system

Abstract

The system according to the embodiment aims to enable even busy people to obtain an accurate diagnosis by inputting symptom information and smoothly consult with a doctor. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a linkage unit. The reception unit receives symptom information from a user. The analysis unit analyzes the information received by the reception unit and generates a diagnosis result. The provision unit provides the diagnosis result generated by the analysis unit. The linkage unit links consultations with a doctor based on the diagnosis result provided by the provision unit.

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] With conventional technology, it was difficult for busy people to obtain an accurate diagnosis even when they input their symptoms, and consultation with a doctor was not carried out smoothly.

[0005] The system according to the embodiment aims to enable even busy people to obtain an accurate diagnosis by inputting symptom information and smoothly consult with a doctor. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a linking unit. The reception unit receives symptom information from a user. The analysis unit analyzes the information received by the reception unit and generates a diagnosis result. The provision unit provides the diagnosis result generated by the analysis unit. The linking unit links consultation with a doctor based on the diagnosis result provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment allows even busy people to obtain an accurate diagnosis by inputting symptom information and smoothly consult with a doctor. [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 non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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) In a diagnostic support system according to an embodiment of the present invention, a user inputs symptom information, and a generation AI performs a diagnosis. The system also provides a doctor consultation function. The user inputs information about their symptoms and health status, and the generation AI analyzes the information to generate a diagnosis and provides it to the user. The system also provides a doctor consultation function based on the diagnosis results. For example, the user inputs specific symptoms, such as fever, cough, and headache, as well as detailed information, such as past medical history and current medication status. This information is input into the generation AI. The diagnostic support system then uses the generation AI to analyze the input information and identify possible illnesses and health problems. For example, if a patient has a fever and cough, the generation AI considers the possibility of a cold or flu. It also takes past medical history and current medication status into account to make a more accurate diagnosis. The diagnostic support system then provides the generated diagnosis result to the user. The user can review the diagnosis result provided by the generation AI and consult a doctor if necessary. For example, if the generation AI suggests the possibility of influenza, the user can consult a doctor and receive appropriate treatment. Furthermore, the diagnostic support system shares the diagnostic results generated by the generative AI with doctors, who then provide advice and treatment to the user based on these results. This allows users to receive a quick and accurate diagnosis and smoothly consult with a doctor. As a result, the diagnostic support system allows even busy people to easily understand their own health condition and receive appropriate diagnosis and treatment. As a result, the diagnostic support system allows users to receive a quick and accurate diagnosis and smoothly consult with a doctor. For example, even people who are too busy at work to have time to go to the hospital can use this system to easily receive a diagnosis at home or at work. In addition, the system is linked to a doctor consultation function, so users can receive doctor's advice immediately if necessary.

[0029] A diagnostic support system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a linking unit. The reception unit receives symptom information from a user. The symptom information may include, but is not limited to, pain level, fever, cough, and the like. The reception unit may also receive, for example, past medical history and current medication status input by the user. The analysis unit uses a generation AI to analyze the information received by the reception unit and generate a diagnostic result. The analysis may be performed based on, for example, a data analysis algorithm and analysis accuracy, but is not limited to, for example. For example, the generation AI analyzes the input symptom information and identifies possible illnesses or health problems. The analysis unit may also make a diagnosis taking into account the past medical history and current medication status. The provision unit provides the user with the diagnostic result generated by the analysis unit. The diagnostic result may include, but is not limited to, a disease name and a recommended treatment. For example, the provision unit provides the user with the diagnostic result generated by the generation AI. The linking unit links a consultation with a doctor based on the diagnostic result provided by the provision unit. For example, the diagnostic results generated by the generation AI can be sent to an electronic medical record system, and a doctor can provide advice to the user based on the results. This allows the diagnostic support system according to the embodiment to efficiently accept and analyze the user's symptom information, provide the diagnostic results, and link the consultation with the doctor.

[0030] The analysis unit can analyze the symptom information input by the generating AI and identify illnesses and health problems. The generating AI, for example, analyzes the input symptom information and identifies possible illnesses and health problems. For example, if there is a fever and cough, the generating AI considers the possibility of a cold or flu. The generating AI can also take into account past medical history and current medication status to make a more accurate diagnosis. As a result, using the generating AI improves the accuracy of symptom information analysis and makes it possible to identify possible illnesses and health problems.

[0031] The providing unit can provide the diagnostic result generated by the generation AI to the user. The providing unit, for example, provides the diagnostic result generated by the generation AI to the user. The diagnostic result includes, for example, a disease name and a recommended treatment, but is not limited to such examples. For example, the providing unit provides the diagnostic result generated by the generation AI to the user, allowing the user to quickly check the diagnostic result. In this way, by providing the diagnostic result generated by the generation AI to the user, the user can quickly check the diagnostic result.

[0032] The interlocking unit transmits the diagnostic results generated by the generation AI to an electronic medical record system, allowing a doctor to provide advice to a user based on the results. The interlocking unit transmits, for example, the diagnostic results generated by the generation AI to an electronic medical record system, allowing a doctor to provide advice to a user based on the results. The electronic medical record system includes, for example, the software used and the data format, but is not limited to such examples. In this way, by transmitting the diagnostic results generated by the generation AI to the electronic medical record system, a doctor can quickly provide advice to a user.

[0033] The reception unit can receive past medical history and current medication status input by the user. The reception unit receives, for example, past medical history and current medication status input by the user. Past medical history includes, for example, past diagnosis results and treatment history, but is not limited to such examples. Current medication status includes, for example, types of medication being taken and frequency of taking medication, but is not limited to such examples. In this way, by receiving the past medical history and current medication status, a more accurate diagnosis can be made.

[0034] The analysis unit can make a diagnosis based on past medical history and current medication status. The analysis unit makes a diagnosis based on, for example, past medical history and current medication status. Past medical history includes, for example, past diagnostic results and treatment history, but is not limited to such examples. Current medication status includes, for example, the type of medication being taken and how often it is taken, but is not limited to such examples. In this way, by taking past medical history and current medication status into consideration, the accuracy of the diagnosis is improved.

[0035] The reception unit can analyze the user's past input history and suggest an appropriate input method. The reception unit, for example, analyzes the user's past input history and suggests an optimal input method. The input history includes, for example, but is not limited to, past input content and input frequency. The appropriate input method includes, for example, but is not limited to, voice input and text input. For example, symptom information that the user has frequently input in the past can be automatically displayed as a candidate. It is also possible to preferentially suggest input methods (voice, text, etc.) that the user has used in the past. It is also possible to predict and suggest symptom information that will be input in a specific time period from the user's past input history. In this way, by analyzing the past input history, it is possible to suggest an optimal input method for the user.

[0036] The reception unit can filter the input content based on the user's current living situation and environment when inputting symptom information. For example, the reception unit filters the input content based on the user's current living situation and environment when inputting symptom information. Examples of living situations include, but are not limited to, work situations and home situations. Examples of environments include, but are not limited to, living environments and workplace environments. For example, when the user is at work, an interface that allows simple and quick input can be provided. Also, when the user is at home, an interface that allows detailed information to be input can be provided. Also, when the user is out, voice input or simple options can be provided to simplify input. In this way, input efficiency can be improved by filtering the input content according to the user's living situation and environment.

[0037] The reception unit can select an appropriate input means according to the user's input method when inputting symptom information. For example, the reception unit selects an appropriate input means according to the user's input method when inputting symptom information. Input methods include, but are not limited to, for example, voice input, text input, and image input. Appropriate input means include, but are not limited to, for example, voice input and text input. For example, if the user selects voice input, the symptom information can be input using voice recognition technology. Also, if the user selects text input, the symptom information can be input using a keyboard or touch panel. Also, if the user selects image input, the symptom information can be input using image analysis technology. In this way, by selecting the optimal input means according to the user's input method, input efficiency is improved.

[0038] The reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information when inputting symptom information. For example, the reception unit prioritizes inputting highly relevant information in consideration of the user's geographical location information when inputting symptom information. Examples of geographical location information include, but are not limited to, an address or GPS data. Examples of highly relevant information include, but are not limited to, diseases specific to a region or environmental factors. For example, when the user is in a specific region, symptom information related to diseases prevalent in that region can be prioritized. Furthermore, when the user is traveling, symptom information related to health risks at the travel destination can be prioritized. Furthermore, when the user is at home, symptom information related to past medical history or current health condition can be prioritized. In this way, highly relevant information can be prioritized by taking geographical location information into consideration.

[0039] The reception unit can analyze the user's social media activity and input related information when symptom information is input. For example, the reception unit analyzes the user's social media activity and inputs related information when symptom information is input. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. Related information includes, for example, but is not limited to, information about other users who have similar symptoms. For example, if a user posts about health on social media, symptom information can be input based on that information. Posts by the user's friends on social media can also be analyzed and related symptom information can be input. Related health information can also be input based on the user's social media activity history. In this way, related information can be input efficiently by analyzing social media activity.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when inputting symptom information. For example, the reception unit customizes the input method by reflecting the user's past feedback when inputting symptom information. Feedback includes, for example, but is not limited to, the user's opinions and evaluations. Input methods include, for example, but are not limited to, voice input and text input. For example, the optimal input method is suggested based on feedback previously input by the user. The input interface can also be improved based on the user's past feedback. The input procedure can also be simplified by referring to the user's past feedback. In this way, the input method can be optimized by reflecting the past feedback.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the symptom during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the symptom during analysis. The importance includes, for example, but is not limited to, the severity and frequency of occurrence of the symptom. The level of detail includes, for example, but is not limited to, the depth of the analysis and the granularity of the information. For example, a detailed analysis can be performed for important symptoms. A brief analysis can also be performed for minor symptoms. The level of detail of the analysis can also be adjusted based on a combination of symptoms. In this way, efficient analysis can be achieved by adjusting the level of detail of the analysis according to the importance of the symptom.

[0042] The analysis unit can apply different analysis algorithms depending on the symptom category during analysis. For example, the analysis unit applies different analysis algorithms depending on the symptom category during analysis. Examples of categories include, but are not limited to, respiratory system, digestive system, and nervous system. Examples of analysis algorithms include, but are not limited to, machine learning algorithms and statistical analysis algorithms. For example, a dedicated analysis algorithm is applied to respiratory system symptoms. Furthermore, a dedicated analysis algorithm can be applied to digestive system symptoms. Furthermore, a dedicated analysis algorithm can be applied to nervous system symptoms. In this way, by applying an analysis algorithm depending on the symptom category, the accuracy of the analysis is improved.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results during analysis. Past analysis results include, for example, past diagnosis results and analysis data, but are not limited to these examples. Analysis accuracy includes, for example, error ranges and confidence intervals, but are not limited to these examples. For example, the current analysis results are corrected based on the user's past analysis results. Furthermore, specific patterns can be extracted from the user's past analysis results to improve the accuracy of the analysis. Furthermore, the analysis algorithm can be adjusted by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the past analysis results.

[0044] The analysis unit can determine the priority of analysis based on the time of symptom occurrence during analysis. For example, the analysis unit determines the priority of analysis based on the time of symptom occurrence during analysis. The time of occurrence includes, for example, but is not limited to, the date and time of symptom occurrence and duration. The priority includes, for example, but is not limited to, the severity and frequency of symptom occurrence. For example, symptoms that have occurred recently can be analyzed preferentially. Symptoms that have continued for a long period of time can also be analyzed preferentially. Symptoms that occur during a specific time period can also be analyzed preferentially. In this way, by determining the priority of analysis based on the time of symptom occurrence, important symptoms can be analyzed preferentially.

[0045] The analysis unit can adjust the order of analysis based on the relevance of symptoms during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of symptoms during analysis. Relevance includes, for example, but is not limited to, co-occurrence or causal relationships between symptoms. The order includes, for example, but is not limited to, analysis priority or processing order. For example, highly related symptoms can be analyzed first. Also, less related symptoms can be postponed. The order of analysis can also be adjusted based on a combination of symptoms. In this way, adjusting the order of analysis based on the relevance of symptoms enables efficient analysis.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. Examples of the level of expertise include, but are not limited to, whether or not the user has medical knowledge and educational background. Examples of technical terms include, but are not limited to, medical terms and technical terms. For example, if the user is a medical professional, analysis results that make heavy use of technical terms can be provided. Furthermore, if the user is a general public, analysis results that avoid technical terms can be provided. Furthermore, the way in which the analysis results are presented can be adjusted according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, analysis results that are easy for the user to understand can be provided.

[0047] The providing unit can select an appropriate display method by referring to the user's past diagnostic results when providing the diagnostic results. For example, the providing unit selects an appropriate display method by referring to the user's past diagnostic results when providing the diagnostic results. Past diagnostic results include, but are not limited to, examples of, for example, past disease names and treatment histories. Display methods include, but are not limited to, examples of, for example, text display and graphical display. For example, information supplementing the current diagnostic result is provided based on the user's past diagnostic results. Furthermore, it is possible to extract specific patterns from the user's past diagnostic results and select an optimal display method. It is also possible to customize the display method by referring to the user's past diagnostic results. In this way, the optimal display method for the user can be selected by referring to the past diagnostic results.

[0048] The providing unit can customize the display content according to the user's current health condition when providing the diagnostic result. For example, the providing unit customizes the display content according to the user's current health condition when providing the diagnostic result. The current health condition includes, for example, but is not limited to, current symptoms and medication status. The display content includes, for example, but is not limited to, diagnostic results and recommended treatments. For example, if the user is currently healthy, display content including preventive advice can be provided. Furthermore, if the user is currently ill, display content including specific treatments and countermeasures can be provided. Furthermore, the display content can be customized based on the user's current health condition. In this way, by customizing the display content according to the user's current health condition, appropriate information can be provided to the user.

[0049] The providing unit can improve the display method by reflecting user feedback when providing a diagnostic result. For example, the providing unit improves the display method by reflecting user feedback when providing a diagnostic result. Feedback includes, for example, user opinions and evaluations, but is not limited to these examples. Display methods include, for example, text display and graphical display, but are not limited to these examples. For example, the display method is improved based on feedback previously provided by the user. Display content can also be customized based on user feedback. Display procedures can also be simplified by referring to user feedback. In this way, the display method can be optimized by reflecting user feedback.

[0050] The providing unit can select an appropriate display method by taking into consideration the geographical location information of the user when providing a diagnostic result. For example, the providing unit selects an appropriate display method by taking into consideration the geographical location information of the user when providing a diagnostic result. Examples of geographical location information include, but are not limited to, an address or GPS data. Examples of display methods include, but are not limited to, a text display or a graphical display. For example, when the user is in a specific area, information about medical institutions and pharmacies in the area can be displayed. Also, when the user is traveling, information about medical resources at the travel destination can be displayed. Also, when the user is at home, information about nearby medical institutions can be displayed. In this way, by taking into consideration the geographical location information, it is possible to provide information that is highly relevant to the user.

[0051] The providing unit can analyze the user's social media activity and display related information when providing a diagnostic result. For example, the providing unit can analyze the user's social media activity and display related information when providing a diagnostic result. Social media activity includes, but is not limited to, for example, the content of posts and the number of likes. Related information includes, but is not limited to, for example, information about other users with similar symptoms. For example, if a user posts health-related information on social media, the diagnostic result can be displayed based on that information. It is also possible to analyze posts by the user's friends on social media and display related diagnostic results. It is also possible to display related health information based on the user's social media activity history. In this way, by analyzing social media activity, it is possible to provide information that is highly relevant to the user.

[0052] The providing unit can customize the display method by reflecting the user's past feedback when providing the diagnostic result. For example, the providing unit customizes the display method by reflecting the user's past feedback when providing the diagnostic result. The feedback includes, for example, but is not limited to, the user's opinions and evaluations. The display method includes, for example, but is not limited to, text display and graphical display. For example, the display method is customized based on feedback provided by the user in the past. The display content can also be improved based on the user's feedback. The display procedure can also be simplified by referring to the user's feedback. In this way, the display method can be optimized by reflecting the past feedback.

[0053] The interlocking unit can select an appropriate consultation method by referring to the user's past consultation history when consulting a doctor. For example, when consulting a doctor, the interlocking unit selects an appropriate consultation method by referring to the user's past consultation history. The consultation history includes, for example, but is not limited to, the content of the past consultation and the date and time of the consultation. For example, the consultation method includes, for example, but is not limited to, face-to-face consultation and online consultation. For example, information supplementing the content of the current consultation can be provided based on the user's past consultation history. Furthermore, a specific pattern can be extracted from the user's past consultation history to select an optimal consultation method. Furthermore, the consultation method can be customized by referring to the user's past consultation history. In this way, the optimal consultation method for the user can be selected by referring to the past consultation history.

[0054] The interlocking unit can customize the consultation content based on the user's current health condition when consulting a doctor. The interlocking unit, for example, customizes the consultation content based on the user's current health condition when consulting a doctor. The current health condition includes, for example, but is not limited to, current symptoms and medication status. The consultation content includes, for example, but is not limited to, diagnosis results and recommended treatments. For example, if the user is currently healthy, consultation content including preventive advice can be provided. Furthermore, if the user is currently ill, consultation content including specific treatments and countermeasures can also be provided. The consultation content can also be customized based on the user's current health condition. In this way, by customizing the consultation content based on the current health condition, appropriate advice can be provided to the user.

[0055] The interlocking unit can improve the consultation method by reflecting user feedback when consulting a doctor. For example, the interlocking unit improves the consultation method by reflecting user feedback when consulting a doctor. Feedback includes, for example, but is not limited to, user opinions and evaluations. Consultation methods include, for example, but are not limited to, face-to-face consultations and online consultations. For example, the consultation method is improved based on feedback previously provided by the user. The content of the consultation can also be customized based on the user feedback. The consultation procedure can also be simplified by referring to the user feedback. In this way, the consultation method can be optimized by reflecting the user feedback.

[0056] The interlocking unit can select an appropriate consultation method by taking into consideration the user's geographical location information when consulting a doctor. For example, the interlocking unit selects an appropriate consultation method by taking into consideration the user's geographical location information when consulting a doctor. Examples of geographical location information include, but are not limited to, an address or GPS data. Examples of consultation methods include, but are not limited to, face-to-face consultations and online consultations. For example, if the user is in a specific area, consultation content can be provided based on information about medical institutions and pharmacies in that area. Also, if the user is traveling, consultation content can be provided based on information about medical resources at the user's travel destination. Also, if the user is at home, consultation content can be provided based on information about nearby medical institutions. In this way, by taking into consideration the geographical location information, it is possible to provide information that is highly relevant to the user.

[0057] The interlocking unit can analyze the user's social media activity and provide relevant information when consulting a doctor. For example, when consulting a doctor, the interlocking unit can analyze the user's social media activity and provide relevant information. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. Related information includes, for example, but is not limited to, information about other users who have similar symptoms. For example, if the user posts health-related content on social media, the interlocking unit can provide consultation content based on that information. It is also possible to analyze posts by the user's friends on social media and provide related consultation content. It is also possible to provide related health information based on the user's social media activity history. In this way, by analyzing social media activity, it is possible to provide information that is highly relevant to the user.

[0058] The interlocking unit can customize the consultation method by reflecting the user's past feedback when consulting a doctor. For example, the interlocking unit customizes the consultation method by reflecting the user's past feedback when consulting a doctor. Feedback includes, for example, but is not limited to, the user's opinions and evaluations. Consultation methods include, for example, but are not limited to, face-to-face consultations and online consultations. For example, the consultation method is customized based on feedback provided by the user in the past. The content of the consultation can also be improved based on the user's feedback. The consultation procedure can also be simplified by referring to the user's feedback. In this way, the consultation method can be optimized by reflecting past feedback.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The diagnostic support system may further include a monitoring unit that monitors the user's lifestyle habits. The monitoring unit collects data on the user's diet, exercise, sleep, etc., and provides it to the analysis unit. For example, it may record the contents of the user's daily meals and evaluate their nutritional balance. It may also track the user's exercise volume and provide appropriate exercise advice. It may also analyze the user's sleep patterns and make suggestions for improving the quality of sleep. This enables comprehensive health management that takes into account the user's overall lifestyle habits.

[0061] The diagnostic support system may further include a location information acquisition unit that acquires the user's geographical location information. The location information acquisition unit identifies the user's current location and provides it to the analysis unit. For example, if the user is in a specific area, the analysis unit may prioritize information related to diseases prevalent in that area. If the user is traveling, the system may also provide information related to health risks at the travel destination. Furthermore, if the user is at home, the system may provide information on nearby medical institutions. This enables diagnosis that takes the user's geographical location information into account.

[0062] The analysis unit can improve the accuracy of the analysis by referring to the user's past diagnostic results and comparing them with the current analysis results. For example, it analyzes current symptoms based on past diagnostic results for similar symptoms. It can also extract specific patterns from past diagnostic results and reflect them in the current analysis. Furthermore, it can increase the reliability of the analysis by checking the consistency between past diagnostic results and current analysis results. This makes it possible to improve the accuracy of the analysis by utilizing past diagnostic results.

[0063] The providing unit can customize the display method of the diagnostic results by reflecting the user's past feedback. For example, the providing unit can suggest the optimal display method based on the feedback provided by the user in the past. The display content can also be improved based on the user's feedback. Furthermore, the display procedure can be simplified by referring to the user's feedback. In this way, the optimal display method for the user can be provided by reflecting the past feedback.

[0064] The linking unit can analyze a user's social media activity and provide relevant information to a doctor. For example, if a user posts about health on social media, the linking unit can provide consultations to a doctor based on that information. It can also analyze posts from the user's friends on social media and provide related consultations. It can also provide related health information based on the user's social media activity history. This makes it possible to provide highly relevant information to doctors by analyzing social media activity.

[0065] The reception unit can analyze the user's past input history and suggest an appropriate input method. For example, symptom information that the user has frequently input in the past can be automatically displayed as a candidate. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest symptom information that will be input during a specific time period based on the user's past input history. In this way, by analyzing the past input history, it is possible to suggest the optimal input method for the user.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The reception unit receives symptom information from the user. The symptom information includes, but is not limited to, the degree of pain, fever, cough, etc. The reception unit can also receive past medical history and current medication status input by the user. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit and generate a diagnosis. The analysis is based on data analysis algorithms and the accuracy of the analysis, and analyzes the input symptom information to identify possible illnesses and health problems. The diagnosis can also take into account past medical history and current medication status. Step 3: The providing unit provides the user with the diagnostic results generated by the analysis unit. The diagnostic results include, for example, but are not limited to, the name of the disease and recommended treatments. The generating AI provides the user with the diagnostic results. Step 4: The linking unit links consultations with doctors based on the diagnosis results provided by the providing unit. For example, the diagnosis results generated by the generation AI can be sent to an electronic medical record system, and doctors can provide advice to users based on the results.

[0068] (Example 2) In a diagnostic support system according to an embodiment of the present invention, a user inputs symptom information, and a generation AI performs a diagnosis. The system also provides a doctor consultation function. The user inputs information about their symptoms and health status, and the generation AI analyzes the information to generate a diagnosis and provides it to the user. The system also provides a doctor consultation function based on the diagnosis results. For example, the user inputs specific symptoms, such as fever, cough, and headache, as well as detailed information, such as past medical history and current medication status. This information is input into the generation AI. The diagnostic support system then uses the generation AI to analyze the input information and identify possible illnesses and health problems. For example, if a patient has a fever and cough, the generation AI considers the possibility of a cold or flu. It also takes past medical history and current medication status into account to make a more accurate diagnosis. The diagnostic support system then provides the generated diagnosis result to the user. The user can review the diagnosis result provided by the generation AI and consult a doctor if necessary. For example, if the generation AI suggests the possibility of influenza, the user can consult a doctor and receive appropriate treatment. Furthermore, the diagnostic support system shares the diagnostic results generated by the generative AI with doctors, who then provide advice and treatment to the user based on these results. This allows users to receive a quick and accurate diagnosis and smoothly consult with a doctor. As a result, the diagnostic support system allows even busy people to easily understand their own health condition and receive appropriate diagnosis and treatment. As a result, the diagnostic support system allows users to receive a quick and accurate diagnosis and smoothly consult with a doctor. For example, even people who are too busy at work to have time to go to the hospital can use this system to easily receive a diagnosis at home or at work. In addition, the system is linked to a doctor consultation function, so users can receive doctor's advice immediately if necessary.

[0069] A diagnostic support system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a linking unit. The reception unit receives symptom information from a user. The symptom information may include, but is not limited to, pain level, fever, cough, and the like. The reception unit may also receive, for example, past medical history and current medication status input by the user. The analysis unit uses a generation AI to analyze the information received by the reception unit and generate a diagnostic result. The analysis may be performed based on, for example, a data analysis algorithm and analysis accuracy, but is not limited to, for example. For example, the generation AI analyzes the input symptom information and identifies possible illnesses or health problems. The analysis unit may also make a diagnosis taking into account the past medical history and current medication status. The provision unit provides the user with the diagnostic result generated by the analysis unit. The diagnostic result may include, but is not limited to, a disease name and a recommended treatment. For example, the provision unit provides the user with the diagnostic result generated by the generation AI. The linking unit links a consultation with a doctor based on the diagnostic result provided by the provision unit. For example, the diagnostic results generated by the generation AI can be sent to an electronic medical record system, and a doctor can provide advice to the user based on the results. This allows the diagnostic support system according to the embodiment to efficiently accept and analyze the user's symptom information, provide the diagnostic results, and link the consultation with the doctor.

[0070] The analysis unit can analyze the symptom information input by the generating AI and identify illnesses and health problems. The generating AI, for example, analyzes the input symptom information and identifies possible illnesses and health problems. For example, if there is a fever and cough, the generating AI considers the possibility of a cold or flu. The generating AI can also take into account past medical history and current medication status to make a more accurate diagnosis. As a result, using the generating AI improves the accuracy of symptom information analysis and makes it possible to identify possible illnesses and health problems.

[0071] The providing unit can provide the diagnostic result generated by the generation AI to the user. The providing unit, for example, provides the diagnostic result generated by the generation AI to the user. The diagnostic result includes, for example, a disease name and a recommended treatment, but is not limited to such examples. For example, the providing unit provides the diagnostic result generated by the generation AI to the user, allowing the user to quickly check the diagnostic result. In this way, by providing the diagnostic result generated by the generation AI to the user, the user can quickly check the diagnostic result.

[0072] The interlocking unit transmits the diagnostic results generated by the generation AI to an electronic medical record system, allowing a doctor to provide advice to a user based on the results. The interlocking unit transmits, for example, the diagnostic results generated by the generation AI to an electronic medical record system, allowing a doctor to provide advice to a user based on the results. The electronic medical record system includes, for example, the software used and the data format, but is not limited to such examples. In this way, by transmitting the diagnostic results generated by the generation AI to the electronic medical record system, a doctor can quickly provide advice to a user.

[0073] The reception unit can receive past medical history and current medication status input by the user. The reception unit receives, for example, past medical history and current medication status input by the user. Past medical history includes, for example, past diagnosis results and treatment history, but is not limited to such examples. Current medication status includes, for example, types of medication being taken and frequency of taking medication, but is not limited to such examples. In this way, by receiving the past medical history and current medication status, a more accurate diagnosis can be made.

[0074] The analysis unit can make a diagnosis based on past medical history and current medication status. The analysis unit makes a diagnosis based on, for example, past medical history and current medication status. Past medical history includes, for example, past diagnostic results and treatment history, but is not limited to such examples. Current medication status includes, for example, the type of medication being taken and how often it is taken, but is not limited to such examples. In this way, by taking past medical history and current medication status into consideration, the accuracy of the diagnosis is improved.

[0075] The reception unit can estimate the user's emotion and customize the symptom information input interface based on the estimated user emotion. For example, the reception unit estimates the user's emotion and customizes the symptom information input interface based on the estimated user emotion. Emotions include, but are not limited to, joy, sadness, anger, and the like. The input interface can include, but is not limited to, text input and voice input. For example, if the user is feeling stressed, a simple and intuitive interface can be provided to minimize input steps. Furthermore, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow the user to quickly input symptom information. This reduces the input burden on the user by customizing the input interface according to the user's emotion.

[0076] The reception unit can analyze the user's past input history and suggest an appropriate input method. The reception unit, for example, analyzes the user's past input history and suggests an optimal input method. The input history includes, for example, but is not limited to, past input content and input frequency. The appropriate input method includes, for example, but is not limited to, voice input and text input. For example, symptom information that the user has frequently input in the past can be automatically displayed as a candidate. It is also possible to preferentially suggest input methods (voice, text, etc.) that the user has used in the past. It is also possible to predict and suggest symptom information that will be input in a specific time period from the user's past input history. In this way, by analyzing the past input history, it is possible to suggest an optimal input method for the user.

[0077] The reception unit can filter the input content based on the user's current living situation and environment when inputting symptom information. For example, the reception unit filters the input content based on the user's current living situation and environment when inputting symptom information. Examples of living situations include, but are not limited to, work situations and home situations. Examples of environments include, but are not limited to, living environments and workplace environments. For example, when the user is at work, an interface that allows simple and quick input can be provided. Also, when the user is at home, an interface that allows detailed information to be input can be provided. Also, when the user is out, voice input or simple options can be provided to simplify input. In this way, input efficiency can be improved by filtering the input content according to the user's living situation and environment.

[0078] The reception unit can select an appropriate input means according to the user's input method when inputting symptom information. For example, the reception unit selects an appropriate input means according to the user's input method when inputting symptom information. Input methods include, but are not limited to, for example, voice input, text input, and image input. Appropriate input means include, but are not limited to, for example, voice input and text input. For example, if the user selects voice input, the symptom information can be input using voice recognition technology. Also, if the user selects text input, the symptom information can be input using a keyboard or touch panel. Also, if the user selects image input, the symptom information can be input using image analysis technology. In this way, by selecting the optimal input means according to the user's input method, input efficiency is improved.

[0079] The reception unit can estimate the user's emotions and determine the priority of symptom information to be input based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and determines the priority of symptom information to be input based on the estimated user emotions. Examples of emotions include, but are not limited to, joy, sadness, and anger. Examples of priorities include, but are not limited to, the severity and frequency of symptoms. For example, if the user is feeling anxious, the user may be prompted to input important symptom information with priority. Also, if the user is relaxed, the user may be prompted to input detailed symptom information. Also, if the user is in a hurry, the user may be prompted to quickly input the most important symptom information. In this way, by determining the priority of symptom information according to the user's emotions, important information can be input with priority.

[0080] The reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information when inputting symptom information. For example, the reception unit prioritizes inputting highly relevant information in consideration of the user's geographical location information when inputting symptom information. Examples of geographical location information include, but are not limited to, an address or GPS data. Examples of highly relevant information include, but are not limited to, diseases specific to a region or environmental factors. For example, when the user is in a specific region, symptom information related to diseases prevalent in that region can be prioritized. Furthermore, when the user is traveling, symptom information related to health risks at the travel destination can be prioritized. Furthermore, when the user is at home, symptom information related to past medical history or current health condition can be prioritized. In this way, highly relevant information can be prioritized by taking geographical location information into consideration.

[0081] The reception unit can analyze the user's social media activity and input related information when symptom information is input. For example, the reception unit analyzes the user's social media activity and inputs related information when symptom information is input. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. Related information includes, for example, but is not limited to, information about other users who have similar symptoms. For example, if a user posts about health on social media, symptom information can be input based on that information. Posts by the user's friends on social media can also be analyzed and related symptom information can be input. Related health information can also be input based on the user's social media activity history. In this way, related information can be input efficiently by analyzing social media activity.

[0082] The reception unit can customize the input method by reflecting the user's past feedback when inputting symptom information. For example, the reception unit customizes the input method by reflecting the user's past feedback when inputting symptom information. Feedback includes, for example, but is not limited to, the user's opinions and evaluations. Input methods include, for example, but are not limited to, voice input and text input. For example, the optimal input method is suggested based on feedback previously input by the user. The input interface can also be improved based on the user's past feedback. The input procedure can also be simplified by referring to the user's past feedback. In this way, the input method can be optimized by reflecting the past feedback.

[0083] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user's emotions. Emotions include, but are not limited to, joy, sadness, anger, etc. The presentation method includes, but is not limited to, wording and display format. For example, if the user is feeling anxious, a simple and easy-to-understand presentation method is used. Furthermore, if the user is relaxed, detailed analysis results can be provided. Furthermore, if the user is in a hurry, concise analysis results that focus on the main points can be provided. In this way, by adjusting the way the analysis is presented according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0084] The analysis unit can adjust the level of detail of the analysis based on the importance of the symptom during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the symptom during analysis. The importance includes, for example, but is not limited to, the severity and frequency of occurrence of the symptom. The level of detail includes, for example, but is not limited to, the depth of the analysis and the granularity of the information. For example, a detailed analysis can be performed for important symptoms. A brief analysis can also be performed for minor symptoms. The level of detail of the analysis can also be adjusted based on a combination of symptoms. In this way, efficient analysis can be achieved by adjusting the level of detail of the analysis according to the importance of the symptom.

[0085] The analysis unit can apply different analysis algorithms depending on the symptom category during analysis. For example, the analysis unit applies different analysis algorithms depending on the symptom category during analysis. Examples of categories include, but are not limited to, respiratory system, digestive system, and nervous system. Examples of analysis algorithms include, but are not limited to, machine learning algorithms and statistical analysis algorithms. For example, a dedicated analysis algorithm is applied to respiratory system symptoms. Furthermore, a dedicated analysis algorithm can be applied to digestive system symptoms. Furthermore, a dedicated analysis algorithm can be applied to nervous system symptoms. In this way, by applying an analysis algorithm depending on the symptom category, the accuracy of the analysis is improved.

[0086] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results during analysis. Past analysis results include, for example, past diagnosis results and analysis data, but are not limited to these examples. Analysis accuracy includes, for example, error ranges and confidence intervals, but are not limited to these examples. For example, the current analysis results are corrected based on the user's past analysis results. Furthermore, specific patterns can be extracted from the user's past analysis results to improve the accuracy of the analysis. Furthermore, the analysis algorithm can be adjusted by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the past analysis results.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, anger, etc. Length includes, but is not limited to, the number of characters and time. For example, if the user is feeling anxious, a short and concise analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided. If the user is in a hurry, a concise and quick analysis result can be provided. In this way, by adjusting the length of the analysis according to the user's emotions, an analysis result of an appropriate length can be provided for the user.

[0088] The analysis unit can determine the priority of analysis based on the time of symptom occurrence during analysis. For example, the analysis unit determines the priority of analysis based on the time of symptom occurrence during analysis. The time of occurrence includes, for example, but is not limited to, the date and time of symptom occurrence and duration. The priority includes, for example, but is not limited to, the severity and frequency of symptom occurrence. For example, symptoms that have occurred recently can be analyzed preferentially. Symptoms that have continued for a long period of time can also be analyzed preferentially. Symptoms that occur during a specific time period can also be analyzed preferentially. In this way, by determining the priority of analysis based on the time of symptom occurrence, important symptoms can be analyzed preferentially.

[0089] The analysis unit can adjust the order of analysis based on the relevance of symptoms during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of symptoms during analysis. Relevance includes, for example, but is not limited to, co-occurrence or causal relationships between symptoms. The order includes, for example, but is not limited to, analysis priority or processing order. For example, highly related symptoms can be analyzed first. Also, less related symptoms can be postponed. The order of analysis can also be adjusted based on a combination of symptoms. In this way, adjusting the order of analysis based on the relevance of symptoms enables efficient analysis.

[0090] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. Examples of the level of expertise include, but are not limited to, whether or not the user has medical knowledge and educational background. Examples of technical terms include, but are not limited to, medical terms and technical terms. For example, if the user is a medical professional, analysis results that make heavy use of technical terms can be provided. Furthermore, if the user is a general public, analysis results that avoid technical terms can be provided. Furthermore, the way in which the analysis results are presented can be adjusted according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, analysis results that are easy for the user to understand can be provided.

[0091] The providing unit can estimate the user's emotions and adjust the display method of the diagnostic results based on the estimated user emotions. For example, the providing unit can estimate the user's emotions and adjust the display method of the diagnostic results based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, anger, etc. Display methods include, but are not limited to, text display and graphical display. For example, if the user is feeling anxious, a simple and highly visible display method can be provided. Furthermore, if the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method of the diagnostic results according to the user's emotions, a display that is easy for the user to understand can be provided.

[0092] The providing unit can select an appropriate display method by referring to the user's past diagnostic results when providing the diagnostic results. For example, the providing unit selects an appropriate display method by referring to the user's past diagnostic results when providing the diagnostic results. Past diagnostic results include, but are not limited to, examples of, for example, past disease names and treatment histories. Display methods include, but are not limited to, examples of, for example, text display and graphical display. For example, information supplementing the current diagnostic result is provided based on the user's past diagnostic results. Furthermore, it is possible to extract specific patterns from the user's past diagnostic results and select an optimal display method. It is also possible to customize the display method by referring to the user's past diagnostic results. In this way, the optimal display method for the user can be selected by referring to the past diagnostic results.

[0093] The providing unit can customize the display content according to the user's current health condition when providing the diagnostic result. For example, the providing unit customizes the display content according to the user's current health condition when providing the diagnostic result. The current health condition includes, for example, but is not limited to, current symptoms and medication status. The display content includes, for example, but is not limited to, diagnostic results and recommended treatments. For example, if the user is currently healthy, display content including preventive advice can be provided. Furthermore, if the user is currently ill, display content including specific treatments and countermeasures can be provided. Furthermore, the display content can be customized based on the user's current health condition. In this way, by customizing the display content according to the user's current health condition, appropriate information can be provided to the user.

[0094] The providing unit can improve the display method by reflecting user feedback when providing a diagnostic result. For example, the providing unit improves the display method by reflecting user feedback when providing a diagnostic result. Feedback includes, for example, user opinions and evaluations, but is not limited to these examples. Display methods include, for example, text display and graphical display, but are not limited to these examples. For example, the display method is improved based on feedback previously provided by the user. Display content can also be customized based on user feedback. Display procedures can also be simplified by referring to user feedback. In this way, the display method can be optimized by reflecting user feedback.

[0095] The providing unit can estimate the user's emotions and determine the priority of diagnostic results based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and determines the priority of diagnostic results based on the estimated user emotions. Examples of emotions include, but are not limited to, joy, sadness, and anger. Examples of priorities include, but are not limited to, the severity and frequency of symptoms. For example, if the user is feeling anxious, important diagnostic results can be displayed preferentially. Also, if the user is relaxed, detailed diagnostic results can be displayed sequentially. Also, if the user is in a hurry, the most important diagnostic results can be displayed quickly. In this way, by determining the priority of diagnostic results according to the user's emotions, important information can be displayed preferentially.

[0096] The providing unit can select an appropriate display method by taking into consideration the geographical location information of the user when providing a diagnostic result. For example, the providing unit selects an appropriate display method by taking into consideration the geographical location information of the user when providing a diagnostic result. Examples of geographical location information include, but are not limited to, an address or GPS data. Examples of display methods include, but are not limited to, a text display or a graphical display. For example, when the user is in a specific area, information about medical institutions and pharmacies in the area can be displayed. Also, when the user is traveling, information about medical resources at the travel destination can be displayed. Also, when the user is at home, information about nearby medical institutions can be displayed. In this way, by taking into consideration the geographical location information, it is possible to provide information that is highly relevant to the user.

[0097] The providing unit can analyze the user's social media activity and display related information when providing a diagnostic result. For example, the providing unit can analyze the user's social media activity and display related information when providing a diagnostic result. Social media activity includes, but is not limited to, for example, the content of posts and the number of likes. Related information includes, but is not limited to, for example, information about other users with similar symptoms. For example, if a user posts health-related information on social media, the diagnostic result can be displayed based on that information. It is also possible to analyze posts by the user's friends on social media and display related diagnostic results. It is also possible to display related health information based on the user's social media activity history. In this way, by analyzing social media activity, it is possible to provide information that is highly relevant to the user.

[0098] The providing unit can customize the display method by reflecting the user's past feedback when providing the diagnostic result. For example, the providing unit customizes the display method by reflecting the user's past feedback when providing the diagnostic result. The feedback includes, for example, but is not limited to, the user's opinions and evaluations. The display method includes, for example, but is not limited to, text display and graphical display. For example, the display method is customized based on feedback provided by the user in the past. The display content can also be improved based on the user's feedback. The display procedure can also be simplified by referring to the user's feedback. In this way, the display method can be optimized by reflecting the past feedback.

[0099] The interlocking unit can estimate the user's emotions and adjust the consultation method with a doctor based on the estimated user emotions. For example, the interlocking unit can estimate the user's emotions and adjust the consultation method with a doctor based on the estimated user emotions. Examples of emotions include, but are not limited to, joy, sadness, anger, etc. Examples of consultation methods include, but are not limited to, face-to-face consultation and online consultation. For example, if the user is feeling anxious, a quick and thorough consultation method can be provided. Also, if the user is relaxed, a detailed consultation method can be provided. Also, if the user is in a hurry, a concise and quick consultation method can be provided. In this way, by adjusting the consultation method with a doctor according to the user's emotions, it is possible to provide an appropriate consultation method for the user.

[0100] The interlocking unit can select an appropriate consultation method by referring to the user's past consultation history when consulting a doctor. For example, when consulting a doctor, the interlocking unit selects an appropriate consultation method by referring to the user's past consultation history. The consultation history includes, for example, but is not limited to, the content of the past consultation and the date and time of the consultation. For example, the consultation method includes, for example, but is not limited to, face-to-face consultation and online consultation. For example, information supplementing the content of the current consultation can be provided based on the user's past consultation history. Furthermore, a specific pattern can be extracted from the user's past consultation history to select an optimal consultation method. Furthermore, the consultation method can be customized by referring to the user's past consultation history. In this way, the optimal consultation method for the user can be selected by referring to the past consultation history.

[0101] The interlocking unit can customize the consultation content based on the user's current health condition when consulting a doctor. The interlocking unit, for example, customizes the consultation content based on the user's current health condition when consulting a doctor. The current health condition includes, for example, but is not limited to, current symptoms and medication status. The consultation content includes, for example, but is not limited to, diagnosis results and recommended treatments. For example, if the user is currently healthy, consultation content including preventive advice can be provided. Furthermore, if the user is currently ill, consultation content including specific treatments and countermeasures can also be provided. The consultation content can also be customized based on the user's current health condition. In this way, by customizing the consultation content based on the current health condition, appropriate advice can be provided to the user.

[0102] The interlocking unit can improve the consultation method by reflecting user feedback when consulting a doctor. For example, the interlocking unit improves the consultation method by reflecting user feedback when consulting a doctor. Feedback includes, for example, but is not limited to, user opinions and evaluations. Consultation methods include, for example, but are not limited to, face-to-face consultations and online consultations. For example, the consultation method is improved based on feedback previously provided by the user. The content of the consultation can also be customized based on the user feedback. The consultation procedure can also be simplified by referring to the user feedback. In this way, the consultation method can be optimized by reflecting the user feedback.

[0103] The interlocking unit can estimate the user's emotions and determine the priority of consultations with a doctor based on the estimated user emotions. The interlocking unit, for example, estimates the user's emotions and determines the priority of consultations with a doctor based on the estimated user emotions. Examples of emotions include, but are not limited to, joy, sadness, and anger. Examples of priorities include, but are not limited to, the severity and frequency of symptoms. For example, if the user is feeling anxious, important consultation details can be conveyed to the doctor first. Also, if the user is relaxed, detailed consultation details can be conveyed to the doctor sequentially. Also, if the user is in a hurry, the most important consultation details can be conveyed to the doctor quickly. In this way, by determining the priority of consultations according to the user's emotions, important consultation details can be conveyed to the doctor first.

[0104] The interlocking unit can select an appropriate consultation method by taking into consideration the user's geographical location information when consulting a doctor. For example, the interlocking unit selects an appropriate consultation method by taking into consideration the user's geographical location information when consulting a doctor. Examples of geographical location information include, but are not limited to, an address or GPS data. Examples of consultation methods include, but are not limited to, face-to-face consultations and online consultations. For example, if the user is in a specific area, consultation content can be provided based on information about medical institutions and pharmacies in that area. Also, if the user is traveling, consultation content can be provided based on information about medical resources at the user's travel destination. Also, if the user is at home, consultation content can be provided based on information about nearby medical institutions. In this way, by taking into consideration the geographical location information, it is possible to provide information that is highly relevant to the user.

[0105] The interlocking unit can analyze the user's social media activity and provide relevant information when consulting a doctor. For example, when consulting a doctor, the interlocking unit can analyze the user's social media activity and provide relevant information. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. Related information includes, for example, but is not limited to, information about other users who have similar symptoms. For example, if the user posts health-related content on social media, the interlocking unit can provide consultation content based on that information. It is also possible to analyze posts by the user's friends on social media and provide related consultation content. It is also possible to provide related health information based on the user's social media activity history. In this way, by analyzing social media activity, it is possible to provide information that is highly relevant to the user.

[0106] The interlocking unit can customize the consultation method by reflecting the user's past feedback when consulting a doctor. For example, the interlocking unit customizes the consultation method by reflecting the user's past feedback when consulting a doctor. Feedback includes, for example, but is not limited to, the user's opinions and evaluations. Consultation methods include, for example, but are not limited to, face-to-face consultations and online consultations. For example, the consultation method is customized based on feedback provided by the user in the past. The content of the consultation can also be improved based on the user's feedback. The consultation procedure can also be simplified by referring to the user's feedback. In this way, the consultation method can be optimized by reflecting past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the reception unit, analysis unit, provision unit, and linkage unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives symptom information, past medical history, and current medication status input by the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received information using a generation AI to generate a diagnosis result. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides the generated diagnosis result to the user. The linkage unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and transmits the diagnosis result to the electronic medical record system and links the consultation with a doctor. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, and interlocking unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives symptom information, past medical history, and current medication status input by the user through voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received information using a generation AI to generate a diagnosis result. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the generated diagnosis result to the user. The interlocking unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and transmits the diagnosis result to the electronic medical record system and interlocks with a consultation with a doctor. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and interlocking unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives symptom information, past medical history, and current medication status input by the user through voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received information using a generation AI to generate a diagnosis result. The provision unit is realized, for example, by the display 343 of the headset-type terminal 314 and provides the generated diagnosis result to the user. The interlocking unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and transmits the diagnosis result to the electronic medical record system and interlocks with a consultation with a doctor. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, analysis unit, provision unit, and interlocking unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives symptom information, past medical history, and current medication status input by the user through voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received information using a generation AI to generate a diagnosis result. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the generated diagnosis result to the user. The interlocking unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and transmits the diagnosis result to the electronic medical record system and interlocks with a consultation with a doctor.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The diagnostic support system may further include a monitoring unit that monitors the user's lifestyle habits. The monitoring unit collects data on the user's diet, exercise, sleep, etc., and provides it to the analysis unit. For example, it may record the contents of the user's daily meals and evaluate their nutritional balance. It may also track the user's exercise volume and provide appropriate exercise advice. It may also analyze the user's sleep patterns and make suggestions for improving the quality of sleep. This enables comprehensive health management that takes into account the user's overall lifestyle habits.

[0109] The analysis unit can estimate the user's emotions and evaluate the reliability of the analysis results based on the estimated emotions. For example, if the user is feeling stressed, the reliability of the analysis results may decrease, prompting the collection of additional data. On the other hand, if the user is relaxed, the reliability of the analysis results increases, allowing for more detailed analysis results to be provided. Furthermore, if the user is feeling anxious, the analysis results can be compared with past data to check for consistency in order to increase their reliability. This allows the reliability of the analysis results to be dynamically evaluated according to the user's emotions.

[0110] The providing unit can estimate the user's emotions and adjust the display method of the diagnosis results based on the estimated emotions. For example, if the user is feeling anxious, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method of the diagnosis results according to the user's emotions, it is possible to display the results in a way that is easy for the user to understand.

[0111] The interlocking unit can estimate the user's emotions and adjust the consultation method with a doctor based on the estimated emotions. For example, if the user feels anxious, a quick and polite consultation method can be provided. If the user feels relaxed, a detailed consultation method can be provided. Furthermore, if the user is in a hurry, a concise and quick consultation method can be provided. In this way, by adjusting the consultation method with a doctor according to the user's emotions, an appropriate consultation method can be provided for the user.

[0112] The reception unit can estimate the user's emotions and determine the priority of symptom information to be input based on the estimated emotions. For example, if the user is feeling anxious, the reception unit can prompt the user to input important symptom information with priority. If the user is relaxed, the reception unit can prompt the user to input detailed symptom information. Furthermore, if the user is in a hurry, the reception unit can prompt the user to quickly input the most important symptom information. In this way, by determining the priority of symptom information according to the user's emotions, important information can be input with priority.

[0113] The diagnostic support system may further include a location information acquisition unit that acquires the user's geographical location information. The location information acquisition unit identifies the user's current location and provides it to the analysis unit. For example, if the user is in a specific area, the analysis unit may prioritize information related to diseases prevalent in that area. If the user is traveling, the system may also provide information related to health risks at the travel destination. Furthermore, if the user is at home, the system may provide information on nearby medical institutions. This enables diagnosis that takes the user's geographical location information into account.

[0114] The analysis unit can improve the accuracy of the analysis by referring to the user's past diagnostic results and comparing them with the current analysis results. For example, it analyzes current symptoms based on past diagnostic results for similar symptoms. It can also extract specific patterns from past diagnostic results and reflect them in the current analysis. Furthermore, it can increase the reliability of the analysis by checking the consistency between past diagnostic results and current analysis results. This makes it possible to improve the accuracy of the analysis by utilizing past diagnostic results.

[0115] The providing unit can customize the display method of the diagnostic results by reflecting the user's past feedback. For example, the providing unit can suggest the optimal display method based on the feedback provided by the user in the past. The display content can also be improved based on the user's feedback. Furthermore, the display procedure can be simplified by referring to the user's feedback. In this way, the optimal display method for the user can be provided by reflecting the past feedback.

[0116] The linking unit can analyze a user's social media activity and provide relevant information to a doctor. For example, if a user posts about health on social media, the linking unit can provide consultations to a doctor based on that information. It can also analyze posts from the user's friends on social media and provide related consultations. It can also provide related health information based on the user's social media activity history. This makes it possible to provide highly relevant information to doctors by analyzing social media activity.

[0117] The reception unit can analyze the user's past input history and suggest an appropriate input method. For example, symptom information that the user has frequently input in the past can be automatically displayed as a candidate. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest symptom information that will be input during a specific time period based on the user's past input history. In this way, by analyzing the past input history, it is possible to suggest the optimal input method for the user.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The reception unit receives symptom information from the user. The symptom information includes, but is not limited to, the degree of pain, fever, cough, etc. The reception unit can also receive past medical history and current medication status input by the user. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit and generate a diagnosis. The analysis is based on data analysis algorithms and the accuracy of the analysis, and analyzes the input symptom information to identify possible illnesses and health problems. The diagnosis can also take into account past medical history and current medication status. Step 3: The providing unit provides the user with the diagnostic results generated by the analysis unit. The diagnostic results include, for example, but are not limited to, the name of the disease and recommended treatments. The generating AI provides the user with the diagnostic results. Step 4: The linking unit links consultations with doctors based on the diagnosis results provided by the providing unit. For example, the diagnosis results generated by the generation AI can be sent to an electronic medical record system, and doctors can provide advice to users based on the results.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] 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.

[0149] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 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 reception unit that receives symptom information from a user; an analysis unit that analyzes the information received by the reception unit and generates a diagnosis result; a providing unit that provides the diagnostic result generated by the analyzing unit; a linking unit that links consultation with a doctor based on the diagnosis result provided by the providing unit; A system characterized by:

2. The analysis unit Generative AI analyzes input symptom information and identifies illnesses and health problems 2. The system of claim 1.

3. The providing unit Providing the user with the diagnostic results generated by the generation AI 2. The system of claim 1.

4. The interlocking portion is The diagnostic results generated by the AI ​​are sent to the electronic medical record system, and the doctor provides advice to the user based on them.

2. The system of claim 1.

5. The reception unit Accepts user-entered medical history and current medication status 2. The system of claim 1.

6. The analysis unit Diagnosis is based on past medical history and current medications 2. The system of claim 1.

7. The reception unit The user's emotions are estimated, and the symptom information input interface is customized based on the estimated user's emotions.

2. The system of claim 1.

8. The reception unit Analyzes the user's past input history and suggests appropriate input methods 2. The system of claim 1.

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