System

Abstract

An object of the system according to the embodiment is to unitarily analyze a plurality of symptoms of an individual and specify the possibility of a potential disease.SOLUTION: A system includes a symptom input part, a symptom analysis part, and a database collation part. The symptom input unit collects symptoms input by a user. The symptom analysis unit analyzes the symptoms collected by the symptom input unit. The database collation unit collates the symptom analyzed by the symptom analysis unit with the medical database.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem of making it difficult to analyze multiple symptoms of an individual in a unified manner and identify possible underlying illnesses.

[0005] The system according to the embodiment aims to centrally analyze multiple symptoms that an individual has and identify the possibility of an underlying illness. [Means for solving the problem]

[0006] The system according to the embodiment includes a symptom input unit, a symptom analysis unit, and a database comparison unit. The symptom input unit collects symptoms input by a user. The symptom analysis unit analyzes the symptoms collected by the symptom input unit. The database comparison unit compares the symptoms analyzed by the symptom analysis unit with a medical database. [Effects of the Invention]

[0007] The system according to the embodiment can analyze multiple symptoms of an individual in a unified manner and identify possible underlying illnesses. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The AI ​​support system according to an embodiment of the present invention centrally analyzes multiple symptoms of an individual and identifies possible underlying illnesses by comparing the results with an accumulated medical database. This allows the AI ​​support system to provide the most likely diagnosis based on the symptoms entered by the user.

[0029] The AI ​​support system according to the embodiment includes a symptom input unit, a symptom analysis unit, and a database comparison unit. The symptom input unit collects symptoms input by a user. For example, a user can input symptoms such as headache, fever, cough, and fatigue. The symptom input unit centrally collects these symptoms. The symptom analysis unit analyzes the symptoms collected by the symptom input unit. For example, the generation AI analyzes the input symptoms and classifies each symptom into an appropriate category. For example, the generation AI classifies headache as a neurological symptom, fever as an infectious disease symptom, and cough as a respiratory symptom. The database comparison unit compares the symptoms analyzed by the symptom analysis unit with a medical database. For example, the generation AI compares the analyzed symptoms with an accumulated medical database to identify diseases that may match the input symptoms. As a result, the AI ​​support system according to the embodiment centrally collects and analyzes a user's symptoms and compares them with a medical database to identify possible underlying diseases. For example, by simply inputting multiple symptoms, the generation AI can quickly and accurately provide a diagnosis, enabling early detection of illnesses and the initiation of appropriate treatment.

[0030] The symptom input unit can collect symptoms using voice input or image recognition technology. For example, the user uses a smartphone or tablet to report symptoms by voice input. For example, when the user says, "I have a headache," the system analyzes the voice and saves it as text data. The symptom input unit also uses image recognition technology to collect symptoms from images taken by the user. For example, the user takes a photo of a rash, and the system analyzes the image to identify the symptoms. This allows for more intuitive operation by using voice input and image recognition technology.

[0031] The symptom input unit can automatically import the user's past health data or lifestyle data to improve the accuracy of symptom input. The symptom input unit, for example, automatically imports the user's past health checkup results and medical history and compares them with current symptoms to improve the accuracy of input. For example, if a user who has been diagnosed with high blood pressure in the past reports a headache, the symptom input unit takes that information into account when conducting analysis. The symptom input unit also imports the user's lifestyle data to improve the accuracy of symptom input. For example, the cause of symptoms can be identified based on the user's dietary records and exercise records. In this way, importing past health data and lifestyle data improves the accuracy of symptom input.

[0032] The symptom input unit can simultaneously collect the user's family history or genetic information, enabling a more comprehensive diagnosis. For example, the symptom input unit provides the user with the option to input family history or genetic information when inputting symptoms. For example, if there are any family members with diabetes, the user can input that information. The symptom input unit also automatically incorporates the user's genetic information to improve the accuracy of symptom input. For example, the risk of a specific disease can be evaluated based on the results of a genetic test. This allows for a more comprehensive diagnosis by collecting family history and genetic information.

[0033] The symptom input unit can gamify the symptom input, allowing the user to enjoy inputting symptoms. The symptom input unit, for example, gamifies the symptom input, allowing the user to enjoy inputting symptoms. For example, a system may be introduced in which points are accumulated and rewards are given each time a symptom is input. The symptom input unit also provides feedback to the user as the game progresses. For example, by accurately inputting symptoms, the character in the game will grow. In this way, gamifying the symptom input allows the user to enjoy inputting symptoms.

[0034] The symptom analysis unit can perform analysis taking into account the severity or frequency of symptoms. For example, when the generation AI analyzes symptoms, the symptom analysis unit classifies them taking into account severity. For example, it distinguishes between mild headaches and severe headaches and classifies each into an appropriate category. The symptom analysis unit also performs analysis taking into account the frequency of symptoms. For example, it distinguishes between frequently occurring symptoms and temporary symptoms and performs an analysis appropriate for each. This allows for a more detailed analysis by taking into account the severity and frequency of symptoms.

[0035] The symptom analysis unit can perform analysis by incorporating the user's living environment or occupational risk. The symptom analysis unit, for example, analyzes symptoms taking into account the user's living environment (e.g., urban or rural area) and classifies them according to the environment. For example, it identifies respiratory symptoms in a user living in an urban area. The symptom analysis unit also performs analysis by incorporating the user's occupational risk. For example, it identifies skin symptoms in a user working in a factory. This allows for more accurate analysis by taking into account the living environment and occupational risk.

[0036] The symptom analysis unit can cooperate with other health applications or wearable devices to realize comprehensive health management. For example, the symptom analysis unit cooperates with a fitness tracker or smartwatch to realize comprehensive health management. For example, the symptom analysis unit integrates heart rate and sleep data with symptoms for analysis. The symptom analysis unit also cooperates with other health applications to centrally manage the user's health data. For example, the symptom analysis unit cooperates with a food recording app to reflect food data in the analysis. This allows comprehensive health management by coordinating with other health applications and wearable devices.

[0037] The symptom analysis unit performs analysis in combination with the user's dietary or exercise data and can make suggestions for improving lifestyle habits. For example, the symptom analysis unit analyzes the symptom analysis results in combination with the user's dietary data and makes suggestions for improving lifestyle habits. For example, it identifies symptoms caused by a specific diet. The symptom analysis unit also performs analysis in combination with the user's exercise data. For example, it identifies symptoms caused by lack of exercise. This makes it possible to make suggestions for improving lifestyle habits by combining the dietary and exercise data.

[0038] The database matching unit can incorporate the latest research papers or clinical trial data for matching. For example, the database matching unit automatically adds the latest research papers to a medical database, and the generation AI performs matching based on that data. For example, it incorporates new treatments and diagnostic criteria. The database matching unit also incorporates clinical trial data for matching. For example, it makes a diagnosis based on the effects of a new drug. In this way, by incorporating the latest research papers and clinical trial data, the accuracy of diagnosis can be improved.

[0039] The database matching unit can suggest preventive measures or lifestyle improvement to the user based on the matching results. The database matching unit, for example, suggests specific preventive measures to the user based on the matching results. For example, if there is a high risk of influenza, vaccination may be recommended. The database matching unit also suggests lifestyle improvement. For example, it recommends dietary improvements and exercise. In this way, by suggesting preventive measures and lifestyle improvement based on the matching results, the user's health management is improved.

[0040] The database collation unit can share the collation results with other medical institutions or specialists to provide a second opinion. The database collation unit, for example, shares the collation results with other medical institutions to build a system for providing a second opinion. For example, the collation results are sent to a medical institution of the user's choice. The database collation unit also works with specialists to provide advice based on the collation results. For example, a specialist reviews the collation results and makes an additional diagnosis. In this way, a second opinion can be provided by sharing the collation results.

[0041] The database matching unit can link the matching results with the user's health insurance information to suggest appropriate medical services. The database matching unit, for example, links the matching results with the user's health insurance information to suggest appropriate medical services. For example, it can suggest treatments that are available within the scope of insurance coverage. The database matching unit also suggests the most suitable medical institution based on the user's insurance information. For example, it can introduce hospitals that accept the user's insurance. In this way, by linking with the health insurance information, it is possible to suggest appropriate medical services.

[0042] When providing a diagnostic result, the symptom analysis unit can automatically generate an explanation that is appropriate for the user's level of understanding and provide it in an easy-to-understand manner. For example, when providing a diagnostic result, the symptom analysis unit automatically generates an explanation that is appropriate for the user's level of understanding. For example, medical terms may be replaced with simpler terms for explanation. The symptom analysis unit also adjusts the format of the explanation depending on the user's level of understanding. For example, illustrations or videos may be used for explanation. In this way, by providing an explanation that is appropriate for the user's level of understanding, the diagnostic result can be communicated in an easy-to-understand manner.

[0043] The symptom analysis unit can present a specific next step to the user based on the diagnosis result. The symptom analysis unit, for example, presents a specific next step to the user based on the diagnosis result. For example, it automatically makes an appointment with a doctor. The symptom analysis unit also suggests medication to the user based on the diagnosis result. For example, it issues a prescription. In this way, by presenting a specific next step based on the diagnosis result, the user can take appropriate action.

[0044] The symptom analysis unit links the diagnosis results with the user's electronic medical record or medical records, thereby making examinations at medical institutions smoother. The symptom analysis unit, for example, links the diagnosis results with the user's electronic medical record, thereby making examinations at medical institutions smoother. For example, the symptom analysis unit transmits the diagnosis results to a doctor in advance. The symptom analysis unit also links with the user's medical records to provide information necessary for examinations. For example, it provides past diagnosis results and treatment history to the doctor. In this way, by linking the diagnosis results with the electronic medical record or medical records, examinations at medical institutions can be made smoother.

[0045] The symptom analysis unit can share the diagnostic results with the user's family and caregivers, thereby strengthening the support system. The symptom analysis unit, for example, shares the diagnostic results with the user's family and strengthens the support system. For example, the diagnostic results are sent to the family by email. The symptom analysis unit also works with the caregiver to provide support based on the diagnostic results. For example, the symptom analysis unit provides the caregiver with a care plan based on the user's symptoms. In this way, by sharing the diagnostic results with the family and caregivers, the support system can be strengthened.

[0046] The symptom analysis unit can set detailed question items when collecting user feedback on the diagnostic results and identify specific areas for improvement. The symptom analysis unit, for example, sets detailed question items when collecting user feedback on the diagnostic results. For example, it asks specific questions such as, "Were the diagnostic results accurate?" The symptom analysis unit also identifies specific areas for improvement based on the user feedback. For example, it identifies areas for improvement in system functionality or the user interface. In this way, by setting detailed question items, it is possible to identify specific areas for improvement.

[0047] The symptom analysis unit periodically updates the algorithm of the generative AI based on the feedback, thereby enabling continuous improvement of diagnostic accuracy. The symptom analysis unit periodically updates the algorithm of the generative AI based on, for example, feedback collected from users. For example, it evaluates the user's satisfaction with the diagnostic results and identifies areas for improvement in the algorithm. The symptom analysis unit also updates the learning data of the generative AI based on the feedback. For example, it adds new case data and improves diagnostic accuracy. In this way, by updating the algorithm based on feedback, diagnostic accuracy can be continuously improved.

[0048] The symptom analysis unit can compare the feedback with data from other users and identify common areas for improvement. The symptom analysis unit, for example, compares feedback collected from a user with data from other users and identifies common areas for improvement. For example, if multiple users are dissatisfied with the same symptom, the diagnostic algorithm for that symptom is improved. The symptom analysis unit also identifies common problems based on the feedback. For example, improving the usability of the user interface. In this way, by comparing the feedback with data from other users, common areas for improvement can be identified.

[0049] The symptom analysis unit can enhance FAQs or support content for users based on the feedback. The symptom analysis unit enhances FAQs and support content based on, for example, feedback collected from users. For example, answers to frequently asked questions may be added. The symptom analysis unit also updates the content of the support content based on user feedback. For example, new tutorials or guidebooks may be created. In this way, by enhancing FAQs and support content based on feedback, it becomes easier to resolve users' questions and problems.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The symptom input unit can generate questions customized based on the user's living environment, enabling more accurate symptom input. For example, for users living in urban areas, questions about air pollution and noise can be added. For users living in rural areas, questions about pesticides and contact with animals can be added. This allows for more detailed symptom input by generating questions tailored to the user's living environment.

[0052] The symptom analysis unit can incorporate the user's dietary data into the analysis to evaluate the impact of a specific meal on symptoms. For example, it can identify symptoms that occur after the user ingests a specific food. It can also make suggestions for improving nutritional balance based on the dietary data. By incorporating dietary data into the analysis, it is possible to evaluate the impact of diet on symptoms and make suggestions for improving lifestyle habits.

[0053] The symptom analysis unit can incorporate the user's exercise data into its analysis and evaluate the impact of exercise on symptoms. For example, it can identify symptoms caused by lack of exercise. It can also suggest appropriate exercise plans based on the exercise data. By incorporating the exercise data into the analysis, it is possible to evaluate the impact of exercise on symptoms and suggest lifestyle improvements.

[0054] The symptom analysis unit can incorporate the user's occupational risk into its analysis. For example, it can identify skin symptoms in a user who works in a factory. It can also identify stiff shoulders and back pain in a user who does a lot of desk work. By taking occupational risk into account, more accurate analysis becomes possible.

[0055] The symptom analysis unit can work with other health applications or wearable devices to achieve comprehensive health management. For example, the symptom analysis results can be linked with a fitness tracker or smartwatch to achieve comprehensive health management. For example, heart rate and sleep data can be integrated with symptoms for analysis. It can also work with other health applications to centrally manage the user's health data. This allows for comprehensive health management by working with other health applications and wearable devices.

[0056] The symptom analysis unit can link the diagnosis results with the user's electronic medical chart or medical records to facilitate smooth examinations at medical institutions. For example, the diagnosis results can be sent to a doctor in advance. It can also link with the user's medical records to provide information necessary for examinations. By linking the diagnosis results with the electronic medical chart or medical records, the diagnosis at medical institutions can be facilitated.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The symptom input unit collects symptoms input by the user. For example, the user can input symptoms such as headache, fever, cough, and fatigue, and these symptoms are collected in a unified manner. Step 2: The symptom analysis unit analyzes the symptoms collected by the symptom input unit. For example, the generation AI analyzes the input symptoms and classifies each symptom into an appropriate category. The generation AI may classify a headache as a neurological symptom, a fever as an infectious disease symptom, and a cough as a respiratory symptom. Step 3: The database comparison unit compares the symptoms analyzed by the symptom analysis unit with a medical database. For example, the generation AI compares the analyzed symptoms with an accumulated medical database to identify diseases that may match the input symptoms.

[0059] (Example 2) The AI ​​support system according to an embodiment of the present invention centrally analyzes multiple symptoms of an individual and identifies possible underlying illnesses by comparing the results with an accumulated medical database. This allows the AI ​​support system to provide the most likely diagnosis based on the symptoms entered by the user.

[0060] The AI ​​support system according to the embodiment includes a symptom input unit, a symptom analysis unit, and a database comparison unit. The symptom input unit collects symptoms input by a user. For example, a user can input symptoms such as headache, fever, cough, and fatigue. The symptom input unit centrally collects these symptoms. The symptom analysis unit analyzes the symptoms collected by the symptom input unit. For example, the generation AI analyzes the input symptoms and classifies each symptom into an appropriate category. For example, the generation AI classifies headache as a neurological symptom, fever as an infectious disease symptom, and cough as a respiratory symptom. The database comparison unit compares the symptoms analyzed by the symptom analysis unit with a medical database. For example, the generation AI compares the analyzed symptoms with an accumulated medical database to identify diseases that may match the input symptoms. As a result, the AI ​​support system according to the embodiment centrally collects and analyzes a user's symptoms and compares them with a medical database to identify possible underlying diseases. For example, by simply inputting multiple symptoms, the generation AI can quickly and accurately provide a diagnosis, enabling early detection of illnesses and the initiation of appropriate treatment.

[0061] The symptom input unit can collect symptoms using voice input or image recognition technology. For example, the user uses a smartphone or tablet to report symptoms by voice input. For example, when the user says, "I have a headache," the system analyzes the voice and saves it as text data. The symptom input unit also uses image recognition technology to collect symptoms from images taken by the user. For example, the user takes a photo of a rash, and the system analyzes the image to identify the symptoms. This allows for more intuitive operation by using voice input and image recognition technology.

[0062] The symptom input unit can automatically import the user's past health data or lifestyle data to improve the accuracy of symptom input. The symptom input unit, for example, automatically imports the user's past health checkup results and medical history and compares them with current symptoms to improve the accuracy of input. For example, if a user who has been diagnosed with high blood pressure in the past reports a headache, the symptom input unit takes that information into account when conducting analysis. The symptom input unit also imports the user's lifestyle data to improve the accuracy of symptom input. For example, the cause of symptoms can be identified based on the user's dietary records and exercise records. In this way, importing past health data and lifestyle data improves the accuracy of symptom input.

[0063] The symptom input unit uses the emotion estimation function to analyze the emotional state of the user when entering symptoms, and can provide an interface that encourages relaxation if the user is experiencing high levels of stress or anxiety. For example, the symptom input unit analyzes facial expressions and voice tones when the user enters symptoms to estimate the emotional state. For example, if the user looks anxious, the system displays a message encouraging relaxation. The symptom input unit also analyzes the user's voice to estimate the level of stress or anxiety. For example, if the user speaks in a tense voice, the system plays music that encourages relaxation. In this way, the emotion estimation function can reduce the user's stress and anxiety, allowing them to enter their symptoms in a relaxed state.

[0064] The symptom input unit can simultaneously collect the user's family history or genetic information, enabling a more comprehensive diagnosis. For example, the symptom input unit provides the user with the option to input family history or genetic information when inputting symptoms. For example, if there are any family members with diabetes, the user can input that information. The symptom input unit also automatically incorporates the user's genetic information to improve the accuracy of symptom input. For example, the risk of a specific disease can be evaluated based on the results of a genetic test. This allows for a more comprehensive diagnosis by collecting family history and genetic information.

[0065] The symptom input unit can gamify the symptom input, allowing the user to enjoy inputting symptoms. The symptom input unit, for example, gamifies the symptom input, allowing the user to enjoy inputting symptoms. For example, a system may be introduced in which points are accumulated and rewards are given each time a symptom is input. The symptom input unit also provides feedback to the user as the game progresses. For example, by accurately inputting symptoms, the character in the game will grow. In this way, gamifying the symptom input allows the user to enjoy inputting symptoms.

[0066] The symptom input unit uses the emotion estimation function to analyze the emotions of the user when entering symptoms in real time and provide positive feedback, thereby improving the accuracy of the input. For example, when the user enters symptoms, the symptom input unit uses the emotion estimation function to analyze the emotions in real time and provide positive feedback. For example, if the user is feeling anxious, the system displays a message such as "Don't worry, we are carefully recording your symptoms." The symptom input unit also adjusts the input interface according to the user's emotional state. For example, if the user is relaxed, the system displays a positive message to encourage input. In this way, the emotion estimation function provides feedback according to the user's emotions and improves the accuracy of the input.

[0067] The symptom analysis unit can perform analysis taking into account the severity or frequency of symptoms. For example, when the generation AI analyzes symptoms, the symptom analysis unit classifies them taking into account severity. For example, it distinguishes between mild headaches and severe headaches and classifies each into an appropriate category. The symptom analysis unit also performs analysis taking into account the frequency of symptoms. For example, it distinguishes between frequently occurring symptoms and temporary symptoms and performs an analysis appropriate for each. This allows for a more detailed analysis by taking into account the severity and frequency of symptoms.

[0068] The symptom analysis unit can perform analysis by incorporating the user's living environment or occupational risk. The symptom analysis unit, for example, analyzes symptoms taking into account the user's living environment (e.g., urban or rural area) and classifies them according to the environment. For example, it identifies respiratory symptoms in a user living in an urban area. The symptom analysis unit also performs analysis by incorporating the user's occupational risk. For example, it identifies skin symptoms in a user working in a factory. This allows for more accurate analysis by taking into account the living environment and occupational risk.

[0069] The symptom analysis unit can use the emotion estimation function to reflect the user's emotional state in the analysis and take into account the impact of stress or anxiety on the symptoms. The symptom analysis unit, for example, uses the emotion estimation function to analyze the user's emotional state and take into account the impact of stress or anxiety on the symptoms. For example, it identifies headaches in users with high stress. The symptom analysis unit also evaluates the severity of symptoms based on the user's emotional state. For example, it takes into account that the severity of symptoms increases when stress is high. In this way, by taking the emotional state into account, it is possible to reflect the impact of stress or anxiety on the symptoms in the analysis.

[0070] The symptom analysis unit can cooperate with other health applications or wearable devices to realize comprehensive health management. For example, the symptom analysis unit cooperates with a fitness tracker or smartwatch to realize comprehensive health management. For example, the symptom analysis unit integrates heart rate and sleep data with symptoms for analysis. The symptom analysis unit also cooperates with other health applications to centrally manage the user's health data. For example, the symptom analysis unit cooperates with a food recording app to reflect food data in the analysis. This allows comprehensive health management by coordinating with other health applications and wearable devices.

[0071] The symptom analysis unit performs analysis in combination with the user's dietary or exercise data and can make suggestions for improving lifestyle habits. For example, the symptom analysis unit analyzes the symptom analysis results in combination with the user's dietary data and makes suggestions for improving lifestyle habits. For example, it identifies symptoms caused by a specific diet. The symptom analysis unit also performs analysis in combination with the user's exercise data. For example, it identifies symptoms caused by lack of exercise. This makes it possible to make suggestions for improving lifestyle habits by combining the dietary and exercise data.

[0072] The database matching unit can incorporate the latest research papers or clinical trial data for matching. For example, the database matching unit automatically adds the latest research papers to a medical database, and the generation AI performs matching based on that data. For example, it incorporates new treatments and diagnostic criteria. The database matching unit also incorporates clinical trial data for matching. For example, it makes a diagnosis based on the effects of a new drug. In this way, by incorporating the latest research papers and clinical trial data, the accuracy of diagnosis can be improved.

[0073] The database matching unit can suggest preventive measures or lifestyle improvement to the user based on the matching results. The database matching unit, for example, suggests specific preventive measures to the user based on the matching results. For example, if there is a high risk of influenza, vaccination may be recommended. The database matching unit also suggests lifestyle improvement. For example, it recommends dietary improvements and exercise. In this way, by suggesting preventive measures and lifestyle improvement based on the matching results, the user's health management is improved.

[0074] The database matching unit can use the emotion estimation function to analyze the user's emotional response to the matching result and provide feedback that reassures the user. For example, the database matching unit collects the user's emotional response to the matching result in real time and provides feedback that gives a sense of security. For example, if the user is feeling anxious, the database matching unit displays a message such as "Don't worry, there are appropriate measures." The database matching unit also adjusts the content of the feedback depending on the user's emotional state. For example, if the user feels relieved, detailed information is provided. In this way, by using the emotion estimation function, feedback that reassures the user can be provided.

[0075] The database collation unit can share the collation results with other medical institutions or specialists to provide a second opinion. The database collation unit, for example, shares the collation results with other medical institutions to build a system for providing a second opinion. For example, the collation results are sent to a medical institution of the user's choice. The database collation unit also works with specialists to provide advice based on the collation results. For example, a specialist reviews the collation results and makes an additional diagnosis. In this way, a second opinion can be provided by sharing the collation results.

[0076] The database matching unit can link the matching results with the user's health insurance information to suggest appropriate medical services. The database matching unit, for example, links the matching results with the user's health insurance information to suggest appropriate medical services. For example, it can suggest treatments that are available within the scope of insurance coverage. The database matching unit also suggests the most suitable medical institution based on the user's insurance information. For example, it can introduce hospitals that accept the user's insurance. In this way, by linking with the health insurance information, it is possible to suggest appropriate medical services.

[0077] The database matching unit can use the emotion estimation function to analyze the user's emotions regarding the matching results in real time and make suggestions that will elicit positive emotions. For example, the database matching unit can analyze the user's emotions regarding the matching results in real time and make suggestions that will elicit positive emotions. For example, if the user is feeling anxious, the database matching unit can display a message such as "This result is a good sign." The database matching unit can also adjust the content of the suggestions depending on the user's emotional state. For example, if the user is feeling positive emotions, the database matching unit can make suggestions for further health improvements. In this way, by using the emotion estimation function, suggestions that will elicit positive emotions from the user can be made.

[0078] When providing a diagnostic result, the symptom analysis unit can automatically generate an explanation that is appropriate for the user's level of understanding and provide it in an easy-to-understand manner. For example, when providing a diagnostic result, the symptom analysis unit automatically generates an explanation that is appropriate for the user's level of understanding. For example, medical terms may be replaced with simpler terms for explanation. The symptom analysis unit also adjusts the format of the explanation depending on the user's level of understanding. For example, illustrations or videos may be used for explanation. In this way, by providing an explanation that is appropriate for the user's level of understanding, the diagnostic result can be communicated in an easy-to-understand manner.

[0079] The symptom analysis unit can present a specific next step to the user based on the diagnosis result. The symptom analysis unit, for example, presents a specific next step to the user based on the diagnosis result. For example, it automatically makes an appointment with a doctor. The symptom analysis unit also suggests medication to the user based on the diagnosis result. For example, it issues a prescription. In this way, by presenting a specific next step based on the diagnosis result, the user can take appropriate action.

[0080] The symptom analysis unit can use the emotion estimation function to analyze the user's emotional response to the diagnosis result and provide feedback that gives a sense of security. For example, the symptom analysis unit collects the user's emotional response to the diagnosis result in real time and provides feedback that gives a sense of security. For example, if the user is feeling anxious, the symptom analysis unit displays a message such as "Don't worry, there are appropriate measures." The symptom analysis unit also adjusts the content of the feedback depending on the user's emotional state. For example, if the user feels relieved, the symptom analysis unit provides detailed information. In this way, by using the emotion estimation function, feedback that gives a sense of security can be provided to the user.

[0081] The symptom analysis unit links the diagnosis results with the user's electronic medical record or medical records, thereby making examinations at medical institutions smoother. The symptom analysis unit, for example, links the diagnosis results with the user's electronic medical record, thereby making examinations at medical institutions smoother. For example, the symptom analysis unit transmits the diagnosis results to a doctor in advance. The symptom analysis unit also links with the user's medical records to provide information necessary for examinations. For example, it provides past diagnosis results and treatment history to the doctor. In this way, by linking the diagnosis results with the electronic medical record or medical records, examinations at medical institutions can be made smoother.

[0082] The symptom analysis unit can share the diagnostic results with the user's family and caregivers, thereby strengthening the support system. The symptom analysis unit, for example, shares the diagnostic results with the user's family and strengthens the support system. For example, the diagnostic results are sent to the family by email. The symptom analysis unit also works with the caregiver to provide support based on the diagnostic results. For example, the symptom analysis unit provides the caregiver with a care plan based on the user's symptoms. In this way, by sharing the diagnostic results with the family and caregivers, the support system can be strengthened.

[0083] The symptom analysis unit can use the emotion estimation function to analyze the user's emotions regarding the diagnosis results in real time and make suggestions that will elicit positive emotions. The symptom analysis unit, for example, analyzes the user's emotions regarding the diagnosis results in real time and makes suggestions that will elicit positive emotions. For example, if the user is feeling anxious, it displays a message such as "This result is a good sign." The symptom analysis unit also adjusts the content of the suggestions depending on the user's emotional state. For example, if the user is feeling positive emotions, it makes suggestions for further health improvements. In this way, by using the emotion estimation function, it is possible to make suggestions that will elicit positive emotions from the user.

[0084] The symptom analysis unit can set detailed question items when collecting user feedback on the diagnostic results and identify specific areas for improvement. The symptom analysis unit, for example, sets detailed question items when collecting user feedback on the diagnostic results. For example, it asks specific questions such as, "Were the diagnostic results accurate?" The symptom analysis unit also identifies specific areas for improvement based on the user feedback. For example, it identifies areas for improvement in system functionality or the user interface. In this way, by setting detailed question items, it is possible to identify specific areas for improvement.

[0085] The symptom analysis unit periodically updates the algorithm of the generative AI based on the feedback, thereby enabling continuous improvement of diagnostic accuracy. The symptom analysis unit periodically updates the algorithm of the generative AI based on, for example, feedback collected from users. For example, it evaluates the user's satisfaction with the diagnostic results and identifies areas for improvement in the algorithm. The symptom analysis unit also updates the learning data of the generative AI based on the feedback. For example, it adds new case data and improves diagnostic accuracy. In this way, by updating the algorithm based on feedback, diagnostic accuracy can be continuously improved.

[0086] The symptom analysis unit can use the emotion estimation function to analyze the user's emotions at the time of feedback and make suggestions to reduce stress or anxiety. The symptom analysis unit, for example, analyzes the user's emotions in real time at the time of feedback and makes suggestions to reduce stress or anxiety. For example, if the user is feeling anxious, it displays a message encouraging the user to relax. The symptom analysis unit also adjusts the content of the suggestions depending on the user's emotional state. For example, if the user is feeling stressed, it provides advice on stress management. In this way, by using the emotion estimation function, suggestions to reduce the user's stress and anxiety can be made.

[0087] The symptom analysis unit can compare the feedback with data from other users and identify common areas for improvement. The symptom analysis unit, for example, compares feedback collected from a user with data from other users and identifies common areas for improvement. For example, if multiple users are dissatisfied with the same symptom, the diagnostic algorithm for that symptom is improved. The symptom analysis unit also identifies common problems based on the feedback. For example, improving the usability of the user interface. In this way, by comparing the feedback with data from other users, common areas for improvement can be identified.

[0088] The symptom analysis unit can enhance FAQs or support content for users based on the feedback. The symptom analysis unit enhances FAQs and support content based on, for example, feedback collected from users. For example, answers to frequently asked questions may be added. The symptom analysis unit also updates the content of the support content based on user feedback. For example, new tutorials or guidebooks may be created. In this way, by enhancing FAQs and support content based on feedback, it becomes easier to resolve users' questions and problems.

[0089] The symptom analysis unit can use the emotion estimation function to analyze the user's emotions in real time when providing feedback and make suggestions that will elicit positive emotions. For example, the symptom analysis unit can analyze the user's emotions in real time when providing feedback and make suggestions that will elicit positive emotions. For example, if the user is feeling anxious, the symptom analysis unit can display a message such as "Your feedback is very helpful." The symptom analysis unit can also adjust the content of the suggestions depending on the user's emotional state. For example, if the user is feeling positive emotions, the symptom analysis unit can display a message encouraging further feedback. In this way, by using the emotion estimation function, suggestions that will elicit positive emotions from the user can be made.

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

[0091] The symptom input unit can generate questions customized based on the user's living environment, enabling more accurate symptom input. For example, for users living in urban areas, questions about air pollution and noise can be added. For users living in rural areas, questions about pesticides and contact with animals can be added. This allows for more detailed symptom input by generating questions tailored to the user's living environment.

[0092] The symptom analysis unit can incorporate the user's dietary data into the analysis to evaluate the impact of a specific meal on symptoms. For example, it can identify symptoms that occur after the user ingests a specific food. It can also make suggestions for improving nutritional balance based on the dietary data. By incorporating dietary data into the analysis, it is possible to evaluate the impact of diet on symptoms and make suggestions for improving lifestyle habits.

[0093] The symptom input unit can use the emotion estimation function to analyze the emotion a user feels when entering symptoms and provide appropriate support during entry. For example, if the user is feeling stressed, a message encouraging the user to relax can be displayed. Also, if the user is feeling anxious, feedback that gives a sense of security can be provided. In this way, by using the emotion estimation function, support can be provided according to the user's emotion, improving the accuracy of entry.

[0094] The symptom analysis unit can incorporate the user's exercise data into its analysis and evaluate the impact of exercise on symptoms. For example, it can identify symptoms caused by lack of exercise. It can also suggest appropriate exercise plans based on the exercise data. By incorporating the exercise data into the analysis, it is possible to evaluate the impact of exercise on symptoms and suggest lifestyle improvements.

[0095] The symptom input unit uses the emotion estimation function to analyze the user's emotions in real time when entering symptoms, and can improve the accuracy of input by providing positive feedback. For example, if the user is feeling anxious, the system can display a message such as "Don't worry, we are carefully recording your symptoms." On the other hand, if the user is relaxed, the system can display a positive message to encourage input. In this way, the emotion estimation function can provide feedback according to the user's emotions, improving the accuracy of input.

[0096] The symptom analysis unit can incorporate the user's occupational risk into its analysis. For example, it can identify skin symptoms in a user who works in a factory. It can also identify stiff shoulders and back pain in a user who does a lot of desk work. By taking occupational risk into account, more accurate analysis becomes possible.

[0097] The symptom analysis unit can use the emotion estimation function to reflect the user's emotional state in the analysis and consider the impact of stress or anxiety on symptoms. For example, it can identify headaches in users who are highly stressed. It can also evaluate the severity of symptoms based on the user's emotional state. By taking the emotional state into consideration, it is possible to reflect the impact of stress and anxiety on symptoms in the analysis.

[0098] The symptom analysis unit can work with other health applications or wearable devices to achieve comprehensive health management. For example, the symptom analysis results can be linked with a fitness tracker or smartwatch to achieve comprehensive health management. For example, heart rate and sleep data can be integrated with symptoms for analysis. It can also work with other health applications to centrally manage the user's health data. This allows for comprehensive health management by working with other health applications and wearable devices.

[0099] The symptom analysis unit can use the emotion estimation function to analyze the user's emotional response to the diagnosis results and provide feedback that gives a sense of security. For example, if the user is feeling anxious, it can display a message such as "Don't worry, there are appropriate measures." It can also adjust the content of the feedback depending on the user's emotional state. In this way, the emotion estimation function can provide feedback that gives the user a sense of security.

[0100] The symptom analysis unit can link the diagnosis results with the user's electronic medical chart or medical records to facilitate smooth examinations at medical institutions. For example, the diagnosis results can be sent to a doctor in advance. It can also link with the user's medical records to provide information necessary for examinations. By linking the diagnosis results with the electronic medical chart or medical records, the diagnosis at medical institutions can be facilitated.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The symptom input unit collects symptoms input by the user. For example, the user can input symptoms such as headache, fever, cough, and fatigue, and these symptoms are collected in a unified manner. Step 2: The symptom analysis unit analyzes the symptoms collected by the symptom input unit. For example, the generation AI analyzes the input symptoms and classifies each symptom into an appropriate category. The generation AI may classify a headache as a neurological symptom, a fever as an infectious disease symptom, and a cough as a respiratory symptom. Step 3: The database comparison unit compares the symptoms analyzed by the symptom analysis unit with a medical database. For example, the generation AI compares the analyzed symptoms with an accumulated medical database to identify diseases that may match the input symptoms.

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

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] 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.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a symptom input unit that collects symptoms input by a user; a symptom analysis unit that analyzes the symptoms collected by the symptom input unit; a database comparison unit that compares the symptoms analyzed by the symptom analysis unit with a medical database; A system characterized by:

2. The symptom input unit Collecting the symptoms using voice input or image recognition technology 2. The system of claim 1.

3. The symptom input unit Collecting the user's family history or genetic information simultaneously allows for a more comprehensive diagnosis 2. The system of claim 1.

4. The symptom analysis unit The analysis is carried out taking into account the severity or frequency of the symptoms.

2. The system of claim 1.

5. The database collation unit Incorporate and collate the latest research papers or clinical trial data 2. The system of claim 1.

6. The symptom input unit Using emotion estimation functionality, the emotional state of the user when entering the symptoms is analyzed, and an interface is provided that encourages relaxation when stress or anxiety is high.

2. The system of claim 1.

7. The symptom analysis unit Emotion estimation functionality is used to incorporate the user's emotional state into the analysis, taking into account the impact of stress or anxiety on the symptoms.

2. The system of claim 1.

8. The database collation unit Using emotion estimation functionality, the system analyzes the user's emotional response to the matching results and provides feedback that reassures the user.

2. The system of claim 1.

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