system

Abstract

The system according to this embodiment aims to quickly and accurately analyze symptom data provided by the user and to provide appropriate medical advice. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a feedback unit. The reception unit receives input of symptom data. The analysis unit analyzes the symptom data received by the reception unit. The provision unit provides the diagnostic results obtained by the analysis unit. The feedback unit provides prompt medical advice based on the diagnostic results provided by the provision 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, there is a problem that it is difficult to quickly and accurately analyze symptom data provided by a user and provide appropriate medical advice.

[0005] The system according to the embodiment aims to quickly and accurately analyze symptom data provided by a user and provide appropriate medical advice.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a feedback unit. The reception unit receives input of symptom data. The analysis unit analyzes the symptom data received by the reception unit. The provision unit provides the diagnostic results obtained by the analysis unit. The feedback unit provides prompt medical advice based on the diagnostic results provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can quickly and accurately analyze symptom data provided by the user and provide appropriate medical advice. [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 applicable to the communication I / F include wireless communication standards including 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 agent system according to an embodiment of the present invention is a system that analyzes symptom data provided by a user and identifies potential diseases by comparing it with an accumulated medical database. The AI ​​agent system analyzes the symptom data provided by the user and compares it with a large-scale accumulated medical database to identify potential diseases and provide the most likely diagnosis. Furthermore, it provides the user with rapid medical advice through a real-time feedback system. This mechanism improves diagnostic accuracy and shortens diagnosis time. It also improves user satisfaction. For example, the user inputs their own symptom data. For example, they input symptoms such as fever, cough, and headache. This information is input to the AI ​​agent. Next, the AI ​​agent analyzes the input symptom data using deep learning. The AI ​​agent analyzes the symptom data and compares it with a large-scale accumulated medical database. For example, it refers to past diagnostic data and medical literature to identify potential diseases that match the symptoms. Based on the identified potential diseases, the AI ​​agent provides the most likely diagnosis. The accuracy of the diagnosis is improved by an algorithm designed by medical professionals. For example, if the user has symptoms of fever and cough, the AI ​​agent identifies possibilities such as influenza or a common cold and provides the most likely diagnosis. Furthermore, it provides the user with rapid medical advice through a real-time feedback system. For example, based on the diagnosis, it provides information on appropriate treatments and medical facilities. This allows users to receive timely and appropriate medical advice. This system improves diagnostic accuracy and reduces diagnosis time. It also improves user satisfaction. For instance, a diagnosis that would take several hours with conventional methods can be completed in minutes using an AI agent. This allows users to receive timely and appropriate medical advice, improving their health management. The AI ​​agent system can efficiently analyze the user's symptom data and provide timely medical advice.

[0029] The AI ​​agent system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a feedback unit. The reception unit receives input of symptom data provided by the user. Symptom data provided by the user includes, but is not limited to, examples of fever, cough, headache, etc. The reception unit receives the symptom data entered by the user in text format, for example. The reception unit can also receive symptom data using voice input. For example, the user describes the symptoms aloud, and this is converted into text data. Furthermore, the reception unit can also receive symptom data using image data. For example, the user takes a picture of a rash and uploads it to the system. The analysis unit uses deep learning to analyze the symptom data received by the reception unit. The analysis unit analyzes the symptom data using, for example, a neural network to identify potential diseases. The analysis unit can also compare the symptom data with an accumulated medical database. For example, it refers to past diagnostic data and medical literature to identify diseases that match the symptoms. Furthermore, the analysis unit transmits the results of the symptom data analysis to the provision unit. The provision unit provides the diagnostic results obtained by the analysis unit. The information provider unit, for example, notifies the user of the diagnosis results. The information provider unit can also provide information on appropriate treatments and medical institutions based on the diagnosis results. For example, it provides the user with information on the nearest medical institution. Furthermore, the information provider unit transmits the diagnosis results to the feedback unit. The feedback unit provides prompt medical advice based on the diagnosis results provided by the information provider unit. For example, the feedback unit advises the user on appropriate treatments and lifestyle improvements based on the diagnosis results. The feedback unit can also provide prompt medical advice to the user through a real-time feedback system. For example, it provides feedback immediately after the user receives the diagnosis results. As a result, the AI ​​agent system according to the embodiment can efficiently analyze the user's symptom data and provide prompt medical advice.

[0030] The reception desk is a system that accepts symptom data provided by users. This symptom data includes, but is not limited to, fever, cough, and headache. The reception desk can accept user-entered symptom data in text format, making it easy for users to input their symptoms. Furthermore, the reception desk can also accept symptom data using voice input; it has a function to convert the user's verbal description of symptoms into text data, thus improving convenience by allowing users to input symptoms without using their hands. The reception desk can also accept symptom data using image data; for example, a user can take a picture of a rash and upload it to the system, allowing for accurate communication of visual symptoms. In this way, the reception desk offers diverse input methods, enabling users to input their symptoms accurately and easily. The analysis unit is a system that uses deep learning to analyze symptom data received by the reception unit. The analysis unit uses a neural network to analyze symptom data and has the function of identifying potential diseases, so it can quickly and accurately identify diseases based on the symptom data provided by the user. Furthermore, the analysis unit can also cross-reference symptom data with an accumulated medical database, so it can refer to past diagnostic data and medical literature to identify diseases that match the symptoms. For example, based on past diagnostic data, it can refer to the diagnostic results of patients with similar symptoms to identify the most likely disease. Also, by referring to medical literature, it can perform diagnoses based on the latest medical information. In this way, the analysis unit can quickly and accurately identify diseases based on the user's symptom data and provide appropriate diagnostic results. The provision unit is a system that provides users with diagnostic results obtained by the analysis unit. The provision unit has a function to notify users of the diagnostic results, allowing users to quickly receive diagnostic results for their symptoms. Furthermore, the provision unit can also provide information on appropriate treatments and medical institutions based on the diagnostic results, allowing users to select appropriate treatments and medical institutions based on the results. For example, by providing users with information on the nearest medical institution, they can quickly visit a medical institution. In addition, the provision unit has a function to transmit diagnostic results to the feedback unit, providing information for the feedback unit to provide prompt medical advice. In this way, the provision unit can provide users with quick and appropriate diagnostic results and information on appropriate treatments and medical institutions. The Feedback Unit is a system that provides rapid medical advice based on the diagnostic results provided by the Provider Unit. The Feedback Unit has the function of advising users on appropriate treatment methods and lifestyle improvements based on the diagnostic results, allowing users to implement appropriate treatment methods and lifestyle improvements based on the diagnostic results. Furthermore, the Feedback Unit can also provide users with rapid medical advice through a real-time feedback system, so users can receive feedback immediately after receiving their diagnostic results. For example, by providing feedback immediately after a user receives their diagnostic results, they can quickly implement appropriate treatment methods and lifestyle improvements. In this way, the Feedback Unit can provide users with rapid and appropriate medical advice and support their health management. Thus, the AI ​​agent system according to this embodiment can efficiently analyze the user's symptom data and provide rapid medical advice, allowing the user to quickly obtain appropriate diagnoses and treatment methods for their symptoms. Furthermore, the system improves convenience by providing diverse input methods, enabling users to accurately and easily input their symptoms. In addition, the system can perform rapid and accurate diagnoses by utilizing deep learning and medical databases, allowing users to obtain highly reliable diagnostic results. In this way, the AI ​​agent system according to this embodiment can support the user's health management and provide rapid and appropriate medical advice.

[0031] The analysis unit can analyze symptom data using deep learning and compare it with an accumulated medical database. For example, the analysis unit can analyze symptom data using a neural network. For example, the analysis unit can receive symptom data as input and perform analysis using a neural network. The analysis unit can also compare symptom data with an accumulated medical database. For example, the analysis unit can refer to past diagnostic data and medical literature to identify diseases that match the symptoms. By utilizing deep learning, the accuracy of symptom data analysis is improved. Deep learning includes, but is not limited to, convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input symptom data into a generative AI, which can analyze the symptom data and identify potential diseases.

[0032] The service provider can provide information on appropriate treatments and medical institutions based on the diagnosis results. For example, the service provider can suggest appropriate treatments to the user based on the diagnosis results. For example, the service provider can advise the user on medication prescriptions and lifestyle improvements based on the diagnosis results. The service provider can also provide the user with information on the nearest medical institutions based on the diagnosis results. For example, the service provider can provide information on the nearest hospitals and clinics based on the user's location information. This allows the user to receive appropriate medical care quickly by providing information on appropriate treatments and medical institutions based on the diagnosis results. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the diagnosis results into AI, and the AI ​​can provide information on appropriate treatments and medical institutions.

[0033] The feedback unit can provide users with prompt medical advice through a real-time feedback system. For example, the feedback unit can provide users with prompt medical advice based on diagnostic results. For example, after receiving diagnostic results, the feedback unit can immediately advise users on appropriate treatments and lifestyle improvements. The feedback unit can also provide users with prompt medical advice through a real-time feedback system. For example, the feedback unit can notify users of diagnostic results in real time and provide prompt feedback. This improves user satisfaction by providing prompt medical advice through a real-time feedback system. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input diagnostic results into AI, which can then provide prompt medical advice.

[0034] The reception desk can receive symptom data such as fever, cough, and headache entered by the user. For example, the reception desk can receive the symptom data entered by the user in text format. For example, the user enters symptoms such as fever, cough, and headache. The reception desk can also receive symptom data using voice input. For example, the user describes the symptoms by voice, and this is converted into text data. Furthermore, the reception desk can also receive symptom data using image data. For example, the user takes a picture of a rash and uploads it to the system. This improves the accuracy of diagnosis by accurately receiving the symptom data entered by the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the symptom data entered by the user into AI, and the AI ​​can analyze and accept the data.

[0035] The reception desk can analyze the user's past symptom data input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also predict and suggest input methods to be used at specific times based on the user's past input history. Furthermore, the reception desk can analyze patterns in the symptom data entered by the user in the past and select the optimal input method. In this way, by analyzing past input history, the reception desk can provide the user with the most suitable input method and improve input efficiency. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past input history data into AI, and the AI ​​can select the optimal input method.

[0036] The reception unit can filter symptom data based on the user's current health status and lifestyle when the data is entered. For example, the reception unit can input only relevant symptom data based on the user's current health status. The reception unit can also filter the input symptom data based on the user's lifestyle (work, family, etc.). Furthermore, the reception unit can determine the priority of the input symptom data according to the user's health status and lifestyle. This improves the accuracy of the input data by filtering according to the user's health status and lifestyle. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input the user's health status data into AI, and the AI ​​can perform the filtering.

[0037] The reception system can prioritize the input of highly relevant symptoms by considering the user's geographical location when entering symptom data. For example, if the user is in a specific region, the reception system can prioritize the input of symptoms of diseases prevalent in that region. Furthermore, if the user is traveling, the reception system can input symptom data based on medical information for the travel destination. Additionally, if the user is in a specific environment (workplace, school, etc.), the reception system can prioritize the input of symptoms related to that environment. This allows for the priority input of highly relevant symptom data by considering geographical location. Some or all of the above processing in the reception system may be performed using AI, or not. For example, the reception system can input the user's geographical location information into the AI, which can then prioritize the input of highly relevant symptoms.

[0038] The reception desk can analyze the user's social media activity when inputting symptom data and input relevant symptom data. For example, the reception desk can extract information about recent health problems from the user's social media posts and input it as symptom data. The reception desk can also extract information about stress and anxiety from the user's social media activity and input it as symptom data. Furthermore, the reception desk can extract information about lifestyle habits from the user's social media activity and input it as symptom data. In this way, by analyzing social media activity, symptom data that reflects the user's lifestyle habits and health condition can be input. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's social media data into AI, and the AI ​​can extract and input relevant symptom data.

[0039] The analysis unit can adjust the level of detail of the analysis based on the severity of the symptoms when analyzing symptom data. For example, the analysis unit can perform a detailed analysis for symptoms with high severity. It can also perform a simplified analysis for mild symptoms. Furthermore, the analysis unit can determine the priority of the analysis according to the severity. This allows for detailed analysis of important symptoms by adjusting the level of detail according to the severity of the symptoms. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input symptom data into AI, and the AI ​​can adjust the level of detail of the analysis based on the severity.

[0040] The analysis unit can apply different analysis algorithms depending on the symptom category when analyzing symptom data. For example, for respiratory symptoms, the analysis unit can use an analysis algorithm specialized for the respiratory system. Similarly, for digestive symptoms, it can use an analysis algorithm specialized for the digestive system. Furthermore, for neurological symptoms, it can use an analysis algorithm specialized for the nervous system. This improves the accuracy of the analysis by applying an analysis algorithm appropriate to the symptom category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input symptom data into AI, which can then apply different analysis algorithms depending on the symptom category.

[0041] The analysis unit can determine the priority of analysis based on the timing of symptom onset when analyzing symptom data. For example, the analysis unit can prioritize the analysis of recently occurring symptoms. The analysis unit can also perform a detailed analysis of symptoms that have persisted for a long period. Furthermore, the analysis unit can determine the priority of analysis according to the timing of symptom onset. This allows for the prioritization of analysis based on the timing of symptom onset, thereby prioritizing the analysis of recently occurring symptoms. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input symptom data into AI, which can then determine the priority of analysis based on the timing of onset.

[0042] The analysis unit can adjust the order of analysis based on the relevance of symptoms when analyzing symptom data. For example, the analysis unit can prioritize the analysis of highly relevant symptoms. It can also postpone the analysis of less relevant symptoms. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of symptoms. This allows for the prioritization of analysis of highly relevant symptoms by adjusting the order of analysis according to the relevance of symptoms. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input symptom data into AI, which can then adjust the order of analysis based on relevance.

[0043] The information provider can adjust the level of detail provided based on the importance of the diagnosis when providing the diagnosis results. For example, the provider can provide detailed information for high-importance diagnoses. It can also provide concise information for low-importance diagnoses. Furthermore, the provider can adjust the level of detail according to the importance of the diagnosis. This allows for the provision of detailed information for important diagnosis results by adjusting the level of detail according to the importance of the diagnosis. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the diagnosis results into AI, which can then adjust the level of detail based on importance.

[0044] The delivery unit can apply different delivery algorithms depending on the diagnostic category when providing diagnostic results. For example, for respiratory system diagnoses, the delivery unit uses a delivery algorithm specialized for the respiratory system. Similarly, for digestive system diagnoses, the delivery unit can use a delivery algorithm specialized for the digestive system. Furthermore, for neurological system diagnoses, the delivery unit can use a delivery algorithm specialized for the neurological system. This improves the accuracy of providing diagnostic results by applying a delivery algorithm appropriate to the diagnostic category. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input diagnostic results into AI, which can then apply different delivery algorithms depending on the diagnostic category.

[0045] The service provider can determine the priority of providing diagnostic results based on when the diagnosis occurred. For example, the service provider will prioritize providing recently occurring diagnoses. The service provider can also provide detailed information for diagnoses that have been ongoing for a long period. Furthermore, the service provider can determine the priority of providing results according to when the diagnosis occurred. This allows for the priority of providing recently occurring diagnostic results by determining the priority of providing results according to when the diagnosis occurred. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input diagnostic results into AI, and the AI ​​can determine the priority of providing results based on when the diagnosis occurred.

[0046] The delivery unit can adjust the order in which diagnostic results are provided based on their relevance. For example, the delivery unit will prioritize providing highly relevant diagnoses. It can also postpone providing less relevant diagnoses. Furthermore, the delivery unit can adjust the order in which diagnoses are provided according to their relevance. This allows for the priority provision of highly relevant diagnostic results by adjusting the order in which they are provided according to their relevance. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input diagnostic results into AI, which can then adjust the order in which they are provided based on their relevance.

[0047] The feedback unit can provide optimal advice by referring to the user's past medical history when offering medical advice. For example, the feedback unit can suggest the most suitable treatment based on the user's past diagnostic history. It can also provide advice for specific illnesses based on the user's past medical history. Furthermore, the feedback unit can analyze the user's past medical history to provide the most effective advice. This allows the feedback unit to provide the user with the most suitable advice by referring to their past medical history. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's past medical history data into an AI, which can then provide optimal advice.

[0048] The feedback unit can customize the means of providing medical advice based on the user's current health condition. For example, if the user is tired, the feedback unit can provide concise and easy-to-understand advice. If the user is healthy, the feedback unit can also provide detailed advice. Furthermore, if the user is unwell, the feedback unit can provide advice that allows for a quick response. In this way, by customizing the means of advice according to the user's health condition, the system can provide the user with the most appropriate advice. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not. For example, the feedback unit can input the user's health condition data into the AI, which can then customize the means of advice.

[0049] The feedback unit can provide optimal medical advice by considering the user's geographical location. For example, if the user is in a specific region, the feedback unit can provide information on medical facilities in that region. Furthermore, if the user is traveling, the feedback unit can provide advice based on medical information for their travel destination. Additionally, if the user is in a specific environment (such as a workplace or school), the feedback unit can provide advice relevant to that environment. This allows the feedback unit to provide optimal medical advice by considering geographical location. Some or all of the above processing in the feedback unit may be performed using AI, or not. For example, the feedback unit can input the user's geographical location into the AI, which can then provide optimal advice.

[0050] The feedback unit can analyze a user's social media activity to suggest methods of providing medical advice. For example, the feedback unit can extract information about recent health problems from a user's social media posts and provide advice. It can also extract information about stress and anxiety from a user's social media activity and provide advice. Furthermore, it can extract information about lifestyle habits from a user's social media activity and provide advice. In this way, by analyzing social media activity, the feedback unit can provide the user with the most suitable method of advice. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not. For example, the feedback unit can input the user's social media data into an AI, which can then suggest methods of advice.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The reception desk can refer to the user's past medical history to complete the input data when receiving the user's symptom data. For example, if the user has suffered from a specific illness in the past, it can automatically complete the symptoms related to that illness. The reception desk can also improve the accuracy of the input data based on the user's past diagnostic results. Furthermore, the reception desk can analyze the user's past medical history to verify the consistency of the input data. By referring to past medical history, the accuracy and consistency of the input data are improved, and the reliability of the diagnosis is enhanced.

[0053] The analysis unit can adjust the analysis results by taking into account the user's lifestyle data when analyzing symptom data. For example, if the user is a smoker, the likelihood of smoking-related diseases can be increased. Conversely, if the user exercises regularly, the likelihood of diseases related to lack of exercise can be decreased. Furthermore, the likelihood of diseases related to nutritional deficiencies or excesses can be adjusted by considering the user's dietary data. In this way, by considering lifestyle data, the accuracy of the analysis results is improved, enabling more appropriate diagnoses.

[0054] The service provider can propose treatment options while considering the user's health insurance information when providing diagnostic results. For example, if the user is enrolled in a specific health insurance plan, the service provider will prioritize suggesting treatment options covered by that plan. Furthermore, if the user desires a treatment option not covered by insurance, the service provider can also provide information on the cost of that treatment. In addition, based on the user's insurance information, the service provider can provide information on the nearest insurance-covered medical facilities. This allows the service provider to propose the most suitable treatment option for the user by considering their health insurance information.

[0055] The feedback unit can adjust the input content when receiving user symptom data, taking into account the user's current activity level. For example, if the user is exercising, symptoms related to exercise will be prioritized. Similarly, if the user is at work, symptoms related to work-related stress and fatigue can be prioritized. Furthermore, if the user is on vacation, symptoms related to the environment of their travel destination can be prioritized. By considering the user's current activity level, the accuracy of the input content is improved, enabling a more appropriate diagnosis.

[0056] The reception desk can complete the input data by considering the user's family history when receiving the user's symptom data. For example, if a user's family has a specific genetic disorder, the system can automatically complete the symptoms related to that disorder. It can also improve the accuracy of the input data based on the user's family history. Furthermore, the reception desk can analyze the user's family history to verify the consistency of the input data. By considering family history, the accuracy and consistency of the input data are improved, and the reliability of the diagnosis is enhanced.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The reception desk accepts symptom data provided by the user. This symptom data may include, for example, fever, cough, and headache. The reception desk accepts symptom data in various formats, including text, voice input, and image data. For example, the user can describe their symptoms verbally, and this will be converted into text data. The user can also take a picture of a rash and upload it to the system. Step 2: The analysis unit uses deep learning to analyze the symptom data received by the reception unit. For example, it uses a neural network to analyze the symptom data and identify potential diseases. It also compares the symptom data with an accumulated medical database and identifies diseases that match the symptoms by referring to past diagnostic data and medical literature. Furthermore, the analysis unit transmits the results of the symptom data analysis to the provision unit. Step 3: The provision unit provides the diagnostic results obtained by the analysis unit. For example, it notifies the user of the diagnostic results and provides information on appropriate treatments and medical institutions. Furthermore, it sends the diagnostic results to the feedback unit. Step 4: The feedback unit provides prompt medical advice based on the diagnostic results provided by the service provider. For example, it advises the user on appropriate treatments and lifestyle improvements based on the diagnostic results. It also provides prompt medical advice to the user through a real-time feedback system.

[0059] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that analyzes symptom data provided by a user and identifies potential diseases by comparing it with an accumulated medical database. The AI ​​agent system analyzes the symptom data provided by the user and compares it with a large-scale accumulated medical database to identify potential diseases and provide the most likely diagnosis. Furthermore, it provides the user with rapid medical advice through a real-time feedback system. This mechanism improves diagnostic accuracy and shortens diagnosis time. It also improves user satisfaction. For example, the user inputs their own symptom data. For example, they input symptoms such as fever, cough, and headache. This information is input to the AI ​​agent. Next, the AI ​​agent analyzes the input symptom data using deep learning. The AI ​​agent analyzes the symptom data and compares it with a large-scale accumulated medical database. For example, it refers to past diagnostic data and medical literature to identify potential diseases that match the symptoms. Based on the identified potential diseases, the AI ​​agent provides the most likely diagnosis. The accuracy of the diagnosis is improved by an algorithm designed by medical professionals. For example, if the user has symptoms of fever and cough, the AI ​​agent identifies possibilities such as influenza or a common cold and provides the most likely diagnosis. Furthermore, it provides the user with rapid medical advice through a real-time feedback system. For example, based on the diagnosis, it provides information on appropriate treatments and medical facilities. This allows users to receive timely and appropriate medical advice. This system improves diagnostic accuracy and reduces diagnosis time. It also improves user satisfaction. For instance, a diagnosis that would take several hours with conventional methods can be completed in minutes using an AI agent. This allows users to receive timely and appropriate medical advice, improving their health management. The AI ​​agent system can efficiently analyze the user's symptom data and provide timely medical advice.

[0060] The AI ​​agent system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a feedback unit. The reception unit receives input of symptom data provided by the user. Symptom data provided by the user includes, but is not limited to, examples of fever, cough, headache, etc. The reception unit receives the symptom data entered by the user in text format, for example. The reception unit can also receive symptom data using voice input. For example, the user describes the symptoms aloud, and this is converted into text data. Furthermore, the reception unit can also receive symptom data using image data. For example, the user takes a picture of a rash and uploads it to the system. The analysis unit uses deep learning to analyze the symptom data received by the reception unit. The analysis unit analyzes the symptom data using, for example, a neural network to identify potential diseases. The analysis unit can also compare the symptom data with an accumulated medical database. For example, it refers to past diagnostic data and medical literature to identify diseases that match the symptoms. Furthermore, the analysis unit transmits the results of the symptom data analysis to the provision unit. The provision unit provides the diagnostic results obtained by the analysis unit. The information provider unit, for example, notifies the user of the diagnosis results. The information provider unit can also provide information on appropriate treatments and medical institutions based on the diagnosis results. For example, it provides the user with information on the nearest medical institution. Furthermore, the information provider unit transmits the diagnosis results to the feedback unit. The feedback unit provides prompt medical advice based on the diagnosis results provided by the information provider unit. For example, the feedback unit advises the user on appropriate treatments and lifestyle improvements based on the diagnosis results. The feedback unit can also provide prompt medical advice to the user through a real-time feedback system. For example, it provides feedback immediately after the user receives the diagnosis results. As a result, the AI ​​agent system according to the embodiment can efficiently analyze the user's symptom data and provide prompt medical advice.

[0061] The reception desk is a system that accepts symptom data provided by users. This symptom data includes, but is not limited to, fever, cough, and headache. The reception desk can accept user-entered symptom data in text format, making it easy for users to input their symptoms. Furthermore, the reception desk can also accept symptom data using voice input; it has a function to convert the user's verbal description of symptoms into text data, thus improving convenience by allowing users to input symptoms without using their hands. The reception desk can also accept symptom data using image data; for example, a user can take a picture of a rash and upload it to the system, allowing for accurate communication of visual symptoms. In this way, the reception desk offers diverse input methods, enabling users to input their symptoms accurately and easily. The analysis unit is a system that uses deep learning to analyze symptom data received by the reception unit. The analysis unit uses a neural network to analyze symptom data and has the function of identifying potential diseases, so it can quickly and accurately identify diseases based on the symptom data provided by the user. Furthermore, the analysis unit can also cross-reference symptom data with an accumulated medical database, so it can refer to past diagnostic data and medical literature to identify diseases that match the symptoms. For example, based on past diagnostic data, it can refer to the diagnostic results of patients with similar symptoms to identify the most likely disease. Also, by referring to medical literature, it can perform diagnoses based on the latest medical information. In this way, the analysis unit can quickly and accurately identify diseases based on the user's symptom data and provide appropriate diagnostic results. The provision unit is a system that provides users with diagnostic results obtained by the analysis unit. The provision unit has a function to notify users of the diagnostic results, allowing users to quickly receive diagnostic results for their symptoms. Furthermore, the provision unit can also provide information on appropriate treatments and medical institutions based on the diagnostic results, allowing users to select appropriate treatments and medical institutions based on the results. For example, by providing users with information on the nearest medical institution, they can quickly visit a medical institution. In addition, the provision unit has a function to transmit diagnostic results to the feedback unit, providing information for the feedback unit to provide prompt medical advice. In this way, the provision unit can provide users with quick and appropriate diagnostic results and information on appropriate treatments and medical institutions. The Feedback Unit is a system that provides rapid medical advice based on the diagnostic results provided by the Provider Unit. The Feedback Unit has the function of advising users on appropriate treatment methods and lifestyle improvements based on the diagnostic results, allowing users to implement appropriate treatment methods and lifestyle improvements based on the diagnostic results. Furthermore, the Feedback Unit can also provide users with rapid medical advice through a real-time feedback system, so users can receive feedback immediately after receiving their diagnostic results. For example, by providing feedback immediately after a user receives their diagnostic results, they can quickly implement appropriate treatment methods and lifestyle improvements. In this way, the Feedback Unit can provide users with rapid and appropriate medical advice and support their health management. Thus, the AI ​​agent system according to this embodiment can efficiently analyze the user's symptom data and provide rapid medical advice, allowing the user to quickly obtain appropriate diagnoses and treatment methods for their symptoms. Furthermore, the system improves convenience by providing diverse input methods, enabling users to accurately and easily input their symptoms. In addition, the system can perform rapid and accurate diagnoses by utilizing deep learning and medical databases, allowing users to obtain highly reliable diagnostic results. In this way, the AI ​​agent system according to this embodiment can support the user's health management and provide rapid and appropriate medical advice.

[0062] The analysis unit can analyze symptom data using deep learning and compare it with an accumulated medical database. For example, the analysis unit can analyze symptom data using a neural network. For example, the analysis unit can receive symptom data as input and perform analysis using a neural network. The analysis unit can also compare symptom data with an accumulated medical database. For example, the analysis unit can refer to past diagnostic data and medical literature to identify diseases that match the symptoms. By utilizing deep learning, the accuracy of symptom data analysis is improved. Deep learning includes, but is not limited to, convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input symptom data into a generative AI, which can analyze the symptom data and identify potential diseases.

[0063] The service provider can provide information on appropriate treatments and medical institutions based on the diagnosis results. For example, the service provider can suggest appropriate treatments to the user based on the diagnosis results. For example, the service provider can advise the user on medication prescriptions and lifestyle improvements based on the diagnosis results. The service provider can also provide the user with information on the nearest medical institutions based on the diagnosis results. For example, the service provider can provide information on the nearest hospitals and clinics based on the user's location information. This allows the user to receive appropriate medical care quickly by providing information on appropriate treatments and medical institutions based on the diagnosis results. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the diagnosis results into AI, and the AI ​​can provide information on appropriate treatments and medical institutions.

[0064] The feedback unit can provide users with prompt medical advice through a real-time feedback system. For example, the feedback unit can provide users with prompt medical advice based on diagnostic results. For example, after receiving diagnostic results, the feedback unit can immediately advise users on appropriate treatments and lifestyle improvements. The feedback unit can also provide users with prompt medical advice through a real-time feedback system. For example, the feedback unit can notify users of diagnostic results in real time and provide prompt feedback. This improves user satisfaction by providing prompt medical advice through a real-time feedback system. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input diagnostic results into AI, which can then provide prompt medical advice.

[0065] The reception desk can receive symptom data such as fever, cough, and headache entered by the user. For example, the reception desk can receive the symptom data entered by the user in text format. For example, the user enters symptoms such as fever, cough, and headache. The reception desk can also receive symptom data using voice input. For example, the user describes the symptoms by voice, and this is converted into text data. Furthermore, the reception desk can also receive symptom data using image data. For example, the user takes a picture of a rash and uploads it to the system. This improves the accuracy of diagnosis by accurately receiving the symptom data entered by the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the symptom data entered by the user into AI, and the AI ​​can analyze and accept the data.

[0066] The reception unit can estimate the user's emotions and adjust the timing of symptom data input based on the estimated emotions. For example, if the user is stressed, the reception unit can delay the input timing to help them relax. If the user is in a hurry, the reception unit can also speed up the input timing to collect data quickly. Furthermore, if the user is anxious, the reception unit can adjust the input timing to provide reassurance. By adjusting the input timing according to the user's emotions, the system reduces user stress and improves input accuracy. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input user facial expression data into a generative AI, which can estimate the user's emotions and adjust the input timing.

[0067] The reception desk can analyze the user's past symptom data input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also predict and suggest input methods to be used at specific times based on the user's past input history. Furthermore, the reception desk can analyze patterns in the symptom data entered by the user in the past and select the optimal input method. In this way, by analyzing past input history, the reception desk can provide the user with the most suitable input method and improve input efficiency. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past input history data into AI, and the AI ​​can select the optimal input method.

[0068] The reception unit can filter symptom data based on the user's current health status and lifestyle when the data is entered. For example, the reception unit can input only relevant symptom data based on the user's current health status. The reception unit can also filter the input symptom data based on the user's lifestyle (work, family, etc.). Furthermore, the reception unit can determine the priority of the input symptom data according to the user's health status and lifestyle. This improves the accuracy of the input data by filtering according to the user's health status and lifestyle. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input the user's health status data into AI, and the AI ​​can perform the filtering.

[0069] The reception unit can estimate the user's emotions and determine the priority of symptom data to be entered based on the estimated emotions. For example, if the user is stressed, the reception unit will prioritize the input of important symptom data. If the user is relaxed, the reception unit can also input detailed symptom data. Furthermore, if the user is in a hurry, the reception unit can prioritize symptom data that can be entered quickly. This allows for the priority of important data to be entered by determining the priority of input data 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. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit can input the user's facial expression data into a generative AI, which can estimate the user's emotions and determine the priority of the input data.

[0070] The reception system can prioritize the input of highly relevant symptoms by considering the user's geographical location when entering symptom data. For example, if the user is in a specific region, the reception system can prioritize the input of symptoms of diseases prevalent in that region. Furthermore, if the user is traveling, the reception system can input symptom data based on medical information for the travel destination. Additionally, if the user is in a specific environment (workplace, school, etc.), the reception system can prioritize the input of symptoms related to that environment. This allows for the priority input of highly relevant symptom data by considering geographical location. Some or all of the above processing in the reception system may be performed using AI, or not. For example, the reception system can input the user's geographical location information into the AI, which can then prioritize the input of highly relevant symptoms.

[0071] The reception desk can analyze the user's social media activity when inputting symptom data and input relevant symptom data. For example, the reception desk can extract information about recent health problems from the user's social media posts and input it as symptom data. The reception desk can also extract information about stress and anxiety from the user's social media activity and input it as symptom data. Furthermore, the reception desk can extract information about lifestyle habits from the user's social media activity and input it as symptom data. In this way, by analyzing social media activity, symptom data that reflects the user's lifestyle habits and health condition can be input. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's social media data into AI, and the AI ​​can extract and input relevant symptom data.

[0072] The analysis unit can estimate the user's emotions and adjust the method of analyzing symptom data based on the estimated user emotions. For example, if the user is stressed, the analysis unit can use a concise analysis method. If the user is relaxed, the analysis unit can also use a detailed analysis method. Furthermore, if the user is in a hurry, the analysis unit can use a rapid analysis method. By adjusting the analysis method according to the user's emotions, the accuracy and efficiency of the analysis are improved. 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. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user facial expression data into a generative AI, which can estimate the user's emotions and adjust the analysis method.

[0073] The analysis unit can adjust the level of detail of the analysis based on the severity of the symptoms when analyzing symptom data. For example, the analysis unit can perform a detailed analysis for symptoms with high severity. It can also perform a simplified analysis for mild symptoms. Furthermore, the analysis unit can determine the priority of the analysis according to the severity. This allows for detailed analysis of important symptoms by adjusting the level of detail according to the severity of the symptoms. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input symptom data into AI, and the AI ​​can adjust the level of detail of the analysis based on the severity.

[0074] The analysis unit can apply different analysis algorithms depending on the symptom category when analyzing symptom data. For example, for respiratory symptoms, the analysis unit can use an analysis algorithm specialized for the respiratory system. Similarly, for digestive symptoms, it can use an analysis algorithm specialized for the digestive system. Furthermore, for neurological symptoms, it can use an analysis algorithm specialized for the nervous system. This improves the accuracy of the analysis by applying an analysis algorithm appropriate to the symptom category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input symptom data into AI, which can then apply different analysis algorithms depending on the symptom category.

[0075] The analysis unit can estimate the user's emotions and determine the priority of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize the analysis of important symptoms. If the user is relaxed, the analysis unit can also perform a detailed symptom analysis. Furthermore, if the user is in a hurry, the analysis unit can prioritize symptoms that can be analyzed quickly. This allows for the priority of analysis of important symptoms by determining the priority of the analysis 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. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user facial expression data into a generative AI, which can estimate the user's emotions and determine the priority of the analysis.

[0076] The analysis unit can determine the priority of analysis based on the timing of symptom onset when analyzing symptom data. For example, the analysis unit can prioritize the analysis of recently occurring symptoms. The analysis unit can also perform a detailed analysis of symptoms that have persisted for a long period. Furthermore, the analysis unit can determine the priority of analysis according to the timing of symptom onset. This allows for the prioritization of analysis based on the timing of symptom onset, thereby prioritizing the analysis of recently occurring symptoms. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input symptom data into AI, which can then determine the priority of analysis based on the timing of onset.

[0077] The analysis unit can adjust the order of analysis based on the relevance of symptoms when analyzing symptom data. For example, the analysis unit can prioritize the analysis of highly relevant symptoms. It can also postpone the analysis of less relevant symptoms. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of symptoms. This allows for the prioritization of analysis of highly relevant symptoms by adjusting the order of analysis according to the relevance of symptoms. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input symptom data into AI, which can then adjust the order of analysis based on relevance.

[0078] The service provider can estimate the user's emotions and adjust the way the diagnostic results are presented based on the estimated emotions. For example, if the user is stressed, the service provider will use a concise and easy-to-understand presentation. If the user is relaxed, the service provider may also use a presentation that includes detailed explanations. Furthermore, if the user is in a hurry, the service provider may use a presentation that can be quickly understood. By adjusting the presentation of the diagnostic results according to the user's emotions, the user can more easily understand the results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user facial expression data into a generative AI, which can estimate the user's emotions and adjust the way the diagnostic results are presented.

[0079] The information provider can adjust the level of detail provided based on the importance of the diagnosis when providing the diagnosis results. For example, the provider can provide detailed information for high-importance diagnoses. It can also provide concise information for low-importance diagnoses. Furthermore, the provider can adjust the level of detail according to the importance of the diagnosis. This allows for the provision of detailed information for important diagnosis results by adjusting the level of detail according to the importance of the diagnosis. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the diagnosis results into AI, which can then adjust the level of detail based on importance.

[0080] The delivery unit can apply different delivery algorithms depending on the diagnostic category when providing diagnostic results. For example, for respiratory system diagnoses, the delivery unit uses a delivery algorithm specialized for the respiratory system. Similarly, for digestive system diagnoses, the delivery unit can use a delivery algorithm specialized for the digestive system. Furthermore, for neurological system diagnoses, the delivery unit can use a delivery algorithm specialized for the neurological system. This improves the accuracy of providing diagnostic results by applying a delivery algorithm appropriate to the diagnostic category. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input diagnostic results into AI, which can then apply different delivery algorithms depending on the diagnostic category.

[0081] The service provider can estimate the user's emotions and adjust the length of the diagnostic result based on the estimated emotions. For example, if the user is stressed, the service provider can provide a short, concise diagnostic result. If the user is relaxed, the service provider can also provide a longer diagnostic result with more detailed explanations. Furthermore, if the user is in a hurry, the service provider can provide a short, easily understandable diagnostic result. By adjusting the length of the diagnostic result according to the user's emotions, the service provider makes it easier for the user to understand the result. 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. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user facial expression data into a generative AI, which can estimate the user's emotions and adjust the length of the diagnostic result.

[0082] The service provider can determine the priority of providing diagnostic results based on when the diagnosis occurred. For example, the service provider will prioritize providing recently occurring diagnoses. The service provider can also provide detailed information for diagnoses that have been ongoing for a long period. Furthermore, the service provider can determine the priority of providing results according to when the diagnosis occurred. This allows for the priority of providing recently occurring diagnostic results by determining the priority of providing results according to when the diagnosis occurred. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input diagnostic results into AI, and the AI ​​can determine the priority of providing results based on when the diagnosis occurred.

[0083] The delivery unit can adjust the order in which diagnostic results are provided based on their relevance. For example, the delivery unit will prioritize providing highly relevant diagnoses. It can also postpone providing less relevant diagnoses. Furthermore, the delivery unit can adjust the order in which diagnoses are provided according to their relevance. This allows for the priority provision of highly relevant diagnostic results by adjusting the order in which they are provided according to their relevance. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input diagnostic results into AI, which can then adjust the order in which they are provided based on their relevance.

[0084] The feedback unit can estimate the user's emotions and adjust the way medical advice is provided based on the estimated emotions. For example, if the user is stressed, the feedback unit can provide concise and easy-to-understand advice. If the user is relaxed, the feedback unit can also provide advice that includes detailed explanations. Furthermore, if the user is in a hurry, the feedback unit can provide advice that can be quickly understood. By adjusting the way medical advice is provided according to the user's emotions, the advice becomes easier 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. Some or all of the above processing in the feedback unit may be performed using AI or not using AI. For example, the feedback unit can input the user's facial expression data into the generative AI, which can estimate the user's emotions and adjust the way medical advice is provided.

[0085] The feedback unit can provide optimal advice by referring to the user's past medical history when offering medical advice. For example, the feedback unit can suggest the most suitable treatment based on the user's past diagnostic history. It can also provide advice for specific illnesses based on the user's past medical history. Furthermore, the feedback unit can analyze the user's past medical history to provide the most effective advice. This allows the feedback unit to provide the user with the most suitable advice by referring to their past medical history. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's past medical history data into an AI, which can then provide optimal advice.

[0086] The feedback unit can customize the means of providing medical advice based on the user's current health condition. For example, if the user is tired, the feedback unit can provide concise and easy-to-understand advice. If the user is healthy, the feedback unit can also provide detailed advice. Furthermore, if the user is unwell, the feedback unit can provide advice that allows for a quick response. In this way, by customizing the means of advice according to the user's health condition, the system can provide the user with the most appropriate advice. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not. For example, the feedback unit can input the user's health condition data into the AI, which can then customize the means of advice.

[0087] The feedback unit can estimate the user's emotions and prioritize medical advice based on those emotions. For example, if the user is stressed, the feedback unit will prioritize important advice. If the user is relaxed, the feedback unit can also provide more detailed advice. Furthermore, if the user is in a hurry, the feedback unit can prioritize advice that can be quickly understood. This ensures that important advice is prioritized by prioritizing medical advice 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the feedback unit may be performed using AI or not. For example, the feedback unit can input user facial expression data into a generative AI, which can estimate the user's emotions and determine the priority of medical advice.

[0088] The feedback unit can provide optimal medical advice by considering the user's geographical location. For example, if the user is in a specific region, the feedback unit can provide information on medical facilities in that region. Furthermore, if the user is traveling, the feedback unit can provide advice based on medical information for their travel destination. Additionally, if the user is in a specific environment (such as a workplace or school), the feedback unit can provide advice relevant to that environment. This allows the feedback unit to provide optimal medical advice by considering geographical location. Some or all of the above processing in the feedback unit may be performed using AI, or not. For example, the feedback unit can input the user's geographical location into the AI, which can then provide optimal advice.

[0089] The feedback unit can analyze a user's social media activity to suggest methods of providing medical advice. For example, the feedback unit can extract information about recent health problems from a user's social media posts and provide advice. It can also extract information about stress and anxiety from a user's social media activity and provide advice. Furthermore, it can extract information about lifestyle habits from a user's social media activity and provide advice. In this way, by analyzing social media activity, the feedback unit can provide the user with the most suitable method of advice. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not. For example, the feedback unit can input the user's social media data into an AI, which can then suggest methods of advice.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The reception desk can refer to the user's past medical history to complete the input data when receiving the user's symptom data. For example, if the user has suffered from a specific illness in the past, it can automatically complete the symptoms related to that illness. The reception desk can also improve the accuracy of the input data based on the user's past diagnostic results. Furthermore, the reception desk can analyze the user's past medical history to verify the consistency of the input data. By referring to past medical history, the accuracy and consistency of the input data are improved, and the reliability of the diagnosis is enhanced.

[0092] The analysis unit can adjust the analysis results by taking into account the user's lifestyle data when analyzing symptom data. For example, if the user is a smoker, the likelihood of smoking-related diseases can be increased. Conversely, if the user exercises regularly, the likelihood of diseases related to lack of exercise can be decreased. Furthermore, the likelihood of diseases related to nutritional deficiencies or excesses can be adjusted by considering the user's dietary data. In this way, by considering lifestyle data, the accuracy of the analysis results is improved, enabling more appropriate diagnoses.

[0093] The service provider can propose treatment options while considering the user's health insurance information when providing diagnostic results. For example, if the user is enrolled in a specific health insurance plan, the service provider will prioritize suggesting treatment options covered by that plan. Furthermore, if the user desires a treatment option not covered by insurance, the service provider can also provide information on the cost of that treatment. In addition, based on the user's insurance information, the service provider can provide information on the nearest insurance-covered medical facilities. This allows the service provider to propose the most suitable treatment option for the user by considering their health insurance information.

[0094] The feedback unit can adjust the input content when receiving user symptom data, taking into account the user's current activity level. For example, if the user is exercising, symptoms related to exercise will be prioritized. Similarly, if the user is at work, symptoms related to work-related stress and fatigue can be prioritized. Furthermore, if the user is on vacation, symptoms related to the environment of their travel destination can be prioritized. By considering the user's current activity level, the accuracy of the input content is improved, enabling a more appropriate diagnosis.

[0095] The reception desk can complete the input data by considering the user's family history when receiving the user's symptom data. For example, if a user's family has a specific genetic disorder, the system can automatically complete the symptoms related to that disorder. It can also improve the accuracy of the input data based on the user's family history. Furthermore, the reception desk can analyze the user's family history to verify the consistency of the input data. By considering family history, the accuracy and consistency of the input data are improved, and the reliability of the diagnosis is enhanced.

[0096] The reception desk can estimate the user's emotions and adjust the method of inputting symptom data based on that estimation. For example, if the user is stressed, it may prompt them to input using simple questions. If the user is relaxed, it may request more detailed input. Furthermore, if the user is in a hurry, it may suggest a method for quick input. By adjusting the input method according to the user's emotions, the accuracy and efficiency of input are improved.

[0097] The analysis unit can estimate the user's emotions and adjust how the analysis results are presented based on those estimated emotions. For example, if the user is stressed, it can present concise and easy-to-understand results. If the user is relaxed, it can present detailed analysis results. Furthermore, if the user is in a hurry, it can present the results in a format that can be quickly understood. By adjusting how the analysis results are presented according to the user's emotions, the results become easier for the user to understand.

[0098] The system can estimate the user's emotions and adjust the treatment suggestions based on those emotions. For example, if the user is stressed, it can suggest a concise and specific treatment. If the user is relaxed, it can suggest a treatment with detailed explanations. Furthermore, if the user is in a hurry, it can suggest a treatment that can be implemented quickly. By adjusting the treatment suggestions according to the user's emotions, the system makes it easier for the user to understand the treatment.

[0099] The feedback unit can estimate the user's emotions and adjust the content of medical advice based on those emotions. For example, if the user is stressed, it can provide advice on how to relax. If the user is relaxed, it can also provide detailed health management advice. Furthermore, if the user is in a hurry, it can provide advice that can be acted upon quickly. By adjusting the content of medical advice according to the user's emotions, the advice becomes more readily accepted by the user.

[0100] The system can estimate the user's emotions and adjust the notification method of the diagnostic results based on those emotions. For example, if the user is stressed, the notification will be less intense. Conversely, if the user is relaxed, a more detailed notification may be provided. Furthermore, if the user is in a hurry, a faster notification method may be chosen. By adjusting the notification method of the diagnostic results according to the user's emotions, the system makes it easier for the user to accept the notifications.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The reception desk accepts symptom data provided by the user. This symptom data may include, for example, fever, cough, and headache. The reception desk accepts symptom data in various formats, including text, voice input, and image data. For example, the user can describe their symptoms verbally, and this will be converted into text data. The user can also take a picture of a rash and upload it to the system. Step 2: The analysis unit uses deep learning to analyze the symptom data received by the reception unit. For example, it uses a neural network to analyze the symptom data and identify potential diseases. It also compares the symptom data with an accumulated medical database and identifies diseases that match the symptoms by referring to past diagnostic data and medical literature. Furthermore, the analysis unit transmits the results of the symptom data analysis to the provision unit. Step 3: The provision unit provides the diagnostic results obtained by the analysis unit. For example, it notifies the user of the diagnostic results and provides information on appropriate treatments and medical institutions. Furthermore, it sends the diagnostic results to the feedback unit. Step 4: The feedback unit provides prompt medical advice based on the diagnostic results provided by the service provider. For example, it advises the user on appropriate treatments and lifestyle improvements based on the diagnostic results. It also provides prompt medical advice to the user through a real-time feedback system.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives symptom data entered by the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the symptom data using deep learning. The provision unit is implemented by the control unit 46A of the smart device 14 and notifies the user of the diagnosis results. The feedback unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides prompt medical advice. 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.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and feedback unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives symptom data entered by the user. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the symptom data using deep learning. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and notifies the user of the diagnosis result. The feedback unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and provides prompt medical advice. 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.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives symptom data entered by the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the symptom data using deep learning. The provision unit is implemented by the control unit 46A of the headset terminal 314 and notifies the user of the diagnosis results. The feedback unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides prompt medical advice. 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.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and feedback unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives symptom data entered by the user. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the symptom data using deep learning. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and notifies the user of the diagnosis results. The feedback unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and provides prompt medical advice. 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] (Note 1) A reception area for receiving symptom data, An analysis unit that analyzes the symptom data received by the reception unit, A providing unit that provides the diagnostic results obtained by the analysis unit, The system includes a feedback unit that provides prompt medical advice based on the diagnostic results provided by the aforementioned supply unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit is Deep learning is used to analyze symptom data and compare it with the accumulated medical database. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Based on the diagnosis, we provide information on appropriate treatment methods and medical facilities. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned feedback unit is Providing users with rapid medical advice through a real-time feedback system. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It accepts symptom data such as fever, cough, and headache entered by the user. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of symptom data input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past symptom data entry history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering symptom 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 9) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of the symptom data to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering symptom data, the system prioritizes inputting symptoms that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering symptom data, the system analyzes the user's social media activity and inputs relevant symptom data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is We estimate the user's emotions and adjust the method of analyzing symptom data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is When analyzing symptom data, adjust the level of detail of the analysis based on the severity of the symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is When analyzing symptom data, different analysis algorithms are applied depending on the symptom category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is The system estimates user sentiment and prioritizes analysis based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is When analyzing symptom data, prioritize the analysis based on the timing of symptom onset. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is When analyzing symptom data, adjust the order of analysis based on the relevance of the symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the diagnostic results are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing diagnostic results, the level of detail provided will be adjusted based on the importance of the diagnosis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing diagnostic results, different delivery algorithms are applied depending on the diagnostic category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the diagnostic result based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing diagnostic results, the priority of provision will be determined based on when the diagnosis occurred. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing diagnostic results, the order in which they are provided will be adjusted based on the relevance of the diagnoses. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback unit is The system estimates the user's emotions and adjusts how medical advice is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is When providing medical advice, we refer to the user's past medical history to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is When providing medical advice, customize the advice based on the user's current health condition. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is The system estimates the user's emotions and prioritizes medical advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is When providing medical advice, we take the user's geographical location into consideration to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is When providing medical advice, we analyze the user's social media activity to suggest methods for providing advice. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0175] 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 reception area for receiving symptom data, An analysis unit that analyzes the symptom data received by the reception unit, A providing unit that provides the diagnostic results obtained by the analysis unit, The system includes a feedback unit that provides prompt medical advice based on the diagnostic results provided by the aforementioned supply unit. A system characterized by the following features.

2. The aforementioned analysis unit is Deep learning is used to analyze symptom data and compare it with the accumulated medical database. The system according to feature 1.

3. The aforementioned supply unit is, Based on the diagnosis, we provide information on appropriate treatment methods and medical facilities. The system according to feature 1.

4. The aforementioned feedback unit is Providing users with rapid medical advice through a real-time feedback system. The system according to feature 1.

5. The aforementioned reception unit is It accepts symptom data such as fever, cough, and headache entered by the user. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of symptom data input based on the estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past symptom data entry history and select the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is When entering symptom data, filtering is performed based on the user's current health status and lifestyle. The system according to feature 1.

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