System

Abstract

To provide a system.SOLUTION: The system includes: means that acquires information on a state of a cognitive function and a lifestyle habit of a patient; means that analyzes patterns of the state of the cognitive function and the lifestyle habit of the patient by using a generation system AI; means that acquires data collected by monitoring the patient; means that analyzes an emotional state of the patient by using an emotion engine; and means that proposes a treatment plan and a lifestyle correction for the patient, based on an analysis result using the generation system AI and an analysis result of the emotional state.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] Patients with mild cognitive impairment (MCI) are required to receive appropriate treatment, exercise, dietary changes, and cognitive function training to prevent the progression of cognitive impairment. Patient monitoring and data collection are also necessary for preliminary screening and symptom detection, and treatment plans are also proposed to the attending physician. [Means for solving the problem]

[0005] Utilizing generative AI, we provide a means to make appropriate suggestions for treatment, exercise, diet, and other lifestyle modification and cognitive function training for MCI, as well as a means to conduct preliminary screening and detect signs based on patient monitoring and data collection, and to propose treatment plans to the attending physician, etc. Furthermore, we provide a means for generative AI to optimize suggestions for treatment, exercise, diet, and other lifestyle modification and cognitive function training according to the state of the patient's cognitive function, and a means to improve the accuracy of preliminary screening and sign detection based on information obtained from patient monitoring and data collection. [Brief explanation of the drawings]

[0006] [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. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] 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.

[0008] First, the terms used in the following description will be explained.

[0009] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] 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.

[0011] 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.

[0012] 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.

[0013] 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."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] One embodiment of the present invention provides a system utilizing generative AI. This system provides appropriate recommendations for treatment, exercise, diet, and other lifestyle modifications, as well as cognitive training, to patients with mild cognitive impairment (MCI). Specifically, the generative AI receives input information on the patient's cognitive function and lifestyle, and generates optimal treatment plans and lifestyle improvement recommendations based on that information.

[0029] "Example 2"

[0030] Furthermore, the system of the present invention monitors patients and collects data to perform preliminary examinations, detect signs, and propose treatment plans to the attending physician. Specifically, the generative AI collects patients' biological information and behavioral data in real time and analyzes this data to perform preliminary examinations and detect signs. It also proposes treatment plans to the attending physician based on this information.

[0031] "Example 3"

[0032] In the system of the present invention, a generative AI optimizes proposals for treatment, exercise, diet, and other lifestyle modification and cognitive training based on the patient's cognitive function status. Specifically, the generative AI receives information on the patient's cognitive function status and lifestyle as input, and generates optimal treatment plans and lifestyle improvement proposals based on that information.

[0033] The processing flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: The generative AI receives information from the patient about their lifestyle and cognitive function status as input.

[0036] Step 2: Based on the information it receives, the generative AI generates suggestions for treatment, exercise, diet, and other lifestyle changes, as well as cognitive function training.

[0037] Step 3: The generative AI presents the generated suggestions to the patient.

[0038] "Example 2"

[0039] Step 1: The generative AI collects the patient's biometric information and behavioral data in real time. Step 2: The generative AI analyzes the collected data and performs preliminary examinations and detects signs.

[0040] Step 3: Based on the analysis results, the generative AI proposes a treatment plan to the attending physician.

[0041] "Example 3"

[0042] Step 1: The generative AI receives information such as the patient's cognitive function and lifestyle as input.

[0043] Step 2: Based on the information it receives, the generative AI optimizes suggestions for treatment, exercise, diet, and other lifestyle changes, as well as cognitive function training.

[0044] Step 3: The generative AI presents optimized recommendations to the patient.

[0045] Example 1

[0046] Next, a description will be given of Example 1 of Form 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."

[0047] There is a need to efficiently provide personalized treatment plans and lifestyle improvement suggestions to patients with mild cognitive impairment (MCI). However, conventional systems have had issues with difficulty in generating optimal suggestions based on the patient's cognitive function status and lifestyle, and with the time-consuming task of collecting and processing data. Furthermore, it is often not possible to provide generated suggestions to patients quickly and accurately.

[0048] 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.

[0049] In this invention, the server includes means for inputting information on the patient's cognitive function status and lifestyle habits using a terminal, means for receiving the data transmitted from the terminal and storing it in a database, means for preprocessing the data and generating prompt sentences to be input to the generative AI model, means for the generative AI model to generate an optimal treatment plan and lifestyle improvement suggestions, and means for transmitting the generated suggestions to the terminal, which then displays the suggestions to the user. This makes it possible to efficiently generate personalized suggestions based on the patient's cognitive function status and lifestyle habits and provide them quickly and accurately.

[0050] "Generative AI" is a type of artificial intelligence that generates optimal proposals and plans based on input data.

[0051] "Mild cognitive impairment (MCI)" is a medical term that refers to a condition in which cognitive function is impaired beyond the normal range but does not significantly interfere with daily life.

[0052] A "device" is an electronic device (e.g., PC, tablet, smartphone) that a user uses to enter information or view generated suggestions.

[0053] A "server" is a computer system that receives, stores, and processes data sent from a terminal.

[0054] A "database" is a system for systematically storing and managing data received by a server.

[0055] A "prompt" is a document to be input into a generative AI model, and contains information about the patient's cognitive function and lifestyle habits.

[0056] "Suggestions" refer to treatment plans and lifestyle improvement suggestions generated by generative AI models.

[0057] "Preprocessing" refers to processes such as formatting data and filling in missing values ​​before inputting the data into a generative AI model.

[0058] This invention is a system that utilizes generative AI to provide appropriate suggestions for lifestyle modification such as treatment, exercise, and diet, as well as cognitive function training, to patients with mild cognitive impairment (MCI). This system is implemented using a terminal, a server, and a generative AI model.

[0059] 1. Data Collection

[0060] The user uses a device (PC, tablet, smartphone, etc.) to input information about the patient's cognitive function status and lifestyle habits. Specific input items include cognitive function test results, daily activity level, dietary habits, and sleep patterns. For example, the user opens the smartphone app and inputs the following information:

[0061] Cognitive function test results: MMSE score 24 points

[0062] Daily activity level: 5,000 steps per day

[0063] Diet: Lack of vegetables, high in fat

[0064] Sleep pattern: 6 hours a day

[0065] 2. Data transmission and storage

[0066] The terminal encrypts the entered data and sends it to the server. The server receives the data sent from the terminal and temporarily stores it in memory. It then saves it in a database (e.g., MySQL (registered trademark), PostgreSQL). When saving the data, it checks for integrity and whether it contains any invalid data.

[0067] 3. Data Processing

[0068] The server retrieves the necessary data from the database and performs preprocessing to input it into the generative AI model. Specifically, it normalizes the data and fills in missing values. Next, it generates a prompt sentence based on the preprocessed data, such as:

[0069] The patient's cognitive function test results show an MMSE score of 24. Their daily activity level is 5000 steps per day, and their diet is high in fat and lacks vegetables. Their sleep pattern is 6 hours per day. Generate an optimal treatment plan and lifestyle modification suggestions for this patient.

[0070] 4. Proposal Generation

[0071] The server invokes a generative AI model (e.g., OpenAI's GPT-4®) and inputs the generated prompt. The generative AI model generates optimal treatment plans and lifestyle improvement suggestions based on the input prompt. For example, the following suggestions may be generated:

[0072] Cognitive training: 30 minutes of puzzle games every day

[0073] Exercise program: Walking three times a week (30 minutes each time)

[0074] Meal plan: Balanced meals with lots of vegetables

[0075] Improved sleep: Go to bed at the same time every night and get 7 hours of sleep

[0076] 5. Providing Proposals

[0077] The server sends the generated proposal to the terminal, and the terminal displays the proposal received from the server to the user, who can confirm and execute the proposal through the terminal.

[0078] In this way, the system can provide MCI patients with personalized treatment plans and lifestyle modification suggestions.

[0079] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0080] Step 1:

[0081] The user uses the device to input information about the patient's cognitive function status and lifestyle habits. Specific input items include cognitive function test results, daily activity level, dietary habits, and sleep patterns. For example, the user opens the smartphone app and inputs an MMSE score of 24, 5,000 steps per day, a diet low in vegetables and high in fat, and a sleep pattern of six hours per day. The input data is temporarily stored on the device.

[0082] Step 2:

[0083] The device encrypts the input data and sends it to the server. The data includes the results of cognitive function tests, daily activity levels, dietary habits, and sleep patterns. The server receives the data and temporarily stores it in its memory.

[0084] Step 3:

[0085] The server stores the received data in a database. When storing the data, it checks the data for consistency and whether it contains any invalid data. The database stores information on the patient's cognitive function status and lifestyle habits in an organized manner.

[0086] Step 4:

[0087] The server retrieves the necessary data from the database and performs preprocessing to input it into the generative AI model. Preprocessing includes normalizing the data and imputing missing values. For example, if there is missing data, it is imputed with the mean or median. The preprocessed data is converted into prompt statements to be input into the generative AI model.

[0088] Step 5:

[0089] The server generates prompts based on the preprocessed data. The generated prompts include information about the patient's cognitive function and lifestyle. For example, the following prompts are generated:

[0090] The patient's cognitive function test results show an MMSE score of 24. Their daily activity level is 5000 steps per day, and their diet is high in fat and lacks vegetables. Their sleep pattern is 6 hours per day. Generate an optimal treatment plan and lifestyle modification suggestions for this patient.

[0091] Step 6:

[0092] The server calls the generative AI model and inputs the generated prompt. The generative AI model generates optimal treatment plans and lifestyle improvement suggestions based on the input prompt. For example, the following suggestions may be generated:

[0093] Cognitive training: 30 minutes of puzzle games every day

[0094] Exercise program: Walking three times a week (30 minutes each time)

[0095] Meal plan: Balanced meals with lots of vegetables

[0096] Improved sleep: Go to bed at the same time every night and get 7 hours of sleep

[0097] Step 7:

[0098] The server sends the generated suggestions to the device. The suggestions include personalized treatment plans and lifestyle improvement suggestions. The device displays the suggestions received from the server to the user. The user can confirm and implement the suggestions through the device.

[0099] (Application example 1)

[0100] Next, a description will be given of Application Example 1 of Embodiment 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."

[0101] It is important to provide appropriate treatment and lifestyle improvement suggestions to patients with mild cognitive impairment (MCI), but current systems make it difficult to provide optimal suggestions tailored to each patient's individual condition. Furthermore, there is a lack of a way for patients to easily input their condition and receive appropriate suggestions in physical stores, which could delay treatment and lifestyle improvement.

[0102] 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.

[0103] In this invention, the server includes: means for utilizing generative AI to make appropriate suggestions for treatment, exercise, diet, and other lifestyle modification and cognitive function training for mild cognitive impairment (MCI); means for monitoring patients and collecting data to conduct preliminary examinations and detect signs, and propose treatment plans to their physicians; and means for patients to use their smartphones at physical stores to input information about their cognitive function status and lifestyle habits, and for the generative AI to provide optimal treatment plans and lifestyle improvement suggestions based on that information. This enables patients to easily input their status at physical stores and receive optimal treatment and lifestyle improvement suggestions tailored to their individual conditions.

[0104] "Generative AI" is an artificial intelligence technology that generates new information and suggestions based on input data.

[0105] "Mild cognitive impairment (MCI)" is a medical term that refers to a condition in which cognitive function is impaired beyond the normal range but does not significantly interfere with daily life.

[0106] "Treatment" refers to medical procedures or measures to cure an illness or disability.

[0107] "Exercise" refers to activities that maintain and improve health by moving the body.

[0108] "Meal" refers to the act of eating to obtain nutrition and the contents of that eating.

[0109] "Lifestyle habits" refer to the actions and habits of daily life, and are factors that affect health.

[0110] "Cognitive function" refers to intellectual functions such as memory, attention, language, and judgment.

[0111] "Training" refers to exercises or practice aimed at improving a particular skill or ability.

[0112] "Monitoring" refers to the continuous observation and recording of a particular condition or activity.

[0113] "Data collection" refers to the act of gathering necessary information.

[0114] "Preliminary screening" refers to an early stage examination aimed at early detection of disease or disability.

[0115] "Early detection" refers to the early detection of signs of illness or disability.

[0116] A "treatment plan" refers to a plan that outlines specific steps and methods for curing a patient's illness or disorder.

[0117] A "physical store" refers to a store that exists in a physical location and where customers can visit in person to purchase products or services.

[0118] A "smartphone" refers to a high-function mobile phone that can connect to the Internet and use applications.

[0119] A system for implementing this invention utilizes generative AI to provide patients with mild cognitive impairment (MCI) with appropriate recommendations for treatment, exercise, diet, and other lifestyle modifications, as well as cognitive training. The system includes means for monitoring patients and collecting data, conducting preliminary screenings and detecting signs, and proposing treatment plans to their physicians. The system also includes means for allowing patients to input information about their cognitive function status and lifestyle habits using their smartphones in a physical store, and for the generative AI to provide optimal treatment plans and lifestyle improvement recommendations based on that information.

[0120] Explanation of program processing

[0121] The server uses a generative AI model to receive input about the patient's cognitive function and lifestyle habits. Specifically, the patient enters their information using a smartphone. The entered information is sent to the server and analyzed by the generative AI model. Based on the analysis results, an optimal treatment plan and lifestyle improvement suggestions are generated.

[0122] The hardware used includes mobile devices such as smartphones and tablets, and the software uses Python and OpenAI's API. The generative AI model uses OpenAI's "text-davinci-003" engine.

[0123] Specific examples

[0124] For example, consider a 65-year-old male patient with mild cognitive impairment, who is physically inactive and has a poor diet. He enters the following information on his smartphone at a brick-and-mortar pharmacy:

[0125] Age: 65

[0126] Gender: Male

[0127] Cognitive status: Mild cognitive impairment

[0128] Lifestyle: Lack of exercise, unbalanced diet

[0129] This information is sent to a server, where the generative AI model analyzes it, and the resulting recommendation is to "walk 30 minutes a day and take a supplement containing B vitamins."

[0130] An example of a prompt to input to a generative AI model is as follows:

[0131] Patient information: Age: 65, Gender: Male, Cognitive status: Mild cognitive impairment, Lifestyle: Lack of exercise, poor diet

[0132] Generate optimal treatment plans and lifestyle modification suggestions.

[0133] In this way, patients can easily enter their own condition at a physical store and receive the most appropriate treatment and lifestyle improvement suggestions based on their individual condition.

[0134] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0135] Step 1:

[0136] Users use their smartphones to input information about their cognitive function status and lifestyle habits. The input information includes age, gender, cognitive function status, and lifestyle habits (lack of exercise, unbalanced diet, etc.). The input data is sent to a server via a smartphone application.

[0137] Input: Age, gender, cognitive function status, lifestyle habits

[0138] Output: Patient information sent to the server

[0139] Step 2:

[0140] The server analyzes the received patient information and generates prompts for the generative AI model. The prompts are instructions for generating optimal treatment plans and lifestyle improvement suggestions based on the patient information.

[0141] Input: Patient information sent to the server

[0142] Output: The prompt to send to the generative AI model

[0143] Step 3:

[0144] The server sends the generated prompts to the generative AI model, which analyzes the prompts and generates optimal treatment plans and lifestyle improvement suggestions based on the patient's condition.

[0145] Input: A prompt to send to the generative AI model

[0146] Output: Suggested results from the generative AI model

[0147] Step 4:

[0148] The server analyzes the proposal results received from the generative AI model and converts them into a format that is easy for the user to understand. Specifically, it converts the proposal results into text format and sends them to a smartphone application.

[0149] Input: Suggested results from a generative AI model

[0150] Output: Text representation of the proposal results to be sent to the user

[0151] Step 5:

[0152] The user can then use the smartphone application to check the proposed results sent from the server, which include specific treatment plans and lifestyle improvement suggestions.

[0153] Input: Text format of the proposal results sent from the server

[0154] Output: Suggested results for user review

[0155] In this way, users can input information about their cognitive function status and lifestyle habits, and receive optimal treatment plans and lifestyle improvement suggestions from the generative AI model.

[0156] Example 2

[0157] Next, a description will be given of Example 2 of Form 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."

[0158] Conventional medical systems have difficulty collecting and analyzing patients' vital signs and behavioral data in real time. Furthermore, they often lack accuracy and speed when it comes to detecting early signs and proposing treatment plans. This makes it difficult to provide treatment and prevention at the appropriate time, and makes it difficult to optimally manage patients' health conditions.

[0159] 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.

[0160] In this invention, the server includes means for the terminal to collect biometric information and behavioral data of the user in real time, means for the terminal to transmit the collected data to the server, means for the server to store the received data in a database, means for the server to analyze the stored data using a generative AI model, means for the server to detect signs based on the results of the data analysis, means for the server to propose a treatment plan to the attending physician based on the results of the sign detection, and means for notifying the attending physician of the proposed treatment plan generated by the server. This enables the patient's biometric information and behavioral data to be collected and analyzed in real time, enabling rapid and accurate sign detection and treatment plan proposals.

[0161] "Generative AI" is a system that uses artificial intelligence technology to generate data and perform analysis and predictions.

[0162] "Mild cognitive impairment" refers to a condition in which cognitive function is impaired beyond the normal range, but does not significantly interfere with daily life.

[0163] "Monitoring" refers to the continuous observation and recording of a patient's vital signs and behavioral data.

[0164] "Data collection" refers to obtaining a patient's biometric information and behavioral data using devices and sensors.

[0165] A "preliminary screening" refers to an examination to assess a patient's health status in advance and detect potential health problems early.

[0166] "Predictive detection" refers to analyzing collected data to predict potential future health problems.

[0167] A "treatment plan" refers to a plan that suggests optimal treatment and preventative measures based on the patient's health condition.

[0168] "Terminal" refers to a device used to collect biometric and behavioral data from a user. Examples include wearable devices and smartphones.

[0169] "Server" refers to the computer system that stores collected data and analyzes it using generative AI.

[0170] "Database" refers to a system for organizing and storing collected data.

[0171] "Generative AI model" refers to an artificial intelligence model used to analyze collected data and make predictions or recommendations.

[0172] "Notification" refers to the means of communicating the generated treatment plan and the results of predictive detection to the attending physician.

[0173] This invention is a system that collects and analyzes a patient's biological information and behavioral data in real time, performs a preliminary examination and detects signs of illness, and proposes a treatment plan to the attending physician. Specific embodiments of this system are described below.

[0174] System configuration

[0175] Hardware Configuration

[0176] 1. Terminal: A device used to collect biometric and behavioral data from a user. Examples include wearable devices (e.g., smartwatches) and smartphones.

[0177] 2. Server: A computer system that stores collected data and analyzes it using generative AI models. The server has a powerful processor and large storage capacity.

[0178] 3. Database: A system for organizing and storing collected data. Specific examples include relational databases such as MySQL and PostgreSQL.

[0179] Software Configuration

[0180] 1. Generative AI model: An artificial intelligence model used to analyze collected data and make predictions or recommendations. Examples include GPT-4 and BERT.

[0181] 2. Data collection application: Software that collects data from devices and sends it to a server. Often implemented as a smartphone app.

[0182] 3. Data analysis software: Software that runs on a server and analyzes data using generative AI models.

[0183] System Operation

[0184] Data collection

[0185] The device (e.g., a smartwatch) collects real-time biometric and behavioral data, such as the user's heart rate, blood pressure, body temperature, number of steps taken, and sleep patterns, and transmits this data to a smartphone via Bluetooth or Wi-Fi.

[0186] Data transmission

[0187] The smartphone app sends the collected data to a server over the internet, where it is encrypted and transmitted using a secure communication protocol (e.g., HTTPS).

[0188] Data storage

[0189] The server stores the received data in a database, organized by user and timestamp.

[0190] Data analysis

[0191] The server analyzes the stored data using a generative AI model (e.g., GPT-4), which detects anomalous patterns based on the collected data and performs predictive detection.

[0192] Predictive detection

[0193] The server uses the results of the data analysis to predict potential future health problems, for example, if abnormal fluctuations in heart rate are detected, it may be a sign of heart disease.

[0194] Treatment plan proposal

[0195] The server uses the generative AI model to propose treatment plans to the patient's doctor based on the results of the symptom detection. For example, if symptoms of heart disease are detected, the server will suggest a consultation with a cardiologist.

[0196] Proposal Notification

[0197] The server then notifies the doctor of the proposed treatment plan via email or a dedicated app.

[0198] Specific examples

[0199] Example 1: Heart rate abnormality detection

[0200] Suppose a user is wearing a smartwatch. The smartwatch measures the user's heart rate every minute and sends it to a server via a smartphone app. The server stores the received heart rate data in a database and analyzes it using a generative AI model. As a result of the analysis, it detects that the heart rate is abnormally high, which may be a sign of heart disease. Based on this information, the server recommends that the user undergo a heart disease examination with their doctor.

[0201] Prompt Sentence Examples

[0202] "Analyze the user's heart rate data and detect any abnormal fluctuations. If abnormalities are detected, suggest possible diseases and recommended treatments."

[0203] In this way, a system is realized in which the server, terminal, and user work together to monitor the patient's health condition in real time and propose an appropriate treatment plan.

[0204] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0205] Step 1: Data collection

[0206] A device (e.g., a smartwatch) collects a user's biometric information (heart rate, blood pressure, body temperature, etc.) and behavioral data (number of steps, sleep patterns, etc.) in real time. Specifically, a smartwatch measures the heart rate and counts the number of steps every minute. The input is the user's biometric information and behavioral data, and the output is the collected data.

[0207] Step 2: Send data

[0208] The device sends the collected data to a server via a smartphone app. Specifically, the smartwatch sends data to the smartphone using Bluetooth, and the smartphone app sends the data to the server via the Internet. The input is the collected data, and the output is the data sent to the server.

[0209] Step 3: Save data

[0210] The server stores the received data in a database. Specifically, the server adds new data to the database whenever it arrives. The input is the data sent to the server, and the output is the data stored in the database.

[0211] Step 4: Data analysis

[0212] The server analyzes the stored data using a generative AI model (e.g., GPT-4). Specifically, the server analyzes the data at a fixed time each day to detect abnormal patterns. The input is the data stored in the database, and the output is the analysis results.

[0213] Step 5: Predictive detection

[0214] The server performs symptom detection based on the results of data analysis. Specifically, the server detects abnormal fluctuations in heart rate and determines whether these are a sign of a specific disease. The input is the analysis results, and the output is the result of symptom detection.

[0215] Step 6: Propose a treatment plan

[0216] The server uses a generative AI model to propose a treatment plan to the attending physician based on the results of the sign detection. Specifically, the server generates a recommendation such as, "You are at high risk of heart disease, so we recommend that you see a cardiologist." The input is the result of the sign detection, and the output is a proposed treatment plan.

[0217] Step 7: Proposal Notification

[0218] The server notifies the doctor of the proposed treatment plan it has created. Specifically, the server sends the proposed plan to the doctor's email address. The input is the proposed treatment plan, and the output is a notification to the doctor.

[0219] (Application example 2)

[0220] Next, a description will be given of Application Example 2 of Form 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."

[0221] Conventional medical systems are not fully optimized for the treatment of mild cognitive impairment (MCI), lifestyle modification, or cognitive function training. Furthermore, there are limited systems that can collect patients' biometric and behavioral data in real time to perform preliminary screening and detect signs of illness. Furthermore, the lack of functionality to detect signs of security risks and propose appropriate countermeasures makes it difficult to ensure user safety.

[0222] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a means for utilizing generative AI to make appropriate suggestions for treatment, lifestyle modification such as exercise and diet, and cognitive function training for mild cognitive impairment (MCI); a means for performing preliminary screening and symptom detection based on patient monitoring and data collection, and proposing treatment plans to the attending physician; a means for collecting user biometric information and behavioral data in real time and detecting signs of security risks; a means for analyzing the collected data and detecting abnormal patterns and high-risk states; and a means for proposing appropriate security measures to the user based on the analysis results. This makes it possible to optimize treatment for mild cognitive impairment, lifestyle modification, and cognitive function training, and further to detect signs of security risks and propose appropriate measures through the collection and analysis of biometric information and behavioral data in real time.

[0223] "Generative AI" is an artificial intelligence technology that generates new information and suggestions based on data.

[0224] Mild cognitive impairment (MCI) is a condition in which cognitive function is somewhere between normal and dementia, and in which there is a decline in cognitive function but not to the point of interfering with daily life.

[0225] "Treatment" refers to medical procedures and drug therapy to cure illness or disability.

[0226] "Exercise" is an activity that maintains and improves health by moving the body.

[0227] "Meal" refers to the act of eating to obtain nutrition and the contents of that eating.

[0228] "Lifestyle modification" refers to actions and habits that improve unhealthy lifestyles and lead a healthy life.

[0229] "Cognitive training" refers to exercises and activities aimed at improving cognitive functions such as memory, attention, and judgment.

[0230] "Monitoring" is the act of continuously observing a specific subject and collecting data.

[0231] "Data collection" is the act of gathering information for a specific purpose.

[0232] A "preliminary examination" refers to an initial test to detect illness or abnormalities early.

[0233] "Predictive detection" is the act of finding signs of abnormalities or problems before they occur.

[0234] "Treatment planning" refers to planning the optimal treatment method according to the patient's condition.

[0235] "Biometric information" refers to physiological data from the human body, such as heart rate, blood pressure, and body temperature.

[0236] "Behavioral Data" refers to information about an individual's behavioral patterns and activity levels.

[0237] "Security risk" refers to factors or situations that could threaten the safety of information or systems.

[0238] "Abnormal patterns" refer to behavior or data fluctuations that are different from the norm.

[0239] "High-risk situation" refers to a situation or condition that is deemed to be high risk.

[0240] "Security measures" refer to the means and methods for ensuring the safety of information and systems.

[0241] As an embodiment of the present invention, the following system is constructed.

[0242] System Configuration

[0243] Hardware

[0244] Smartphone: A device for collecting user biometric and behavioral data. Use an iOS or ANDROID (registered trademark) smartphone.

[0245] Sensors: Sensors for collecting biometric information such as heart rate, activity level, stress level, etc. Specifically, APIs such as Apple HealthKit and GOOGLE FI (registered trademark) are used.

[0246] software

[0247] Generative AI model: An artificial intelligence technology that generates new information and suggestions based on data. It is implemented using programming languages ​​such as Python.

[0248] Data Collection API: API for collecting biometric and behavioral data. Uses Apple HealthKit and GOOGLE FIT (registered trademark).

[0249] Data analysis module: This module analyzes collected data in real time to detect abnormal patterns and high-risk conditions.

[0250] Processing flow

[0251] 1. Data collection: Using the smartphone's sensor API, biometric information such as the user's heart rate, activity level, and stress level is collected in real time.

[0252] 2. Data analysis: Collected data is fed into generative AI models to detect abnormal patterns and high-risk conditions.

[0253] 3. Presenting the results: Based on the analysis results, appropriate security measures are suggested to the user.

[0254] Specific examples

[0255] For example, if a user shows high stress levels and high activity levels late at night, the system will notify them, "High risk detected. We recommend you take immediate rest."

[0256] Prompt Sentence Examples

[0257] If the device detects that the user's heart rate is above 90, their activity level is high, or their stress level is 7 or higher, it will notify the user, saying, "High risk has been detected. We recommend that you rest immediately."

[0258] In this way, it will be possible to treat mild cognitive impairment, correct lifestyle habits, and optimize cognitive function training. Furthermore, by collecting and analyzing biometric and behavioral data in real time, it will be possible to detect signs of security risks and propose appropriate countermeasures.

[0259] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0260] Step 1:

[0261] The server uses the smartphone's sensor API to collect biometric information such as the user's heart rate, activity level, and stress level in real time. As input, it receives data from the sensor API and generates a biometric dataset as output. This dataset includes the heart rate, activity level, and stress level.

[0262] Step 2:

[0263] The server inputs the collected biometric data set into a generative AI model. It uses the biometric data set as input and generates analysis results for detecting abnormal patterns and high-risk conditions as output. Specifically, the AI ​​model analyzes the data and identifies abnormal patterns and high-risk conditions.

[0264] Step 3:

[0265] The server proposes appropriate security measures to the user based on the analysis results from the generative AI model. It receives the analysis results of the AI ​​model as input and generates a message to notify the user as output. Specifically, the server evaluates the analysis results and, if necessary, sends a notification to the user, such as "High risk has been detected. We recommend that you take immediate rest."

[0266] Step 4:

[0267] The user receives the notification from the server and takes appropriate security measures. The input is the notification message from the server, and the output is taking action based on the notification. Specifically, the user takes measures such as taking a break according to the notification content.

[0268] Step 5:

[0269] The server recollects the user's behavioral data and evaluates the effectiveness of the countermeasures taken in the previous step. As input, it receives the user's new biometric and behavioral data, and as output, it generates a report to evaluate the effectiveness of the countermeasures. Specifically, the server analyzes the new data and determines whether the countermeasures were effective.

[0270] Example 3

[0271] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0272] Conventional systems for suggesting treatments for cognitive impairment and lifestyle improvements have difficulty providing optimal suggestions tailored to each patient's individual condition, and data collection and analysis are performed manually, resulting in inefficiency and low accuracy.Furthermore, even systems that utilize generative AI have had issues with providing appropriate treatment plans and lifestyle improvement suggestions to patients because prompt generation and optimization of suggestions are not adequately performed.

[0273] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0274] In this invention, the server includes a means for the user to input patient information and the terminal to transmit the information to the server, a means for the server to analyze the received data and generate a prompt sentence, and a means for the server to input the prompt sentence into a generative AI model and generate a proposal, thereby enabling the efficient and accurate provision of optimal treatment plans and lifestyle improvement proposals tailored to the individual condition of the patient.

[0275] "Generative AI" is an artificial intelligence technology that generates optimal proposals and plans based on input data.

[0276] "Mild cognitive impairment" refers to a condition in which cognitive function is impaired beyond the normal range, but does not significantly interfere with daily life.

[0277] "Correcting lifestyle habits" refers to improving daily habits such as diet, exercise, and sleep in order to maintain and improve health.

[0278] "Cognitive function training" refers to training and activities aimed at maintaining and improving cognitive functions such as memory, attention, and judgment.

[0279] "Monitoring" refers to the continuous observation and recording of a patient's health condition and lifestyle habits.

[0280] "Data collection" refers to the process of gathering information about patients, including their health status, lifestyle habits, and cognitive status.

[0281] "Preliminary screening" refers to initial testing and evaluation before a formal diagnosis is made.

[0282] "Predictive detection" refers to detecting signs that allow for early detection of illness or a deterioration in health.

[0283] A "treatment plan" refers to the specific treatment strategies and procedures developed by a medical professional to improve a patient's health.

[0284] A "prompt" is an instruction to be input into generative AI, and specifically describes the patient's condition and necessary suggestions.

[0285] "Terminal" refers to an electronic device through which a user inputs information and receives suggestions from a server.

[0286] "Server" refers to a central processing unit that analyzes data, runs generative AI models, generates proposals, etc.

[0287] This invention is a system that utilizes generative AI to provide appropriate suggestions for treatment, lifestyle modification such as exercise and diet, and cognitive function training for mild cognitive impairment. The system consists of three main components: the user, the terminal, and the server.

[0288] Data collection

[0289] The user uses the device to input information about the patient's cognitive function and lifestyle habits. Specifically, the user inputs data such as the patient's age, gender, medical history, current symptoms, daily diet, and exercise habits. For example, when inputting information about a 65-year-old female patient with mild dementia, the user would enter information such as "65-year-old female, mild dementia, takes walks daily, but has high-calorie and irregular meals."

[0290] The terminal transmits the input data to the server in real time. When data is entered into the input form on the terminal and the "Send" button is pressed, the data is transmitted to the server.

[0291] Data Processing

[0292] The server analyzes the received data. Specifically, it stores it in a database and extracts the necessary information. The server generates a prompt based on the extracted information. For example, it generates a prompt such as "65-year-old female with mild dementia, takes walks daily, but eats high-calorie, irregular meals."

[0293] Generative AI processing

[0294] The server inputs a prompt into a generative AI model (e.g., GPT-4). Specifically, it calls the generative AI model's API and sends the prompt. The generative AI model generates an optimal treatment plan and lifestyle improvement suggestions based on the input prompt. For example, it generates a suggestion such as, "For this patient, we recommend aerobic exercise three times a week and a balanced, low-calorie diet. We also recommend daily crossword puzzles and reading to maintain cognitive function."

[0295] Providing results

[0296] The server sends the generated proposal to the terminal. Specifically, the generated proposal is sent to the terminal in JSON format. The user confirms the proposal through the terminal. For example, the user confirms the proposal displayed on the terminal screen and applies it to the patient.

[0297] Specific examples

[0298] For example, Patient A is a 65-year-old woman with mild dementia. She takes walks daily, but her diet is high in calories and irregular. The user enters this information into a device and sends it to the server. The server generates the following prompt: "65-year-old woman with mild dementia, takes walks daily, but her diet is high in calories and irregular." The generative AI model generates the following suggestion: "We recommend that this patient do aerobic exercise three times a week and eat a balanced, low-calorie diet. We also recommend that she do crossword puzzles and read regularly to maintain cognitive function."

[0299] In this way, the system can efficiently and accurately provide optimal treatment plans and lifestyle improvement suggestions according to the state of the patient's cognitive function. The flow of the identification process in Example 3 will be described with reference to FIG.

[0300] Step 1:

[0301] The user uses the device to input information about the patient's cognitive function and lifestyle habits. Specifically, the user inputs data such as the patient's age, gender, medical history, current symptoms, daily diet, and exercise habits. For example, the user might input information such as "65-year-old female with mild dementia, takes walks daily, but has high-calorie and irregular meals." The input data is saved in the device's input form.

[0302] Step 2:

[0303] The terminal sends the entered data to the server. Specifically, when the user presses the "Send" button, the terminal sends the entered data to the server in real time. The sent data is saved in the server's database.

[0304] Step 3:

[0305] The server analyzes the data it receives. Specifically, it extracts data stored in the database and organizes the necessary information. For example, it extracts information such as the patient's age, gender, cognitive function status, and lifestyle habits. Based on the results of this analysis, it generates a prompt.

[0306] Step 4:

[0307] The server inputs a prompt to the generative AI model. Specifically, it calls the API of the generative AI model and sends the prompt. For example, it generates a prompt such as "65-year-old female with mild dementia, takes walks daily, but eats high-calorie, irregular meals" and sends it to the generative AI model.

[0308] Step 5:

[0309] The generative AI model generates optimal treatment plans and lifestyle improvement suggestions based on the input prompts. Specifically, the generative AI model analyzes the prompts and generates appropriate suggestions. For example, it generates a suggestion such as, "We recommend this patient engage in aerobic exercise three times a week and a balanced, low-calorie diet. We also recommend daily crossword puzzles and reading to maintain cognitive function."

[0310] Step 6:

[0311] The server sends the generated proposal to the device. Specifically, it sends the generated proposal in JSON format to the device. The sent proposal is displayed on the device screen.

[0312] Step 7:

[0313] The user confirms the proposals through the device. Specifically, the user confirms the proposals displayed on the device screen and applies them to the patient. For example, the user explains the proposed treatment plan and lifestyle improvements to the patient and instructs them to implement them.

[0314] (Application example 3)

[0315] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0316] Providing appropriate treatment and lifestyle improvement suggestions to patients with mild cognitive impairment (MCI) is important, but conventional systems have had difficulty making optimal suggestions based on the patient's cognitive function and lifestyle. Furthermore, there was a lack of means to provide appropriate suggestions to support patients' cognitive function, even while shopping in physical stores. This made it difficult for patients to make appropriate choices in their daily lives, potentially delaying the improvement of their cognitive function.

[0317] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0318] In this invention, the server includes: means for utilizing generative AI to make appropriate suggestions for treatment, exercise, diet, and other lifestyle modification and cognitive function training for mild cognitive impairment (MCI); means for monitoring patients and collecting data to conduct preliminary examinations and detect signs, and propose treatment plans to their physicians; means for making suggestions to support the user's cognitive function while shopping in a physical store; means for generating optimal product and service suggestions based on the user's cognitive function status and lifestyle data; and means for notifying the user of the generated suggestions. This allows patients to receive cognitive support in their daily lives and make appropriate choices.

[0319] "Generative AI" is an artificial intelligence technology that generates new information and suggestions based on input data.

[0320] "Mild cognitive impairment (MCI)" refers to a condition in which cognitive function declines beyond the normal range but does not significantly interfere with daily life.

[0321] "Treatment" refers to medical procedures and drug therapy to cure illness or disability.

[0322] "Exercise" refers to activities that maintain and improve health by moving the body.

[0323] "Diet" refers to the act of consuming food to obtain nutrients.

[0324] "Lifestyle" refers to the actions and habits of daily life, and includes factors that affect health.

[0325] "Cognitive function" refers to intellectual functions such as memory, attention, language, and judgment.

[0326] "Training" refers to exercises or practice aimed at improving a particular ability or skill.

[0327] "Monitoring" refers to the continuous observation and recording of a particular condition or activity.

[0328] "Data collection" refers to the act of gathering necessary information.

[0329] "Preliminary screening" refers to an early stage examination aimed at early detection of disease or disability.

[0330] "Early detection" refers to the early detection of signs of illness or disability.

[0331] "Treatment planning" refers to planning the optimal treatment method according to the patient's condition.

[0332] "Brick and Mortar Store" means a retailer that has a physical location.

[0333] "Shopping" refers to the act of purchasing goods and services.

[0334] "User" refers to a person who uses a system or service.

[0335] "Notification" refers to the act of making information known.

[0336] The following system configuration will be described as an embodiment of the present invention.

[0337] System Configuration

[0338] This system utilizes generative AI to provide appropriate recommendations for treatment, exercise, diet, and other lifestyle changes, as well as cognitive training for mild cognitive impairment (MCI). The system includes the following main components:

[0339] Hardware used

[0340] Smartphone (iOS or Android)

[0341] Sensors (heart rate monitor, pedometer, etc.)

[0342] Software used

[0343] Generative AI models (e.g., GPT-4)

[0344] Database (e.g. Firebase)

[0345] Health data collection APIs (e.g., Google Fit, Apple HealthKit)

[0346] Program processing explanation

[0347] Data collection

[0348] The device (smartphone) collects data on the user's cognitive function status and lifestyle habits, including data from smartphone sensors and external APIs (Google Fit, Apple HealthKit).

[0349] Data analysis

[0350] The server inputs the collected data into a generative AI model (GPT-4) to evaluate the user's current cognitive function state.

[0351] Proposal generation

[0352] The server uses a generative AI model to generate optimal product and service recommendations based on the user's cognitive state, such as foods containing specific nutrients or exercise equipment to enhance cognitive function.

[0353] Notifications and Displays

[0354] The device then notifies the user of the generated suggestions via a smartphone application, where the user can check the suggestions and use them to help them with their shopping in physical stores.

[0355] Specific examples

[0356] While shopping in a physical store, the app will notify users, "We recommend purchasing foods high in B vitamins to support your cognitive function." Users can review the suggested products in the app and search for them in-store.

[0357] Prompt Sentence Examples

[0358] Generate optimal product and service recommendations based on the user's cognitive status and lifestyle data, such as foods containing specific nutrients or exercise equipment to improve cognitive function. User data includes:

[0359] Cognitive status: mild cognitive decline

[0360] Lifestyle: Lack of exercise, unbalanced diet

[0361] In this way, applications can be created that support users' cognitive functions through the physical shopping experience.

[0362] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0363] Step 1:

[0364] The device collects data on the user's cognitive function and lifestyle habits. Specifically, it obtains data from smartphone sensors (heart rate monitor, pedometer, etc.) and external APIs (Google Fit, Apple HealthKit). Input data includes heart rate, number of steps, dietary details, etc. This data is collected and sent to a server.

[0365] Step 2:

[0366] The server inputs the received data into a generative AI model (GPT-4). The input data includes information on the user's cognitive function status and lifestyle habits. The generative AI model analyzes this data and evaluates the user's current cognitive function status. The output is an evaluation result of the user's cognitive function.

[0367] Step 3:

[0368] The server generates optimal product and service recommendations based on the cognitive function assessment results output by the generative AI model. Specifically, it makes recommendations such as foods containing specific nutrients and exercise equipment to improve cognitive function. The input data includes the cognitive function assessment results, and the recommendations are obtained as the output.

[0369] Step 4:

[0370] The server sends the generated proposal to the device. The device notifies the user of the received proposal via a smartphone application. Specifically, the proposal is displayed using a push notification or an in-app message. The proposal is included in the input data, and a notification is sent to the user as the output.

[0371] Step 5:

[0372] The user confirms the proposed products and services within the app. Specifically, the user views the list or detail page within the app and confirms the proposed content. The input data includes the proposed content, and the output is the user's confirmation action.

[0373] Step 6:

[0374] Users search for and purchase suggested products and services in physical stores. Specifically, they use information in the app to explore the store and find the suggested products. The input data includes the suggestions, and the output is the user's purchasing behavior.

[0375] In this way, the system can support the user's cognitive functions and suggest appropriate products and services.

[0376] 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.

[0377] "Example 1"

[0378] In one embodiment of the present invention, the generative AI includes an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's emotions from facial expressions, tone of voice, text input, etc., and feeds the results back to the generative AI. Based on the information obtained from the emotion engine, the generative AI suggests lifestyle modifications such as treatment, exercise, and diet, as well as cognitive function training, in accordance with the user's emotional state. For example, if the user feels stressed, the generative AI suggests exercises and meals that promote relaxation. Also, if the user feels joy, the generative AI suggests activities to maintain that joy.

[0379] "Example 2"

[0380] In another embodiment of the present invention, the emotion engine improves the accuracy of preliminary screening and sign detection by taking into account the user's emotional state. Specifically, if the user's emotional state shows a certain pattern, this can be used as an indicator for preliminary screening and sign detection. For example, if the user feels anxious or sad for a certain period of time, this can be interpreted as a sign of cognitive decline and early intervention can be suggested.

[0381] "Example 3"

[0382] In another embodiment of the present invention, the emotion engine may consider the user's emotional state when proposing a treatment plan. Specifically, if the user's emotional state exhibits a certain pattern, that pattern may be used as an indicator for adjusting the treatment plan. For example, if the user experiences a sense of joy over a period of time, that may be used as an indicator of improved cognitive function, and the progress of the treatment plan may be adjusted accordingly.

[0383] The processing flow of each embodiment will be described below.

[0384] "Example 1"

[0385] Step 1: Receive input from the user (facial expression, tone of voice, text, etc.).

[0386] Step 2: The emotion engine analyzes the user's emotions.

[0387] Step 3: The analysis results are fed back to the generative AI.

[0388] Step 4: The generative AI will suggest treatment, exercise, diet, and other lifestyle changes based on the emotional state, as well as cognitive function training.

[0389] "Example 2"

[0390] Step 1: Receive input from the user (facial expression, tone of voice, text, etc.).

[0391] Step 2: The emotion engine analyzes the user's emotions.

[0392] Step 3: The analysis results are fed back to the generative AI.

[0393] Step 4: Generative AI uses emotional states as indicators for preliminary screening and early warning signs.

[0394] "Example 3"

[0395] Step 1: Receive input from the user (facial expression, tone of voice, text, etc.).

[0396] Step 2: The emotion engine analyzes the user's emotions.

[0397] Step 3: The analysis results are fed back to the generative AI.

[0398] Step 4: Generative AI uses emotional state as a guide to adjust treatment plans.

[0399] Example 1

[0400] Next, a description will be given of Example 1 of Form 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."

[0401] Patients with mild cognitive impairment (MCI) need appropriate recommendations for treatment, exercise, diet, and other lifestyle changes, as well as cognitive function training. However, conventional systems do not take into account the patient's emotional state, making it difficult to provide optimal treatment plans and lifestyle improvement recommendations for each individual patient. Another challenge was improving the accuracy of preliminary screening and symptom detection based on information obtained from patient monitoring and data collection.

[0402] 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.

[0403] In this invention, the server includes: means for utilizing generative AI to make appropriate suggestions for treatment, exercise, diet, and other lifestyle modification and cognitive training for mild cognitive impairment (MCI); means for monitoring patients and collecting data to conduct preliminary screening and detect signs, and propose treatment plans to attending physicians; means including an emotion engine that analyzes emotions from a user's facial expressions, tone of voice, text input, etc.; means for proposing treatment, exercise, diet, and other lifestyle modification and cognitive training according to the user's emotional state based on information obtained from the emotion engine; means for inputting prompt sentences into the generative AI model to generate optimal suggestions; and means for transmitting the generated suggestions to the user's terminal and displaying them to the user. This makes it possible to provide optimal treatment plans and lifestyle improvement suggestions that take into account the patient's emotional state.

[0404] "Generative AI" is a type of artificial intelligence that generates optimal proposals and plans based on user input data.

[0405] "Mild cognitive impairment (MCI)" is a medical term that refers to a condition in which cognitive function is below the normal range but does not significantly interfere with daily life.

[0406] "Treatment" refers to medical procedures or measures to cure an illness or disability.

[0407] "Exercise" refers to activities that maintain and improve health by moving the body.

[0408] "Meal" refers to the act of eating to obtain nutrition and the contents of that eating.

[0409] "Lifestyle habits" is a general term for actions and habits in daily life.

[0410] "Cognitive training" refers to exercises or activities designed to improve cognitive functions such as memory and attention.

[0411] "Monitoring" refers to the act of continuously observing and recording a specific subject.

[0412] "Data collection" refers to the act of gathering necessary information or data.

[0413] "Preliminary screening" refers to an early stage examination aimed at early detection of disease or disability.

[0414] "Early detection" refers to a means of detecting early signs of disease or disability.

[0415] An "emotion engine" refers to technology for analyzing emotions from a user's facial expressions, tone of voice, text input, etc.

[0416] A "prompt" refers to an instruction or question that is input into a generative AI model.

[0417] "Terminal" refers to a device used by a user, such as a computer or smartphone.

[0418] A "server" refers to a computer system that stores and processes data over a network.

[0419] This invention is a system that provides appropriate suggestions for lifestyle modification, such as treatment, exercise, and diet, as well as cognitive function training for patients with mild cognitive impairment (MCI). The system uses software such as generative AI, an emotion engine, and a database management system (DBMS), as well as hardware such as servers and terminals (PCs, smartphones, tablets, etc.).

[0420] Hardware and software used

[0421] Hardware: Servers, devices (PCs, smartphones, tablets, etc.)

[0422] Software: Generative AI model, emotion engine, database management system (DBMS)

[0423] System Overview

[0424] 1. Data Collection:

[0425] The user uses the device to input information about the state of their cognitive function and lifestyle habits. Specifically, the user opens the device's application and answers questions on a questionnaire form. For example, the user answers questions such as "What have you eaten recently?" and "How often do you exercise?"

[0426] The device collects data such as the user's facial expressions, tone of voice, and text input. Specifically, the device's camera and microphone are used to analyze the user's facial expressions and tone of voice in real time. The user inputs text using a keyboard or touch screen.

[0427] 2. Data transmission and storage:

[0428] The terminal transmits the collected data to a server. Specifically, the terminal encrypts the collected data and transmits it to the server via the Internet.

[0429] The server stores the received data in a database. Specifically, the server uses a database management system (DBMS) to store the received data in the appropriate tables.

[0430] 3. Emotion analysis:

[0431] The server uses an emotion engine to analyze emotions from the user's facial expressions, tone of voice, and text input. Specifically, the emotion engine uses a machine learning model to classify emotions from the user's facial expressions and tone of voice. For example, it analyzes a smile as "joy" and a furrowed brow as "stress."

[0432] 4. Proposal generation:

[0433] The server uses a generative AI model to generate optimal treatment plans and lifestyle improvement suggestions based on the user's cognitive function status, lifestyle information, and emotion analysis results. Specifically, the server inputs the following prompt into the generative AI model:

[0434] User's cognitive status: Mild Cognitive Impairment (MCI)

[0435] User's lifestyle: Irregular eating habits, lack of exercise

[0436] User's emotional state: stressed

[0437] Based on the above information, generate optimal treatment plans and lifestyle modification suggestions for the user.

[0438] Based on this prompt, the generative AI model generates specific suggestions, such as "We recommend walking 30 minutes daily" or "Do yoga to reduce stress."

[0439] 5. Providing suggestions:

[0440] The server transmits the generated proposal to the user's terminal. Specifically, the server transmits the generated proposal to the terminal in a data format such as JSON format.

[0441] The device displays the received suggestion to the user. Specifically, the device application notifies the user of the suggestion and displays it on the screen. The user can then confirm and implement the suggestion.

[0442] Specific examples

[0443] For example, if a user enters "A lot of fast food" as their "recent diet" and the emotion engine analyzes this as "feeling stressed," the generative AI model will generate suggestions such as "recommended 30 minutes of walking every day" or "do yoga to reduce stress." These suggestions are sent to the user's device via the server, and the user can view them on their device screen.

[0444] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0445] Step 1:

[0446] The user uses the terminal to input information about the state of cognitive function and lifestyle habits.

[0447] Input: The user opens the application on their device and answers questions in a questionnaire, such as "What have you eaten recently?" or "How often do you exercise?"

[0448] Output: The user's input data is saved to the terminal.

[0449] What happens: The user enters information using a keyboard or touchscreen and presses the submit button.

[0450] Step 2:

[0451] The device collects data such as the user's facial expressions, tone of voice, and text input.

[0452] Input: The device's camera and microphone capture the user's facial expressions and tone of voice in real time.

[0453] Output: Collected data is saved to the device.

[0454] What happens: The device's camera captures the user's face, the microphone records the user's voice, and the user types text using a keyboard or touchscreen.

[0455] Step 3:

[0456] The terminal transmits the collected data to the server.

[0457] Input: User input and collected data stored on the device.

[0458] Output: Data is sent to the server.

[0459] Specific operation: The device encrypts the collected data and sends it to a server via the Internet.

[0460] Step 4:

[0461] The server stores the received data in a database.

[0462] Input: User input data and collected data sent from the device.

[0463] Output: Data is stored in a database.

[0464] What happens: The server uses a database management system (DBMS) to store the received data in the appropriate tables.

[0465] Step 5:

[0466] The server uses an emotion engine to analyze emotions from the user's facial expressions, tone of voice, and text input.

[0467] Input: User facial expressions, tone of voice, and text input data stored in a database.

[0468] Output: The sentiment analysis results are generated.

[0469] How it works: The emotion engine uses machine learning models to classify emotions from a user's facial expressions and tone of voice. For example, a smile might indicate "happiness" and a furrowed brow might indicate "stress."

[0470] Step 6:

[0471] The server uses a generative AI model to generate optimal treatment plans and lifestyle improvement suggestions based on the user's cognitive function status, lifestyle information, and emotion analysis results.

[0472] Input: User's cognitive function status, lifestyle information, and emotion analysis results stored in the database.

[0473] Output: An optimal treatment plan and lifestyle modification suggestions are generated.

[0474] How it works: Enter the following prompt into the generative AI model:

[0475] User's cognitive status: Mild Cognitive Impairment (MCI)

[0476] User's lifestyle: Irregular eating habits, lack of exercise

[0477] User's emotional state: stressed

[0478] Based on the above information, generate optimal treatment plans and lifestyle modification suggestions for the user.

[0479] Based on this prompt, the generative AI model generates specific suggestions, such as "We recommend walking 30 minutes daily" or "Do yoga to reduce stress."

[0480] Step 7:

[0481] The server transmits the generated proposal to the user's terminal.

[0482] Input: Generated treatment plan and lifestyle modification suggestions.

[0483] Output: The proposal is sent to the user's device.

[0484] Specific operation: The server sends the generated proposal to the device in a data format such as JSON.

[0485] Step 8:

[0486] The terminal displays the received suggestions to the user.

[0487] Input: Treatment plan and lifestyle improvement suggestions sent from the server.

[0488] Output: The suggestions are displayed on the device screen.

[0489] Specific operation: The device application notifies the user of the suggestion and displays it on the screen. The user can then confirm and implement the suggestion.

[0490] (Application example 1)

[0491] Next, a description will be given of Application Example 1 of Embodiment 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."

[0492] It is important to provide appropriate suggestions for treatment, exercise, dietary changes, and cognitive function training to patients with mild cognitive impairment (MCI). However, conventional systems do not take into account the patient's emotional state when making suggestions, making it difficult to maximize the patient's motivation and treatment effectiveness. Furthermore, in the shopping experience at physical stores, there is also the problem of not being able to improve patient satisfaction because product and service suggestions are not tailored to the patient's emotional state.

[0493] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes: a means for utilizing generative AI to make appropriate suggestions for treatment, exercise, diet, and other lifestyle modification and cognitive function training for mild cognitive impairment (MCI); a means for performing preliminary screening and symptom detection based on patient monitoring and data collection, and proposing treatment plans to the attending physician; a means including an emotion engine that recognizes the user's emotions, analyzing the user's emotions from facial expressions, tone of voice, text input, etc., and feeding the results back to the generative AI; and a means for suggesting products and services based on the user's emotional state through smart glasses. This makes it possible to suggest optimal treatments and lifestyle changes that take the patient's emotional state into consideration, thereby improving patient satisfaction even in shopping experiences at physical stores.

[0494] "Generative AI" is an artificial intelligence system that generates optimal proposals and plans based on user input data.

[0495] "Mild cognitive impairment (MCI)" is a medical term that refers to a condition in which cognitive function is impaired beyond the normal range but does not significantly interfere with daily life.

[0496] An "emotion engine" is software or hardware that analyzes emotions from a user's facial expressions, tone of voice, text input, etc., and feeds the results back to other systems.

[0497] "Smart glasses" are wearable devices equipped with functions such as a display, camera, and microphone, and provide information to the user.

[0498] "Monitoring" is the process of continuously observing and collecting data on a patient's condition and behavior.

[0499] "Data collection" is the act of gathering information about a patient's condition or behavior.

[0500] A "preliminary examination" is an early stage examination conducted with the aim of early detection of disease.

[0501] "Early warning signs" is a technology for early detection of signs of illness or abnormalities.

[0502] A "treatment plan" is a plan for determining the optimal treatment method depending on the patient's condition.

[0503] "Correcting lifestyle habits" refers to the act of improving daily habits such as diet, exercise, and sleep in order to maintain and improve health.

[0504] "Cognitive training" refers to exercises or activities designed to improve cognitive functions such as memory and attention.

[0505] "Product and service suggestions" refers to the act of recommending optimal products and services based on the user's needs and situation.

[0506] The following system configuration will be described as an embodiment of the present invention.

[0507] System Configuration

[0508] The system consists of a generative AI, an emotion engine, smart glasses, a monitoring device, a data collection device, a preliminary examination device, a symptom detection device, and a treatment plan generation device.

[0509] Program processing

[0510] The server utilizes generative AI to make appropriate recommendations for treatment, exercise, diet, and other lifestyle changes, as well as cognitive training for mild cognitive impairment (MCI). The generative AI receives information on the patient's cognitive function and lifestyle habits as input, and generates optimal treatment plans and lifestyle improvement recommendations based on that information.

[0511] The emotion engine analyzes the user's emotions from facial expressions, tone of voice, text input, etc., and feeds the results back to the generative AI, which then suggests treatment, exercise, dietary and other lifestyle changes, as well as cognitive function training, based on the user's emotional state.

[0512] The smart glasses analyze the user's facial expressions and tone of voice in real time and send the data to the emotion engine. The emotion engine analyzes the data and feeds it back to the generative AI. The generative AI then generates product and service suggestions based on the user's emotional state and displays them on the smart glasses' display.

[0513] Hardware and software used

[0514] Hardware: Smart glasses (camera, microphone, display), monitoring device, data collection device, preliminary examination device, symptom detection device

[0515] Software: OpenCV (image processing), EmotionRecognizer (emotion analysis), AIGenerator (generative AI)

[0516] Specific examples

[0517] For example, if a user is feeling stressed, the emotion engine analyzes that information and feeds it back to the generative AI, which then generates suggestions for relaxation products (e.g., aroma oils) or services (e.g., massages) and displays them on the smart glasses' display.

[0518] Example prompts to input to the generative AI model:

[0519] If the user is feeling stressed, suggest products or services that will help them relax.

[0520] In this way, it becomes possible to propose optimal treatments and lifestyle habits that take into account the patient's emotional state, improving patient satisfaction even during the shopping experience in a physical store.

[0521] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0522] Step 1:

[0523] The user puts on the smart glasses and starts the system. The camera and microphone in the smart glasses capture the user's facial expressions and voice in real time. The input is the user's facial expression data and voice data, which are then sent to the emotion engine.

[0524] Step 2:

[0525] The emotion engine analyzes the user's facial expression and voice data to recognize the user's emotional state. Specifically, it uses OpenCV for facial expression analysis and EmotionRecognizer for voice tone analysis. The input is facial expression data and voice data, and the output is the user's emotional state (e.g., stress, joy).

[0526] Step 3:

[0527] The emotional state recognized by the emotion engine is fed back to the generative AI. The input is the user's emotional state, and the generative AI generates a prompt sentence based on this information. An example of a prompt sentence is, "If the user is feeling stressed, please suggest products or services that have a relaxation effect."

[0528] Step 4:

[0529] Generative AI generates optimal suggestions based on the prompt. Specifically, it uses AI Generator to generate product and service suggestions based on the user's emotional state. The input is the prompt, and the output is the suggestion (e.g., aroma oil, massage service).

[0530] Step 5:

[0531] The suggestions generated by the generative AI are displayed on the smart glasses' display. The input is the suggestions, and the output is the information displayed on the smart glasses' display. The user can check the suggestions through the display.

[0532] Step 6:

[0533] The user selects an action based on the suggestions. For example, if the user wants to purchase a product with a relaxation effect, the purchase can be completed through the smart glasses. The input is the user's selection, and the output is the completion of the purchase.

[0534] In this way, the system can make optimal suggestions that take into account the user's emotional state, improving user satisfaction.

[0535] Example 2

[0536] Next, a description will be given of Example 2 of Form 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."

[0537] Conventional medical systems have difficulty collecting and analyzing patients' biometric and behavioral data in real time, resulting in low accuracy in preliminary examinations and early warning signs. Furthermore, early warning signs and treatment plans do not take into account the patient's emotional state, which often results in delayed early intervention. This increases the patient's health risks and creates the problem of not being able to provide optimal treatment.

[0538] 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.

[0539] In this invention, the server includes: a means for utilizing generative AI to appropriately recommend treatment, exercise, lifestyle modification (e.g., diet), and cognitive training for mild cognitive impairment (MCI); a means for monitoring patients and collecting data to conduct preliminary screening and detect signs, and propose treatment plans to the attending physician; a means for collecting the patient's biometric information and behavioral data in real time; a means for transmitting the collected data to the server using a secure communication protocol; a means for the server to analyze the data using a generative AI model and detect abnormal patterns and health risks; a means for conducting preliminary screening and detecting signs based on the analysis results of the generative AI model; a means for generating a treatment plan based on the results of the sign detection and notifying the attending physician; a means for monitoring the user's emotional state, analyzing the emotional data, and detecting certain patterns; and a means for suggesting early intervention based on the emotional state pattern. This enables the real-time collection and analysis of the patient's biometric information and behavioral data to improve the accuracy of preliminary screening and sign detection. Furthermore, sign detection and treatment plan proposals that take emotional state into account enable early intervention and reduce the patient's health risks.

[0540] "Generative AI" is an artificial intelligence technology that analyzes patients' biometric information and behavioral data to conduct preliminary examinations, detect signs, and propose treatment plans.

[0541] "Mild cognitive impairment (MCI)" is a medical concept that refers to a condition in which cognitive function is impaired beyond the normal range but does not significantly interfere with daily life.

[0542] "Treatment, exercise, diet, and other lifestyle changes" refers to medical procedures and lifestyle changes carried out to improve a patient's health.

[0543] "Cognitive training" refers to exercises or activities designed to improve cognitive functions such as memory and attention.

[0544] "Patient monitoring" refers to the continuous observation and recording of a patient's vital signs and behavioral data.

[0545] "Data collection" refers to the process of obtaining patient biometric and behavioral data.

[0546] A "preliminary examination" refers to a simple test conducted for the purpose of early detection of disease.

[0547] "Early detection" refers to the early detection of signs of disease or health risks.

[0548] "Treatment plan proposal" refers to proposing the optimal treatment method based on the patient's health condition.

[0549] "Biometric information" refers to data that indicates the patient's physical condition, such as heart rate and blood pressure.

[0550] "Behavioral data" refers to data that indicates the patient's daily behavior, such as the number of steps taken and the amount of activity.

[0551] "Real-time" means that data is processed immediately as it is generated.

[0552] A "secure communication protocol" refers to a communication protocol for securely sending and receiving data.

[0553] An "unusual pattern" refers to fluctuations or trends in data that fall outside the normal range.

[0554] "Health risk" refers to the possibility of developing a disease or health problem.

[0555] "Emotional state" refers to data that indicates the psychological state of a user.

[0556] "Emotion data" refers to data that expresses the user's emotional state in numerical or textual form.

[0557] "Early intervention" refers to preventative medical treatment that occurs before health risks increase.

[0558] This invention is a system that utilizes generative AI to collect and analyze patient biometric information and behavioral data in real time, and then performs preliminary examinations, detects signs, and proposes treatment plans. Specific embodiments of this system are described below.

[0559] 1. Program Generation

[0560] The server generates a program using generative AI. This program has the ability to collect and analyze patients' biometric and behavioral data in real time, and also incorporates algorithms for preliminary screening, symptom detection, and treatment plan proposals.

[0561] 2. Program Processing

[0562] The server processes and calculates data using the following hardware and software:

[0563] Hardware: Wearable devices for collecting patient biometric information (e.g., heart rate monitors, blood pressure monitors, activity monitors)

[0564] Software: Data collection and analysis platforms (e.g., AWS, Google Cloud), generative AI models (e.g., GPT-4)

[0565] The terminal collects biometric and behavioral data in real time from the wearable device worn by the patient. For example, it periodically acquires data such as heart rate, blood pressure, and number of steps. The collected data is sent to a server using a secure communication protocol (e.g., HTTPS). The server stores the received data in a database.

[0566] The server inputs the stored data into a generative AI model. The generative AI model analyzes this data and detects abnormal patterns and health risks. For example, it detects abnormal fluctuations in heart rate or sudden increases in blood pressure. Based on the results of the generative AI model's analysis, the server performs preliminary screening and detects signs of illness. For example, it determines that abnormal fluctuations in heart rate are a sign of heart disease. The server then notifies the patient's doctor of this information and suggests early examination.

[0567] The server then generates a treatment plan based on the results of the sign detection. The generative AI model proposes an optimal treatment plan by referring to past data and medical guidelines. For example, if signs of heart disease are detected, the generative AI model will suggest lifestyle improvements and drug therapy. The attending physician can review these suggestions and make any necessary adjustments.

[0568] 3. Examples of concrete examples and prompts

[0569] As a concrete example, the following scenario can be considered.

[0570] Scenario: Patient A is wearing a wearable device. The device measures heart rate, blood pressure, and activity in real time and sends the data to a server. A generative AI model detects abnormal heart rate fluctuations and determines this to be a sign of heart disease. The server notifies the patient's doctor and suggests early examination.

[0571] Example prompt sentence:

[0572] Analyze Patient A's heart rate data to detect any abnormal fluctuations. If any abnormalities are detected, provide details and a recommended treatment plan.

[0573] Embodiment of Emotion Engine

[0574] The device uses an emotion engine to improve the accuracy of pre-screening and pre-sign detection by taking into account the user's emotional state. Specifically, if the user's emotional state shows a certain pattern, it can be used as an indicator for pre-screening and pre-sign detection. For example, if the device detects that the user has been feeling anxious or sad for a certain period of time, it can determine that this is a sign of cognitive decline. The server then uses this information to suggest early intervention.

[0575] Scenario: User B is using an app equipped with an emotion engine. The app records the user's emotional state daily and detects that anxiety or sadness persists over a period of time. The emotion engine interprets this as a sign of cognitive decline and suggests early counseling.

[0576] Example prompt sentence:

[0577] Analyze User B's emotional data to detect whether he has experienced persistent anxiety or sadness over a period of time, and if so, provide details and suggested interventions.

[0578] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0579] Step 1: Data collection

[0580] The terminal collects biological information and behavioral data in real time from wearable devices (e.g., heart rate monitors, blood pressure monitors, activity monitors) worn by the patient. Specifically, it periodically acquires data such as heart rate, blood pressure, and number of steps. The input is the biological information and behavioral data from the wearable devices, and the output is the collected data.

[0581] Step 2: Send data

[0582] The device sends the collected data to the server using a secure communication protocol (e.g., HTTPS). Specifically, the device sends heart rate and blood pressure data to the server at regular intervals. The input is the collected data, and the output is the data sent to the server.

[0583] Step 3: Save data

[0584] The server stores the received data in a database. Specifically, the server stores data such as heart rate, blood pressure, and number of steps in the database. The input is the data sent to the server, and the output is the data stored in the database.

[0585] Step 4: Data analysis

[0586] The server inputs the stored data into a generative AI model, which analyzes the data and detects abnormal patterns and health risks, such as abnormal heart rate fluctuations or sudden increases in blood pressure. The input is the data stored in the database, and the output is the analysis results.

[0587] Step 5: Preliminary screening and symptom detection

[0588] The server performs preliminary screening and sign detection based on the analysis results of the generative AI model. Specifically, it determines that abnormal fluctuations in heart rate are signs of heart disease. The input is the analysis results of the generative AI model, and the output is the results of the preliminary screening and sign detection.

[0589] Step 6: Propose a treatment plan

[0590] The server generates a treatment plan based on the results of the sign detection. The generative AI model proposes an optimal treatment plan by referring to past data and medical guidelines. Specifically, if signs of heart disease are detected, the generative AI model suggests lifestyle improvements and drug therapy. The input is the sign detection results, and the output is a proposed treatment plan.

[0591] Step 7: Monitoring your emotional state

[0592] The device monitors the user's emotional state. The emotion engine analyzes the user's emotional data (e.g., diary app, voice analysis) and detects certain patterns. Specifically, it detects when the user is feeling anxious or sad over a certain period of time. The input is the user's emotional data, and the output is a pattern of emotional states.

[0593] Step 8: Early intervention recommendations

[0594] The server suggests early intervention based on patterns of emotional states. Specifically, if the emotion engine detects signs of cognitive decline, it suggests early counseling. The input is patterns of emotional states, and the output is early intervention suggestions.

[0595] (Application example 2)

[0596] Next, a description will be given of Application Example 2 of Form 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."

[0597] Conventional medical systems have difficulty collecting patients' biometric and behavioral data in real time and detecting abnormalities early. Furthermore, there has been a lack of advances in the accuracy of preliminary examinations and symptom detection that take into account the patient's emotional state. This can lead to delays in timely treatment and intervention, putting patients at risk of worsening their health. Furthermore, there is a lack of a way to quickly notify personnel when an abnormality is detected.

[0598] 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.

[0599] In this invention, the server includes: means for utilizing generative AI to appropriately propose treatment, exercise, diet, and other lifestyle modification and cognitive function training for mild cognitive impairment (MCI); means for performing preliminary screening and symptom detection based on patient monitoring and data collection, and proposing treatment plans to the attending physician; means for improving the accuracy of preliminary screening and symptom detection by taking the user's emotional state into consideration using an emotion engine; means for collecting biometric information and behavioral data in real time using a smart device and detecting abnormalities; and means for generating an alert and notifying a responsible person when an abnormality is detected. This enables real-time monitoring of the patient's health condition, early detection of abnormalities, and prompt response.

[0600] "Generative AI" is an artificial intelligence technology that analyzes patients' biometric information and behavioral data to perform preliminary examinations, detect signs, and propose treatment plans.

[0601] "Mild cognitive impairment (MCI)" is a medical concept that refers to a condition in which cognitive function is impaired beyond the normal range but does not significantly interfere with daily life.

[0602] "Treatment, exercise, diet, and other lifestyle modification" is a general term for medical procedures, exercise programs, dietary advice, and other measures taken to improve a patient's health.

[0603] "Cognitive training" refers to exercises and activities aimed at improving cognitive functions such as memory, attention, and judgment.

[0604] "Patient monitoring" refers to the act of continuously observing and recording a patient's vital signs and behavioral data.

[0605] "Data collection" is the process of obtaining patient biometric and behavioral data using sensors and devices.

[0606] A "preliminary examination" is an early stage examination conducted with the aim of early detection and prevention of disease.

[0607] "Predictive detection" refers to analysis and diagnosis to detect early signs of disease or abnormalities.

[0608] "Proposing a treatment plan to the attending physician" refers to the act of the generative AI proposing optimal treatment methods and intervention measures to the physician based on the analysis results.

[0609] An "emotion engine" is a technology for detecting and analyzing a user's emotional state.

[0610] A "smart device" is an electronic device equipped with sensors and communication functions, and is capable of collecting and transmitting data.

[0611] "Biometric information" refers to data obtained from the human body, such as heart rate, body temperature, and blood pressure.

[0612] "Behavioral Data" refers to information about a user's movements and activities.

[0613] "Detecting anomalies" means discovering data or conditions that are outside the normal range.

[0614] "Generating an alert" is the act of creating a warning or notification when an anomaly is detected.

[0615] "Notifying the person in charge" refers to the act of conveying information to the person in charge when an abnormality occurs so that they can take appropriate action.

[0616] A system for implementing this invention is configured using generative AI, an emotion engine, and a smart device. Specific embodiments are described below.

[0617] System configuration

[0618] 1. Generative AI

[0619] The server uses generative AI to analyze the patient's biometric and behavioral data, perform preliminary screening, detect signs, and propose treatment plans. Based on the collected data, generative AI detects abnormalities and suggests appropriate treatment methods and interventions.

[0620] 2. Emotion Engine

[0621] The server detects and analyzes the user's emotional state using an emotion engine, which takes the user's emotional state into account to improve the accuracy of preliminary screening and symptom detection.

[0622] 3. Smart Devices

[0623] The terminal collects biometric information and behavioral data in real time using smart devices. The smart devices are equipped with sensors that measure heart rate, body temperature, blood pressure, etc. They also collect behavioral data using cameras and accelerometers.

[0624] 4. Alert Generation and Notification

[0625] If an abnormality is detected, the server generates an alert and notifies the person in charge, enabling a prompt response.

[0626] Data processing and calculation

[0627] The server analyzes the collected biometric and behavioral data using generative AI and an emotion engine. Specifically, it processes the data and performs the following calculations:

[0628] Data collection: Collect biometric information such as heart rate, body temperature, and blood pressure from smart devices, and collect behavioral data using cameras and accelerometers.

[0629] Data analysis: Generative AI analyzes collected data and detects anomalies. An emotion engine analyzes the user's emotional state to improve the accuracy of preliminary screening and symptom detection.

[0630] Alert generation: When an abnormality is detected, an alert is generated and the responsible person is notified.

[0631] Specific examples

[0632] For example, if an employee experiences a sudden spike in heart rate while at work and simultaneously detects an anxious emotional state, the system will send an alert to security personnel, allowing for early detection of the anomaly and appropriate response.

[0633] Prompt Sentence Examples

[0634] Below is an example of a prompt sentence to input to a generative AI model.

[0635] Create a program that collects user biometric and behavioral data in real time and uses generative AI to detect anomalies. Also, add a function that uses an emotion engine to analyze the user's emotional state and send an alert if an anomaly is detected.

[0636] In this way, by combining generative AI, emotion engines, and smart devices, this invention makes it possible to monitor a patient's health condition in real time, detect abnormalities early, and respond quickly.

[0637] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0638] Step 1:

[0639] The terminal uses a smart device to collect the user's biometric information (heart rate, body temperature, blood pressure, etc.) in real time. The input is sensor data from the smart device, and the output is the collected biometric information. The terminal sends this data to a server.

[0640] Step 2:

[0641] The terminal collects user behavior data (walking, sitting, running, etc.) using the smart device's camera and acceleration sensor. The input is sensor data from the smart device, and the output is the collected behavior data. The terminal sends this data to the server.

[0642] Step 3:

[0643] The server analyzes the collected biometric and behavioral data using generative AI. The input is the biometric and behavioral data sent from the device, and the output is the analysis results. The server determines whether an abnormality has been detected.

[0644] Step 4:

[0645] The server uses an emotion engine to analyze the user's emotional state. The input is the user's behavioral data and biometric information, and the output is the analysis result of the emotional state. The server takes the emotional state into account to improve the accuracy of preliminary screening and symptom detection.

[0646] Step 5:

[0647] The server integrates the analysis results of the generative AI and the emotion engine, and generates an alert if an abnormality is detected. The input is the analysis results of the generative AI and the emotion engine, and the output is alert information.

[0648] Step 6:

[0649] The server notifies the person in charge of the generated alert. The input is the alert information, and the output is the notification to the person in charge. The server notifies quickly so that the person in charge can take appropriate action.

[0650] In this way, by clarifying the specific operations and data flow that take place at each step, it is possible to understand how the processing of the entire system is carried out.

[0651] Example 3

[0652] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0653] Patients with mild cognitive impairment (MCI) need appropriate recommendations for treatment, exercise, diet, and other lifestyle changes, as well as cognitive training. However, conventional systems have difficulty making optimal recommendations based on the patient's cognitive function status and lifestyle, and do not adjust treatment plans to take into account the patient's emotional state. As a result, effective treatment and lifestyle improvements tailored to each patient's individual condition have not been realized.

[0654] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0655] In this invention, the server includes: means for utilizing generative artificial intelligence to make appropriate suggestions for treatment, exercise, diet, and other lifestyle modification and cognitive training for mild cognitive impairment; means for monitoring the patient and collecting data to conduct preliminary screening and detect signs, and propose a treatment plan to the attending physician; means for the user to input information about the state of their cognitive function and lifestyle; means for the terminal to collect the data input by the user and transmit it to the server; means for the server to analyze the received data and use a generative artificial intelligence model to analyze the user's state of cognitive function and lifestyle patterns; means for the server to generate an optimal treatment plan and lifestyle improvement suggestions based on the analysis results; means for the server to transmit the generated suggestions to the terminal and display the suggestions to the user; and means for analyzing the user's emotional state using an emotion engine and adjusting the treatment plan. This enables the system to propose an optimal treatment plan and lifestyle improvement suggestions based on the patient's cognitive function, lifestyle, and emotional state.

[0656] "Generative AI" is an AI technology that generates optimal proposals and plans based on input data.

[0657] "Mild cognitive impairment" refers to a condition in which cognitive function is impaired beyond the normal range, but does not significantly interfere with daily life.

[0658] A "treatment plan" is a plan that includes a series of medical interventions and lifestyle changes designed to improve a patient's health.

[0659] "Correcting lifestyle habits" refers to improving daily habits such as diet, exercise, and sleep in order to maintain and improve health.

[0660] "Cognitive training" refers to exercises and activities designed to improve cognitive functions such as memory, attention, and problem-solving skills.

[0661] "Monitoring" refers to the continuous observation and recording of a patient's health condition and lifestyle habits.

[0662] "Data collection" refers to the process of gathering information obtained from patients.

[0663] "Preliminary screening" refers to an early stage examination conducted for the early detection and prevention of disease.

[0664] "Early warning signs" refers to technologies and methods for early detection of signs of disease or abnormalities.

[0665] An "emotion engine" is a technology that analyzes a user's emotional state and generates appropriate suggestions and plans based on that.

[0666] "Terminal" refers to an electronic device that a user uses to enter information or receive suggestions.

[0667] "Server" refers to a central processing unit for analyzing data and generating recommendations.

[0668] This invention is a system that utilizes generative artificial intelligence to provide appropriate suggestions for lifestyle modification such as treatment, exercise, and diet, as well as cognitive function training, to patients with mild cognitive impairment (MCI). This system includes functions such as patient monitoring and data collection, preliminary screening and symptom detection, and proposing treatment plans to the attending physician.

[0669] Hardware and software used

[0670] Devices: Electronic devices such as smartphones, tablets, and computers

[0671] Server: A central processing unit that analyzes data and generates proposals

[0672] Generative artificial intelligence models: For example, OpenAI's GPT-4

[0673] Emotion engines: for example, Affectiva's emotion recognition software

[0674] Data entry and collection

[0675] Users use devices such as smartphones and tablets to input information about their cognitive function and lifestyle habits. Specifically, they input information about their recent memory and concentration levels, dietary habits, exercise habits, and emotional state into the application. For example, a user might input, "I feel like my memory has been declining recently. My diet is unbalanced and I hardly exercise at all. Please suggest the best treatment plan and lifestyle habits to improve this condition."

[0676] Data transmission and analysis

[0677] The device collects data entered by the user and sends it to a server via the Internet. The data is encrypted before transmission to ensure security. The server analyzes the received data. Specifically, it uses a generative artificial intelligence model to analyze the state of the user's cognitive function and lifestyle patterns. It also uses an emotion engine to analyze the user's emotional state.

[0678] Proposal generation and delivery

[0679] Based on the analysis results, the server generates optimal treatment plans and lifestyle improvement suggestions. The generative AI model generates specific suggestions based on the user's cognitive function state and lifestyle. For example, it might suggest a "memory improvement training program" and a "balanced meal plan." The emotion engine takes the user's emotional state into account and adjusts the treatment plan. For example, if the user has recently felt joy, this can be used as an indicator of cognitive function improvement, and the progress of the treatment plan can be adjusted.

[0680] View Suggestions

[0681] The server sends the generated suggestions to the device. The device displays the suggestions received from the server to the user. The user can review and implement the suggestions. For example, the user opens the app and checks "Training program to improve memory," "Balanced meal plan," and "Yoga for relaxation." The user can see specific steps to implement the suggestions within the app.

[0682] The above is a specific embodiment of the system of the present invention. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0683] Step 1:

[0684] The user enters the data.

[0685] Users use devices such as smartphones and tablets to input information about their cognitive function status and lifestyle habits. Specifically, they input information such as recent memory and concentration status, dietary habits, exercise habits, and emotional state into the application. The input data includes details of the user's cognitive function status and lifestyle habits.

[0686] Step 2:

[0687] The device sends the data to the server.

[0688] The device collects data entered by the user and sends it to a server via the Internet. The data is encrypted before transmission to ensure security. The input is data on the state of cognitive function and lifestyle habits entered by the user, and the output is the encrypted data sent to the server.

[0689] Step 3:

[0690] The server analyzes the data.

[0691] The server analyzes the received data. Specifically, it uses a generative artificial intelligence model to analyze the user's cognitive function and lifestyle patterns. It also uses an emotion engine to analyze the user's emotional state. The input is encrypted data sent from the device, and the output is the analysis results.

[0692] Step 4:

[0693] The server generates the proposal.

[0694] Based on the analysis results, the server generates optimal treatment plans and lifestyle improvement suggestions. A generative AI model generates specific suggestions based on the user's cognitive function state and lifestyle. For example, it suggests a "training program to improve memory" and a "balanced meal plan." An emotion engine takes the user's emotional state into account and adjusts the treatment plan. The input is the analysis results, and the output is the generated suggestions.

[0695] Step 5:

[0696] The server sends the proposal to the device.

[0697] The server sends the generated proposal to the terminal. The proposal is encrypted and transmitted to ensure security. The input is the generated proposal, and the output is the encrypted proposal sent to the terminal.

[0698] Step 6:

[0699] The terminal displays the suggestions to the user.

[0700] The device displays the suggestions received from the server to the user. The user can review and implement the suggestions. For example, the user opens the app and reviews "Training program to improve memory," "Balanced meal plan," and "Yoga for relaxation." The input is the encrypted suggestion sent from the server, and the output is the suggestion displayed to the user.

[0701] (Application example 3)

[0702] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0703] While conventional cognitive function improvement systems can make suggestions based on the patient's cognitive function and lifestyle habits, they lack support for proposing treatment plans that take the user's emotional state into account and for increasing motivation. Therefore, there is a need for systems that can suggest optimal fitness programs and meal plans based on the user's emotional state, as well as provide messages and music to increase motivation.

[0704] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0705] In this invention, the server includes means for utilizing generative AI to make appropriate suggestions for treatment, exercise, dietary and other lifestyle modification and cognitive function training for mild cognitive impairment (MCI), means for monitoring patients and collecting data to conduct preliminary examinations and detect signs, and propose treatment plans to attending physicians, means for evaluating the user's emotional state and, based on the results, proposing optimal fitness programs and meal plans, and means for providing motivational messages and music according to the user's emotional state. This makes it possible to propose optimal treatment plans and lifestyle improvements that take the user's emotional state into consideration.

[0706] "Generative AI" is an artificial intelligence technology that generates new information and suggestions based on data.

[0707] "Mild cognitive impairment (MCI)" refers to a condition in which cognitive function declines beyond the normal range but does not significantly interfere with daily life.

[0708] "Correcting lifestyle habits" refers to improving lifestyle habits such as diet, exercise, and sleep in order to maintain and improve health.

[0709] "Cognitive training" refers to exercises and activities aimed at improving cognitive functions such as memory, attention, and judgment.

[0710] "Monitoring" refers to the continuous observation and recording of a patient's health condition and behavior.

[0711] "Data collection" refers to collecting information about a patient's health and lifestyle.

[0712] "Preliminary examination" refers to an early stage examination conducted for the purpose of early detection and prevention of disease.

[0713] "Early detection" refers to the early detection of signs of illness or abnormalities.

[0714] A "treatment plan" refers to formulating the optimal treatment method and schedule based on the patient's health condition.

[0715] "Emotional state" refers to emotional states such as joy, sadness, anger, etc.

[0716] A "fitness program" refers to an exercise plan aimed at improving physical fitness and maintaining health.

[0717] "Meal plan" refers to a meal plan and schedule designed to maintain or improve health.

[0718] "Motivational messages" refer to words of encouragement and support that motivate and motivate users.

[0719] "Music" refers to a combination of sounds that affect emotions and moods through the sense of hearing.

[0720] A system for implementing this invention utilizes generative AI to provide appropriate suggestions for treatment, exercise, dietary and other lifestyle modification, and cognitive training for mild cognitive impairment (MCI). It also includes a function to assess the user's emotional state, suggest optimal fitness programs and meal plans based on that, and provide motivational messages and music.

[0721] System configuration

[0722] The system consists of the following major components:

[0723] 1. Generative AI:

[0724] Generate optimal treatment plans and lifestyle improvement suggestions based on the patient's cognitive function and lifestyle habits.

[0725] Software used: Python, generative AI model (e.g., GPT-3 (registered trademark))

[0726] 2. Emotion Engine:

[0727] It assesses the user's emotional state and adjusts treatment plans, fitness programs, and meal plans accordingly.

[0728] Software used: Emotion recognition API (e.g., Microsoft® Azure® Emotion API)

[0729] 3. Monitoring System:

[0730] Continuously observe and record the patient's health and behavior.

[0731] Hardware used: Smartphones, wearable devices

[0732] 4. Data Collection System:

[0733] Collect information about the patient's health and lifestyle.

[0734] Hardware used: Smartphones, wearable devices

[0735] 5. User Interface:

[0736] It provides an interface for the user to interact with the system.

[0737] Hardware used: Smartphone, tablet

[0738] Processing flow

[0739] 1. Data Collection:

[0740] Users input data about their cognitive function, lifestyle habits, and emotional state via smartphones or wearable devices.

[0741] 2. Data Analysis:

[0742] The server analyzes the collected data, and the generative AI generates optimal treatment plans and suggestions for improving lifestyle habits.

[0743] 3. Emotional assessment:

[0744] An emotional engine assesses the user's emotional state and adjusts suggestions accordingly.

[0745] 4. Providing Proposals:

[0746] It provides users with fitness programs, meal plans, motivational messages and music tailored to their smartphone.

[0747] Specific examples

[0748] For example, a user visits a fitness gym and launches the app. The app evaluates the user's cognitive function and emotional state and suggests an optimal training plan. The user follows the suggestions and the app displays motivational messages.

[0749] Prompt Sentence Examples

[0750] The user's cognitive state is 80 and their emotional state is 90. Suggest an optimal fitness program and meal plan.

[0751] In this way, it becomes possible to propose optimal treatment plans and lifestyle improvements that take into account the user's emotional state.

[0752] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0753] Step 1:

[0754] Users input data about their cognitive function, lifestyle habits, and emotional state via smartphones or wearable devices.

[0755] Input: Cognitive function test results, lifestyle data (diet, exercise, sleep, etc.), emotional state data

[0756] Output: The entered data is sent to the server.

[0757] What it does: The user launches a smartphone app and completes a cognitive function test. The wearable device records their heart rate and activity, and answers questions to assess their emotional state.

[0758] Step 2:

[0759] The server analyzes the collected data, and the generative AI generates optimal treatment plans and suggestions for improving lifestyle habits.

[0760] Input: Cognitive function data, lifestyle data, and emotional state data submitted by the user

[0761] Output: Optimal treatment plan and lifestyle improvement suggestions

[0762] What it does: The server analyzes the data using Python and uses a generative AI model (e.g., GPT-3) to generate optimal treatment plans and lifestyle improvement suggestions.

[0763] Step 3:

[0764] An emotional engine assesses the user's emotional state and adjusts suggestions accordingly.

[0765] Input: User emotional state data, suggestions generated by generative AI

[0766] Output: Recommendations tailored to emotional state

[0767] What it does: The server uses an emotion recognition API (e.g., Microsoft Azure Emotion API) to assess the user's emotional state and adjust the generated suggestions.

[0768] Step 4:

[0769] It provides users with fitness programs, meal plans, motivational messages and music tailored to their smartphone.

[0770] Input: Adjusted proposal

[0771] Output: Fitness programs, meal plans, messages, music displayed on the user's smartphone

[0772] How it works: The server sends tailored recommendations to the user's smartphone, where they are displayed by the app. The user then follows the recommendations to train and eat, and receives motivational messages and music.

[0773] In this way, it becomes possible to propose optimal treatment plans and lifestyle improvements that take into account the user's emotional state.

[0774] 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.

[0775] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) 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.

[0776] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0777] 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.

[0778] [Second embodiment]

[0779] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0780] 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.

[0781] 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).

[0782] 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.

[0783] 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.

[0784] 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).

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0791] "Example 1"

[0792] One embodiment of the present invention provides a system utilizing generative AI. This system provides appropriate recommendations for treatment, exercise, diet, and other lifestyle modifications, as well as cognitive training, to patients with mild cognitive impairment (MCI). Specifically, the generative AI receives input information on the patient's cognitive function and lifestyle, and generates optimal treatment plans and lifestyle improvement recommendations based on that information.

[0793] "Example 2"

[0794] Furthermore, the system of the present invention monitors patients and collects data to perform preliminary examinations, detect signs, and propose treatment plans to the attending physician. Specifically, the generative AI collects patients' biological information and behavioral data in real time and analyzes this data to perform preliminary examinations and detect signs. It also proposes treatment plans to the attending physician based on this information.

[0795] "Example 3"

[0796] In the system of the present invention, a generative AI optimizes proposals for treatment, exercise, diet, and other lifestyle modification and cognitive training based on the patient's cognitive function status. Specifically, the generative AI receives information on the patient's cognitive function status and lifestyle as input, and generates optimal treatment plans and lifestyle improvement proposals based on that information.

[0797] The processing flow of each embodiment will be described below.

[0798] "Example 1"

[0799] Step 1: The generative AI receives information from the patient about their lifestyle and cognitive function status as input.

[0800] Step 2: Based on the information it receives, the generative AI generates suggestions for treatment, exercise, diet, and other lifestyle changes, as well as cognitive function training.

[0801] Step 3: The generative AI presents the generated suggestions to the patient.

[0802] "Example 2"

[0803] Step 1: The generative AI collects the patient's biometric information and behavioral data in real time. Step 2: The generative AI analyzes the collected data and performs preliminary examinations and detects signs.

[0804] Step 3: Based on the analysis results, the generative AI proposes a treatment plan to the attending physician.

[0805] "Example 3"

[0806] Step 1: The generative AI receives information such as the patient's cognitive function and lifestyle as input.

[0807] Step 2: Based on the information it receives, the generative AI optimizes suggestions for treatment, exercise, diet, and other lifestyle changes, as well as cognitive function training.

[0808] Step 3: The generative AI presents optimized recommendations to the patient.

[0809] Example 1

[0810] Next, a description will be given of Example 1 of Form 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."

[0811] There is a need to efficiently provide personalized treatment plans and lifestyle improvement suggestions to patients with mild cognitive impairment (MCI). However, conventional systems have had issues with difficulty in generating optimal suggestions based on the patient's cognitive function status and lifestyle, and with the time-consuming task of collecting and processing data. Furthermore, it is often not possible to provide generated suggestions to patients quickly and accurately.

[0812] 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.

[0813] In this invention, the server includes means for inputting information on the patient's cognitive function status and lifestyle habits using a terminal, means for receiving the data transmitted from the terminal and storing it in a database, means for preprocessing the data and generating prompt sentences to be input to the generative AI model, means for the generative AI model to generate an optimal treatment plan and lifestyle improvement suggestions, and means for transmitting the generated suggestions to the terminal, which then displays the suggestions to the user. This makes it possible to efficiently generate personalized suggestions based on the patient's cognitive function status and lifestyle habits and provide them quickly and accurately.

[0814] "Generative AI" is a type of artificial intelligence that generates optimal proposals and plans based on input data.

[0815] "Mild cognitive impairment (MCI)" is a medical term that refers to a condition in which cognitive function is impaired beyond the normal range but does not significantly interfere with daily life.

[0816] A "device" is an electronic device (e.g., PC, tablet, smartphone) that a user uses to enter information or view generated suggestions.

[0817] A "server" is a computer system that receives, stores, and processes data sent from a terminal.

[0818] A "database" is a system for systematically storing and managing data received by a server.

[0819] A "prompt" is a document to be input into a generative AI model, and contains information about the patient's cognitive function and lifestyle habits.

[0820] "Suggestions" refer to treatment plans and lifestyle improvement suggestions generated by generative AI models.

[0821] "Preprocessing" refers to processes such as formatting data and filling in missing values ​​before inputting the data into a generative AI model.

[0822] This invention is a system that utilizes generative AI to provide appropriate suggestions for lifestyle modification such as treatment, exercise, and diet, as well as cognitive function training, to patients with mild cognitive impairment (MCI). This system is implemented using a terminal, a server, and a generative AI model.

[0823] 1. Data Collection

[0824] The user uses a device (PC, tablet, smartphone, etc.) to input information about the patient's cognitive function status and lifestyle habits. Specific input items include cognitive function test results, daily activity level, dietary habits, and sleep patterns. For example, the user opens the smartphone app and inputs the following information:

[0825] Cognitive function test results: MMSE score 24 points

[0826] Daily activity level: 5,000 steps per day

[0827] Diet: Lack of vegetables, high in fat

[0828] Sleep pattern: 6 hours a day

[0829] 2. Data transmission and storage

[0830] The terminal encrypts the entered data and sends it to the server. The server receives the data sent from the terminal and temporarily stores it in memory. It then saves it in a database (e.g., MySQL, PostgreSQL). When saving, the data integrity is checked to see if it contains any invalid data.

[0831] 3. Data Processing

[0832] The server retrieves the necessary data from the database and performs preprocessing to input it into the generative AI model. Specifically, it normalizes the data and fills in missing values. Next, it generates a prompt sentence based on the preprocessed data, such as:

[0833] The patient's cognitive function test results show an MMSE score of 24. Their daily activity level is 5000 steps per day, and their diet is high in fat and lacks vegetables. Their sleep pattern is 6 hours per day. Generate an optimal treatment plan and lifestyle modification suggestions for this patient.

[0834] 4. Proposal Generation

[0835] The server calls a generative AI model (e.g., OpenAI's GPT-4) and inputs the generated prompt. The generative AI model generates optimal treatment plans and lifestyle improvement suggestions based on the input prompt. For example, the following suggestions may be generated:

[0836] Cognitive training: 30 minutes of puzzle games every day

[0837] Exercise program: Walking three times a week (30 minutes each time)

[0838] Meal plan: Balanced meals with lots of vegetables

[0839] Improved sleep: Go to bed at the same time every night and get 7 hours of sleep

[0840] 5. Providing Proposals

[0841] The server sends the generated proposal to the terminal, and the terminal displays the proposal received from the server to the user, who can confirm and execute the proposal through the terminal.

[0842] In this way, the system can provide MCI patients with personalized treatment plans and lifestyle modification suggestions.

[0843] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0844] Step 1:

[0845] The user uses the device to input information about the patient's cognitive function status and lifestyle habits. Specific input items include cognitive function test results, daily activity level, dietary habits, and sleep patterns. For example, the user opens the smartphone app and inputs an MMSE score of 24, 5,000 steps per day, a diet low in vegetables and high in fat, and a sleep pattern of six hours per day. The input data is temporarily stored on the device.

[0846] Step 2:

[0847] The device encrypts the input data and sends it to the server. The data includes the results of cognitive function tests, daily activity levels, dietary habits, and sleep patterns. The server receives the data and temporarily stores it in its memory.

[0848] Step 3:

[0849] The server stores the received data in a database. When storing the data, it checks the data for consistency and whether it contains any invalid data. The database stores information on the patient's cognitive function status and lifestyle habits in an organized manner.

[0850] Step 4:

[0851] The server retrieves the necessary data from the database and performs preprocessing to input it into the generative AI model. Preprocessing includes normalizing the data and imputing missing values. For example, if there is missing data, it is imputed with the mean or median. The preprocessed data is converted into prompt statements to be input into the generative AI model.

[0852] Step 5:

[0853] The server generates prompts based on the preprocessed data. The generated prompts include information about the patient's cognitive function and lifestyle. For example, the following prompts are generated:

[0854] The patient's cognitive function test results show an MMSE score of 24. Their daily activity level is 5000 steps per day, and their diet is high in fat and lacks vegetables. Their sleep pattern is 6 hours per day. Generate an optimal treatment plan and lifestyle modification suggestions for this patient.

[0855] Step 6:

[0856] The server calls the generative AI model and inputs the generated prompt. The generative AI model generates optimal treatment plans and lifestyle improvement suggestions based on the input prompt. For example, the following suggestions may be generated:

[0857] Cognitive training: 30 minutes of puzzle games every day

[0858] Exercise program: Walking three times a week (30 minutes each time)

[0859] Meal plan: Balanced meals with lots of vegetables

[0860] Improved sleep: Go to bed at the same time every night and get 7 hours of sleep

[0861] Step 7:

[0862] The server sends the generated suggestions to the device. The suggestions include personalized treatment plans and lifestyle improvement suggestions. The device displays the suggestions received from the server to the user. The user can confirm and implement the suggestions through the device.

[0863] (Application example 1)

[0864] Next, a description will be given of Application Example 1 of Form 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."

[0865] It is important to provide appropriate treatment and lifestyle improvement suggestions to patients with mild cognitive impairment (MCI), but current systems make it difficult to provide optimal suggestions tailored to each patient's individual condition. Furthermore, there is a lack of a way for patients to easily input their condition and receive appropriate suggestions in physical stores, which could delay treatment and lifestyle improvement.

[0866] 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.

[0867] In this invention, the server includes: means for utilizing generative AI to make appropriate suggestions for treatment, exercise, diet, and other lifestyle modification and cognitive function training for mild cognitive impairment (MCI); means for monitoring patients and collecting data to conduct preliminary examinations and detect signs, and propose treatment plans to their physicians; and means for patients to use their smartphones at physical stores to input information about their cognitive function status and lifestyle habits, and for the generative AI to provide optimal treatment plans and lifestyle improvement suggestions based on that information. This enables patients to easily input their status at physical stores and receive optimal treatment and lifestyle improvement suggestions tailored to their individual conditions.

[0868] "Generative AI" is an artificial intelligence technology that generates new information and suggestions based on input data.

[0869] "Mild cognitive impairment (MCI)" is a medical term that refers to a condition in which cognitive function is impaired beyond the normal range but does not significantly interfere with daily life.

[0870] "Treatment" refers to medical procedures or measures to cure an illness or disability.

[0871] "Exercise" refers to activities that maintain and improve health by moving the body.

[0872] "Meal" refers to the act of eating to obtain nutrition and the contents of that eating.

[0873] "Lifestyle habits" refer to the actions and habits of daily life, and are factors that affect health.

[0874] "Cognitive function" refers to intellectual functions such as memory, attention, language, and judgment.

[0875] "Training" refers to exercises or practice aimed at improving a particular skill or ability.

[0876] "Monitoring" refers to the continuous observation and recording of a particular condition or activity.

[0877] "Data collection" refers to the act of gathering necessary information.

[0878] "Preliminary screening" refers to an early stage examination aimed at early detection of disease or disability.

[0879] "Early detection" refers to the early detection of signs of illness or disability.

[0880] A "treatment plan" refers to a plan that outlines specific steps and methods for curing a patient's illness or disorder.

[0881] A "physical store" refers to a store that exists in a physical location and where customers can visit in person to purchase products or services.

[0882] A "smartphone" refers to a high-function mobile phone that can connect to the Internet and use applications.

[0883] A system for implementing this invention utilizes generative AI to provide patients with mild cognitive impairment (MCI) with appropriate recommendations for treatment, exercise, diet, and other lifestyle modifications, as well as cognitive training. The system includes means for monitoring patients and collecting data, conducting preliminary screenings and detecting signs, and proposing treatment plans to their physicians. The system also includes means for allowing patients to input information about their cognitive function status and lifestyle habits using their smartphones in a physical store, and for the generative AI to provide optimal treatment plans and lifestyle improvement recommendations based on that information.

[0884] Explanation of program processing

[0885] The server uses a generative AI model to receive input about the patient's cognitive function and lifestyle habits. Specifically, the patient enters their information using a smartphone. The entered information is sent to the server and analyzed by the generative AI model. Based on the analysis results, an optimal treatment plan and lifestyle improvement suggestions are generated.

[0886] The hardware used includes mobile devices such as smartphones and tablets, and the software uses Python and OpenAI's API. The generative AI model uses OpenAI's "text-davinci-003" engine.

[0887] Specific examples

[0888] For example, consider a 65-year-old male with mild cognitive impairment, who is physically inactive and has a poor diet. He enters the following information on his smartphone at a brick-and-mortar pharmacy:

[0889] Age: 65

[0890] Gender: Male

[0891] Cognitive status: Mild cognitive impairment

[0892] Lifestyle: Lack of exercise, unbalanced diet

[0893] This information is sent to a server, where the generative AI model analyzes it, and the resulting recommendation is to "walk 30 minutes a day and take a supplement containing B vitamins."

[0894] An example of a prompt to input to a generative AI model is as follows:

[0895] Patient information: Age: 65, Gender: Male, Cognitive status: Mild cognitive impairment, Lifestyle: Lack of exercise, poor diet

[0896] Generate optimal treatment plans and lifestyle modification suggestions.

[0897] In this way, patients can easily enter their own condition at a physical store and receive the most appropriate treatment and lifestyle improvement suggestions based on their individual condition.

[0898] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0899] Step 1:

[0900] Users use their smartphones to input information about their cognitive function status and lifestyle habits. The input information includes age, gender, cognitive function status, and lifestyle habits (lack of exercise, unbalanced diet, etc.). The input data is sent to a server via a smartphone application.

[0901] Input: Age, gender, cognitive function status, lifestyle habits

[0902] Output: Patient information sent to the server

[0903] Step 2:

[0904] The server analyzes the received patient information and generates prompts for the generative AI model. The prompts are instructions for generating optimal treatment plans and lifestyle improvement suggestions based on the patient information.

[0905] Input: Patient information sent to the server

[0906] Output: The prompt to send to the generative AI model

[0907] Step 3:

[0908] The server sends the generated prompts to the generative AI model, which analyzes the prompts and generates optimal treatment plans and lifestyle improvement suggestions based on the patient's condition.

[0909] Input: A prompt to send to the generative AI model

[0910] Output: Suggested results from the generative AI model

[0911] Step 4:

[0912] The server analyzes the proposal results received from the generative AI model and converts them into a format that is easy for the user to understand. Specifically, it converts the proposal results into text format and sends them to the smartphone application.

[0913] Input: Suggested results from a generative AI model

[0914] Output: Text representation of the proposal results to be sent to the user

[0915] Step 5:

[0916] The user can then use the smartphone application to check the proposed results sent from the server, which include specific treatment plans and lifestyle improvement suggestions.

[0917] Input: Text format of the proposal results sent from the server

[0918] Output: Suggested results for user review

[0919] In this way, users can input information about their cognitive function status and lifestyle habits, and receive optimal treatment plans and lifestyle improvement suggestions from the generative AI model.

[0920] Example 2

[0921] Next, a description will be given of Example 2 of Form 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."

[0922] Conventional medical systems have difficulty collecting and analyzing patients' vital signs and behavioral data in real time. Furthermore, they often lack accuracy and speed when it comes to detecting early signs and proposing treatment plans. This makes it difficult to provide treatment and prevention at the appropriate time, and makes it difficult to optimally manage patients' health conditions.

[0923] 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.

[0924] In this invention, the server includes means for the terminal to collect biometric information and behavioral data of the user in real time, means for the terminal to transmit the collected data to the server, means for the server to store the received data in a database, means for the server to analyze the stored data using a generative AI model, means for the server to detect signs based on the results of the data analysis, means for the server to propose a treatment plan to the attending physician based on the results of the sign detection, and means for notifying the attending physician of the proposed treatment plan generated by the server. This enables the patient's biometric information and behavioral data to be collected and analyzed in real time, enabling rapid and accurate sign detection and treatment plan proposals.

[0925] "Generative AI" is a system that uses artificial intelligence technology to generate data and perform analysis and predictions.

[0926] "Mild cognitive impairment" refers to a condition in which cognitive function is impaired beyond the normal range, but does not significantly interfere with daily life.

[0927] "Monitoring" refers to the continuous observation and recording of a patient's vital signs and behavioral data.

[0928] "Data collection" refers to obtaining a patient's biometric information and behavioral data using devices and sensors.

[0929] A "preliminary screening" refers to an examination to assess a patient's health status in advance and detect potential health problems early.

[0930] "Predictive detection" refers to analyzing collected data to predict potential future health problems.

[0931] A "treatment plan" refers to a plan that suggests optimal treatment and preventative measures based on the patient's health condition.

[0932] "Terminal" refers to a device used to collect biometric and behavioral data from a user. Examples include wearable devices and smartphones.

[0933] "Server" refers to the computer system that stores collected data and analyzes it using generative AI.

[0934] "Database" refers to a system for organizing and storing collected data.

[0935] "Generative AI model" refers to an artificial intelligence model used to analyze collected data and make predictions or recommendations.

[0936] "Notification" refers to the means of communicating the generated treatment plan and the results of predictive detection to the attending physician.

[0937] This invention is a system that collects and analyzes a patient's biological information and behavioral data in real time, performs a preliminary examination and detects signs of illness, and proposes a treatment plan to the attending physician. Specific embodiments of this system are described below.

[0938] System configuration

[0939] Hardware Configuration

[0940] 1. Terminal: A device used to collect biometric and behavioral data from a user. Examples include wearable devices (e.g., smartwatches) and smartphones.

[0941] 2. Server: A computer system that stores collected data and analyzes it using generative AI models. The server has a powerful processor and large storage capacity.

[0942] 3. Database: A system for organizing and storing collected data. Specific examples include relational databases such as MySQL and PostgreSQL.

[0943] Software Configuration

[0944] 1. Generative AI model: An artificial intelligence model used to analyze collected data and make predictions or recommendations. Examples include GPT-4 and BERT.

[0945] 2. Data collection application: Software that collects data from devices and sends it to a server. Often implemented as a smartphone app.

[0946] 3. Data analysis software: Software that runs on a server and analyzes data using generative AI models.

[0947] System Operation

[0948] Data collection

[0949] The device (e.g., a smartwatch) collects real-time biometric and behavioral data, such as the user's heart rate, blood pressure, body temperature, number of steps taken, and sleep patterns, and transmits this data to a smartphone via Bluetooth or Wi-Fi.

[0950] Data transmission

[0951] The smartphone app sends the collected data to a server over the internet, where it is encrypted and transmitted using a secure communication protocol (e.g., HTTPS).

[0952] Data storage

[0953] The server stores the received data in a database, organized by user and timestamp.

[0954] Data analysis

[0955] The server analyzes the stored data using a generative AI model (e.g., GPT-4), which detects anomalous patterns based on the collected data and performs predictive detection.

[0956] Predictive detection

[0957] The server uses the results of the data analysis to predict potential future health problems, for example, if abnormal fluctuations in heart rate are detected, it may be a sign of heart disease.

[0958] Treatment plan proposal

[0959] The server uses the generative AI model to propose treatment plans to the patient's doctor based on the results of the symptom detection. For example, if symptoms of heart disease are detected, the server will suggest a consultation with a cardiologist.

[0960] Proposal Notification

[0961] The server then notifies the doctor of the proposed treatment plan via email or a dedicated app.

[0962] Specific examples

[0963] Example 1: Heart rate abnormality detection

[0964] Suppose a user is wearing a smartwatch. The smartwatch measures the user's heart rate every minute and sends it to a server via a smartphone app. The server stores the received heart rate data in a database and analyzes it using a generative AI model. As a result of the analysis, it detects that the heart rate is abnormally high, which may be a sign of heart disease. Based on this information, the server recommends that the user undergo a heart disease examination with their doctor.

[0965] Prompt Sentence Examples

[0966] "Analyze the user's heart rate data and detect any abnormal fluctuations. If abnormalities are detected, suggest possible diseases and recommended treatments."

[0967] In this way, a system is realized in which the server, terminal, and user work together to monitor the patient's health condition in real time and propose an appropriate treatment plan.

[0968] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0969] Step 1: Data collection

[0970] A device (e.g., a smartwatch) collects a user's biometric information (heart rate, blood pressure, body temperature, etc.) and behavioral data (number of steps, sleep patterns, etc.) in real time. Specifically, a smartwatch measures the heart rate and counts the number of steps every minute. The input is the user's biometric information and behavioral data, and the output is the collected data.

[0971] Step 2: Send data

[0972] The device sends the collected data to a server via a smartphone app. Specifically, the smartwatch sends data to the smartphone using Bluetooth, and the smartphone app sends the data to the server via the Internet. The input is the collected data, and the output is the data sent to the server.

[0973] Step 3: Save data

[0974] The server stores the received data in a database. Specifically, the server adds new data to the database whenever it arrives. The input is the data sent to the server, and the output is the data stored in the database.

[0975] Step 4: Data analysis

[0976] The server analyzes the stored data using a generative AI model (e.g., GPT-4). Specifically, the server analyzes the data at a fixed time each day to detect abnormal patterns. The input is the data stored in the database, and the output is the analysis results.

[0977] Step 5: Predictive detection

[0978] The server performs symptom detection based on the results of data analysis. Specifically, the server detects abnormal fluctuations in heart rate and determines whether these are a sign of a specific disease. The input is the analysis results, and the output is the result of symptom detection.

[0979] Step 6: Propose a treatment plan

[0980] The server uses a generative AI model to propose a treatment plan to the attending physician based on the results of the sign detection. Specifically, the server generates a recommendation such as, "You are at high risk of heart disease, so we recommend that you see a cardiologist." The input is the result of the sign detection, and the output is a proposed treatment plan.

[0981] Step 7: Proposal Notification

[0982] The server notifies the doctor of the proposed treatment plan it has created. Specifically, the server sends the proposed plan to the doctor's email address. The input is the proposed treatment plan, and the output is a notification to the doctor.

[0983] (Application example 2)

[0984] Next, a description will be given of Application Example 2 of Form 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."

[0985] Conventional medical systems are not fully optimized for the treatment of mild cognitive impairment (MCI), lifestyle modification, or cognitive function training. Furthermore, there are limited systems that can collect patients' biometric and behavioral data in real time to perform preliminary screening and detect signs of illness. Furthermore, the lack of functionality to detect signs of security risks and propose appropriate countermeasures makes it difficult to ensure user safety.

[0986] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a means for utilizing generative AI to make appropriate suggestions for treatment, lifestyle modification such as exercise and diet, and cognitive function training for mild cognitive impairment (MCI); a means for performing preliminary screening and symptom detection based on patient monitoring and data collection, and proposing treatment plans to the attending physician; a means for collecting user biometric information and behavioral data in real time and detecting signs of security risks; a means for analyzing the collected data and detecting abnormal patterns and high-risk states; and a means for proposing appropriate security measures to the user based on the analysis results. This makes it possible to optimize treatment for mild cognitive impairment, lifestyle modification, and cognitive function training, and further to detect signs of security risks and propose appropriate measures through the collection and analysis of biometric information and behavioral data in real time.

[0987] "Generative AI" is an artificial intelligence technology that generates new information and suggestions based on data.

[0988] "Mild cognitive impairment (MCI)" is a condition that lies between normal cognitive function and dementia, in which cognitive function is impaired but not to the point of interfering with daily life.

[0989] "Treatment" refers to medical procedures and drug therapy to cure illness or disability.

[0990] "Exercise" is an activity that maintains and improves health by moving the body.

[0991] "Meal" refers to the act of eating to obtain nutrition and the contents of that eating.

[0992] "Lifestyle modification" refers to actions and habits that improve unhealthy lifestyles and lead a healthy life.

[0993] "Cognitive training" refers to exercises and activities aimed at improving cognitive functions such as memory, attention, and judgment.

[0994] "Monitoring" is the act of continuously observing a specific subject and collecting data.

[0995] "Data collection" is the act of gathering information for a specific purpose.

[0996] A "preliminary examination" refers to an initial test to detect illness or abnormalities early.

[0997] "Predictive detection" is the act of finding signs of abnormalities or problems before they occur.

[0998] "Treatment planning" refers to planning the optimal treatment method according to the patient's condition.

[0999] "Biometric information" refers to physiological data from the human body, such as heart rate, blood pressure, and body temperature.

[1000] "Behavioral Data" refers to information about an individual's behavioral patterns and activity levels.

[1001] "Security risk" refers to factors or situations that could threaten the safety of information or systems.

[1002] "Abnormal patterns" refer to behavior or data fluctuations that are different from the norm.

[1003] "High-risk situation" refers to a situation or condition that is deemed to be high risk.

[1004] "Security measures" refer to the means and methods for ensuring the safety of information and systems.

[1005] As an embodiment of the present invention, the following system is constructed.

[1006] System Configuration

[1007] Hardware

[1008] Smartphone: A device used to collect biometric and behavioral data from users. It can be an iOS or Android smartphone.

[1009] Sensors: Sensors for collecting biometric information such as heart rate, activity level, and stress level. Specifically, APIs such as Apple HealthKit and Google Fit are used.

[1010] software

[1011] Generative AI model: An artificial intelligence technology that generates new information and suggestions based on data. It is implemented using programming languages ​​such as Python.

[1012] Data Collection API: API for collecting biometric and behavioral data. Uses Apple HealthKit and Google Fit.

[1013] Data analysis module: This module analyzes collected data in real time to detect abnormal patterns and high-risk conditions.

[1014] Processing flow

[1015] 1. Data collection: Using the smartphone's sensor API, biometric information such as the user's heart rate, activity level, and stress level is collected in real time.

[1016] 2. Data analysis: Collected data is fed into generative AI models to detect abnormal patterns and high-risk conditions.

[1017] 3. Presenting the results: Based on the analysis results, appropriate security measures are suggested to the user.

[1018] Specific examples

[1019] For example, if a user shows high stress levels and high activity levels late at night, the system will notify them, "High risk detected. We recommend you take immediate rest."

[1020] Prompt Sentence Examples

[1021] If the device detects that the user's heart rate is above 90, their activity level is high, or their stress level is 7 or higher, it will notify the user, saying, "High risk has been detected. We recommend that you rest immediately."

[1022] In this way, it will be possible to treat mild cognitive impairment, correct lifestyle habits, and optimize cognitive function training. Furthermore, by collecting and analyzing biometric and behavioral data in real time, it will be possible to detect signs of security risks and propose appropriate countermeasures.

[1023] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1024] Step 1:

[1025] The server uses the smartphone's sensor API to collect biometric information such as the user's heart rate, activity level, and stress level in real time. As input, it receives data from the sensor API and generates a biometric dataset as output. This dataset includes the heart rate, activity level, and stress level.

[1026] Step 2:

[1027] The server inputs the collected biometric data set into a generative AI model. It uses the biometric data set as input and generates analysis results for detecting abnormal patterns and high-risk conditions as output. Specifically, the AI ​​model analyzes the data and identifies abnormal patterns and high-risk conditions.

[1028] Step 3:

[1029] The server proposes appropriate security measures to the user based on the analysis results from the generative AI model. It receives the analysis results of the AI ​​model as input and generates a message to notify the user as output. Specifically, the server evaluates the analysis results and, if necessary, sends a notification to the user, such as "High risk has been detected. We recommend that you take immediate rest."

[1030] Step 4:

[1031] The user receives the notification from the server and takes appropriate security measures. The input is the notification message from the server, and the output is taking action based on the notification. Specifically, the user takes measures such as taking a break according to the notification content.

[1032] Step 5:

[1033] The server recollects the user's behavioral data and evaluates the effectiveness of the countermeasures taken in the previous step. As input, it receives the user's new biometric and behavioral data, and as output, it generates a report to evaluate the effectiveness of the countermeasures. Specifically, the server analyzes the new data and determines whether the countermeasures were effective.

[1034] Example 3

[1035] Next, a description will be given of Example 3 of Form Example 3. 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."

[1036] Conventional systems for suggesting treatments for cognitive impairment and lifestyle improvements have difficulty providing optimal suggestions tailored to each patient's individual condition, and data collection and analysis are performed manually, resulting in inefficiency and low accuracy.Furthermore, even systems that utilize generative AI have had issues with providing appropriate treatment plans and lifestyle improvement suggestions to patients because prompt generation and optimization of suggestions are not adequately performed.

[1037] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1038] In this invention, the server includes a means for the user to input patient information and the terminal to transmit the information to the server, a means for the server to analyze the received data and generate a prompt sentence, and a means for the server to input the prompt sentence into a generative AI model and generate a proposal, thereby enabling the efficient and accurate provision of optimal treatment plans and lifestyle improvement proposals tailored to the individual condition of the patient.

[1039] "Generative AI" is an artificial intelligence technology that generates optimal proposals and plans based on input data.

[1040] "Mild cognitive impairment" refers to a condition in which cognitive function is impaired beyond the normal range, but does not significantly interfere with daily life.

[1041] "Correcting lifestyle habits" refers to improving daily habits such as diet, exercise, and sleep in order to maintain and improve health.

[1042] "Cognitive function training" refers to training and activities aimed at maintaining and improving cognitive functions such as memory, attention, and judgment.

[1043] "Monitoring" refers to the continuous observation and recording of a patient's health condition and lifestyle habits.

[1044] "Data collection" refers to the process of gathering information about patients, including their health status, lifestyle habits, and cognitive status.

[1045] "Preliminary screening" refers to initial testing and evaluation before a formal diagnosis is made.

[1046] "Predictive detection" refers to detecting signs that allow for early detection of illness or a deterioration in health.

[1047] A "treatment plan" refers to the specific treatment strategies and procedures developed by a medical professional to improve a patient's health.

[1048] A "prompt" is an instruction to be input into generative AI, and specifically describes the patient's condition and necessary suggestions.

[1049] "Terminal" refers to an electronic device through which a user inputs information and receives suggestions from a server.

[1050] "Server" refers to a central processing unit that analyzes data, runs generative AI models, generates proposals, etc.

[1051] This invention is a system that utilizes generative AI to provide appropriate suggestions for treatment, lifestyle modification such as exercise and diet, and cognitive function training for mild cognitive impairment. The system consists of three main components: the user, the terminal, and the server.

[1052] Data collection

[1053] The user uses the device to input information about the patient's cognitive function and lifestyle habits. Specifically, the user inputs data such as the patient's age, gender, medical history, current symptoms, daily diet, and exercise habits. For example, when inputting information about a 65-year-old female patient with mild dementia, the user would enter information such as "65-year-old female, mild dementia, takes walks daily, but has high-calorie and irregular meals."

[1054] The terminal transmits the input data to the server in real time. When data is entered into the input form on the terminal and the "Send" button is pressed, the data is transmitted to the server.

[1055] Data Processing

[1056] The server analyzes the received data. Specifically, it stores it in a database and extracts the necessary information. The server generates a prompt based on the extracted information. For example, it generates a prompt such as "65-year-old female with mild dementia, takes walks daily, but eats high-calorie, irregular meals."

[1057] Generative AI processing

[1058] The server inputs a prompt into a generative AI model (e.g., GPT-4). Specifically, it calls the generative AI model's API and sends the prompt. The generative AI model generates an optimal treatment plan and lifestyle improvement suggestions based on the input prompt. For example, it generates a suggestion such as, "For this patient, we recommend aerobic exercise three times a week and a balanced, low-calorie diet. We also recommend daily crossword puzzles and reading to maintain cognitive function."

[1059] Providing results

[1060] The server sends the generated proposal to the terminal. Specifically, the generated proposal is sent to the terminal in JSON format. The user confirms the proposal through the terminal. For example, the user confirms the proposal displayed on the terminal screen and applies it to the patient.

[1061] Specific examples

[1062] For example, Patient A is a 65-year-old woman with mild dementia. She takes walks daily, but her diet is high in calories and irregular. The user enters this information into a device and sends it to the server. The server generates the following prompt: "65-year-old woman with mild dementia, takes walks daily, but her diet is high in calories and irregular." The generative AI model generates the following suggestion: "We recommend that this patient do aerobic exercise three times a week and eat a balanced, low-calorie diet. We also recommend that she do crossword puzzles and read regularly to maintain cognitive function."

[1063] In this way, the system can efficiently and accurately provide optimal treatment plans and lifestyle improvement suggestions according to the state of the patient's cognitive function. The flow of the identification process in Example 3 will be described with reference to FIG.

[1064] Step 1:

[1065] The user uses the device to input information about the patient's cognitive function and lifestyle habits. Specifically, the user inputs data such as the patient's age, gender, medical history, current symptoms, daily diet, and exercise habits. For example, the user might input information such as "65-year-old female with mild dementia, takes walks daily, but has high-calorie and irregular meals." The input data is saved in the device's input form.

[1066] Step 2:

[1067] The terminal sends the entered data to the server. Specifically, when the user presses the "Send" button, the terminal sends the entered data to the server in real time. The sent data is saved in the server's database.

[1068] Step 3:

[1069] The server analyzes the data it receives. Specifically, it extracts data stored in the database and organizes the necessary information. For example, it extracts information such as the patient's age, gender, cognitive function status, and lifestyle habits. Based on the results of this analysis, it generates a prompt.

[1070] Step 4:

[1071] The server inputs a prompt to the generative AI model. Specifically, it calls the API of the generative AI model and sends the prompt. For example, it generates a prompt such as "65-year-old female with mild dementia, takes walks daily, but eats high-calorie, irregular meals" and sends it to the generative AI model.

[1072] Step 5:

[1073] The generative AI model generates optimal treatment plans and lifestyle improvement suggestions based on the input prompts. Specifically, the generative AI model analyzes the prompts and generates appropriate suggestions. For example, it generates a suggestion such as, "We recommend this patient engage in aerobic exercise three times a week and a balanced, low-calorie diet. We also recommend daily crossword puzzles and reading to maintain cognitive function."

[1074] Step 6:

[1075] The server sends the generated proposal to the device. Specifically, it sends the generated proposal in JSON format to the device. The sent proposal is displayed on the device screen.

[1076] Step 7:

[1077] The user confirms the proposals through the device. Specifically, the user confirms the proposals displayed on the device screen and applies them to the patient. For example, the user explains the proposed treatment plan and lifestyle improvements to the patient and instructs them to implement them.

[1078] (Application example 3)

[1079] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1080] Providing appropriate treatment and lifestyle improvement suggestions to patients with mild cognitive impairment (MCI) is important, but conventional systems have had difficulty making optimal suggestions based on the patient's cognitive function and lifestyle. Furthermore, there was a lack of means to provide appropriate suggestions to support patients' cognitive function, even while shopping in physical stores. This made it difficult for patients to make appropriate choices in their daily lives, potentially delaying the improvement of their cognitive function.

[1081] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1082] In this invention, the server includes: means for utilizing generative AI to make appropriate suggestions for treatment, exercise, diet, and other lifestyle modification and cognitive function training for mild cognitive impairment (MCI); means for monitoring patients and collecting data to conduct preliminary examinations and detect signs, and propose treatment plans to their physicians; means for making suggestions to support the user's cognitive function while shopping in a physical store; means for generating optimal product and service suggestions based on the user's cognitive function status and lifestyle data; and means for notifying the user of the generated suggestions. This allows patients to receive cognitive support in their daily lives and make appropriate choices.

[1083] "Generative AI" is an artificial intelligence technology that generates new information and suggestions based on input data.

[1084] "Mild cognitive impairment (MCI)" refers to a condition in which cognitive function declines beyond the normal range but does not significantly interfere with daily life.

[1085] "Treatment" refers to medical procedures and drug therapy to cure illness or disability.

[1086] "Exercise" refers to activities that maintain and improve health by moving the body.

[1087] "Diet" refers to the act of consuming food to obtain nutrients.

[1088] "Lifestyle" refers to the actions and habits of daily life, and includes factors that affect health.

[1089] "Cognitive function" refers to intellectual functions such as memory, attention, language, and judgment.

[1090] "Training" refers to exercises or practice aimed at improving a particular ability or skill.

[1091] "Monitoring" refers to the continuous observation and recording of a particular condition or activity.

[1092] "Data collection" refers to the act of gathering necessary information.

[1093] "Preliminary screening" refers to an early stage examination aimed at early detection of disease or disability.

[1094] "Early detection" refers to the early detection of signs of illness or disability.

[1095] "Treatment planning" refers to planning the optimal treatment method according to the patient's condition.

[1096] "Brick and Mortar Store" means a retailer that has a physical location.

[1097] "Shopping" refers to the act of purchasing goods and services.

[1098] "User" refers to a person who uses a system or service.

[1099] "Notification" refers to the act of making information known.

[1100] The following system configuration will be described as an embodiment of the present invention.

[1101] System Configuration

[1102] This system utilizes generative AI to provide appropriate recommendations for treatment, exercise, diet, and other lifestyle changes, as well as cognitive training for mild cognitive impairment (MCI). The system includes the following main components:

[1103] Hardware used

[1104] Smartphone (iOS or Android)

[1105] Sensors (heart rate monitor, pedometer, etc.)

[1106] Software used

[1107] Generative AI models (e.g., GPT-4)

[1108] Database (e.g. Firebase)

[1109] Health data collection APIs (e.g., Google Fit, Apple HealthKit)

[1110] Program processing explanation

[1111] Data collection

[1112] The device (smartphone) collects data on the user's cognitive function status and lifestyle habits, including data from smartphone sensors and external APIs (Google Fit, Apple HealthKit).

[1113] Data analysis

[1114] The server inputs the collected data into a generative AI model (GPT-4) to evaluate the user's current cognitive function state.

[1115] Proposal generation

[1116] The server uses a generative AI model to generate optimal product and service recommendations based on the user's cognitive state, such as foods containing specific nutrients or exercise equipment to enhance cognitive function.

[1117] Notifications and Displays

[1118] The device then notifies the user of the generated suggestions via a smartphone application, where the user can check the suggestions and use them to help them with their shopping in physical stores.

[1119] Specific examples

[1120] While shopping in a physical store, the app will notify users, "We recommend purchasing foods high in B vitamins to support your cognitive function." Users can review the suggested products in the app and search for them in-store.

[1121] Prompt Sentence Examples

[1122] Generate optimal product and service recommendations based on the user's cognitive status and lifestyle data, such as foods containing specific nutrients or exercise equipment to improve cognitive function. User data includes:

[1123] Cognitive status: mild cognitive decline

[1124] Lifestyle: Lack of exercise, unbalanced diet

[1125] In this way, applications can be created that support users' cognitive functions through the physical shopping experience.

[1126] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1127] Step 1:

[1128] The device collects data on the user's cognitive function and lifestyle habits. Specifically, it obtains data from smartphone sensors (heart rate monitor, pedometer, etc.) and external APIs (Google Fit, Apple HealthKit). Input data includes heart rate, number of steps, dietary details, etc. This data is collected and sent to a server.

[1129] Step 2:

[1130] The server inputs the received data into a generative AI model (GPT-4). The input data includes information on the user's cognitive function status and lifestyle habits. The generative AI model analyzes this data and evaluates the user's current cognitive function status. The output is an evaluation result of the user's cognitive function.

[1131] Step 3:

[1132] The server generates optimal product and service recommendations based on the cognitive function assessment results output by the generative AI model. Specifically, it makes recommendations such as foods containing specific nutrients and exercise equipment to improve cognitive function. The input data includes the cognitive function assessment results, and the recommendations are obtained as the output.

[1133] Step 4:

[1134] The server sends the generated proposal to the device. The device notifies the user of the received proposal via a smartphone application. Specifically, the proposal is displayed using a push notification or an in-app message. The proposal is included in the input data, and a notification is sent to the user as the output.

[1135] Step 5:

[1136] The user confirms the proposed products and services within the app. Specifically, the user views the list or detail page within the app and confirms the proposed content. The input data includes the proposed content, and the output is the user's confirmation action.

[1137] Step 6:

[1138] Users search for and purchase suggested products and services in physical stores. Specifically, they use information in the app to explore the store and find the suggested products. The input data includes the suggestions, and the output is the user's purchasing behavior.

[1139] In this way, the system can support the user's cognitive functions and suggest appropriate products and services.

[1140] 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.

[1141] "Example 1"

[1142] In one embodiment of the present invention, the generative AI includes an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's emotions from facial expressions, tone of voice, text input, etc., and feeds the results back to the generative AI. Based on the information obtained from the emotion engine, the generative AI suggests lifestyle modifications such as treatment, exercise, and diet, as well as cognitive function training, in accordance with the user's emotional state. For example, if the user feels stressed, the generative AI suggests exercises and meals that promote relaxation. Also, if the user feels joy, the generative AI suggests activities to maintain that joy.

[1143] "Example 2"

[1144] In another embodiment of the present invention, the emotion engine improves the accuracy of preliminary screening and sign detection by taking into account the user's emotional state. Specifically, if the user's emotional state shows a certain pattern, this can be used as an indicator for preliminary screening and sign detection. For example, if the user feels anxious or sad for a certain period of time, this can be interpreted as a sign of cognitive decline and early intervention can be suggested.

[1145] "Example 3"

[1146] In another embodiment of the present invention, the emotion engine may consider the user's emotional state when proposing a treatment plan. Specifically, if the user's emotional state exhibits a certain pattern, that pattern may be used as an indicator for adjusting the treatment plan. For example, if the user experiences a sense of joy over a period of time, that may be used as an indicator of improved cognitive function, and the progress of the treatment plan may be adjusted accordingly.

[1147] The processing flow of each embodiment will be described below.

[1148] "Example 1"

[1149] Step 1: Receive input from the user (facial expression, tone of voice, text, etc.).

[1150] Step 2: The emotion engine analyzes the user's emotions.

[1151] Step 3: The analysis results are fed back to the generative AI.

[1152] Step 4: The generative AI will suggest treatment, exercise, diet, and other lifestyle changes based on the emotional state, as well as cognitive function training.

[1153] "Example 2"

[1154] Step 1: Receive input from the user (facial expression, tone of voice, text, etc.).

[1155] Step 2: The emotion engine analyzes the user's emotions.

[1156] Step 3: The analysis results are fed back to the generative AI.

[1157] Step 4: Generative AI uses emotional states as indicators for preliminary screening and early warning signs.

[1158] "Example 3"

[1159] Step 1: Receive input from the user (facial expression, tone of voice, text, etc.).

[1160] Step 2: The emotion engine analyzes the user's emotions.

[1161] Step 3: The analysis results are fed back to the generative AI.

[1162] Step 4: Generative AI uses emotional state as a guide to adjust treatment plans.

[1163] Example 1

[1164] Next, a description will be given of Example 1 of Form 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."

[1165] Patients with mild cognitive impairment (MCI) need appropriate recommendations for treatment, exercise, diet, and other lifestyle changes, as well as cognitive function training. However, conventional systems do not take into account the patient's emotional state, making it difficult to provide optimal treatment plans and lifestyle improvement recommendations for each individual patient. Another challenge was improving the accuracy of preliminary screening and symptom detection based on information obtained from patient monitoring and data collection.

[1166] 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.

[1167] In this invention, the server includes: means for utilizing generative AI to make appropriate suggestions for treatment, exercise, diet, and other lifestyle modification and cognitive training for mild cognitive impairment (MCI); means for monitoring patients and collecting data to conduct preliminary screening and detect signs, and propose treatment plans to attending physicians; means including an emotion engine that analyzes emotions from a user's facial expressions, tone of voice, text input, etc.; means for proposing treatment, exercise, diet, and other lifestyle modification and cognitive training according to the user's emotional state based on information obtained from the emotion engine; means for inputting prompt sentences into the generative AI model to generate optimal suggestions; and means for transmitting the generated suggestions to the user's terminal and displaying them to the user. This makes it possible to provide optimal treatment plans and lifestyle improvement suggestions that take into account the patient's emotional state.

[1168] "Generative AI" is a type of artificial intelligence that generates optimal proposals and plans based on user input data.

[1169] "Mild cognitive impairment (MCI)" is a medical term that refers to a condition in which cognitive function is below the normal range but does not significantly interfere with daily life.

[1170] "Treatment" refers to medical procedures or measures to cure an illness or disability.

[1171] "Exercise" refers to activities that maintain and improve health by moving the body.

[1172] "Meal" refers to the act of eating to obtain nutrition and the contents of that eating.

[1173] "Lifestyle habits" is a general term for actions and habits in daily life.

[1174] "Cognitive training" refers to exercises or activities designed to improve cognitive functions such as memory and attention.

[1175] "Monitoring" refers to the act of continuously observing and recording a specific subject.

[1176] "Data collection" refers to the act of gathering necessary information or data.

[1177] "Preliminary screening" refers to an early stage examination aimed at early detection of disease or disability.

[1178] "Early detection" refers to a means of detecting early signs of disease or disability.

[1179] An "emotion engine" refers to technology for analyzing emotions from a user's facial expressions, tone of voice, text input, etc.

[1180] A "prompt" refers to an instruction or question that is input into a generative AI model.

[1181] "Terminal" refers to a device used by a user, such as a computer or smartphone.

[1182] A "server" refers to a computer system that stores and processes data over a network.

[1183] This invention is a system that provides appropriate suggestions for lifestyle modification, such as treatment, exercise, and diet, as well as cognitive function training for patients with mild cognitive impairment (MCI). The system uses software such as generative AI, an emotion engine, and a database management system (DBMS), as well as hardware such as servers and terminals (PCs, smartphones, tablets, etc.).

[1184] Hardware and software used

[1185] Hardware: Servers, devices (PCs, smartphones, tablets, etc.)

[1186] Software: Generative AI model, emotion engine, database management system (DBMS)

[1187] System Overview

[1188] 1. Data Collection:

[1189] The user uses the device to input information about the state of their cognitive function and lifestyle habits. Specifically, the user opens the device's application and answers questions on a questionnaire form. For example, the user answers questions such as "What have you eaten recently?" and "How often do you exercise?"

[1190] The device collects data such as the user's facial expressions, tone of voice, and text input. Specifically, the device's camera and microphone are used to analyze the user's facial expressions and tone of voice in real time. The user inputs text using a keyboard or touch screen.

[1191] 2. Data transmission and storage:

[1192] The terminal transmits the collected data to a server. Specifically, the terminal encrypts the collected data and transmits it to the server via the Internet.

[1193] The server stores the received data in a database. Specifically, the server uses a database management system (DBMS) to store the received data in the appropriate tables.

[1194] 3. Emotion analysis:

[1195] The server uses an emotion engine to analyze emotions from the user's facial expressions, tone of voice, and text input. Specifically, the emotion engine uses a machine learning model to classify emotions from the user's facial expressions and tone of voice. For example, it analyzes a smile as "joy" and a furrowed brow as "stress."

[1196] 4. Proposal generation:

[1197] The server uses a generative AI model to generate optimal treatment plans and lifestyle improvement suggestions based on the user's cognitive function status, lifestyle information, and emotion analysis results. Specifically, the server inputs the following prompt into the generative AI model:

[1198] User's cognitive status: Mild Cognitive Impairment (MCI)

[1199] User's lifestyle: Irregular eating habits, lack of exercise

[1200] User's emotional state: stressed

[1201] Based on the above information, generate optimal treatment plans and lifestyle modification suggestions for the user.

[1202] Based on this prompt, the generative AI model generates specific suggestions, such as "We recommend walking 30 minutes daily" or "Do yoga to reduce stress."

[1203] 5. Providing suggestions:

[1204] The server transmits the generated proposal to the user's terminal. Specifically, the server transmits the generated proposal to the terminal in a data format such as JSON format.

[1205] The device displays the received suggestion to the user. Specifically, the device application notifies the user of the suggestion and displays it on the screen. The user can then confirm and implement the suggestion.

[1206] Specific examples

[1207] For example, if a user enters "A lot of fast food" as their "recent diet" and the emotion engine analyzes this as "feeling stressed," the generative AI model will generate suggestions such as "recommended 30 minutes of walking every day" or "do yoga to reduce stress." These suggestions are sent to the user's device via the server, and the user can view them on their device screen.

[1208] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1209] Step 1:

[1210] The user uses the terminal to input information about the state of cognitive function and lifestyle habits.

[1211] Input: The user opens the application on their device and answers questions in a questionnaire, such as "What have you eaten recently?" or "How often do you exercise?"

[1212] Output: The user's input data is saved to the terminal.

[1213] What happens: The user enters information using a keyboard or touchscreen and presses the submit button.

[1214] Step 2:

[1215] The device collects data such as the user's facial expressions, tone of voice, and text input.

[1216] Input: The device's camera and microphone capture the user's facial expressions and tone of voice in real time.

[1217] Output: Collected data is saved to the device.

[1218] What happens: The device's camera captures a picture of the user's face, the microphone records the user's voice, and the user inputs text using a keyboard or touchscreen.

[1219] Step 3:

[1220] The terminal transmits the collected data to the server.

[1221] Input: User input and collected data stored on your device.

[1222] Output: Data is sent to the server.

[1223] Specific operation: The device encrypts the collected data and sends it to a server via the Internet.

[1224] Step 4:

[1225] The server stores the received data in a database.

[1226] Input: User input data and collected data sent from the device.

[1227] Output: Data is stored in a database.

[1228] What happens: The server uses a database management system (DBMS) to store the received data in the appropriate tables.

[1229] Step 5:

[1230] The server uses an emotion engine to analyze emotions from the user's facial expressions, tone of voice, and text input.

[1231] Input: User facial expressions, tone of voice, and text input data stored in a database.

[1232] Output: The sentiment analysis results are generated.

[1233] How it works: The emotion engine uses machine learning models to classify emotions from a user's facial expressions and tone of voice. For example, a smile might indicate "happiness" and a furrowed brow might indicate "stress."

[1234] Step 6:

[1235] The server uses a generative AI model to generate optimal treatment plans and lifestyle improvement suggestions based on the user's cognitive function status, lifestyle information, and emotion analysis results.

[1236] Input: User's cognitive function status, lifestyle information, and emotion analysis results stored in the database.

[1237] Output: An optimal treatment plan and lifestyle modification suggestions are generated.

[1238] How it works: Enter the following prompt into the generative AI model:

[1239] User's cognitive status: Mild Cognitive Impairment (MCI)

[1240] User's lifestyle: Irregular eating habits, lack of exercise

[1241] User's emotional state: stressed

[1242] Based on the above information, generate optimal treatment plans and lifestyle modification suggestions for the user.

[1243] Based on this prompt, the generative AI model generates specific suggestions, such as "We recommend walking 30 minutes daily" or "Do yoga to reduce stress."

[1244] Step 7:

[1245] The server transmits the generated proposal to the user's terminal.

[1246] Input: Generated treatment plan and lifestyle modification suggestions.

[1247] Output: The proposal is sent to the user's device.

[1248] Specific operation: The server sends the generated proposal to the device in a data format such as JSON.

[1249] Step 8:

[1250] The terminal displays the received suggestions to the user.

[1251] Input: Treatment plan and lifestyle improvement suggestions sent from the server.

[1252] Output: The suggestions are displayed on the device screen.

[1253] Specific operation: The application on the device notifies the user of the suggestion and displays it on the screen. The user can then confirm and implement the suggestion.

[1254] (Application example 1)

[1255] Next, a description will be given of Application Example 1 of Form 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."

[1256] It is important to provide appropriate suggestions for treatment, exercise, dietary changes, and cognitive function training to patients with mild cognitive impairment (MCI). However, conventional systems do not take into account the patient's emotional state when making suggestions, making it difficult to maximize the patient's motivation and treatment effectiveness. Furthermore, in the shopping experience at physical stores, there is also the problem of not being able to improve patient satisfaction because product and service suggestions are not tailored to the patient's emotional state.

[1257] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes: a means for utilizing generative AI to make appropriate suggestions for treatment, exercise, diet, and other lifestyle modification and cognitive function training for mild cognitive impairment (MCI); a means for performing preliminary screening and symptom detection based on patient monitoring and data collection, and proposing treatment plans to the attending physician; a means including an emotion engine that recognizes the user's emotions, analyzing the user's emotions from facial expressions, tone of voice, text input, etc., and feeding the results back to the generative AI; and a means for suggesting products and services based on the user's emotional state through smart glasses. This makes it possible to suggest optimal treatments and lifestyle changes that take the patient's emotional state into consideration, thereby improving patient satisfaction even in shopping experiences at physical stores.

[1258] "Generative AI" is an artificial intelligence system that generates optimal proposals and plans based on user input data.

[1259] "Mild cognitive impairment (MCI)" is a medical term that refers to a condition in which cognitive function is impaired beyond the normal range but does not significantly interfere with daily life.

[1260] An "emotion engine" is software or hardware that analyzes emotions from a user's facial expressions, tone of voice, text input, etc., and feeds the results back to other systems.

[1261] "Smart glasses" are wearable devices equipped with functions such as a display, camera, and microphone, and provide information to the user.

[1262] "Monitoring" is the process of continuously observing and collecting data on a patient's condition and behavior.

[1263] "Data collection" is the act of gathering information about a patient's condition or behavior.

[1264] A "preliminary examination" is an early stage examination conducted with the aim of early detection of disease.

[1265] "Early warning signs" is a technology for early detection of signs of illness or abnormalities.

[1266] A "treatment plan" is a plan for determining the optimal treatment method depending on the patient's condition.

[1267] "Correcting lifestyle habits" refers to the act of improving daily habits such as diet, exercise, and sleep in order to maintain and improve health.

[1268] "Cognitive training" refers to exercises or activities designed to improve cognitive functions such as memory and attention.

[1269] "Product and service suggestions" refers to the act of recommending optimal products and services based on the user's needs and situation.

[1270] The following system configuration will be described as an embodiment of the present invention.

[1271] System Configuration

[1272] The system consists of a generative AI, an emotion engine, smart glasses, a monitoring device, a data collection device, a preliminary examination device, a symptom detection device, and a treatment plan generation device.

[1273] Program processing

[1274] The server utilizes generative AI to make appropriate recommendations for treatment, exercise, diet, and other lifestyle changes, as well as cognitive training for mild cognitive impairment (MCI). The generative AI receives information on the patient's cognitive function and lifestyle habits as input, and generates optimal treatment plans and lifestyle improvement recommendations based on that information.

[1275] The emotion engine analyzes the user's emotions from facial expressions, tone of voice, text input, etc., and feeds the results back to the generative AI, which then suggests treatment, exercise, dietary and other lifestyle changes, as well as cognitive function training, based on the user's emotional state.

[1276] The smart glasses analyze the user's facial expressions and tone of voice in real time and send the data to the emotion engine. The emotion engine analyzes the data and feeds it back to the generative AI. The generative AI then generates product and service suggestions based on the user's emotional state and displays them on the smart glasses' display.

[1277] Hardware and software used

[1278] Hardware: Smart glasses (camera, microphone, display), monitoring device, data collection device, preliminary examination device, symptom detection device

[1279] Software: OpenCV (image processing), EmotionRecognizer (emotion analysis), AIGenerator (generative AI)

[1280] Specific examples

[1281] For example, if a user is feeling stressed, the emotion engine analyzes that information and feeds it back to the generative AI, which then generates suggestions for relaxation products (e.g., aroma oils) or services (e.g., massages) and displays them on the smart glasses' display.

[1282] Example prompts to input to a generative AI model:

[1283] If the user is feeling stressed, suggest products or services that will help them relax.

[1284] In this way, it becomes possible to propose optimal treatments and lifestyle habits that take into account the patient's emotional state, improving patient satisfaction even during the shopping experience in a physical store.

[1285] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1286] Step 1:

[1287] The user puts on the smart glasses and starts the system. The camera and microphone in the smart glasses capture the user's facial expressions and voice in real time. The input is the user's facial expression data and voice data, which are then sent to the emotion engine.

[1288] Step 2:

[1289] The emotion engine analyzes the user's facial expression and voice data to recognize the user's emotional state. Specifically, it uses OpenCV for facial expression analysis and EmotionRecognizer for voice tone analysis. The input is facial expression data and voice data, and the output is the user's emotional state (e.g., stress, joy).

[1290] Step 3:

[1291] The emotional state recognized by the emotion engine is fed back to the generative AI. The input is the user's emotional state, and the generative AI generates a prompt sentence based on this information. An example of a prompt sentence is, "If the user is feeling stressed, please suggest products or services that have a relaxation effect."

[1292] Step 4:

[1293] Generative AI generates optimal suggestions based on the prompt. Specifically, it uses AI Generator to generate product and service suggestions based on the user's emotional state. The input is the prompt, and the output is the suggestion (e.g., aroma oil, massage service).

[1294] Step 5:

[1295] The suggestions generated by the generative AI are displayed on the smart glasses' display. The input is the suggestions, and the output is the information displayed on the smart glasses' display. The user can check the suggestions through the display.

[1296] Step 6:

[1297] The user selects an action based on the suggestions. For example, if the user wants to purchase a product with a relaxation effect, the purchase can be completed through the smart glasses. The input is the user's selection, and the output is the completion of the purchase.

[1298] In this way, the system can make optimal suggestions that take into account the user's emotional state, improving user satisfaction.

[1299] Example 2

[1300] Next, a description will be given of Example 2 of Form 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."

[1301] Conventional medical systems have difficulty collecting and analyzing patients' biometric and behavioral data in real time, resulting in low accuracy in preliminary examinations and early warning signs. Furthermore, early warning signs and treatment plans do not take into account the patient's emotional state, which often results in delayed early intervention. This increases the patient's health risks and creates the problem of not being able to provide optimal treatment.

[1302] 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.

[1303] In this invention, the server includes: a means for utilizing generative AI to appropriately recommend treatment, exercise, lifestyle modification (e.g., diet), and cognitive training for mild cognitive impairment (MCI); a means for monitoring patients and collecting data to conduct preliminary screening and detect signs, and propose treatment plans to the attending physician; a means for collecting the patient's biometric information and behavioral data in real time; a means for transmitting the collected data to the server using a secure communication protocol; a means for the server to analyze the data using a generative AI model and detect abnormal patterns and health risks; a means for conducting preliminary screening and detecting signs based on the analysis results of the generative AI model; a means for generating a treatment plan based on the results of the sign detection and notifying the attending physician; a means for monitoring the user's emotional state, analyzing the emotional data, and detecting certain patterns; and a means for suggesting early intervention based on the emotional state pattern. This enables the real-time collection and analysis of the patient's biometric information and behavioral data, improving the accuracy of preliminary screening and sign detection. Furthermore, sign detection and treatment plan proposals that take emotional state into account enable early intervention and reduce the patient's health risks.

[1304] "Generative AI" is an artificial intelligence technology that analyzes patients' biometric information and behavioral data to perform preliminary examinations, detect signs, and propose treatment plans.

[1305] "Mild cognitive impairment (MCI)" is a medical concept that refers to a condition in which cognitive function is impaired beyond the normal range but does not significantly interfere with daily life.

[1306] "Treatment, exercise, diet, and other lifestyle changes" refers to medical procedures and lifestyle changes carried out to improve a patient's health.

[1307] "Cognitive training" refers to exercises or activities designed to improve cognitive functions such as memory and attention.

[1308] "Patient monitoring" refers to the continuous observation and recording of a patient's vital signs and behavioral data.

[1309] "Data collection" refers to the process of obtaining patient biometric and behavioral data.

[1310] A "preliminary examination" refers to a simple test conducted for the purpose of early detection of disease.

[1311] "Early detection" refers to the early detection of signs of disease or health risks.

[1312] "Treatment plan proposal" refers to proposing the optimal treatment method based on the patient's health condition.

[1313] "Biometric information" refers to data that indicates the patient's physical condition, such as heart rate and blood pressure.

[1314] "Behavioral data" refers to data that indicates the patient's daily behavior, such as the number of steps taken and the amount of activity.

[1315] "Real-time" means that data is processed immediately at the moment it is generated.

[1316] A "secure communication protocol" refers to a communication protocol for securely sending and receiving data.

[1317] An "unusual pattern" refers to fluctuations or trends in data that fall outside the normal range.

[1318] "Health risk" refers to the possibility of developing a disease or health problem.

[1319] "Emotional state" refers to data that indicates the psychological state of a user.

[1320] "Emotion data" refers to data that expresses the user's emotional state in numerical or text form.

[1321] "Early intervention" refers to preventative medical treatment that occurs before health risks increase.

[1322] This invention is a system that utilizes generative AI to collect and analyze patient biometric information and behavioral data in real time, and then performs preliminary examinations, detects signs, and proposes treatment plans. Specific embodiments of this system are described below.

[1323] 1. Program Generation

[1324] The server generates a program using generative AI. This program has the ability to collect and analyze patients' biometric and behavioral data in real time, and also incorporates algorithms for preliminary screening, symptom detection, and treatment plan proposals.

[1325] 2. Program Processing

[1326] The server processes and calculates data using the following hardware and software:

[1327] Hardware: Wearable devices for collecting patient biometric information (e.g., heart rate monitors, blood pressure monitors, activity monitors)

[1328] Software: Data collection and analysis platforms (e.g., AWS, Google Cloud), generative AI models (e.g., GPT-4)

[1329] The terminal collects biometric and behavioral data in real time from the wearable device worn by the patient. For example, it periodically acquires data such as heart rate, blood pressure, and number of steps. The collected data is sent to a server using a secure communication protocol (e.g., HTTPS). The server stores the received data in a database.

[1330] The server inputs the stored data into a generative AI model. The generative AI model analyzes this data and detects abnormal patterns and health risks. For example, it detects abnormal fluctuations in heart rate or sudden increases in blood pressure. Based on the results of the generative AI model's analysis, the server performs preliminary screening and detects signs of illness. For example, it determines that abnormal fluctuations in heart rate are a sign of heart disease. The server then notifies the patient's doctor of this information and suggests early examination.

[1331] The server then generates a treatment plan based on the results of the sign detection. The generative AI model proposes an optimal treatment plan by referring to past data and medical guidelines. For example, if signs of heart disease are detected, the generative AI model will suggest lifestyle improvements and drug therapy. The attending physician can review these suggestions and make any necessary adjustments.

[1332] 3. Examples of concrete examples and prompts

[1333] As a concrete example, the following scenario can be considered.

[1334] Scenario: Patient A is wearing a wearable device. The device measures heart rate, blood pressure, and activity in real time and sends the data to a server. A generative AI model detects abnormal heart rate fluctuations and determines this to be a sign of heart disease. The server notifies the patient's doctor and suggests early examination.

[1335] Example prompt sentence:

[1336] Analyze Patient A's heart rate data to detect any abnormal fluctuations. If any abnormalities are detected, provide details and a recommended treatment plan.

[1337] Embodiment of Emotion Engine

[1338] The device uses an emotion engine to improve the accuracy of pre-screening and pre-sign detection by taking into account the user's emotional state. Specifically, if the user's emotional state shows a certain pattern, it can be used as an indicator for pre-screening and pre-sign detection. For example, if the device detects that the user has been feeling anxious or sad for a certain period of time, it can determine that this is a sign of cognitive decline. The server then uses this information to suggest early intervention.

[1339] Scenario: User B is using an app equipped with an emotion engine. The app records the user's emotional state daily and detects that anxiety or sadness persists over a period of time. The emotion engine interprets this as a sign of cognitive decline and suggests early counseling.

[1340] Example prompt sentence:

[1341] Analyze User B's emotional data to detect whether he has experienced persistent anxiety or sadness over a period of time, and if so, provide details and suggested interventions.

[1342] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1343] Step 1: Data collection

[1344] The terminal collects biological information and behavioral data in real time from wearable devices (e.g., heart rate monitors, blood pressure monitors, activity monitors) worn by the patient. Specifically, it periodically acquires data such as heart rate, blood pressure, and number of steps. The input is the biological information and behavioral data from the wearable devices, and the output is the collected data.

[1345] Step 2: Send data

[1346] The device sends the collected data to the server using a secure communication protocol (e.g., HTTPS). Specifically, the device sends heart rate and blood pressure data to the server at regular intervals. The input is the collected data, and the output is the data sent to the server.

[1347] Step 3: Save data

[1348] The server stores the received data in a database. Specifically, the server stores data such as heart rate, blood pressure, and number of steps in the database. The input is the data sent to the server, and the output is the data stored in the database.

[1349] Step 4: Data analysis

[1350] The server inputs the stored data into a generative AI model, which analyzes the data and detects abnormal patterns and health risks, such as abnormal heart rate fluctuations or sudden increases in blood pressure. The input is the data stored in the database, and the output is the analysis results.

[1351] Step 5: Preliminary screening and symptom detection

[1352] The server performs preliminary screening and sign detection based on the analysis results of the generative AI model. Specifically, it determines that abnormal fluctuations in heart rate are signs of heart disease. The input is the analysis results of the generative AI model, and the output is the results of the preliminary screening and sign detection.

[1353] Step 6: Propose a treatment plan

[1354] The server generates a treatment plan based on the results of the sign detection. The generative AI model proposes an optimal treatment plan by referring to past data and medical guidelines. Specifically, if signs of heart disease are detected, the generative AI model suggests lifestyle improvements and drug therapy. The input is the sign detection results, and the output is a proposed treatment plan.

[1355] Step 7: Monitoring your emotional state

[1356] The device monitors the user's emotional state. The emotion engine analyzes the user's emotional data (e.g., diary app, voice analysis) and detects certain patterns. Specifically, it detects when the user is feeling anxious or sad over a certain period of time. The input is the user's emotional data, and the output is a pattern of emotional states.

[1357] Step 8: Early intervention recommendations

[1358] The server suggests early intervention based on patterns of emotional states. Specifically, if the emotion engine detects signs of cognitive decline, it suggests early counseling. The input is patterns of emotional states, and the output is early intervention suggestions.

[1359] (Application example 2)

[1360] Next, a description will be given of Application Example 2 of Form 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."

[1361] Conventional medical systems have difficulty collecting patients' biometric and behavioral data in real time and detecting abnormalities early. Furthermore, there has been a lack of advances in the accuracy of preliminary examinations and symptom detection that take into account the patient's emotional state. This can lead to delays in timely treatment and intervention, putting patients at risk of worsening their health. Furthermore, there is a lack of a way to quickly notify personnel when an abnormality is detected.

[1362] 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.

[1363] In this invention, the server includes: means for utilizing generative AI to make appropriate suggestions for treatment, exercise, diet, and other lifestyle modification and cognitive function training for mild cognitive impairment (MCI); means for performing preliminary screening and symptom detection based on patient monitoring and data collection, and proposing treatment plans to the attending physician; means for improving the accuracy of preliminary screening and symptom detection by taking the user's emotional state into consideration using an emotion engine; means for collecting biometric information and behavioral data in real time using a smart device and detecting abnormalities; and means for generating an alert and notifying a responsible person when an abnormality is detected. This enables real-time monitoring of a patient's health condition, early detection of abnormalities, and prompt response.

[1364] "Generative AI" is an artificial intelligence technology that analyzes patients' biometric information and behavioral data to perform preliminary examinations, detect signs, and propose treatment plans.

[1365] "Mild cognitive impairment (MCI)" is a medical concept that refers to a condition in which cognitive function is impaired beyond the normal range but does not significantly interfere with daily life.

[1366] "Treatment, exercise, diet, and other lifestyle modification" is a general term for medical procedures, exercise programs, dietary advice, and other measures carried out to improve a patient's health.

[1367] "Cognitive training" refers to exercises and activities aimed at improving cognitive functions such as memory, attention, and judgment.

[1368] "Patient monitoring" refers to the act of continuously observing and recording a patient's vital signs and behavioral data.

[1369] "Data collection" is the process of obtaining patient biometric and behavioral data using sensors and devices.

[1370] A "preliminary examination" is an early stage examination conducted with the aim of early detection and prevention of disease.

[1371] "Predictive detection" refers to analysis and diagnosis to detect early signs of disease or abnormalities.

[1372] "Proposing a treatment plan to the attending physician" refers to the act of the generative AI proposing optimal treatment methods and intervention measures to the physician based on the analysis results.

[1373] An "emotion engine" is a technology for detecting and analyzing a user's emotional state.

[1374] A "smart device" is an electronic device equipped with sensors and communication functions, and is a device that can collect and transmit data.

[1375] "Biometric information" refers to data obtained from the human body, such as heart rate, body temperature, and blood pressure.

[1376] "Behavioral Data" refers to information about a user's movements and activities.

[1377] "Detecting anomalies" means discovering data or conditions that are outside the normal range.

[1378] "Generating an alert" is the act of creating a warning or notification when an anomaly is detected.

[1379] "Notifying the person in charge" refers to the act of conveying information to the person in charge when an abnormality occurs so that they can take appropriate action.

[1380] A system for implementing this invention is configured using generative AI, an emotion engine, and a smart device. Specific embodiments are described below.

[1381] System configuration

[1382] 1. Generative AI

[1383] The server uses generative AI to analyze the patient's biometric and behavioral data, perform preliminary screening, detect signs, and propose treatment plans. Based on the collected data, generative AI detects abnormalities and suggests appropriate treatment methods and interventions.

[1384] 2. Emotion Engine

[1385] The server detects and analyzes the user's emotional state using an emotion engine, which takes the user's emotional state into account to improve the accuracy of preliminary screening and symptom detection.

[1386] 3. Smart Devices

[1387] The terminal collects biometric information and behavioral data in real time using smart devices. The smart devices are equipped with sensors that measure heart rate, body temperature, blood pressure, etc. They also collect behavioral data using cameras and accelerometers.

[1388] 4. Alert Generation and Notification

[1389] If an abnormality is detected, the server generates an alert and notifies the person in charge, enabling a prompt response.

[1390] Data processing and calculation

[1391] The server analyzes the collected biometric and behavioral data using generative AI and an emotion engine. Specifically, it processes the data and performs the following calculations:

[1392] Data collection: Collect biometric information such as heart rate, body temperature, and blood pressure from smart devices, and collect behavioral data using cameras and accelerometers.

[1393] Data analysis: Generative AI analyzes collected data and detects anomalies. An emotion engine analyzes the user's emotional state to improve the accuracy of preliminary screening and symptom detection.

[1394] Alert generation: When an abnormality is detected, an alert is generated and the responsible person is notified.

[1395] Specific examples

[1396] For example, if an employee experiences a sudden spike in heart rate while at work and simultaneously detects an anxious emotional state, the system will send an alert to security personnel, allowing for early detection of the anomaly and appropriate response.

[1397] Prompt Sentence Examples

[1398] Below is an example of a prompt sentence to input to a generative AI model.

[1399] Create a program that collects user biometric and behavioral data in real time and uses generative AI to detect anomalies. Also, add a function that uses an emotion engine to analyze the user's emotional state and send an alert if an anomaly is detected.

[1400] In this way, by combining generative AI, emotion engines, and smart devices, this invention makes it possible to monitor a patient's health condition in real time, detect abnormalities early, and respond quickly.

[1401] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1402] Step 1:

[1403] The terminal uses a smart device to collect the user's biometric information (heart rate, body temperature, blood pressure, etc.) in real time. The input is sensor data from the smart device, and the output is the collected biometric information. The terminal sends this data to a server.

[1404] Step 2:

[1405] The terminal collects user behavior data (walking, sitting, running, etc.) using the smart device's camera and acceleration sensor. The input is sensor data from the smart device, and the output is the collected behavior data. The terminal sends this data to the server.

[1406] Step 3:

[1407] The server analyzes the collected biometric and behavioral data using generative AI. The input is the biometric and behavioral data sent from the device, and the output is the analysis results. The server determines whether an abnormality has been detected.

[1408] Step 4:

[1409] The server uses an emotion engine to analyze the user's emotional state. The input is the user's behavioral data and biometric information, and the output is the analysis result of the emotional state. The server takes the emotional state into account to improve the accuracy of preliminary screening and symptom detection.

[1410] Step 5:

[1411] The server integrates the analysis results of the generative AI and the emotion engine, and generates an alert if an abnormality is detected. The input is the analysis results of the generative AI and the emotion engine, and the output is alert information.

[1412] Step 6:

[1413] The server notifies the person in charge of the generated alert. The input is the alert information, and the output is the notification to the person in charge. The server notifies quickly so that the person in charge can take appropriate action.

[1414] In this way, by clarifying the specific operations and data flow that take place at each step, it is possible to understand how the processing of the entire system is carried out.

[1415] Example 3

[1416] Next, a description will be given of Example 3 of Form Example 3. 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."

[1417] Patients with mild cognitive impairment (MCI) need appropriate recommendations for treatment, exercise, diet, and other lifestyle changes, as well as cognitive training. However, conventional systems have difficulty making optimal recommendations based on the patient's cognitive function status and lifestyle, and do not adjust treatment plans to take into account the patient's emotional state. As a result, effective treatment and lifestyle improvements tailored to each patient's individual condition have not been realized.

[1418] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1419] In this invention, the server includes: means for utilizing generative artificial intelligence to make appropriate suggestions for treatment, exercise, diet, and other lifestyle modification and cognitive training for mild cognitive impairment; means for monitoring the patient and collecting data to conduct preliminary screening and detect signs, and propose a treatment plan to the attending physician; means for the user to input information about the state of their cognitive function and lifestyle; means for the terminal to collect the data input by the user and transmit it to the server; means for the server to analyze the received data and use a generative artificial intelligence model to analyze the user's state of cognitive function and lifestyle patterns; means for the server to generate an optimal treatment plan and lifestyle improvement suggestions based on the analysis results; means for the server to transmit the generated suggestions to the terminal and display the suggestions to the user; and means for analyzing the user's emotional state using an emotion engine and adjusting the treatment plan. This enables the system to propose an optimal treatment plan and lifestyle improvement suggestions based on the patient's cognitive function, lifestyle, and emotional state.

[1420] "Generative AI" is an AI technology that generates optimal proposals and plans based on input data.

[1421] "Mild cognitive impairment" refers to a condition in which cognitive function is impaired beyond the normal range, but does not significantly interfere with daily life.

[1422] A "treatment plan" is a plan that includes a series of medical interventions and lifestyle changes designed to improve a patient's health.

[1423] "Correcting lifestyle habits" refers to improving daily habits such as diet, exercise, and sleep in order to maintain and improve health.

[1424] "Cognitive training" refers to exercises and activities designed to improve cognitive functions such as memory, attention, and problem-solving skills.

[1425] "Monitoring" refers to the continuous observation and recording of a patient's health condition and lifestyle habits.

[1426] "Data collection" refers to the process of gathering information obtained from patients.

[1427] "Preliminary screening" refers to an early stage examination conducted for the early detection and prevention of disease.

[1428] "Early warning signs" refers to technologies and methods for early detection of signs of disease or abnormalities.

[1429] An "emotion engine" is a technology that analyzes a user's emotional state and generates appropriate suggestions and plans based on that.

[1430] "Terminal" refers to an electronic device that a user uses to enter information or receive suggestions.

[1431] "Server" refers to a central processing unit for analyzing data and generating recommendations.

[1432] This invention is a system that utilizes generative artificial intelligence to provide appropriate suggestions for lifestyle modification such as treatment, exercise, and diet, as well as cognitive function training, to patients with mild cognitive impairment (MCI). This system includes functions such as patient monitoring and data collection, preliminary screening and symptom detection, and proposing treatment plans to the attending physician.

[1433] Hardware and software used

[1434] Devices: Electronic devices such as smartphones, tablets, and computers

[1435] Server: A central processing unit that analyzes data and generates proposals

[1436] Generative artificial intelligence models: For example, OpenAI's GPT-4

[1437] Emotion engines: for example, Affectiva's emotion recognition software

[1438] Data entry and collection

[1439] Users use devices such as smartphones and tablets to input information about their cognitive function and lifestyle habits. Specifically, they input information about their recent memory and concentration levels, dietary habits, exercise habits, and emotional state into the application. For example, a user might input, "I feel like my memory has been declining recently. My diet is unbalanced and I hardly exercise at all. Please suggest the best treatment plan and lifestyle habits to improve this condition."

[1440] Data transmission and analysis

[1441] The device collects data entered by the user and sends it to a server via the Internet. The data is encrypted before transmission to ensure security. The server analyzes the received data. Specifically, it uses a generative artificial intelligence model to analyze the state of the user's cognitive function and lifestyle patterns. It also uses an emotion engine to analyze the user's emotional state.

[1442] Proposal generation and delivery

[1443] Based on the analysis results, the server generates optimal treatment plans and lifestyle improvement suggestions. The generative AI model generates specific suggestions based on the user's cognitive function state and lifestyle. For example, it might suggest a "memory improvement training program" and a "balanced meal plan." The emotion engine takes the user's emotional state into account and adjusts the treatment plan. For example, if the user has recently felt joy, this can be used as an indicator of cognitive function improvement, and the progress of the treatment plan can be adjusted.

[1444] View Suggestions

[1445] The server sends the generated suggestions to the device. The device displays the suggestions received from the server to the user. The user can review and implement the suggestions. For example, the user opens the app and checks "Training program to improve memory," "Balanced meal plan," and "Yoga for relaxation." The user can see specific steps to implement the suggestions within the app.

[1446] The above is a specific embodiment of the system of the present invention. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1447] Step 1:

[1448] The user enters the data.

[1449] Users use devices such as smartphones and tablets to input information about their cognitive function status and lifestyle habits. Specifically, they input information such as recent memory and concentration status, dietary habits, exercise habits, and emotional state into the application. The input data includes details of the user's cognitive function status and lifestyle habits.

[1450] Step 2:

[1451] The device sends the data to the server.

[1452] The device collects data entered by the user and sends it to a server via the Internet. The data is encrypted before transmission to ensure security. The input is data on the state of cognitive function and lifestyle habits entered by the user, and the output is the encrypted data sent to the server.

[1453] Step 3:

[1454] The server analyzes the data.

[1455] The server analyzes the received data. Specifically, it uses a generative artificial intelligence model to analyze the user's cognitive function and lifestyle patterns. It also uses an emotion engine to analyze the user's emotional state. The input is encrypted data sent from the device, and the output is the analysis results.

[1456] Step 4:

[1457] The server generates the proposal.

[1458] Based on the analysis results, the server generates optimal treatment plans and lifestyle improvement suggestions. A generative AI model generates specific suggestions based on the user's cognitive function state and lifestyle. For example, it suggests a "training program to improve memory" and a "balanced meal plan." An emotion engine takes the user's emotional state into account and adjusts the treatment plan. The input is the analysis results, and the output is the generated suggestions.

[1459] Step 5:

[1460] The server sends the proposal to the device.

[1461] The server sends the generated proposal to the terminal. The proposal is encrypted and transmitted to ensure security. The input is the generated proposal, and the output is the encrypted proposal sent to the terminal.

[1462] Step 6:

[1463] The terminal displays the suggestions to the user.

[1464] The device displays the suggestions received from the server to the user. The user can review and implement the suggestions. For example, the user opens the app and reviews "Training program to improve memory," "Balanced meal plan," and "Yoga for relaxation." The input is the encrypted suggestion sent from the server, and the output is the suggestion displayed to the user.

[1465] (Application example 3)

[1466] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1467] While conventional cognitive function improvement systems can make suggestions based on the patient's cognitive function and lifestyle habits, they lack support for proposing treatment plans that take the user's emotional state into account and for increasing motivation. Therefore, there is a need for systems that can suggest optimal fitness programs and meal plans based on the user's emotional state, as well as provide messages and music to increase motivation.

[1468] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1469] In this invention, the server includes means for utilizing generative AI to make appropriate suggestions for treatment, exercise, dietary and other lifestyle modification and cognitive function training for mild cognitive impairment (MCI), means for monitoring patients and collecting data to conduct preliminary examinations and detect signs, and propose treatment plans to attending physicians, means for evaluating the user's emotional state and, based on the results, proposing optimal fitness programs and meal plans, and means for providing motivational messages and music according to the user's emotional state. This makes it possible to propose optimal treatment plans and lifestyle improvements that take the user's emotional state into consideration.

[1470] "Generative AI" is an artificial intelligence technology that generates new information and suggestions based on data.

[1471] "Mild cognitive impairment (MCI)" refers to a condition in which cognitive function declines beyond the normal range but does not significantly interfere with daily life.

[1472] "Correcting lifestyle habits" refers to improving lifestyle habits such as diet, exercise, and sleep in order to maintain and improve health.

[1473] "Cognitive training" refers to exercises and activities aimed at improving cognitive functions such as memory, attention, and judgment.

[1474] "Monitoring" refers to the continuous observation and recording of a patient's health condition and behavior.

[1475] "Data collection" refers to collecting information about a patient's health and lifestyle.

[1476] A "preliminary examination" refers to an early stage examination conducted for the purpose of early detection and prevention of disease.

[1477] "Early detection" refers to the early detection of signs of illness or abnormalities.

[1478] A "treatment plan" refers to formulating the optimal treatment method and schedule based on the patient's health condition.

[1479] "Emotional state" refers to emotional states such as joy, sadness, anger, etc.

[1480] A "fitness program" refers to an exercise plan aimed at improving physical fitness and maintaining health.

[1481] "Meal plan" refers to a meal plan and schedule designed to maintain or improve health.

[1482] "Motivational messages" refer to words of encouragement and support that motivate and motivate users.

[1483] "Music" refers to a combination of sounds that affect emotions and moods through the sense of hearing.

[1484] A system for implementing this invention utilizes generative AI to provide appropriate suggestions for treatment, exercise, dietary and other lifestyle modification, and cognitive training for mild cognitive impairment (MCI). It also includes a function to assess the user's emotional state, suggest optimal fitness programs and meal plans based on that, and provide motivational messages and music.

[1485] System configuration

[1486] The system consists of the following major components:

[1487] 1. Generative AI:

[1488] Generate optimal treatment plans and lifestyle improvement suggestions based on the patient's cognitive function and lifestyle habits.

[1489] Software used: Python, generative AI models (e.g., GPT-3)

[1490] 2. Emotion Engine:

[1491] It assesses the user's emotional state and adjusts treatment plans, fitness programs, and meal plans accordingly.

[1492] Software used: Emotion recognition API (e.g. Microsoft Azure Emotion API)

[1493] 3. Monitoring System:

[1494] Continuously observe and record the patient's health and behavior.

[1495] Hardware used: Smartphones, wearable devices

[1496] 4. Data Collection System:

[1497] Collect information about the patient's health and lifestyle.

[1498] Hardware used: Smartphones, wearable devices

[1499] 5. User Interface:

[1500] It provides an interface for the user to interact with the system.

[1501] Hardware used: Smartphone, tablet

[1502] Processing flow

[1503] 1. Data Collection:

[1504] Users input data about their cognitive function, lifestyle habits, and emotional state via smartphones or wearable devices.

[1505] 2. Data Analysis:

[1506] The server analyzes the collected data, and the generative AI generates optimal treatment plans and suggestions for improving lifestyle habits.

[1507] 3. Emotional assessment:

[1508] An emotional engine assesses the user's emotional state and adjusts suggestions accordingly.

[1509] 4. Providing Proposals:

[1510] It provides users with fitness programs, meal plans, motivational messages and music tailored to their smartphone.

[1511] Specific examples

[1512] For example, a user visits a fitness gym and launches the app. The app evaluates the user's cognitive function and emotional state and suggests an optimal training plan. The user follows the suggestions and the app displays motivational messages.

[1513] Prompt Sentence Examples

[1514] The user's cognitive state is 80 and their emotional state is 90. Suggest an optimal fitness program and meal plan.

[1515] In this way, it becomes possible to propose optimal treatment plans and lifestyle improvements that take into account the user's emotional state.

[1516] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1517] Step 1:

[1518] Users input data about their cognitive function, lifestyle habits, and emotional state via smartphones or wearable devices.

[1519] Input: Cognitive function test results, lifestyle data (diet, exercise, sleep, etc.), emotional state data

[1520] Output: The entered data is sent to the server.

[1521] What it does: The user launches a smartphone app and completes a cognitive function test. The wearable device records their heart rate and activity, and answers questions to assess their emotional state.

[1522] Step 2:

[1523] The server analyzes the collected data, and the generative AI generates optimal treatment plans and suggestions for improving lifestyle habits.

[1524] Input: Cognitive function data, lifestyle data, and emotional state data submitted by the user

[1525] Output: Optimal treatment plan and lifestyle improvement suggestions

[1526] What it does: The server analyzes the data using Python and uses a generative AI model (e.g., GPT-3) to generate optimal treatment plans and lifestyle improvement suggestions.

[1527] Step 3:

[1528] An emotional engine assesses the user's emotional state and adjusts suggestions accordingly.

[1529] Input: User emotional state data, suggestions generated by generative AI

[1530] Output: Recommendations tailored to emotional state

[1531] What it does: The server uses an emotion recognition API (e.g., Microsoft Azure Emotion API) to assess the user's emotional state and adjust the generated suggestions.

[1532] Step 4:

[1533] It provides users with fitness programs, meal plans, motivational messages and music tailored to their smartphone.

[1534] Input: Adjusted proposal

[1535] Output: Fitness programs, meal plans, messages, music displayed on the user's smartphone

[1536] How it works: The server sends tailored recommendations to the user's smartphone, where they are displayed by the app. The user then follows the recommendations to train and eat, and receives motivational messages and music.

[1537] In this way, it becomes possible to propose optimal treatment plans and lifestyle improvements that take into account the user's emotional state.

[1538] 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.

[1539] 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> ) 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.

[1540] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1541] 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.

[1542] [Third embodiment]

[1543] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1544] 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.

[1545] 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).

[1546] 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.

[1547] 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.

[1548] 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).

[1549] 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.

[1550] 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.

[1551] 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.

[1552] 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.

[1553] 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.

[1554] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1555] "Example 1"

[1556] One embodiment of the present invention provides a system utilizing generative AI. This system provides appropriate recommendations for treatment, exercise, diet, and other lifestyle modifications, as well as cognitive training, to patients with mild cognitive impairment (MCI). Specifically, the generative AI receives input information on the patient's cognitive function and lifestyle, and generates optimal treatment plans and lifestyle improvement recommendations based on that information.

[1557] "Example 2"

[1558] Furthermore, the system of the present invention monitors patients and collects data to perform preliminary examinations, detect signs, and propose treatment plans to the attending physician. Specifically, the generative AI collects patients' biological information and behavioral data in real time and analyzes this data to perform preliminary examinations and detect signs. It also proposes treatment plans to the attending physician based on this information.

[1559] "Example 3"

[1560] In the system of the present invention, a generative AI optimizes proposals for treatment, exercise, diet, and other lifestyle modification and cognitive training based on the patient's cognitive function status. Specifically, the generative AI receives information on the patient's cognitive function status and lifestyle as input, and generates optimal treatment plans and lifestyle improvement proposals based on that information.

[1561] The processing flow of each embodiment will be described below.

[1562] "Example 1"

[1563] Step 1: The generative AI receives information from the patient about their lifestyle and cognitive function status as input.

[1564] Step 2: Based on the information it receives, the generative AI generates suggestions for treatment, exercise, diet, and other lifestyle changes, as well as cognitive function training.

[1565] Step 3: The generative AI presents the generated suggestions to the patient.

[1566] "Example 2"

[1567] Step 1: The generative AI collects the patient's biometric information and behavioral data in real time. Step 2: The generative AI analyzes the collected data and performs preliminary examinations and detects signs. Step 3: Based on the analysis results, the generative AI proposes a treatment plan to the attending physician.

[1568] "Example 3"

[1569] Step 1: The generative AI receives information such as the patient's cognitive function and lifestyle as input.

[1570] Step 2: Based on the information it receives, the generative AI optimizes suggestions for treatment, exercise, diet, and other lifestyle changes, as well as cognitive function training.

[1571] Step 3: The generative AI presents optimized recommendations to the patient.

[1572] Example 1

[1573] Next, a description will be given of Example 1 of Form 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."

[1574] There is a need to efficiently provide personalized treatment plans and lifestyle improvement suggestions to patients with mild cognitive impairment (MCI). However, conventional systems have had issues with difficulty in generating optimal suggestions based on the patient's cognitive function status and lifestyle, and with the time-consuming task of collecting and processing data. Furthermore, it is often not possible to provide generated suggestions to patients quickly and accurately.

[1575] 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.

[1576] In this invention, the server includes means for inputting information on the patient's cognitive function status and lifestyle habits using a terminal, means for receiving the data transmitted from the terminal and storing it in a database, means for preprocessing the data and generating prompt sentences to be input to the generative AI model, means for the generative AI model to generate an optimal treatment plan and lifestyle improvement suggestions, and means for transmitting the generated suggestions to the terminal, which then displays the suggestions to the user. This makes it possible to efficiently generate personalized suggestions based on the patient's cognitive function status and lifestyle habits and provide them quickly and accurately.

[1577] "Generative AI" is a type of artificial intelligence that generates optimal proposals and plans based on input data.

[1578] "Mild cognitive impairment (MCI)" is a medical term that refers to a condition in which cognitive function is impaired beyond the normal range but does not significantly interfere with daily life.

[1579] A "device" is an electronic device (e.g., PC, tablet, smartphone) that a user uses to enter information or view generated suggestions.

[1580] A "server" is a computer system that receives, stores, and processes data sent from a terminal.

[1581] A "database" is a system for systematically storing and managing data received by a server.

[1582] A "prompt" is a document to be input into a generative AI model, and contains information about the patient's cognitive function and lifestyle habits.

[1583] "Suggestions" refer to treatment plans and lifestyle improvement suggestions generated by generative AI models.

[1584] "Preprocessing" refers to processes such as formatting data and filling in missing values ​​before inputting the data into a generative AI model.

[1585] This invention is a system that utilizes generative AI to provide appropriate suggestions for lifestyle modification such as treatment, exercise, and diet, as well as cognitive function training, to patients with mild cognitive impairment (MCI). This system is implemented using a terminal, a server, and a generative AI model.

[1586] 1. Data Collection

[1587] The user uses a device (PC, tablet, smartphone, etc.) to input information about the patient's cognitive function status and lifestyle habits. Specific input items include cognitive function test results, daily activity level, dietary habits, and sleep patterns. For example, the user opens the smartphone app and inputs the following information:

[1588] Cognitive function test results: MMSE score 24 points

[1589] Daily activity level: 5,000 steps per day

[1590] Diet: Lack of vegetables, high in fat

[1591] Sleep pattern: 6 hours a day

[1592] 2. Data transmission and storage

[1593] The terminal encrypts the entered data and sends it to the server. The server receives the data sent from the terminal and temporarily stores it in memory. It then saves it in a database (e.g., MySQL, PostgreSQL). When saving, the data integrity is checked to see if it contains any invalid data.

[1594] 3. Data Processing

[1595] The server retrieves the necessary data from the database and performs preprocessing to input it into the generative AI model. Specifically, it normalizes the data and fills in missing values. Next, it generates a prompt sentence based on the preprocessed data, such as:

[1596] The patient's cognitive function test results show an MMSE score of 24. Their daily activity level is 5000 steps per day, and their diet is high in fat and lacks vegetables. Their sleep pattern is 6 hours per day. Generate an optimal treatment plan and lifestyle modification suggestions for this patient.

[1597] 4. Proposal Generation

[1598] The server calls a generative AI model (e.g., OpenAI's GPT-4) and inputs the generated prompt. The generative AI model generates optimal treatment plans and lifestyle improvement suggestions based on the input prompt. For example, the following suggestions may be generated:

[1599] Cognitive training: 30 minutes of puzzle games every day

[1600] Exercise program: Walking three times a week (30 minutes each time)

[1601] Meal plan: Balanced meals with lots of vegetables

[1602] Improved sleep: Go to bed at the same time every night and get 7 hours of sleep

[1603] 5. Providing Proposals

[1604] The server sends the generated proposal to the terminal, and the terminal displays the proposal received from the server to the user, who can confirm and execute the proposal through the terminal.

[1605] In this way, the system can provide MCI patients with personalized treatment plans and lifestyle modification suggestions.

[1606] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1607] Step 1:

[1608] The user uses the device to input information about the patient's cognitive function status and lifestyle habits. Specific input items include cognitive function test results, daily activity level, dietary habits, and sleep patterns. For example, the user opens the smartphone app and inputs an MMSE score of 24, 5,000 steps per day, a diet low in vegetables and high in fat, and a sleep pattern of six hours per day. The input data is temporarily stored on the device.

[1609] Step 2:

[1610] The device encrypts the input data and sends it to the server. The data includes the results of cognitive function tests, daily activity levels, dietary habits, and sleep patterns. The server receives the data and temporarily stores it in its memory.

[1611] Step 3:

[1612] The server stores the received data in a database. When storing the data, it checks the data for consistency and whether it contains any invalid data. The database stores information on the patient's cognitive function status and lifestyle habits in an organized manner.

[1613] Step 4:

[1614] The server retrieves the necessary data from the database and performs preprocessing to input it into the generative AI model. Preprocessing includes normalizing the data and imputing missing values. For example, if there is missing data, it is imputed with the mean or median. The preprocessed data is converted into prompt statements to be input into the generative AI model.

[1615] Step 5:

[1616] The server generates prompts based on the preprocessed data. The generated prompts include information about the patient's cognitive function and lifestyle. For example, the following prompts are generated:

[1617] The patient's cognitive function test results show an MMSE score of 24. Their daily activity level is 5000 steps per day, and their diet is high in fat and lacks vegetables. Their sleep pattern is 6 hours per day. Generate an optimal treatment plan and lifestyle modification suggestions for this patient.

[1618] Step 6:

[1619] The server calls the generative AI model and inputs the generated prompt. The generative AI model generates optimal treatment plans and lifestyle improvement suggestions based on the input prompt. For example, the following suggestions may be generated:

[1620] Cognitive training: 30 minutes of puzzle games every day

[1621] Exercise program: Walking three times a week (30 minutes each time)

[1622] Meal plan: Balanced meals with lots of vegetables

[1623] Improved sleep: Go to bed at the same time every night and get 7 hours of sleep

[1624] Step 7:

[1625] The server sends the generated suggestions to the device. The suggestions include personalized treatment plans and lifestyle improvement suggestions. The device displays the suggestions received from the server to the user. The user can confirm and implement the suggestions through the device.

[1626] (Application example 1)

[1627] Next, a description will be given of Application Example 1 of Form 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."

[1628] It is important to provide appropriate treatment and lifestyle improvement suggestions to patients with mild cognitive impairment (MCI), but current systems make it difficult to provide optimal suggestions tailored to each patient's individual condition. Furthermore, there is a lack of a way for patients to easily input their condition and receive appropriate suggestions in physical stores, which could delay treatment and lifestyle improvement.

[1629] 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.

[1630] In this invention, the server includes: means for utilizing generative AI to make appropriate suggestions for treatment, exercise, diet, and other lifestyle modification and cognitive function training for mild cognitive impairment (MCI); means for monitoring patients and collecting data to conduct preliminary examinations and detect signs, and propose treatment plans to their physicians; and means for patients to use their smartphones at physical stores to input information about their cognitive function status and lifestyle habits, and for the generative AI to provide optimal treatment plans and lifestyle improvement suggestions based on that information. This enables patients to easily input their status at physical stores and receive optimal treatment and lifestyle improvement suggestions tailored to their individual conditions.

[1631] "Generative AI" is an artificial intelligence technology that generates new information and suggestions based on input data.

[1632] "Mild cognitive impairment (MCI)" is a medical term that refers to a condition in which cognitive function is impaired beyond the normal range but does not significantly interfere with daily life.

[1633] "Treatment" refers to medical procedures or measures to cure an illness or disability.

[1634] "Exercise" refers to activities that maintain and improve health by moving the body.

[1635] "Meal" refers to the act of eating to obtain nutrition and the contents of that eating.

[1636] "Lifestyle habits" refer to the actions and habits of daily life, and are factors that affect health.

[1637] "Cognitive function" refers to intellectual functions such as memory, attention, language, and judgment.

[1638] "Training" refers to exercises or practice aimed at improving a particular skill or ability.

[1639] "Monitoring" refers to the continuous observation and recording of a particular condition or activity.

[1640] "Data collection" refers to the act of gathering necessary information.

[1641] "Preliminary screening" refers to an early stage examination aimed at early detection of disease or disability.

[1642] "Early detection" refers to the early detection of signs of illness or disability.

[1643] A "treatment plan" refers to a plan that outlines specific steps and methods for curing a patient's illness or disorder.

[1644] A "physical store" refers to a store that exists in a physical location and where customers can visit in person to purchase products or services.

[1645] A "smartphone" refers to a high-function mobile phone that can connect to the Internet and use applications.

[1646] A system for implementing this invention utilizes generative AI to provide patients with mild cognitive impairment (MCI) with appropriate recommendations for treatment, exercise, diet, and other lifestyle modifications, as well as cognitive training. The system includes means for monitoring patients and collecting data, conducting preliminary screenings and detecting signs, and proposing treatment plans to their physicians. The system also includes means for allowing patients to input information about their cognitive function status and lifestyle habits using their smartphones in a physical store, and for the generative AI to provide optimal treatment plans and lifestyle improvement recommendations based on that information.

[1647] Explanation of program processing

[1648] The server uses a generative AI model to receive input about the patient's cognitive function and lifestyle habits. Specifically, the patient enters their information using a smartphone. The entered information is sent to the server and analyzed by the generative AI model. Based on the analysis results, an optimal treatment plan and lifestyle improvement suggestions are generated.

[1649] The hardware used includes mobile devices such as smartphones and tablets, and the software uses Python and OpenAI's API. The generative AI model uses OpenAI's "text-davinci-003" engine.

[1650] Specific examples

[1651] For example, consider a 65-year-old male patient with mild cognitive impairment, who is physically inactive and has a poor diet. He enters the following information on his smartphone at a brick-and-mortar pharmacy:

[1652] Age: 65

[1653] Gender: Male

[1654] Cognitive status: Mild cognitive impairment

[1655] Lifestyle: Lack of exercise, unbalanced diet

[1656] This information is sent to a server, where the generative AI model analyzes it, and the resulting recommendation is to "walk 30 minutes a day and take a supplement containing B vitamins."

[1657] An example of a prompt to input to a generative AI model is as follows:

[1658] Patient information: Age: 65, Gender: Male, Cognitive status: Mild cognitive impairment, Lifestyle: Lack of exercise, poor diet

[1659] Generate optimal treatment plans and lifestyle modification suggestions.

[1660] In this way, patients can easily enter their condition at a physical store and receive the most appropriate treatment and lifestyle improvement suggestions based on their individual condition.

[1661] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1662] Step 1:

[1663] Users use their smartphones to input information about their cognitive function status and lifestyle habits. The input information includes age, gender, cognitive function status, and lifestyle habits (lack of exercise, unbalanced diet, etc.). The input data is sent to a server via a smartphone application.

[1664] Input: Age, gender, cognitive function status, lifestyle habits

[1665] Output: Patient information sent to the server

[1666] Step 2:

[1667] The server analyzes the received patient information and generates prompts for the generative AI model. The prompts are instructions for generating optimal treatment plans and lifestyle improvement suggestions based on the patient information.

[1668] Input: Patient information sent to the server

[1669] Output: The prompt to send to the generative AI model

[1670] Step 3:

[1671] The server sends the generated prompts to the generative AI model, which analyzes the prompts and generates optimal treatment plans and lifestyle improvement suggestions based on the patient's condition.

[1672] Input: A prompt to send to the generative AI model

[1673] Output: Suggested results from the generative AI model

[1674] Step 4:

[1675] The server analyzes the proposal results received from the generative AI model and converts them into a format that is easy for the user to understand. Specifically, it converts the proposal results into text format and sends them to a smartphone application.

[1676] Input: Suggested results from a generative AI model

[1677] Output: Text representation of the proposal results to be sent to the user

[1678] Step 5:

[1679] The user can then use the smartphone application to check the proposed results sent from the server, which include specific treatment plans and lifestyle improvement suggestions.

[1680] Input: Text format of the proposal results sent from the server

[1681] Output: Suggested results for user review

[1682] In this way, users can input information about their cognitive function status and lifestyle habits, and receive optimal treatment plans and lifestyle improvement suggestions from the generative AI model.

[1683] Example 2

[1684] Next, a description will be given of Example 2 of Form 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."

[1685] Conventional medical systems have difficulty collecting and analyzing patients' vital signs and behavioral data in real time. Furthermore, they often lack accuracy and speed when it comes to detecting early signs and proposing treatment plans. This makes it difficult to provide treatment and prevention at the appropriate time, and makes it difficult to optimally manage patients' health conditions.

[1686] 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.

[1687] In this invention, the server includes means for the terminal to collect biometric information and behavioral data of the user in real time, means for the terminal to transmit the collected data to the server, means for the server to store the received data in a database, means for the server to analyze the stored data using a generative AI model, means for the server to detect signs based on the results of the data analysis, means for the server to propose a treatment plan to the attending physician based on the results of the sign detection, and means for notifying the attending physician of the proposed treatment plan generated by the server. This enables the patient's biometric information and behavioral data to be collected and analyzed in real time, enabling rapid and accurate sign detection and treatment plan proposals.

[1688] "Generative AI" is a system that uses artificial intelligence technology to generate data and perform analysis and predictions.

[1689] "Mild cognitive impairment" refers to a condition in which cognitive function is impaired beyond the normal range, but does not significantly interfere with daily life.

[1690] "Monitoring" refers to the continuous observation and recording of a patient's vital signs and behavioral data.

[1691] "Data collection" refers to obtaining a patient's biometric information and behavioral data using devices and sensors.

[1692] A "preliminary screening" refers to an examination to assess a patient's health status in advance and detect potential health problems early.

[1693] "Predictive detection" refers to analyzing collected data to predict potential future health problems.

[1694] A "treatment plan" refers to a plan that suggests optimal treatment and preventative measures based on the patient's health condition.

[1695] "Terminal" refers to a device used to collect biometric and behavioral data from a user. Examples include wearable devices and smartphones.

[1696] "Server" refers to the computer system that stores collected data and analyzes it using generative AI.

[1697] "Database" refers to a system for organizing and storing collected data.

[1698] "Generative AI model" refers to an artificial intelligence model used to analyze collected data and make predictions or recommendations.

[1699] "Notification" refers to the means of communicating the generated treatment plan and the results of predictive detection to the attending physician.

[1700] This invention is a system that collects and analyzes a patient's biological information and behavioral data in real time, performs a preliminary examination and detects signs of illness, and proposes a treatment plan to the attending physician. Specific embodiments of this system are described below.

[1701] System configuration

[1702] Hardware Configuration

[1703] 1. Terminal: A device used to collect biometric and behavioral data from a user. Examples include wearable devices (e.g., smartwatches) and smartphones.

[1704] 2. Server: A computer system that stores collected data and analyzes it using generative AI models. The server has a powerful processor and large storage capacity.

[1705] 3. Database: A system for organizing and storing collected data. Specific examples include relational databases such as MySQL and PostgreSQL.

[1706] Software Configuration

[1707] 1. Generative AI model: An artificial intelligence model used to analyze collected data and make predictions or recommendations. Examples include GPT-4 and BERT.

[1708] 2. Data collection application: Software that collects data from devices and sends it to a server. Often implemented as a smartphone app.

[1709] 3. Data analysis software: Software that runs on a server and analyzes data using generative AI models.

[1710] System Operation

[1711] Data collection

[1712] The device (e.g., a smartwatch) collects real-time biometric and behavioral data, such as the user's heart rate, blood pressure, body temperature, number of steps taken, and sleep patterns, and transmits this data to a smartphone via Bluetooth or Wi-Fi.

[1713] Data transmission

[1714] The smartphone app sends the collected data to a server over the internet, where it is encrypted and transmitted using a secure communication protocol (e.g., HTTPS).

[1715] Data storage

[1716] The server stores the received data in a database, organized by user and timestamp.

[1717] Data analysis

[1718] The server analyzes the stored data using a generative AI model (e.g., GPT-4), which detects anomalous patterns based on the collected data and performs predictive detection.

[1719] Predictive detection

[1720] The server uses the results of the data analysis to predict potential future health problems, for example, if abnormal fluctuations in heart rate are detected, it may be a sign of heart disease.

[1721] Treatment plan proposal

[1722] The server uses the generative AI model to propose treatment plans to the patient's doctor based on the results of the symptom detection. For example, if symptoms of heart disease are detected, the server will suggest a consultation with a cardiologist.

[1723] Proposal Notification

[1724] The server then notifies the doctor of the proposed treatment plan via email or a dedicated app.

[1725] Specific examples

[1726] Example 1: Heart rate abnormality detection

[1727] Suppose a user is wearing a smartwatch. The smartwatch measures the user's heart rate every minute and sends it to a server via a smartphone app. The server stores the received heart rate data in a database and analyzes it using a generative AI model. As a result of the analysis, it detects that the heart rate is abnormally high, which may be a sign of heart disease. Based on this information, the server recommends that the user undergo a heart disease examination with their doctor.

[1728] Prompt Sentence Examples

[1729] "Analyze the user's heart rate data and detect any abnormal fluctuations. If abnormalities are detected, suggest possible diseases and recommended treatments."

[1730] In this way, a system is realized in which the server, terminal, and user work together to monitor the patient's health condition in real time and propose an appropriate treatment plan.

[1731] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1732] Step 1: Data collection

[1733] A device (e.g., a smartwatch) collects a user's biometric information (heart rate, blood pressure, body temperature, etc.) and behavioral data (number of steps, sleep patterns, etc.) in real time. Specifically, a smartwatch measures the heart rate and counts the number of steps every minute. The input is the user's biometric information and behavioral data, and the output is the collected data.

[1734] Step 2: Send data

[1735] The device sends the collected data to a server via a smartphone app. Specifically, the smartwatch sends data to the smartphone using Bluetooth, and the smartphone app sends the data to the server via the Internet. The input is the collected data, and the output is the data sent to the server.

[1736] Step 3: Save Data

[1737] The server stores the received data in a database. Specifically, the server adds new data to the database whenever it arrives. The input is the data sent to the server, and the output is the data stored in the database.

[1738] Step 4: Data analysis

[1739] The server analyzes the stored data using a generative AI model (e.g., GPT-4). Specifically, the server analyzes the data at a fixed time each day to detect abnormal patterns. The input is the data stored in the database, and the output is the analysis results.

[1740] Step 5: Predictive detection

[1741] The server performs symptom detection based on the results of data analysis. Specifically, the server detects abnormal fluctuations in heart rate and determines whether these are a sign of a specific disease. The input is the analysis results, and the output is the result of symptom detection.

[1742] Step 6: Propose a treatment plan

[1743] The server uses a generative AI model to propose a treatment plan to the attending physician based on the results of the sign detection. Specifically, the server generates a recommendation such as, "You are at high risk of heart disease, so we recommend that you see a cardiologist." The input is the result of the sign detection, and the output is a proposed treatment plan.

[1744] Step 7: Proposal Notification

[1745] The server notifies the doctor of the proposed treatment plan it has created. Specifically, the server sends the proposed plan to the doctor's email address. The input is the proposed treatment plan, and the output is a notification to the doctor.

[1746] (Application example 2)

[1747] Next, a description will be given of Application Example 2 of Form 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."

[1748] Conventional medical systems are not fully optimized for the treatment of mild cognitive impairment (MCI), lifestyle modification, or cognitive function training. Furthermore, there are limited systems that can collect patients' biometric and behavioral data in real time to perform preliminary screening and detect signs of illness. Furthermore, the lack of functionality to detect signs of security risks and propose appropriate countermeasures makes it difficult to ensure user safety.

[1749] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a means for utilizing generative AI to make appropriate suggestions for treatment, lifestyle modification such as exercise and diet, and cognitive function training for mild cognitive impairment (MCI); a means for performing preliminary screening and symptom detection based on patient monitoring and data collection, and proposing treatment plans to the attending physician; a means for collecting user biometric information and behavioral data in real time and detecting signs of security risks; a means for analyzing the collected data and detecting abnormal patterns and high-risk states; and a means for proposing appropriate security measures to the user based on the analysis results. This makes it possible to optimize treatment for mild cognitive impairment, lifestyle modification, and cognitive function training, and further to detect signs of security risks and propose appropriate measures through the collection and analysis of biometric information and behavioral data in real time.

[1750] "Generative AI" is an artificial intelligence technology that generates new information and suggestions based on data.

[1751] "Mild cognitive impairment (MCI)" is a condition in which cognitive function is somewhere between normal and dementia, and in which there is a decline in cognitive function, but not to the point of interfering with daily life.

[1752] "Treatment" refers to medical procedures and drug therapy to cure illness or disability.

[1753] "Exercise" is an activity that maintains and improves health by moving the body.

[1754] "Meal" refers to the act of eating to obtain nutrition and the contents of that eating.

[1755] "Lifestyle modification" refers to actions and habits that improve unhealthy lifestyles and lead a healthy life.

[1756] "Cognitive training" refers to exercises and activities aimed at improving cognitive functions such as memory, attention, and judgment.

[1757] "Monitoring" is the act of continuously observing a specific subject and collecting data.

[1758] "Data collection" is the act of gathering information for a specific purpose.

[1759] A "preliminary examination" refers to an initial test to detect illness or abnormalities early.

[1760] "Predictive detection" is the act of finding signs of abnormalities or problems before they occur.

[1761] "Treatment planning" refers to planning the optimal treatment method according to the patient's condition.

[1762] "Biometric information" refers to physiological data from the human body, such as heart rate, blood pressure, and body temperature.

[1763] "Behavioral Data" refers to information about an individual's behavioral patterns and activity levels.

[1764] "Security risk" refers to factors or situations that could threaten the safety of information or systems.

[1765] "Abnormal patterns" refer to behavior or data fluctuations that are different from the norm.

[1766] "High-risk situation" refers to a situation or condition that is deemed to be high risk.

[1767] "Security measures" refer to the means and methods for ensuring the safety of information and systems.

[1768] As an embodiment of the present invention, the following system is constructed.

[1769] System Configuration

[1770] Hardware

[1771] Smartphone: A device used to collect biometric and behavioral data from users. It can be an iOS or Android smartphone.

[1772] Sensors: Sensors for collecting biometric information such as heart rate, activity level, and stress level. Specifica...

Claims

1. A means for obtaining information regarding the patient's cognitive function status and lifestyle habits; A means for analyzing the patient's cognitive function state and lifestyle pattern using a generative AI; means for acquiring data collected by monitoring said patient; means for analyzing an emotional state of the patient using an emotion engine; A means for proposing a treatment plan and lifestyle modification for the patient based on the analysis result using the generative AI and the analysis result of the emotional state; A system including:

2. The system of claim 1, wherein the means for proposing a treatment plan and lifestyle corrections for the patient uses a prompt sentence that instructs the system to propose a treatment plan and lifestyle corrections for the patient based on the analysis results using the generative AI and the analysis results of the emotional state, and the generative AI to propose a treatment plan and lifestyle corrections for the patient.

3. The system of claim 1 further comprising means for performing a pre-diagnosis or prognostic detection using data collected by monitoring the patient.

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