system

Abstract

The system according to this embodiment aims to analyze the user's medical data and test results to detect early signs of cancer. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, an evaluation unit, and a recommendation unit. The data collection unit collects the user's medical data and test results. The analysis unit analyzes the data collected by the data collection unit and detects early signs of cancer. The evaluation unit performs a risk assessment based on the data obtained by the analysis unit. The recommendation unit provides the user with the most suitable test recommendations based on the risk assessment performed by the evaluation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the medical data and test results of users have not been sufficiently utilized to effectively detect early signs of cancer, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the medical data and test results of users and detect early signs of cancer.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, an evaluation unit, and a recommendation unit. The data collection unit collects the user's medical data and test results. The analysis unit analyzes the data collected by the data collection unit and detects early signs of cancer. The evaluation unit performs a risk assessment based on the data obtained by the analysis unit. The recommendation unit provides the user with the most suitable test recommendations based on the risk assessment performed by the evaluation unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's medical data and test results to detect early signs of cancer. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. 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. Also, the database 24 and the communication I / F 26 are 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).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An AI-based application according to an embodiment of the present invention is a system that analyzes a user's medical data and test results to detect early signs of cancer. This system performs risk assessments based on the latest medical research and data and provides the user with optimal test recommendations. For example, the AI-based application collects the user's medical data and test results. For example, it collects medical data such as medical records, test results, and prescription information. Next, the AI ​​analyzes the collected data and detects early signs of cancer. For example, the AI ​​detects abnormal values ​​of specific biomarkers. Furthermore, the AI ​​performs a risk assessment and provides the user with optimal test recommendations. For example, the AI ​​uses statistical methods and scoring systems to perform risk assessments and determine the types and frequencies of tests to be recommended. This improves the rate of early cancer detection and increases the success rate of treatment. It also contributes to reducing medical costs. Thus, the AI-based application can efficiently collect, analyze, assess risk, and recommend tests based on the user's medical data and test results.

[0029] The AI-based application according to the embodiment comprises a data collection unit, an analysis unit, an evaluation unit, and a recommendation unit. The data collection unit collects the user's medical data and test results. The data collection unit can collect medical data such as medical records, test results, and prescription information. The data collection unit can periodically collect medical data provided by the user. The data collection unit can also collect test results provided by medical institutions. Furthermore, with the user's consent, the data collection unit can build a system that automatically collects medical data. The analysis unit analyzes the data collected by the data collection unit and detects early signs of cancer. The analysis unit can analyze the data using AI to detect abnormal values ​​of specific biomarkers. The analysis unit can also detect early signs of cancer by referring to the latest medical research data. The analysis unit can automate the process of using AI to analyze medical data and detect early signs of cancer. The evaluation unit performs a risk assessment based on the data obtained by the analysis unit. The evaluation unit performs a risk assessment using statistical methods or scoring systems. The evaluation unit can assess the user's health risk using AI to perform a risk assessment. The evaluation unit can, for example, save risk assessment results and refer to past evaluation results. The recommendation unit provides the user with the most suitable test recommendations based on the risk assessment performed by the evaluation unit. The recommendation unit can, for example, notify the user of test recommendations based on the risk assessment results using AI. The recommendation unit can, for example, build a system for notifying the user of test recommendations. The recommendation unit can also, for example, update test recommendations based on the user's risk assessment results. This enables the AI-based application according to the embodiment to efficiently collect, analyze, assess risks, and recommend tests based on the user's medical data and test results.

[0030] The data collection unit collects users' medical data and test results. Specifically, it can collect medical data such as medical records, test results, and prescription information. The data collection unit has an interface for regularly collecting medical data provided by users, allowing users to easily upload their own medical data. The data collection unit also has an API for automatically acquiring test results provided by medical institutions, and can collect data in conjunction with medical institution systems. Furthermore, with the user's consent, the data collection unit can build a system that automatically collects medical data. This system automatically acquires data from the user's electronic medical records and wearable devices and stores it in a central database. For example, if a user is wearing a smartwatch, data such as heart rate, blood pressure, and exercise level can be collected from the device in real time and integrated with medical data. This allows the data collection unit to comprehensively understand the user's health status and efficiently collect necessary data. In addition, the data collection unit employs encryption technology to ensure data privacy and security, protecting users' personal information. This allows the data collection unit to safely collect medical data while gaining the user's trust.

[0031] The analysis unit analyzes data collected by the data collection unit to detect early signs of cancer. Specifically, it uses AI to analyze data and detect abnormal values ​​of specific biomarkers. The AI ​​learns from large amounts of medical data using machine learning algorithms, enabling it to detect early signs of cancer with high accuracy. For example, it uses deep learning technology to analyze blood test results and image data to detect abnormal patterns. The analysis unit can also detect early signs of cancer by referring to the latest medical research data. This allows the analysis unit to always incorporate the latest knowledge into its analysis. Furthermore, the analysis unit automates the process by which the AI ​​analyzes medical data and detects early signs of cancer. This allows the analysis unit to analyze data quickly and efficiently and provide early warnings to users. For example, if the AI ​​analyzes blood test results and detects abnormal values ​​of a specific biomarker, it sends a notification to the user recommending early testing. This allows the analysis unit to monitor the user's health status in real time and enable early intervention. In addition, the analysis unit can detect abnormal changes by comparing data with historical data. This allows the analysis unit to continuously monitor changes in the user's health status and ensure that early signs are not missed.

[0032] The evaluation unit performs risk assessments based on data obtained by the analysis unit. Specifically, it uses statistical methods and scoring systems to perform risk assessments. The evaluation unit is equipped with an AI-powered algorithm for assessing users' health risks. For example, the AI ​​calculates a user's cancer risk score based on collected medical data and analysis results. This score is calculated considering factors such as the user's age, gender, family history, and lifestyle. The evaluation unit can save risk assessment results and refer to past assessment results. This allows the evaluation unit to track changes in the user's health risks and support long-term health management. Furthermore, the evaluation unit provides a dashboard to visually display the risk assessment results. Through this dashboard, users can grasp their health risks at a glance. For example, if the risk score is high, a warning will be displayed on the dashboard, recommending early testing or consultation with a doctor. The evaluation unit can also share risk assessment results with healthcare institutions. This allows doctors to understand the user's health risks and provide appropriate diagnosis and treatment. The evaluation unit plays a crucial role in comprehensively assessing users' health risks and supporting early intervention.

[0033] The recommendation unit provides users with the most suitable test recommendations based on risk assessments conducted by the evaluation unit. Specifically, AI notifies users of test recommendations based on the risk assessment results. The recommendation unit has built a system for notifying users of test recommendations, and users can receive these recommendations via smartphones or computers. For example, if a user's risk score is high, the recommendation unit will notify the user to undergo a specific test. This notification is made via email, app push notifications, SMS, etc. Furthermore, the recommendation unit can also update test recommendations based on the user's risk assessment results. For example, if a user provides new test results, the recommendation unit re-evaluates the test recommendations based on the latest data and updates the recommendations as necessary. This ensures that the recommendation unit always provides test recommendations based on the most up-to-date information. In addition, the recommendation unit can collect user feedback and continuously improve the accuracy and effectiveness of its recommendations. For example, by providing feedback on the results and impressions after a test, the recommendation unit can improve the accuracy of future test recommendations. This allows the recommendation unit to provide users with the most suitable test recommendations and support early disease detection and prevention.

[0034] The analysis unit can detect early signs of cancer by referring to the latest medical research data. For example, the analysis unit can detect early signs of cancer by referring to specific papers or databases. For example, the analysis unit can input the latest medical research data into AI, which then analyzes the data to detect early signs of cancer. For example, the analysis unit can automate the process by which AI detects early signs of cancer based on the latest medical research data. This improves the accuracy of detecting early signs of cancer by referring to the latest medical research data.

[0035] The recommendation unit can notify users of testing recommendations. The recommendation unit can notify users of testing recommendations via methods such as email, app notifications, and SMS. The recommendation unit can automate the process of notifying users of testing recommendations based on their risk assessment results, for example, using AI. The recommendation unit can also build a system that periodically notifies users of testing recommendations. This allows users to get tested at the appropriate time.

[0036] The data collection unit can periodically collect users' medical data and test results. The data collection unit can collect users' medical data at frequencies such as daily, weekly, or monthly. The data collection unit can automate the process of periodically collecting users' medical data, for example, using AI. The data collection unit can also build a system for periodically collecting users' medical data. This ensures that up-to-date medical data is always available by collecting data regularly.

[0037] The evaluation unit can save the user's risk assessment results. For example, the evaluation unit saves the risk assessment results to cloud storage or a database. The evaluation unit can automate the process of saving risk assessment results, for example, using AI. The evaluation unit can also build a system for long-term storage of risk assessment results. This allows for referencing past assessment results by saving the current ones.

[0038] The recommendation unit can update inspection recommendations based on the user's risk assessment results. For example, the recommendation unit updates inspection recommendations in response to changes in risk assessment results. For example, the recommendation unit automates the process of AI updating inspection recommendations based on risk assessment results. For example, the recommendation unit can also build a system that periodically updates inspection recommendations based on the user's risk assessment results. This ensures that the optimal inspection recommendations are always provided by updating them based on risk assessment results.

[0039] The data collection unit can analyze the user's past medical data submission history and select the optimal collection method. For example, the data collection unit can analyze the frequency of data previously submitted by the user and set the optimal collection interval. For example, the data collection unit can analyze the format of data previously submitted by the user and suggest the optimal data format. For example, the data collection unit can analyze the content of data previously submitted by the user and select the necessary data items. In this way, the optimal collection method can be selected by analyzing the past submission history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0040] The data collection unit can filter medical data based on the user's current health status and lifestyle. For example, the data collection unit can select necessary data items considering the user's current health status. For example, the data collection unit can analyze the user's lifestyle and prioritize the collection of relevant data. For example, the data collection unit can adjust the scope of data to be collected based on the user's health status and lifestyle. This allows for efficient collection of necessary data by filtering the data based on the user's health status and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting medical data. For example, the data collection unit can collect data related to region-specific health risks based on the user's place of residence. For example, the data collection unit can collect data related to places visited by considering the user's travel history. For example, the data collection unit can collect data related to environmental factors based on the user's geographical location. This allows for the priority collection of highly relevant data by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0042] The data collection unit can analyze a user's social media activity and collect relevant data when collecting medical data. For example, the data collection unit can analyze the content of a user's social media posts and collect health-related information. For example, the data collection unit can consider a user's social media friendships and collect data related to health risks. For example, the data collection unit can collect lifestyle-related data from a user's social media activity. This allows for the efficient collection of relevant data by analyzing social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the medical data during the analysis. For example, the analysis unit performs a detailed analysis on important data. For example, the analysis unit performs a simplified analysis on less important data. For example, the analysis unit adjusts the depth of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the medical data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0044] The analysis unit can apply different analysis algorithms depending on the category of medical data during analysis. For example, the analysis unit applies a specific analysis algorithm to blood test data. For example, the analysis unit applies a different analysis algorithm to diagnostic imaging data. For example, the analysis unit applies a dedicated analysis algorithm to genetic data. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the category of medical data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0045] The analysis unit can determine the priority of analysis based on the submission date of the medical data during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may postpone the analysis of older data. For example, the analysis unit may adjust the order of analysis based on the submission date. This allows for the prioritization of analysis based on the submission date of the medical data, thereby prioritizing the analysis of the most recent data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the medical data during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant data. For example, the analysis unit postpones the analysis of less relevant data. The analysis unit adjusts the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the medical data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0047] The evaluation unit can analyze the user's past medical data to select the optimal evaluation method during risk assessment. For example, the evaluation unit selects the optimal risk assessment method based on the user's past medical data. For example, the evaluation unit analyzes the user's past medical data to improve the accuracy of the risk assessment. For example, the evaluation unit customizes the risk assessment method by referring to the user's past medical data. This allows the evaluation unit to select the optimal risk assessment method by analyzing past medical data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI.

[0048] The evaluation unit can customize the evaluation methods based on the user's current health status during risk assessment. For example, the evaluation unit selects the optimal risk assessment method considering the user's current health status. For example, the evaluation unit adjusts the risk assessment methods based on the user's health status. For example, the evaluation unit improves the accuracy of the risk assessment based on the user's current health status. This makes it possible to perform a more accurate risk assessment by customizing the evaluation methods based on the current health status. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI.

[0049] The evaluation unit can select the optimal evaluation method when conducting risk assessments, taking into account the user's geographical location information. For example, the evaluation unit may select an evaluation method that considers region-specific health risks based on the user's place of residence. For example, the evaluation unit may consider the user's travel history and conduct risk assessments related to places visited. For example, the evaluation unit may conduct risk assessments that consider environmental factors based on the user's geographical location information. This makes it possible to conduct assessments that take into account region-specific health risks by considering geographical location information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI.

[0050] The evaluation unit can analyze a user's social media activity and propose evaluation methods during risk assessment. For example, the evaluation unit can analyze the content of a user's social media posts and propose evaluation methods related to health risks. For example, the evaluation unit can consider a user's social media friendships and propose evaluation methods related to health risks. For example, the evaluation unit can propose evaluation methods related to lifestyle habits from a user's social media activity. In this way, by analyzing social media activity, evaluation methods related to health risks can be proposed. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI.

[0051] The recommendation unit can adjust the level of detail of recommendations based on the importance of the user's medical data when recommending tests. For example, the recommendation unit provides detailed test recommendations based on important medical data. For example, the recommendation unit provides simplified test recommendations based on less important medical data. The recommendation unit adjusts the level of detail of recommendations according to the importance of the medical data. This enables efficient test recommendations by adjusting the level of detail of recommendations based on the importance of the medical data. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without using AI.

[0052] The recommendation unit can apply different recommendation algorithms depending on the category of the user's medical data when recommending tests. For example, the recommendation unit may apply a specific recommendation algorithm to blood test data. For example, the recommendation unit may apply a different recommendation algorithm to diagnostic imaging data. For example, the recommendation unit may apply a dedicated recommendation algorithm to genetic data. This improves recommendation accuracy by applying the appropriate recommendation algorithm according to the category of medical data. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI.

[0053] The recommendation unit can determine the priority of recommendations based on when the user's medical data was submitted, when recommending tests. For example, the recommendation unit may prioritize test recommendations based on the most recent medical data. For example, the recommendation unit may postpone test recommendations based on older medical data. For example, the recommendation unit may adjust the order of test recommendations based on the submission date. This allows for recommendations based on the most recent data by determining the priority of recommendations based on when the medical data was submitted. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not using AI.

[0054] The recommendation unit can adjust the order of recommendations based on the relevance of the user's medical data when recommending tests. For example, the recommendation unit may prioritize test recommendations based on highly relevant medical data. For example, the recommendation unit may postpone test recommendations based on less relevant medical data. The recommendation unit adjusts the order of test recommendations based on the relevance of the medical data. This allows for efficient test recommendations by adjusting the order of recommendations based on the relevance of the medical data. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without using AI.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit can analyze the user's past medical data submission history and select the optimal collection method. For example, the data collection unit can analyze the frequency of data previously submitted by the user and set the optimal collection interval. For example, the data collection unit can analyze the format of data previously submitted by the user and suggest the optimal data format. For example, the data collection unit can analyze the content of data previously submitted by the user and select the necessary data items. In this way, the optimal collection method can be selected by analyzing the past submission history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0057] The data collection unit can filter medical data based on the user's current health status and lifestyle. For example, the data collection unit can select necessary data items considering the user's current health status. For example, the data collection unit can analyze the user's lifestyle and prioritize the collection of relevant data. For example, the data collection unit can adjust the scope of data to be collected based on the user's health status and lifestyle. This allows for efficient collection of necessary data by filtering the data based on the user's health status and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI.

[0058] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting medical data. For example, the data collection unit can collect data related to region-specific health risks based on the user's place of residence. For example, the data collection unit can collect data related to places visited by considering the user's travel history. For example, the data collection unit can collect data related to environmental factors based on the user's geographical location. This allows for the priority collection of highly relevant data by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0059] The data collection unit can analyze a user's social media activity and collect relevant data when collecting medical data. For example, the data collection unit can analyze the content of a user's social media posts and collect health-related information. For example, the data collection unit can consider a user's social media friendships and collect data related to health risks. For example, the data collection unit can collect lifestyle-related data from a user's social media activity. This allows for the efficient collection of relevant data by analyzing social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI.

[0060] The analysis unit can adjust the level of detail of the analysis based on the importance of the medical data during the analysis. For example, the analysis unit performs a detailed analysis on important data. For example, the analysis unit performs a simplified analysis on less important data. For example, the analysis unit adjusts the depth of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the medical data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The collection unit collects the user's medical data and test results. The collection unit can collect medical data such as medical records, test results, and prescription information. The collection unit can periodically collect medical data provided by the user and can also collect test results provided by medical institutions. Furthermore, with the user's consent, the collection unit can build a system that automatically collects medical data. Step 2: The analysis unit analyzes the data collected by the collection unit to detect early signs of cancer. The analysis unit uses AI to analyze the data and detect abnormal values ​​of specific biomarkers. The analysis unit can also detect early signs of cancer by referring to the latest medical research data, and the process of AI analyzing medical data and detecting early signs of cancer is automated. Step 3: The evaluation unit performs a risk assessment based on the data obtained by the analysis unit. The evaluation unit uses statistical methods and scoring systems to perform the risk assessment, and AI also performs the risk assessment to evaluate the user's health risk. The evaluation unit can save the risk assessment results and also refer to past assessment results. Step 4: The recommendation unit provides the user with the most suitable test recommendations based on the risk assessment performed by the evaluation unit. The recommendation unit can also build a system in which AI notifies the user of test recommendations based on the risk assessment results. The recommendation unit can also update test recommendations based on the user's risk assessment results.

[0063] (Example of form 2) An AI-based application according to an embodiment of the present invention is a system that analyzes a user's medical data and test results to detect early signs of cancer. This system performs risk assessments based on the latest medical research and data and provides the user with optimal test recommendations. For example, the AI-based application collects the user's medical data and test results. For example, it collects medical data such as medical records, test results, and prescription information. Next, the AI ​​analyzes the collected data and detects early signs of cancer. For example, the AI ​​detects abnormal values ​​of specific biomarkers. Furthermore, the AI ​​performs a risk assessment and provides the user with optimal test recommendations. For example, the AI ​​uses statistical methods and scoring systems to perform risk assessments and determine the types and frequencies of tests to be recommended. This improves the rate of early cancer detection and increases the success rate of treatment. It also contributes to reducing medical costs. Thus, the AI-based application can efficiently collect, analyze, assess risk, and recommend tests based on the user's medical data and test results.

[0064] The AI-based application according to the embodiment comprises a data collection unit, an analysis unit, an evaluation unit, and a recommendation unit. The data collection unit collects the user's medical data and test results. The data collection unit can collect medical data such as medical records, test results, and prescription information. The data collection unit can periodically collect medical data provided by the user. The data collection unit can also collect test results provided by medical institutions. Furthermore, with the user's consent, the data collection unit can build a system that automatically collects medical data. The analysis unit analyzes the data collected by the data collection unit and detects early signs of cancer. The analysis unit can analyze the data using AI to detect abnormal values ​​of specific biomarkers. The analysis unit can also detect early signs of cancer by referring to the latest medical research data. The analysis unit can automate the process of using AI to analyze medical data and detect early signs of cancer. The evaluation unit performs a risk assessment based on the data obtained by the analysis unit. The evaluation unit performs a risk assessment using statistical methods or scoring systems. The evaluation unit can assess the user's health risk using AI to perform a risk assessment. The evaluation unit can, for example, save risk assessment results and refer to past evaluation results. The recommendation unit provides the user with the most suitable test recommendations based on the risk assessment performed by the evaluation unit. The recommendation unit can, for example, notify the user of test recommendations based on the risk assessment results using AI. The recommendation unit can, for example, build a system for notifying the user of test recommendations. The recommendation unit can also, for example, update test recommendations based on the user's risk assessment results. This enables the AI-based application according to the embodiment to efficiently collect, analyze, assess risks, and recommend tests based on the user's medical data and test results.

[0065] The data collection unit collects users' medical data and test results. Specifically, it can collect medical data such as medical records, test results, and prescription information. The data collection unit has an interface for regularly collecting medical data provided by users, allowing users to easily upload their own medical data. The data collection unit also has an API for automatically acquiring test results provided by medical institutions, and can collect data in conjunction with medical institution systems. Furthermore, with the user's consent, the data collection unit can build a system that automatically collects medical data. This system automatically acquires data from the user's electronic medical records and wearable devices and stores it in a central database. For example, if a user is wearing a smartwatch, data such as heart rate, blood pressure, and exercise level can be collected from the device in real time and integrated with medical data. This allows the data collection unit to comprehensively understand the user's health status and efficiently collect necessary data. In addition, the data collection unit employs encryption technology to ensure data privacy and security, protecting users' personal information. This allows the data collection unit to safely collect medical data while gaining the user's trust.

[0066] The analysis unit analyzes data collected by the data collection unit to detect early signs of cancer. Specifically, it uses AI to analyze data and detect abnormal values ​​of specific biomarkers. The AI ​​learns from large amounts of medical data using machine learning algorithms, enabling it to detect early signs of cancer with high accuracy. For example, it uses deep learning technology to analyze blood test results and image data to detect abnormal patterns. The analysis unit can also detect early signs of cancer by referring to the latest medical research data. This allows the analysis unit to always incorporate the latest knowledge into its analysis. Furthermore, the analysis unit automates the process by which the AI ​​analyzes medical data and detects early signs of cancer. This allows the analysis unit to analyze data quickly and efficiently and provide early warnings to users. For example, if the AI ​​analyzes blood test results and detects abnormal values ​​of a specific biomarker, it sends a notification to the user recommending early testing. This allows the analysis unit to monitor the user's health status in real time and enable early intervention. In addition, the analysis unit can detect abnormal changes by comparing data with historical data. This allows the analysis unit to continuously monitor changes in the user's health status and ensure that early signs are not missed.

[0067] The evaluation unit performs risk assessments based on data obtained by the analysis unit. Specifically, it uses statistical methods and scoring systems to perform risk assessments. The evaluation unit is equipped with an AI-powered algorithm for assessing users' health risks. For example, the AI ​​calculates a user's cancer risk score based on collected medical data and analysis results. This score is calculated considering factors such as the user's age, gender, family history, and lifestyle. The evaluation unit can save risk assessment results and refer to past assessment results. This allows the evaluation unit to track changes in the user's health risks and support long-term health management. Furthermore, the evaluation unit provides a dashboard to visually display the risk assessment results. Through this dashboard, users can grasp their health risks at a glance. For example, if the risk score is high, a warning will be displayed on the dashboard, recommending early testing or consultation with a doctor. The evaluation unit can also share risk assessment results with healthcare institutions. This allows doctors to understand the user's health risks and provide appropriate diagnosis and treatment. The evaluation unit plays a crucial role in comprehensively assessing users' health risks and supporting early intervention.

[0068] The recommendation unit provides users with the most suitable test recommendations based on risk assessments conducted by the evaluation unit. Specifically, AI notifies users of test recommendations based on the risk assessment results. The recommendation unit has built a system for notifying users of test recommendations, and users can receive these recommendations via smartphones or computers. For example, if a user's risk score is high, the recommendation unit will notify the user to undergo a specific test. This notification is made via email, app push notifications, SMS, etc. Furthermore, the recommendation unit can also update test recommendations based on the user's risk assessment results. For example, if a user provides new test results, the recommendation unit re-evaluates the test recommendations based on the latest data and updates the recommendations as necessary. This ensures that the recommendation unit always provides test recommendations based on the most up-to-date information. In addition, the recommendation unit can collect user feedback and continuously improve the accuracy and effectiveness of its recommendations. For example, by providing feedback on the results and impressions after a test, the recommendation unit can improve the accuracy of future test recommendations. This allows the recommendation unit to provide users with the most suitable test recommendations and support early disease detection and prevention.

[0069] The analysis unit can detect early signs of cancer by referring to the latest medical research data. For example, the analysis unit can detect early signs of cancer by referring to specific papers or databases. For example, the analysis unit can input the latest medical research data into AI, which then analyzes the data to detect early signs of cancer. For example, the analysis unit can automate the process by which AI detects early signs of cancer based on the latest medical research data. This improves the accuracy of detecting early signs of cancer by referring to the latest medical research data.

[0070] The recommendation unit can notify users of testing recommendations. The recommendation unit can notify users of testing recommendations via methods such as email, app notifications, and SMS. The recommendation unit can automate the process of notifying users of testing recommendations based on their risk assessment results, for example, using AI. The recommendation unit can also build a system that periodically notifies users of testing recommendations. This allows users to get tested at the appropriate time.

[0071] The data collection unit can periodically collect users' medical data and test results. The data collection unit can collect users' medical data at frequencies such as daily, weekly, or monthly. The data collection unit can automate the process of periodically collecting users' medical data, for example, using AI. The data collection unit can also build a system for periodically collecting users' medical data. This ensures that up-to-date medical data is always available by collecting data regularly.

[0072] The evaluation unit can save the user's risk assessment results. For example, the evaluation unit saves the risk assessment results to cloud storage or a database. The evaluation unit can automate the process of saving risk assessment results, for example, using AI. The evaluation unit can also build a system for long-term storage of risk assessment results. This allows for referencing past assessment results by saving the current ones.

[0073] The recommendation unit can update inspection recommendations based on the user's risk assessment results. For example, the recommendation unit updates inspection recommendations in response to changes in risk assessment results. For example, the recommendation unit automates the process of AI updating inspection recommendations based on risk assessment results. For example, the recommendation unit can also build a system that periodically updates inspection recommendations based on the user's risk assessment results. This ensures that the optimal inspection recommendations are always provided by updating them based on risk assessment results.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of medical data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to reduce the user's burden. For example, if the user is relaxed, the data collection unit can advance the collection timing to maintain data freshness. For example, if the user is busy, the data collection unit can adjust the collection timing to match the user's schedule. In this way, the user's burden can be reduced by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] The data collection unit can analyze the user's past medical data submission history and select the optimal collection method. For example, the data collection unit can analyze the frequency of data previously submitted by the user and set the optimal collection interval. For example, the data collection unit can analyze the format of data previously submitted by the user and suggest the optimal data format. For example, the data collection unit can analyze the content of data previously submitted by the user and select the necessary data items. In this way, the optimal collection method can be selected by analyzing the past submission history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0076] The data collection unit can filter medical data based on the user's current health status and lifestyle. For example, the data collection unit can select necessary data items considering the user's current health status. For example, the data collection unit can analyze the user's lifestyle and prioritize the collection of relevant data. For example, the data collection unit can adjust the scope of data to be collected based on the user's health status and lifestyle. This allows for efficient collection of necessary data by filtering the data based on the user's health status and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI.

[0077] The data collection unit can estimate the user's emotions and prioritize the medical data to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting important data. For example, if the user is relaxed, the data collection unit will prioritize collecting detailed data. For example, if the user is in a hurry, the data collection unit will prioritize collecting the most necessary data. This allows for the priority collection of important data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting medical data. For example, the data collection unit can collect data related to region-specific health risks based on the user's place of residence. For example, the data collection unit can collect data related to places visited by considering the user's travel history. For example, the data collection unit can collect data related to environmental factors based on the user's geographical location. This allows for the priority collection of highly relevant data by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0079] The data collection unit can analyze a user's social media activity and collect relevant data when collecting medical data. For example, the data collection unit can analyze the content of a user's social media posts and collect health-related information. For example, the data collection unit can consider a user's social media friendships and collect data related to health risks. For example, the data collection unit can collect lifestyle-related data from a user's social media activity. This allows for the efficient collection of relevant data by analyzing social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit provides simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is excited, the analysis unit provides visually appealing analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the medical data during the analysis. For example, the analysis unit performs a detailed analysis on important data. For example, the analysis unit performs a simplified analysis on less important data. For example, the analysis unit adjusts the depth of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the medical data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0082] The analysis unit can apply different analysis algorithms depending on the category of medical data during analysis. For example, the analysis unit applies a specific analysis algorithm to blood test data. For example, the analysis unit applies a different analysis algorithm to diagnostic imaging data. For example, the analysis unit applies a dedicated analysis algorithm to genetic data. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the category of medical data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will provide a short, concise analysis. For example, if the user is relaxed, the analysis unit will provide a detailed analysis. For example, if the user is excited, the analysis unit will provide a visually appealing analysis. By adjusting the length of the analysis according to the user's emotions, the system can provide the user with an appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The analysis unit can determine the priority of analysis based on the submission date of the medical data during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may postpone the analysis of older data. For example, the analysis unit may adjust the order of analysis based on the submission date. This allows for the prioritization of analysis based on the submission date of the medical data, thereby prioritizing the analysis of the most recent data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the medical data during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant data. For example, the analysis unit postpones the analysis of less relevant data. The analysis unit adjusts the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the medical data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0086] The evaluation unit can estimate the user's emotions and adjust the risk assessment method based on the estimated user emotions. For example, if the user is feeling anxious, the evaluation unit provides a simple and easy-to-understand risk assessment. For example, if the user is relaxed, the evaluation unit provides a detailed risk assessment. For example, if the user is excited, the evaluation unit provides a visually appealing risk assessment. In this way, by adjusting the risk assessment method according to the user's emotions, an easy-to-understand risk assessment can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The evaluation unit can analyze the user's past medical data to select the optimal evaluation method during risk assessment. For example, the evaluation unit selects the optimal risk assessment method based on the user's past medical data. For example, the evaluation unit analyzes the user's past medical data to improve the accuracy of the risk assessment. For example, the evaluation unit customizes the risk assessment method by referring to the user's past medical data. This allows the evaluation unit to select the optimal risk assessment method by analyzing past medical data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI.

[0088] The evaluation unit can customize the evaluation methods based on the user's current health status during risk assessment. For example, the evaluation unit selects the optimal risk assessment method considering the user's current health status. For example, the evaluation unit adjusts the risk assessment methods based on the user's health status. For example, the evaluation unit improves the accuracy of the risk assessment based on the user's current health status. This makes it possible to perform a more accurate risk assessment by customizing the evaluation methods based on the current health status. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI.

[0089] The evaluation unit can estimate the user's emotions and determine the priority of risk assessments based on the estimated emotions. For example, if the user is feeling anxious, the evaluation unit will prioritize important risk assessments. For example, if the user is relaxed, the evaluation unit will perform detailed risk assessments. For example, if the user is in a hurry, the evaluation unit will prioritize the most necessary risk assessments. In this way, by determining the priority of risk assessments according to the user's emotions, important risk assessments can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The evaluation unit can select the optimal evaluation method when conducting risk assessments, taking into account the user's geographical location information. For example, the evaluation unit may select an evaluation method that considers region-specific health risks based on the user's place of residence. For example, the evaluation unit may consider the user's travel history and conduct risk assessments related to places visited. For example, the evaluation unit may conduct risk assessments that consider environmental factors based on the user's geographical location information. This makes it possible to conduct assessments that take into account region-specific health risks by considering geographical location information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI.

[0091] The evaluation unit can analyze a user's social media activity and propose evaluation methods during risk assessment. For example, the evaluation unit can analyze the content of a user's social media posts and propose evaluation methods related to health risks. For example, the evaluation unit can consider a user's social media friendships and propose evaluation methods related to health risks. For example, the evaluation unit can propose evaluation methods related to lifestyle habits from a user's social media activity. In this way, by analyzing social media activity, evaluation methods related to health risks can be proposed. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI.

[0092] The recommendation section can estimate the user's emotions and adjust the way test recommendations are presented based on those emotions. For example, if the user is feeling anxious, the recommendation section will provide simple and easy-to-understand test recommendations. For example, if the user is relaxed, the recommendation section will provide detailed test recommendations. For example, if the user is excited, the recommendation section will provide visually appealing test recommendations. By adjusting the way test recommendations are presented according to the user's emotions, the system can provide test recommendations that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The recommendation unit can adjust the level of detail of recommendations based on the importance of the user's medical data when recommending tests. For example, the recommendation unit provides detailed test recommendations based on important medical data. For example, the recommendation unit provides simplified test recommendations based on less important medical data. The recommendation unit adjusts the level of detail of recommendations according to the importance of the medical data. This enables efficient test recommendations by adjusting the level of detail of recommendations based on the importance of the medical data. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without using AI.

[0094] The recommendation unit can apply different recommendation algorithms depending on the category of the user's medical data when recommending tests. For example, the recommendation unit may apply a specific recommendation algorithm to blood test data. For example, the recommendation unit may apply a different recommendation algorithm to diagnostic imaging data. For example, the recommendation unit may apply a dedicated recommendation algorithm to genetic data. This improves recommendation accuracy by applying the appropriate recommendation algorithm according to the category of medical data. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI.

[0095] The recommendation section can estimate the user's emotions and adjust the length of the test recommendations based on the estimated emotions. For example, if the user is in a hurry, the recommendation section will provide short, concise test recommendations. For example, if the user is relaxed, the recommendation section will provide detailed test recommendations. For example, if the user is excited, the recommendation section will provide visually appealing test recommendations. By adjusting the length of the test recommendations according to the user's emotions, the system can provide appropriate test recommendations for the user. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The recommendation unit can determine the priority of recommendations based on when the user's medical data was submitted, when recommending tests. For example, the recommendation unit may prioritize test recommendations based on the most recent medical data. For example, the recommendation unit may postpone test recommendations based on older medical data. For example, the recommendation unit may adjust the order of test recommendations based on the submission date. This allows for recommendations based on the most recent data by determining the priority of recommendations based on when the medical data was submitted. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not using AI.

[0097] The recommendation unit can adjust the order of recommendations based on the relevance of the user's medical data when recommending tests. For example, the recommendation unit may prioritize test recommendations based on highly relevant medical data. For example, the recommendation unit may postpone test recommendations based on less relevant medical data. The recommendation unit adjusts the order of test recommendations based on the relevance of the medical data. This allows for efficient test recommendations by adjusting the order of recommendations based on the relevance of the medical data. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without using AI.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit will prioritize analyzing important data. For example, if the user is relaxed, the analysis unit will analyze detailed data. For example, if the user is in a hurry, the analysis unit will prioritize analyzing the most necessary data. In this way, by determining the priority of analysis according to the user's emotions, important data can be analyzed preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The evaluation unit can estimate the user's emotions and adjust the level of detail in the risk assessment based on the estimated emotions. For example, if the user is feeling anxious, the evaluation unit provides a simple and easy-to-understand risk assessment. For example, if the user is relaxed, the evaluation unit provides a detailed risk assessment. For example, if the user is excited, the evaluation unit provides a visually appealing risk assessment. In this way, by adjusting the level of detail in the risk assessment according to the user's emotions, an easy-to-understand risk assessment can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The recommendation unit can estimate the user's emotions and adjust the timing of test recommendations based on those emotions. For example, if the user is stressed, the recommendation unit will delay the timing of test recommendations to reduce the user's burden. For example, if the user is relaxed, the recommendation unit will advance the timing of test recommendations to maintain data freshness. For example, if the user is busy, the recommendation unit will adjust the timing of test recommendations to match the user's schedule. In this way, the user's burden can be reduced by adjusting the timing of test recommendations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The data collection unit can estimate the user's emotions and determine the type of medical data to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting important data. For example, if the user is relaxed, the data collection unit will prioritize collecting detailed data. For example, if the user is in a hurry, the data collection unit will prioritize collecting the most necessary data. This allows for the priority collection of important data by determining the type of medical data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit provides simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is excited, the analysis unit provides visually appealing analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The data collection unit can analyze the user's past medical data submission history and select the optimal collection method. For example, the data collection unit can analyze the frequency of data previously submitted by the user and set the optimal collection interval. For example, the data collection unit can analyze the format of data previously submitted by the user and suggest the optimal data format. For example, the data collection unit can analyze the content of data previously submitted by the user and select the necessary data items. In this way, the optimal collection method can be selected by analyzing the past submission history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0105] The data collection unit can filter medical data based on the user's current health status and lifestyle. For example, the data collection unit can select necessary data items considering the user's current health status. For example, the data collection unit can analyze the user's lifestyle and prioritize the collection of relevant data. For example, the data collection unit can adjust the scope of data to be collected based on the user's health status and lifestyle. This allows for efficient collection of necessary data by filtering the data based on the user's health status and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI.

[0106] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting medical data. For example, the data collection unit can collect data related to region-specific health risks based on the user's place of residence. For example, the data collection unit can collect data related to places visited by considering the user's travel history. For example, the data collection unit can collect data related to environmental factors based on the user's geographical location. This allows for the priority collection of highly relevant data by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0107] The data collection unit can analyze a user's social media activity and collect relevant data when collecting medical data. For example, the data collection unit can analyze the content of a user's social media posts and collect health-related information. For example, the data collection unit can consider a user's social media friendships and collect data related to health risks. For example, the data collection unit can collect lifestyle-related data from a user's social media activity. This allows for the efficient collection of relevant data by analyzing social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI.

[0108] The analysis unit can adjust the level of detail of the analysis based on the importance of the medical data during the analysis. For example, the analysis unit performs a detailed analysis on important data. For example, the analysis unit performs a simplified analysis on less important data. For example, the analysis unit adjusts the depth of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the medical data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The collection unit collects the user's medical data and test results. The collection unit can collect medical data such as medical records, test results, and prescription information. The collection unit can periodically collect medical data provided by the user and can also collect test results provided by medical institutions. Furthermore, with the user's consent, the collection unit can build a system that automatically collects medical data. Step 2: The analysis unit analyzes the data collected by the collection unit to detect early signs of cancer. The analysis unit uses AI to analyze the data and detect abnormal values ​​of specific biomarkers. The analysis unit can also detect early signs of cancer by referring to the latest medical research data, and the process of AI analyzing medical data and detecting early signs of cancer is automated. Step 3: The evaluation unit performs a risk assessment based on the data obtained by the analysis unit. The evaluation unit uses statistical methods and scoring systems to perform the risk assessment, and AI also performs the risk assessment to evaluate the user's health risk. The evaluation unit can save the risk assessment results and also refer to past assessment results. Step 4: The recommendation unit provides the user with the most suitable test recommendations based on the risk assessment performed by the evaluation unit. The recommendation unit can also build a system in which AI notifies the user of test recommendations based on the risk assessment results. The recommendation unit can also update test recommendations based on the user's risk assessment results.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the data collection unit, analysis unit, evaluation unit, and recommendation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 and collects the user's medical data and test results. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to detect early signs of cancer. The evaluation unit is implemented by the identification processing unit 290 of the data processing unit 12 and performs a risk assessment based on the analysis results. The recommendation unit is implemented by the control unit 46A of the smart device 14 and provides the user with the most suitable test recommendations based on the risk assessment results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the data collection unit, analysis unit, evaluation unit, and recommendation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and collects the user's medical data and test results. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to detect early signs of cancer. The evaluation unit is implemented by the identification processing unit 290 of the data processing unit 12 and performs a risk assessment based on the analysis results. The recommendation unit is implemented by the control unit 46A of the smart glasses 214 and provides the user with the most suitable test recommendations based on the risk assessment results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the data collection unit, analysis unit, evaluation unit, and recommendation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and collects the user's medical data and test results. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to detect early signs of cancer. The evaluation unit is implemented by the identification processing unit 290 of the data processing unit 12 and performs a risk assessment based on the analysis results. The recommendation unit is implemented by the control unit 46A of the headset terminal 314 and provides the user with the most suitable test recommendations based on the risk assessment results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the data collection unit, analysis unit, evaluation unit, and recommendation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414 and collects the user's medical data and test results. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to detect early signs of cancer. The evaluation unit is implemented by the identification processing unit 290 of the data processing unit 12 and performs a risk assessment based on the analysis results. The recommendation unit is implemented by the control unit 46A of the robot 414 and provides the user with the most suitable test recommendations based on the risk assessment results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) A collection unit that collects users' medical data and test results, An analysis unit analyzes the data collected by the aforementioned collection unit to detect early signs of cancer, An evaluation unit performs a risk assessment based on the data obtained by the analysis unit, The system includes a recommendation unit that provides the user with the most suitable test recommendation based on the risk assessment performed by the evaluation unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Detecting early signs of cancer by referring to the latest medical research data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned recommendation section is, Notify users of recommended tests. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Regularly collect users' medical data and test results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The evaluation unit, Save the user's risk assessment results. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned recommendation section is, We update testing recommendations based on the user's risk assessment results. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of medical data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past medical data submission history to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting medical data, filtering is performed based on the user's current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the medical data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting medical data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting medical data, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the medical data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of medical data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on when the medical data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the medical data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit, We estimate user sentiment and adjust the risk assessment method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit, During risk assessment, the system analyzes the user's past medical data to select the most appropriate assessment method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, During risk assessment, the assessment method is customized based on the user's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, The system estimates user sentiment and prioritizes risk assessments based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, When conducting risk assessments, the optimal assessment method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit, When conducting risk assessments, we propose methods for evaluation by analyzing users' social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned recommendation section is, The system estimates the user's emotions and adjusts the way test recommendations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned recommendation section is, When recommending tests, the level of detail of the recommendation is adjusted based on the importance of the user's medical data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned recommendation section is, When recommending tests, different recommendation algorithms are applied depending on the user's medical data category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned recommendation section is, It estimates the user's emotions and adjusts the length of the test recommendations based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned recommendation section is, When recommending tests, the priority of recommendations is determined based on when the user's medical data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned recommendation section is, When recommending tests, the order of recommendations is adjusted based on the relevance of the user's medical data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects users' medical data and test results, An analysis unit analyzes the data collected by the aforementioned collection unit to detect early signs of cancer, An evaluation unit performs a risk assessment based on the data obtained by the analysis unit, The system includes a recommendation unit that provides the user with the most suitable test recommendation based on the risk assessment performed by the evaluation unit. A system characterized by the following features.

2. The aforementioned analysis unit, Detecting early signs of cancer by referring to the latest medical research data. The system according to feature 1.

3. The aforementioned recommendation section is, Notify users of recommended tests. The system according to feature 1.

4. The aforementioned collection unit is Regularly collect users' medical data and test results. The system according to feature 1.

5. The evaluation unit, Save the user's risk assessment results. The system according to feature 1.

6. The aforementioned recommendation section is, We update testing recommendations based on the user's risk assessment results. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of medical data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past medical data submission history to select the optimal data collection method. The system according to feature 1.

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