lung cancer risk.

[1700] 6. The server analyzes your ancestral roots based on your genetic information.

[1701] 7. The server generates a result report, encrypts it, and sends it to the user's terminal.

[1702] 8. The device receives the results and displays them in the user interface.

[1703] Example 2: Comprehensive health check using blood samples

[1704] 1. The user collects a blood sample using a dedicated collection kit.

[1705] 2. The user places the blood sample into the terminal.

[1706] 3. The device sends the sample data to the server.

[1707] 4. The server receives the blood data and performs preprocessing.

[1708] 5. The server performs analysis using the generated AI model to assess the risk of diabetes and cardiovascular disease.

[1709] 6. The server analyzes your ancestral roots based on your genetic information.

[1710] 7. The server generates a result report, encrypts it, and sends it to the user's terminal.

[1711] 8. The device receives the results and displays them in the user interface.

[1712] The present invention can be carried out as described above.

[1713] The processing flow will be explained below.

[1714] Step 1:

[1715] The user collects a saliva or blood sample using a special collection kit.

[1716] Action: Follow the instructions on the collection kit to collect the correct amount of sample. Seal the sample in the collection kit to prevent leakage.

[1717] Step 2:

[1718] The user sets the collected sample in the terminal.

[1719] Operation: The collected sample is placed in a dedicated device and secured in place, starting the terminal's sample scanning mode.

[1720] Step 3:

[1721] The device formats the sample data and sends it to the server.

[1722] Operation: Scans the sample, stores it as digital data, converts it into the appropriate data format, and initiates the process of sending it to the server.

[1723] Step 4:

[1724] A server receives the biological sample data.

[1725] Action: Checks and receives data sent from the terminal. Sends a message confirming receipt to the terminal.

[1726] Step 5:

[1727] The server performs preprocessing on the data received.

[1728] Actions: Performs noise removal, data normalization, and missing data imputation. Converts data into an analysis-ready format.

[1729] Step 6:

[1730] The server begins analyzing the data using the generative AI model.

[1731] How it works: Input data into a generative AI model and run analysis using machine learning algorithms, including parameter tuning and feature engineering.

[1732] Step 7:

[1733] The server predicts disease risk and diagnoses it.

[1734] How it works: Calculates the risk of certain diseases, such as lung cancer or colon cancer, based on biological sample data. Generates specific diagnostic results.

[1735] Step 8:

[1736] The server analyzes an individual's ancestral roots based on their genetic information.

[1737] How it works: Analyzes DNA data and matches genetic characteristics with historical data to identify ancestral geographic roots and genetic patterns.

[1738] Step 9:

[1739] The server generates a report of the data analysis results.

[1740] What it does: Organizes the results of each analysis and creates a report for the user, including disease risk scores, health advice, and ancestry information.

[1741] Step 10:

[1742] The server encrypts the report and sends it to the user terminal.

[1743] How it works: The generated report is encrypted and sent to the user's device using a secure communication protocol.

[1744] Step 11:

[1745] The terminal visually displays the received reports to the user.

[1746] What it does: It decodes reports sent from the server and displays them in a user interface, allowing users to review results and understand health risks and ancestry information.

[1747] Example 1

[1748] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1749] In modern society, it is important for individuals to regularly monitor their health status and take appropriate health management measures. However, conventional health checkups and disease risk assessment methods are often time-consuming, costly, and have limited access. Furthermore, ancestral root analysis requires specialized knowledge and equipment, making it difficult for average users to access. The present invention aims to provide a system that allows users to easily monitor their health status and genetic roots.

[1750] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1751] In this invention, the server includes means for preprocessing received digital data, means for analyzing biological sample data using a generative AI model, and means for assessing disease risk scores. This enables users to easily and quickly understand their own health status and disease risk, and further analyze their ancestral roots.

[1752] A "user" is an individual who collects a saliva or blood sample using a biological sample collection kit and sets it in a dedicated terminal.

[1753] A "biological sample" is a sample, such as saliva or blood, collected from a living organism and is a source of data for analyzing health status and genetic information.

[1754] A "terminal" is a device that scans biological samples collected by a user, converts them into digital data, and sends them to a server.

[1755] "Digital data" refers to information obtained from a biological sample that has been converted into a format that can be stored and transmitted electronically.

[1756] The "server" is a central processing unit that analyzes the received digital data and performs disease risk assessments and ancestral root analysis.

[1757] "Preprocessing" refers to processes such as noise removal, data normalization, and missing data completion that are performed on the data received by the server.

[1758] A "generative AI model" is an artificial intelligence model used to analyze biological sample data and perform disease risk assessment and genetic information analysis.

[1759] A "disease risk score" is a quantified indicator of the likelihood of contracting a particular disease, as assessed by a generative AI model.

[1760] "Ancestry analysis" is the process of analyzing past genetic patterns based on an individual's genetic information to identify the region and roots of one's ancestors.

[1761] A "report" is a written or digital document provided to a user that includes the results of a disease risk assessment or ancestry analysis.

[1762] "Encryption" is the process of transforming data using a specific algorithm to secure the data being transmitted.

[1763] The present invention is a system that allows users to analyze their own health status, predict disease risks, and analyze their ancestral roots. This system is composed of three elements: a user, a terminal, and a server.

[1764] First, the user uses a dedicated collection kit to collect a saliva or blood sample, which is a biological specimen. The collected sample is sealed to prevent leakage and placed in the terminal. The terminal scans the biological specimen and converts it into digital data. The terminal then transmits this digital data to a server.

[1765] The server performs preprocessing on the received digital data. This preprocessing includes noise removal, data normalization, and missing data completion. After preprocessing is complete, the data is analyzed using a generative AI model. Machine learning libraries such as TensorFlow and PyTorch can be used for the generative AI model.

[1766] As part of the analysis, the server will assess a disease risk score, calculating a numerical value for the risk of certain diseases, such as lung cancer or colon cancer. It will also perform ancestry analysis based on an individual's genetic information. By analyzing specific patterns in the DNA sample, the server can pinpoint the region and origins of an individual's ancestors based on their past genetic patterns.

[1767] The analysis results are compiled by the server and generated into a detailed report, which includes disease risk assessment, health advice, and ancestry information. The report is encrypted and sent to the device using a secure communication protocol (e.g., HTTPS).

[1768] The device interprets the received report and visually displays it to the user, who can then access detailed risk assessments, health advice, and ancestral information through a user interface.

[1769] Specific examples

[1770] Example 1: Disease risk prediction using saliva samples

[1771] 1. The user collects a saliva sample using a dedicated collection kit.

[1772] 2. The user places the saliva sample on the terminal and starts the sample scan.

[1773] 3. The device sends the sample data to the server.

[1774] 4. The server receives the saliva data and performs preprocessing.

[1775] 5. The server performs analysis using the generative AI model to assess lung cancer risk.

[1776] 6. The server analyzes your ancestral roots based on your genetic information.

[1777] 7. The server generates a result report, encrypts it, and sends it to the user's terminal.

[1778] 8. The device receives the results and displays them in the user interface.

[1779] Example 2: Comprehensive health check using blood samples

[1780] 1. The user collects a blood sample using a dedicated collection kit.

[1781] 2. The user places the blood sample into the terminal.

[1782] 3. The device sends the sample data to the server.

[1783] 4. The server receives the blood data and performs preprocessing.

[1784] 5. The server performs analysis using the generated AI model to assess the risk of diabetes and cardiovascular disease.

[1785] 6. The server analyzes your ancestral roots based on your genetic information.

[1786] 7. The server generates a result report, encrypts it, and sends it to the user's terminal.

[1787] 8. The device receives the results and displays them in the user interface.

[1788] Example prompt

[1789] Example of server prompt:

[1790] "Sample data has been received. Please perform preprocessing and analyze the biological sample data. As analysis results, please output disease risk assessment and ancestral roots information."

[1791] Example prompt for a server-side AI model:

[1792] "Given your genetic sample data, calculate risk scores for the following diseases: lung cancer, colon cancer, diabetes, and cardiovascular disease. Additionally, analyze your personal genetic information to identify your ancestral roots."

[1793] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1794] Step 1:

[1795] Sample collection

[1796] The user collects a saliva or blood sample using a dedicated collection kit, which is then sealed to prevent leakage.

[1797] Input: Dedicated collection kit, user

[1798] Output: Collected biological sample (saliva or blood)

[1799] Step 2:

[1800] Set of data

[1801] The user places the collected sample into the terminal and inserts the sample into the terminal's sample scanner.

[1802] Input: collected biological sample, terminal

[1803] Output: Set sample

[1804] Step 3:

[1805] Scanning Data

[1806] The terminal scans the sample and stores it as digital data. The scanner reads the necessary information from the biological specimen and converts it into a digital format.

[1807] Input: Set sample

[1808] Output: Digital data

[1809] Step 4:

[1810] Sending data

[1811] The device sends the digital data to the server, using a network communication protocol (e.g., HTTPS) in an encrypted format.

[1812] Input: Digital data

[1813] Output: Data sent to the server

[1814] Step 5:

[1815] Data reception and preprocessing

[1816] The server preprocesses the digital data it receives, removing noise, normalizing the data, and filling in missing data.

[1817] Input: Data sent to the server

[1818] Output: Pre-processed digital data

[1819] Step 6:

[1820] Analysis using generative AI models

[1821] The server analyzes the data using a generative AI model, performs parameter tuning and feature engineering on the data, and then uses a predictive model (e.g., using TensorFlow or PyTorch) to assess disease risk.

[1822] Input: Preprocessed digital data

[1823] Output: Analysis results (disease risk score, ancestral roots information)

[1824] Step 7:

[1825] Disease risk assessment

[1826] The server calculates the risk of a specific disease (e.g., lung cancer, colon cancer, etc.) based on the analysis results of the generated AI model, generates a risk score, and provides a numerical assessment.

[1827] Input: Analysis results

[1828] Output: Disease risk score

[1829] Step 8:

[1830] Ancestry analysis

[1831] The server analyzes ancestral roots based on genetic information, referencing specific DNA patterns and identifying regions and roots based on past genetic patterns.

[1832] Input: Analysis results

[1833] Output: Ancestor roots information

[1834] Step 9:

[1835] Report Generation

[1836] The server generates a detailed report based on your disease risk score and ancestry information, including risk assessment, health advice, and ancestry information.

[1837] Input: Disease risk score, ancestry information

[1838] Output: Generated report

[1839] Step 10:

[1840] Data encryption and transmission

[1841] The server generates reports and encrypts them and sends them to the device using a secure communication protocol (e.g., SSL / TLS).

[1842] Input: Generated report

[1843] Output: Encrypted report

[1844] Step 11:

[1845] Displaying the results

[1846] The device interprets the received report and visually displays it to the user, who can then view the analysis results and receive detailed health advice through the user interface.

[1847] Input: Encrypted report

[1848] Output: Decoded report (displayed in the user interface)

[1849] (Application example 1)

[1850] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1851] Conventional health analysis systems simply provide users with analysis results, but do not offer appropriate health promotion product recommendations or services. This makes it difficult for users to take appropriate measures based on the analysis results, making consistent health management difficult. Another issue is the existence of security risks in the process of receiving and checking the analysis results.

[1852] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1853] In this invention, the server includes: [means for generating analysis results and transmitting them to the terminal as a report including personalized health promotion product advertisements;] [means for encrypting the analysis results and transmitting them to the terminal using a secure communication protocol; and] [means for predicting cancer risk in specific locations using the generative AI model.] This allows users to use optimal health promotion products and services based on the analysis results, enabling safe and consistent health management. Furthermore, the analysis results can be received in a form with enhanced security.

[1854] A "user" is an individual who uses the system or an entity who provides a biological sample and receives the analysis results.

[1855] A "biological sample" is a biological substance such as saliva or blood collected from a user, and is input data for analyzing the health condition.

[1856] A "terminal" is a device that allows a user to convert a biological sample into a data format and transmit it to a server.

[1857] A "data format" is a biological sample that has been digitized and converted into an analyzable format.

[1858] The "server" is a central processing unit that analyzes the received biometric sample data and performs disease risk prediction, health checkups, ancestral root analysis, and advertising suggestions.

[1859] "Preprocessing" refers to processing of biological sample data, such as noise removal, data normalization, and missing data complementation.

[1860] A "generative AI model" is a machine learning model that predicts and diagnoses disease risk based on biological sample data.

[1861] "Disease prediction" is the analysis of biological sample data to assess the risk of developing a particular disease.

[1862] "Diagnosis" is the medical evaluation of a specific health condition with a prognosis for disease.

[1863] "Genetic information" refers to an individual's genetic pattern obtained from genes such as DNA.

[1864] "Ancestral roots" refers to information about past genetic patterns and lineages identified based on genetic information.

[1865] A "report" is a document that summarizes the analysis results and includes a disease risk score, health advice, ancestry, and personalized recommendations for health-promoting products and services.

[1866] "Personalized health promotion products" are products and services that promote health that are individually suggested based on the analysis results.

[1867] "Advertisements" are introductions of health promotion products and services suggested based on the user's health condition.

[1868] "Visually displaying" means displaying the analysis results and advertisements on the screen of the terminal so that the user can check them.

[1869] "Encryption" refers to converting the analysis results so that they cannot be read by third parties, and is a technology that transmits them via a secure communication protocol.

[1870] A "secure communication protocol" is a standard communication specification for securely sending and receiving data.

[1871] MODE FOR CARRYING OUT THE INVENTION

[1872] System Configuration

[1873] The present invention is a system that analyzes a user's health status, predicts disease risk, and suggests appropriate health promotion products and services. The system is composed of the following elements:

[1874] 1. User terminal: A device that allows a user to collect a biological sample and transmit the data to the server.

[1875] 2. Server: A central processing unit that analyzes the received biological sample data and performs disease risk prediction, health checkups, ancestral root analysis, and advertising suggestions.

[1876] Program processing

[1877] Sample collection and submission

[1878] The user collects a saliva or blood sample using a dedicated collection kit. The collected sample is then placed in the device to prevent leakage. The terminal converts the biological sample into an appropriate data format and sends it to the server. The terminal also scans the biological sample and stores it as digital data. The process of sending it to the server then begins.

[1879] Data reception and analysis

[1880] The server receives the sent sample data and performs preprocessing, including noise removal, data normalization, and missing data completion. The server then begins analyzing the biological sample data using the generated AI model. This allows for disease prediction and diagnosis. For example, it calculates the risk of cancer in specific areas, such as lung cancer or colon cancer. The server also performs ancestral analysis based on an individual's genetic information. By analyzing the DNA sample, individual genetic patterns are identified and ancestral information is generated based on past genetic patterns.

[1881] Generating and notifying results

[1882] The server compiles the analysis results, generates a report containing personalized health promotion product advertisements, encrypts it, and sends it to the user's device. The device visually displays the received results through a user interface. The user can review the results and obtain a detailed risk assessment, health advice, and details of the advertised products.

[1883] Hardware and software used

[1884] User terminals are general mobile devices such as smartphones and tablet terminals.

[1885] The server is a high-performance cloud server or on-premise server.

[1886] The software uses libraries for data analysis and machine learning (e.g., Python, Flask, and some AI model libraries).

[1887] A generative AI model is a machine learning model that predicts and diagnoses disease risk based on biological sample data.

[1888] We use encryption technology and secure communication protocols (e.g., SSL / TLS) to ensure the secure transmission and reception of data.

[1889] Specific examples

[1890] For example, if a user opens the app and submits a saliva sample, the following occurs:

[1891] User Action:

[1892] 1. Collect a saliva sample and enter it into the smartphone app.

[1893] 2. Follow the instructions in the app to send sample data to the server.

[1894] Server processing example:

[1895] The server receives the sample data and inputs it into the analysis model.

[1896] Generate reports of the user's health risks (e.g., lung cancer risk) and ancestry information.

[1897] Advertisement proposal example:

[1898] Based on the report, the app will display recommendations such as "recommended supplements for those at high risk of lung cancer" and "fitness programs suited to your constitution."

[1899] Prompt Sentence Examples

[1900] Recommend appropriate health promotion products and services based on the user's health risks and ancestry information. Implement a program that receives the user's saliva data, analyzes it with an AI model, and suggests advertisements based on the generated report.

[1901] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1902] Processing flow and steps

[1903] Step 1:

[1904] The user collects a biological sample.

[1905] Specific behavior:

[1906] Users collect a saliva or blood sample using a special collection kit and place it in the device to prevent leakage.

[1907] Input: User's biological sample (saliva or blood)

[1908] Output: A biological sample is placed in the device.

[1909] Step 2:

[1910] The terminal scans the biological sample and converts it into a data format.

[1911] Specific behavior:

[1912] The scanning function of the terminal is used to convert the biological sample into digital data, which is then formatted into an appropriate data format.

[1913] Input: Biological sample placed on the device

[1914] Output: Digital data converted into a data format

[1915] Step 3:

[1916] The terminal transmits the digital data to the server.

[1917] Specific behavior:

[1918] This initiates the process of sending digital data from the terminal to the server, and the data is encrypted and sent securely.

[1919] Input: Digital data converted into a data format

[1920] Output: Digital data sent to the server

[1921] Step 4:

[1922] The server receives the transmitted data and performs pre-processing.

[1923] Specific behavior:

[1924] The server performs preprocessing on the received data, such as noise removal, data normalization, and missing data completion.

[1925] Input: Digital data sent from the terminal

[1926] Output: Preprocessed data

[1927] Step 5:

[1928] The server performs data analysis using the generated AI model.

[1929] Specific behavior:

[1930] The server inputs the preprocessed data into a generative AI model, performs parameter tuning and feature engineering, and then performs analysis to predict disease risk and health status from biological samples.

[1931] Input: Preprocessed digital data

[1932] Output: Analysis results (disease risk score and health status assessment)

[1933] Step 6:

[1934] The server analyzes ancestral roots based on genetic information.

[1935] Specific behavior:

[1936] The server uses analytical models to identify genetic patterns from a user's DNA sample and analyze their ancestral roots.

[1937] Input: Preprocessed digital data and DNA information

[1938] Output: Ancestor roots analysis results

[1939] Step 7:

[1940] A server generates a report containing personalized health and wellness product promotions.

[1941] Specific behavior:

[1942] The server combines the analysis results with information such as past purchasing history and interests and preferences to create a report that includes suggestions for health-promoting products and services that are appropriate for the user.

[1943] Input: Analysis results, purchase history, interest and preference information

[1944] Output: Personalized health report and advertising suggestions

[1945] Step 8:

[1946] The server encrypts the report and sends it to the device.

[1947] Specific behavior:

[1948] The server encrypts the generated report and sends it to the terminal using a secure communication protocol (e.g., SSL / TLS).

[1949] Input: Personalized health reports and advertising suggestions

[1950] Output: Encrypted report

[1951] Step 9:

[1952] The device visually displays the analysis results and advertisements to the user.

[1953] Specific behavior:

[1954] The device decrypts the received encrypted report and visually displays it through a user interface, allowing the user to view the analysis results and details of suggested health promotion products and services.

[1955] Input: Encrypted report

[1956] Output: Analysis results and advertising suggestions displayed on the user interface

[1957] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1958] System Configuration

[1959] The present invention is a system for users to analyze their own health status and predict their risk of disease. The system comprises the following components:

[1960] 1. User Device

[1961] This device allows users to collect saliva or blood samples and transmits the data to a server. The device also has an emotion engine that can recognize the user's emotions.

[1962] 2. Server

[1963] It is a central processing unit that analyzes received biometric sample data and emotional data to predict disease risk, conduct health checks, and analyze ancestral roots.

[1964] Program processing

[1965] Sample collection and submission

[1966] 1. The user collects a saliva or blood sample using a dedicated collection kit, which is then placed in the device to prevent leakage.

[1967] 2. When the user sets the sample, they use the device camera to scan their face. The emotion engine analyzes the user's facial expressions and obtains emotion data.

[1968] 3. The device converts the sample data and emotion data into an appropriate data format and sends it to the server. The device scans the sample, saves it as digital data, and then starts the process of sending it to the server along with the emotion data.

[1969] Data reception and analysis

[1970] 4. The server receives the sample data and emotion data and performs preprocessing, including noise removal, data normalization, and missing data completion.

[1971] 5. The server begins analyzing the biometric sample data and emotional data using the generated AI model.

[1972] Data is input into the model and analysis is performed using machine learning algorithms, including parameter tuning and feature engineering.

[1973] 6. The server performs disease risk prediction and diagnosis. It performs risk assessment taking into account emotional data and calculates the risk of cancer in specific areas, such as lung cancer or colon cancer.

[1974] 7. The server also performs ancestry analysis based on an individual's genetic information. By analyzing DNA samples, it identifies individual genetic patterns and generates ancestry information based on past genetic patterns.

[1975] Generating and notifying results

[1976] 8. The server compiles the analysis results and generates a report for the user, which may include a disease risk score, health advice based on the user's emotional state, and information about their ancestry.

[1977] 9. The server encrypts the result report and sends it to the user terminal using the appropriate communication protocol.

[1978] 10. The device receives the transmitted results and visually displays them through the user interface. The user can then review the results and receive detailed risk assessments and health advice, while also receiving interactive feedback based on the user's emotional state.

[1979] Specific examples

[1980] Example 1: Disease risk prediction and sentiment analysis using saliva samples

[1981] 1. The user collects a saliva sample using a dedicated collection kit.

[1982] 2. The user places a saliva sample on the device and scans their face to obtain emotional data.

[1983] 3. The device sends the sample data and emotion data to the server.

[1984] 4. The server receives the saliva data and emotion data and performs preprocessing.

[1985] 5. The server analyzes the generated AI model and assesses the risk of lung cancer, taking emotional data into account.

[1986] 6. The server analyzes your ancestral roots based on your genetic information.

[1987] 7. The server generates a result report, encrypts it, and sends it to the user's terminal.

[1988] 8. The device receives the results and displays them on the user interface, providing health advice based on the user's emotional state.

[1989] Example 2: Comprehensive health check and sentiment analysis using blood samples

[1990] 1. The user collects a blood sample using a dedicated collection kit.

[1991] 2. The user places a blood sample into the device and scans their face to obtain emotional data.

[1992] 3. The device sends the sample data and emotion data to the server.

[1993] 4. The server receives the blood data and emotion data and performs preprocessing.

[1994] 5. The server performs analysis using the generated AI model to assess the risk of diabetes and cardiovascular disease, taking emotional data into account.

[1995] 6. The server analyzes your ancestral roots based on your genetic information.

[1996] 7. The server generates a result report, encrypts it, and sends it to the user's terminal.

[1997] 8. The device receives the results and displays them on the user interface, providing health advice based on the user's emotional state.

[1998] The present invention can be carried out as described above.

[1999] The processing flow will be explained below.

[2000] Step 1:

[2001] The user collects a saliva or blood sample using a special collection kit.

[2002] Action: Collect the appropriate amount of biological sample according to the instructions on the collection kit. Seal the sample in the collection kit to prevent leakage.

[2003] Step 2:

[2004] The user sets the collected sample in the terminal.

[2005] Operation: The collected sample is placed in a dedicated device and secured in place, starting the terminal's sample scanning mode.

[2006] Step 3:

[2007] The user uses the device's camera to scan their face and obtain emotional data.

[2008] How it works: The device's emotion engine analyzes the user's facial expressions and generates emotion data in real time.

[2009] Step 4:

[2010] The terminal formats the sample data and emotion data and sends them to the server.

[2011] Operation: Sample data is scanned and converted into digital data. At the same time, emotion data is formatted and the process of sending it to the server begins.

[2012] Step 5:

[2013] A server receives the biometric sample data and the emotion data.

[2014] Operation: Checks and receives data sent from the terminal. Sends a receipt confirmation to the terminal.

[2015] Step 6:

[2016] The server performs preprocessing.

[2017] What it does: It denoises, normalizes, and fills in missing data from biological samples. It also preprocesses emotion data and prepares it for analysis.

[2018] Step 7:

[2019] The server begins analyzing the data using the generative AI model.

[2020] How it works: Input data into a generative AI model to perform disease risk and diagnostic analysis, taking emotional data into account.

[2021] Step 8:

[2022] The server takes emotion data into account when performing disease risk prediction and diagnosis.

[2023] How it works: Combines biological sample data with emotional data to assess the risk of certain diseases, such as lung or colon cancer.

[2024] Step 9:

[2025] The server analyzes an individual's genetic information to identify their ancestral roots.

[2026] How it works: Analyzes DNA samples to identify genetic patterns, then matches them with historical data to identify your ancestral geographic roots.

[2027] Step 10:

[2028] The server generates a report of the analysis results.

[2029] What it does: Generates a detailed report with risk scores, emotion-based health advice, and ancestry information.

[2030] Step 11:

[2031] The server encrypts the report and sends it to the user terminal.

[2032] Operation: The generated report is encrypted and sent to the user's device using a secure communication protocol.

[2033] Step 12:

[2034] The terminal receives the analysis results and visually displays them to the user.

[2035] What it does: It interprets the received reports and displays them through a user interface, allowing users to view detailed risk assessments, health advice, and ancestry information, while also providing interactive feedback based on emotional state.

[2036] Example 2

[2037] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2038] Current health diagnostic systems face challenges in enabling users to comprehensively analyze their own health status and predict disease risk. Disease risk prediction requires consideration of both biomaterials and emotional state, but conventional systems are unable to integrate these factors. Providing analysis results to users quickly and conveniently is also a challenge.

[2039] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2040] In this invention, the server includes: [means for analyzing biomaterial data and emotional data using a generative AI model]; [means for predicting and diagnosing disease from the biomaterial data]; and [means for analyzing an individual's genetic information and identifying ancestry information]. This allows the user to obtain integrated analysis results of their own biomaterial and emotional state, enabling prediction and early diagnosis of health risks.

[2041] A "biological material" is a biological sample such as saliva or blood taken from a user.

[2042] "Digital data" refers to an electronic data format for analyzing, storing, and transmitting samples and emotional data by computer.

[2043] A "generative AI model" is an artificial intelligence system that uses machine learning algorithms to analyze data and make predictions and diagnoses.

[2044] An "emotion engine" is software or a device that analyzes a user's face and facial expressions to identify their emotional state.

[2045] "Preprocessing" refers to the process of removing noise from biomaterial data and emotion data, normalizing the data, and filling in missing data.

[2046] "Disease prediction and diagnosis" refers to assessing the risk of contracting a specific disease based on analyzed data and diagnosing it early.

[2047] "Genetic Information" refers to a user's genetic characteristics based on their individual genetic patterns or DNA data.

[2048] "Ancestry information" analyzes past genetic patterns based on the user's genetic information and provides information about their ancestors.

[2049] A "communications protocol" defines the procedures and rules for sending and receiving data over a network.

[2050] "Interactive feedback" refers to responses and advice provided in real time based on the user's behavior and emotional state.

[2051] System Configuration

[2052] The present invention is a system for users to analyze their own health status and predict their risk of disease. The system comprises the following components:

[2053] 1. User Device

[2054] This device allows users to collect biomaterials such as saliva or blood and transmits the data to a server. The device also has an emotion engine and is capable of recognizing the user's emotions.

[2055] 2. Server

[2056] This is a central processing unit that analyzes the received biomaterial data and emotional data, and performs disease risk prediction, health checks, and ancestral roots analysis.

[2057] Program processing

[2058] Sample collection and submission

[2059] 1. The user collects a saliva or blood sample using a dedicated collection kit, which is then placed in the device to prevent leakage.

[2060] 2. When the user sets the sample, they use the device camera to scan their face. The emotion engine analyzes the user's facial expressions and obtains emotion data.

[2061] 3. The device converts the sample data and emotion data into an appropriate data format and sends it to the server. The device scans the sample, saves it as digital data, and then starts the process of sending it to the server along with the emotion data.

[2062] Data reception and analysis

[2063] 4. The server receives the sample data and emotion data and performs preprocessing, including noise removal, data normalization, and missing data completion.

[2064] 5. The server begins analyzing the biometric sample data and emotional data using the generated AI model.

[2065] Data is input into the model and analysis is performed using machine learning algorithms, including parameter tuning and feature engineering.

[2066] 6. The server performs disease risk prediction and diagnosis. It performs risk assessment taking into account emotional data and calculates the risk of cancer in specific areas, such as lung cancer or colon cancer.

[2067] 7. The server also performs ancestry analysis based on an individual's genetic information. By analyzing DNA samples, it identifies individual genetic patterns and generates ancestry information based on past genetic patterns.

[2068] Generating and notifying results

[2069] 8. The server compiles the analysis results and generates a report for the user, which may include a disease risk score, health advice based on the user's emotional state, and information about their ancestry.

[2070] 9. The server encrypts the result report and sends it to the user terminal using the appropriate communication protocol.

[2071] 10. The device receives the transmitted results and visually displays them through the user interface. The user can then review the results and receive detailed risk assessments and health advice, while also receiving interactive feedback based on the user's emotional state.

[2072] Specific examples

[2073] Example 1: Disease risk prediction and sentiment analysis using saliva samples

[2074] 1. The user collects a saliva sample using a dedicated collection kit.

[2075] 2. The user places a saliva sample on the device and scans their face to obtain emotional data.

[2076] 3. The device sends the sample data and emotion data to the server.

[2077] 4. The server receives the data and performs preprocessing.

[2078] 5. The server begins analysis using the generated AI model, for example, to assess lung cancer risk, taking emotional data into account.

[2079] 6. The server performs ancestral roots analysis based on the genetic information.

[2080] 7. The server generates a result report, encrypts it, and sends it to the user's terminal.

[2081] 8. The device receives the results and displays them on the user interface, providing health advice based on the emotional state.

[2082] Example 2: Comprehensive health check and sentiment analysis using blood samples

[2083] 1. The user collects a blood sample using a dedicated collection kit.

[2084] 2. The user inserts a blood sample into the device and scans their face to obtain emotional data.

[2085] 3. The device sends the sample data and emotion data to the server.

[2086] 4. The server receives the data and performs preprocessing.

[2087] 5. The server begins analysis using the generated AI model, assessing, for example, the risk of diabetes or cardiovascular disease, taking emotional data into account.

[2088] 6. The server performs ancestral roots analysis based on the genetic information.

[2089] 7. The server generates a result report, encrypts it, and sends it to the user's terminal.

[2090] 8. The device receives the results and displays them on the user interface, providing health advice based on the emotional state.

[2091] In this way, the present invention can be implemented.

[2092] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2093] Step 1:

[2094] The user collects a saliva or blood sample using a special collection kit.

[2095] Input: Specialized collection kit, user's saliva or blood.

[2096] Output: sealed biomaterial sample.

[2097] How it works: The user follows the instructions on the collection kit, spitting saliva into a test tube or pricking their finger with a needle to collect blood. The sample is then sealed to prevent leakage.

[2098] Step 2:

[2099] When the user sets the sample, they scan their face using the device's camera.

[2100] Input: User's face, device camera.

[2101] Output: Emotion data.

[2102] Specific operation: The user follows the instructions displayed on the device screen, faces the camera, and remains still for a certain period of time. The emotion engine analyzes the user's facial expressions and generates emotion data.

[2103] Step 3:

[2104] The terminal converts the sample data and emotion data into an appropriate data format and transmits it to the server.

[2105] Input: Biomaterial samples, emotion data.

[2106] Output: Digitized sample data and emotion data.

[2107] How it works: The device scans the sample and stores it as digital data, which is then packaged with emotion data into a data packet and sent to a server using an encryption protocol.

[2108] Step 4:

[2109] The server receives the sample data and emotion data and performs preprocessing.

[2110] Input: Digitized sample data and emotion data.

[2111] Output: Preprocessed sample data and emotion data.

[2112] Specific operation: The server removes noise from the received data, normalizes the data, and fills in missing data, thereby maintaining data consistency and preparing it for analysis.

[2113] Step 5:

[2114] The server begins analyzing the data using the generative AI model.

[2115] Input: Preprocessed sample data and emotion data.

[2116] Output: Analysis results.

[2117] How it works: Data is fed into a generative AI model, and analysis is performed using machine learning algorithms, tuning parameters and performing feature engineering to extract meaningful features from the data.

[2118] Step 6:

[2119] The server predicts disease risk and diagnoses it.

[2120] Input: Analysis result data.

[2121] Output: Disease risk prediction and diagnostic results.

[2122] Specific operation: For example, calculate the risk of lung cancer, colon cancer, etc. from specific biomarkers and genetic information, and perform risk assessment taking emotional data into account.

[2123] Step 7:

[2124] The server analyzes an individual's ancestral roots based on their genetic information.

[2125] Input: Personal genetic information data.

[2126] Output: Ancestry information.

[2127] What it does: Identifies genetic patterns from DNA samples and matches them with historical genetic databases to generate ancestry information.

[2128] Step 8:

[2129] The server compiles the analysis results and generates a report to provide to the user.

[2130] Input: Disease risk prediction results, emotional state data, ancestry information.

[2131] Output: Results report.

[2132] What it does: Generates reports with disease risk scores, health advice based on emotional state, and ancestry information.

[2133] Step 9:

[2134] The server encrypts the results report and transmits it to the user terminal using a secure communication protocol.

[2135] Input: Results report.

[2136] Output: Encrypted results report.

[2137] Specific operation: Using encryption protocols such as SSL / TLS, the report data is protected and sent to the user terminal.

[2138] Step 10:

[2139] The terminal receives the transmitted results and visually displays them through a user interface.

[2140] Input: Encrypted results report.

[2141] Output: A visual display to the user.

[2142] How it works: The analysis results are displayed through a device application, allowing users to check risk assessments, health advice, ancestry information, etc. Interactive feedback based on emotional state is also displayed on the screen.

[2143] (Application example 2)

[2144] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2145] Conventional health management systems predict disease risk using only a user's biometric sample data, making it difficult to provide advice that takes into account the user's emotional state and stress level. Furthermore, health advice in virtual reality environments is not commonly provided, and real-time feedback is lacking. This makes it difficult for users to comprehensively understand their own health status.

[2146] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2147] In this invention, the server includes: [means for a user to collect a biological sample;] [means for a terminal to convert the biological sample into a data format and send it to the server;] [means for the server to perform preprocessing and analyze the biological sample data;] [means for the server to predict and diagnose disease using a generative AI model;] [means for the server to analyze an individual's genetic information and identify ancestral roots;] [means for the server to generate analysis results and send them to the terminal;] [means for the terminal to visually display the analysis results to the user;] [means for acquiring the user's emotional data using a camera-equipped head-mounted display; and [means for the server to analyze the emotional data and provide advice based on the user's health condition in real time.] This enables users to understand their own health condition in real time in a virtual reality environment and receive personalized health advice that takes their emotional state into consideration.

[2148] A "biological sample" is a biological component, such as a user's saliva or blood, that is collected to understand the user's health condition.

[2149] A "terminal" is a device on which a user sets a biological sample and transmits the data to a server, and is also a device that has the function of acquiring emotion data using a camera.

[2150] The "server" is a central processing unit that analyzes the received biological sample data and emotion data to predict disease risk, perform diagnosis, and analyze ancestry.

[2151] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to analyze received data and predict disease risk and diagnose disease.

[2152] "Genetic information" refers to a user's DNA data, which is used to analyze ancestral roots and disease risks.

[2153] "Emotion data" is data that indicates the emotional state of the user, and is data that is acquired by the user scanning their face with a camera.

[2154] A "head-mounted display" is a device worn by the user that displays information visually and acquires emotional data using a camera.

[2155] "Real-time" means that the process of collecting data on the user's health and emotional state, analyzing it, and providing feedback is carried out instantly.

[2156] "Personalization" means providing information and advice that is individually tailored to each user based on their health and emotional state.

[2157] The present invention provides a system for enabling a user to analyze his or her own health and emotional state, and obtain health risk predictions and personalized advice. This system is implemented in the following manner.

[2158] System Configuration

[2159] The main components of this system include:

[2160] 1. User terminal: A device where the user collects biometric samples and sends them to the server. The terminal is equipped with a camera and has the function of scanning the user's face and acquiring emotional data.

[2161] 2. Server: A central processing unit that analyzes received biometric sample data and emotional data to predict disease risk, conduct health checks, and analyze ancestral roots. Generative AI models are used for the analysis.

[2162] 3. Head-mounted display (HMD): A device worn by the user to visually check the analysis results. It also captures emotional data using a camera.

[2163] The specific hardware and software used

[2164] Hardware:

[2165] User device (smartphone or dedicated device)

[2166] Head-mounted display (HMD) with camera

[2167] Server device

[2168] software:

[2169] Sentiment analysis software (OpenCV, Keras)

[2170] Generative AI model (machine learning algorithm) on the server

[2171] Data transmission protocol (HTTP, HTTPS)

[2172] Processing flow explanation

[2173] 1. Biological sample collection and transmission:

[2174] The user collects a saliva or blood sample using a dedicated kit and inserts it into the device, which converts the biometric sample into digital data and sends it to a server. At the same time, the device's camera scans the face and captures emotional data.

[2175] 2. Data Receipt and Analysis:

[2176] The server analyzes the received biometric sample data and emotional data. It performs preprocessing, such as noise removal and data normalization, and then inputs the data into a generative AI model to predict disease risk. It also analyzes emotional data and generates advice tailored to the user's health condition.

[2177] 3. Results generation and notification:

[2178] The server compiles the analysis results, generates a report for the user, encrypts it, and sends it to the device. The device receives the report and displays it visually to the user through a head-mounted display, while also providing interactive feedback based on the user's emotional state.

[2179] Examples of specific examples and prompts

[2180] Example: A user can track their health status while shopping in a virtual store and receive real-time health advice. For example, if the system determines that the user is feeling stressed, it will suggest relaxing music or products.

[2181] Example prompt sentence:

[2182] "Design a VR application that tracks users' health status and provides real-time health advice while they shop in a virtual store. Combine sentiment analysis with biometric sample data to provide optimal personalized feedback."

[2183] The system allows users to gain a comprehensive understanding of their health status in a virtual reality environment and receive personalized health advice in real time.

[2184] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2185] Step 1:

[2186] The user collects a biological sample (saliva or blood) using a dedicated kit. The input is the user's biological sample, and the output is the collected biological sample. The specific operation is to place the biological sample in a dedicated container to prevent leakage.

[2187] Step 2:

[2188] The user places a biometric sample on the device, and the device's camera scans their face to obtain emotional data. The input is the biometric sample and the user's facial information, and the output is digital biometric sample data and emotional data. Specifically, the system scans the face with the camera and performs facial expression analysis.

[2189] Step 3:

[2190] The device converts the biometric sample data and emotion data into an appropriate data format and sends it to the server. The input is the digital biometric sample data and emotion data, and the output is the data sent to the server. Specifically, the data is converted into an appropriate format and sent to the server via an HTTP request.

[2191] Step 4:

[2192] The server receives the biometric sample data and emotion data and performs preprocessing including noise removal, data normalization, and missing data completion. The input is the biometric sample data and emotion data sent to the server, and the output is the preprocessed data. Specific operations include applying noise filtering and data normalization algorithms.

[2193] Step 5:

[2194] The server uses the generated AI model to analyze the biometric sample data and emotional data. The input is the preprocessed biometric sample data and emotional data, and the output is the analysis results. Specifically, the data is input into a model using a machine learning algorithm to perform risk prediction and diagnosis.

[2195] Step 6:

[2196] The server generates a report based on the analysis results, including the user's disease risk score and health advice, encrypts it, and sends it to the device. The input is the analysis results, and the output is an encrypted report. Specifically, the report for the user is constructed using a report generation algorithm and encrypted using SSL / TLS.

[2197] Step 7:

[2198] The terminal decrypts the received report and visually displays it through the user interface. The input is the encrypted report, and the output is the analysis result displayed on the user interface. Specifically, after decryption, the report is displayed on the GUI so that the user can check the results.

[2199] Step 8:

[2200] Based on the reports displayed on the device, interactive feedback is provided according to the user's emotional state. The input is feedback information based on emotional data, and the output is personalized advice provided to the user. Specifically, advice based on the results of emotion analysis is generated and presented in real time.

[2201] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2202] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2203] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2204] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2205] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2206] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2207] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2208] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2209] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2210] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2211] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2212] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2213] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2214] 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.

[2215] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2216] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2217] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2218] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2219] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2220] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2221] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2222] The following is further disclosed regarding the above embodiment.

[2223] (Claim 1)

[2224] [a means by which a user may collect a biological sample;

[2225] [Means for the terminal to convert the biological sample into a data format and transmit it to the server;

[2226] [Means for the server to preprocess and analyze the biological sample data;

[2227] [Means for the server to use the generated AI model to predict and diagnose diseases;

[2228] [Means for the server to analyze an individual's genetic information and identify their ancestral roots;

[2229] [Means for the server to generate analysis results and transmit the results to the terminal;

[2230] [A system including a means for the terminal to visually display the analysis results to the user.

[2231] (Claim 2)

[2232] [The system of claim 1, wherein the server uses the generated AI model to predict the risk of cancer in a specific area.

[2233] (Claim 3)

[2234] [The system according to claim 1, wherein the server encrypts the analysis results and transmits them to the terminal using a secure communication protocol.

[2235] "Example 1"

[2236] (Claim 1)

[2237] [means for a user to collect a biological sample;

[2238] [Means for the terminal to convert the biological sample into digital data and transmit it to the server;

[2239] [means for preprocessing the digital data received by the server;

[2240] [Means for the server to analyze the biological specimen data using the generated AI model;

[2241] [means for the server to evaluate a disease risk score;

[2242] [Means for the server to analyze ancestral roots based on individual genetic information,

[2243] [Means for the server to generate reports based on the analysis results;

[2244] [Means for the server to encrypt the report and send it to the device;

[2245] [A system in which the terminal includes a means for visually displaying the report to the user.

[2246] (Claim 2)

[2247] [The system of claim 1, wherein the server uses the generative AI model to assess the risk of a particular disease.

[2248] (Claim 3)

[2249] [The system of claim 1, wherein the server transmits the analysis results to the terminal using a secure communication protocol.

[2250] "Application Example 1"

[2251] (Claim 1)

[2252] [a means by which a user may collect a biological sample;

[2253] [Means for the terminal to convert the biological sample into a data format and transmit it to the server;

[2254] [Means for the server to preprocess and analyze the biological sample data;

[2255] [Means for the server to use the generated AI model to predict and diagnose diseases;

[2256] [Means for the server to analyze an individual's genetic information and identify their ancestral roots;

[2257] [Means for the server to generate analysis results and transmit them to the terminal as a report including personalized health promotion product advertisements;

[2258] [A system including a means for the terminal to visually display the analysis results and advertisements to the user and provide details of the advertised products.

[2259] (Claim 2)

[2260] [The system of claim 1, wherein the server uses the generated AI model to predict the risk of cancer in a specific area.

[2261] (Claim 3)

[2262] [The system according to claim 1, wherein the server encrypts the analysis results and transmits them to the terminal using a secure communication protocol.

[2263] "Example 2: Combining Emotion Engines"

[2264] (Claim 1)

[2265] [means for a user to collect biomaterial;

[2266] [Means for the terminal to convert the biomaterial into digital data and transmit it to the server;

[2267] [Means for the server to pre-process the biomaterial data and emotion data;

[2268] [Means for the server to analyze the biomaterial data and emotion data using the generated AI model;

[2269] [Means for the server to predict and diagnose diseases from biomaterial data;

[2270] [Means for the server to analyze an individual's genetic information and identify ancestry information;

[2271] [Means for the server to generate analysis results and transmit the results to the terminal;

[2272] [A system including a means for the terminal to visually display the analysis results to the user.

[2273] (Claim 2)

[2274] [The system of claim 1, wherein the server uses the generated AI model to predict the risk of disease in a specific area.

[2275] (Claim 3)

[2276] [The system according to claim 1, wherein the server encrypts the analysis results and transmits them to the terminal using a secure communication protocol.

[2277] (Claim 4)

[2278] [The system according to claim 1, wherein the terminal includes means for acquiring user emotion data using an emotion engine.

[2279] (Claim 5)

[2280] [The system of claim 1, wherein the server performs health risk assessment taking into account emotional data.

[2281] "Application example 2 when combining emotion engines"

[2282] (Claim 1)

[2283] [a means by which a user may collect a biological sample;

[2284] [Means for the terminal to convert the biological sample into a data format and transmit it to the server;

[2285] [Means for the server to preprocess and analyze the biological sample data;

[2286] [Means for the server to use the generated AI model to predict and diagnose diseases;

[2287] [Means for the server to analyze an individual's genetic information and identify their ancestral roots;

[2288] [Means for the server to generate analysis results and transmit the results to the terminal;

[2289] [Means for the device to visually display the analysis results to the user;

[2290] [Means for acquiring user emotion data using a head-mounted display with a camera;

[2291] [Means for the server to analyze emotional data and provide advice based on health status in real time;

[2292] A system including:

[2293] (Claim 2)

[2294] [The system of claim 1, wherein the server uses the generated AI model to predict the risk of cancer in a specific area.

[2295] (Claim 3)

[2296] [The system according to claim 1, wherein the server encrypts the analysis results and transmits them to the terminal using a secure communication protocol. [Explanation of symbols]

[2297] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for a user to obtain a biological sample; A means for the terminal to convert the biological sample into a data format and transmit the data to the server; a server for preprocessing and analyzing the biological sample data; A means for the server to use the generated AI model to predict and diagnose diseases; The server analyzes an individual's genetic information to identify their ancestral roots, A means for the server to generate an analysis result and transmit the result to the terminal; The system includes a means for the terminal to visually display the analysis results to the user.

2. The system of claim 1, wherein the server uses the generative AI model to predict the risk of cancer in a specific location.

3. 2. The system of claim 1, wherein the server encrypts the analysis results and transmits them to the terminal using a secure communication protocol.

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