system

Abstract

The system according to this embodiment aims to analyze a patient's symptoms and medical data to provide appropriate diagnoses and treatments. [Solution] The system according to the embodiment comprises an input unit, an analysis unit, a reference unit, and a provision unit. The input unit inputs the patient's symptoms and medical data. The analysis unit analyzes the data input by the input unit and lists diagnoses. The reference unit references similar cases and medical literature from a medical database based on the diagnoses listed by the analysis unit. The provision unit provides recommendations for treatments and prescriptions, as well as information on side effects, based on the information obtained by the reference unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to efficiently analyze the symptoms and medical data of patients and provide appropriate diagnoses and treatment methods.

[0005] The system according to the embodiment aims to analyze the symptoms and medical data of patients and provide appropriate diagnoses and treatment methods.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an input unit, an analysis unit, a reference unit, and a provision unit. The input unit inputs the patient's symptoms and medical data. The analysis unit analyzes the data input by the input unit and lists diagnoses. The reference unit references similar cases and medical literature from a medical database based on the diagnoses listed by the analysis unit. The provision unit provides recommendations for treatments and prescriptions, as well as information on side effects, based on the information obtained by the reference unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze a patient's symptoms and medical data to provide appropriate diagnoses and treatments. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI assistant for medical diagnosis according to an embodiment of the present invention is a system that analyzes a patient's symptoms and medical data and lists possible diagnoses. This system takes the patient's symptoms and medical data as input, analyzes them, and lists diagnoses. Furthermore, based on the listed diagnoses, it refers to similar cases and the latest medical literature from a medical database. Finally, based on the obtained information, it provides recommendations for treatments and medications, as well as information on side effects. For example, data such as the patient's symptoms, medical records, and test results can be input. If the patient complains of symptoms such as fever or cough, that information is input. Past medical records and test results can also be input. Next, the input data is analyzed. The analysis unit lists possible diagnoses based on the input data. For example, from symptoms such as fever and cough, it can list diagnoses such as colds, influenza, and pneumonia. It can also estimate the patient's emotions and list diagnoses based on the estimated emotions. Furthermore, based on the listed diagnoses, it refers to similar cases and the latest medical literature from a medical database. The reference unit prioritizes referring to information related to the listed diagnoses. For example, it can prioritize referencing information about other patients with similar attributes to the patient's own. Finally, based on the information obtained, it provides recommendations for treatments and medications, as well as information on side effects. For each listed diagnosis, the system provides recommendations for treatments and prescriptions, as well as information on side effects. For example, for a diagnosis of the common cold, it can provide recommendations for appropriate medications and information on side effects. This allows for efficient analysis of the patient's symptoms and medical data, enabling the provision of appropriate diagnoses and treatments. Furthermore, filtering based on the patient's emotions and living situation can lead to more accurate diagnoses. For example, if a patient is experiencing stress, this information can be taken into consideration when making a diagnosis. As a result, the medical diagnostic support AI assistant can efficiently analyze the patient's symptoms and medical data, providing appropriate diagnoses and treatments.

[0029] The AI ​​assistant for medical diagnosis according to this embodiment comprises an input unit, an analysis unit, a reference unit, and a provision unit. The input unit inputs the patient's symptoms and medical data. Patient symptoms include, for example, fever, cough, and pain, but are not limited to such examples. Medical data includes, for example, medical records, test results, and image data, but are not limited to such examples. The input unit inputs, for example, the patient's symptoms in text format. The input unit can also obtain medical records from an electronic medical record system. Furthermore, the input unit can also obtain test results from a medical institution's database. The analysis unit analyzes the data input by the input unit and lists diagnoses. The analysis unit analyzes the data using, for example, statistical analysis or machine learning algorithms. For example, the analysis unit lists diagnoses such as a cold, influenza, and pneumonia based on symptoms such as fever and cough. The analysis unit can also estimate the patient's emotions and list diagnoses based on the estimated emotions. For example, the analysis unit estimates the patient's emotions using facial expression analysis or voice analysis. The reference unit searches medical databases for similar cases and medical literature based on the diagnoses listed by the analysis unit. For example, the reference unit prioritizes information on cases of other patients with similar attributes to the patient's. For instance, it searches for similar cases based on attributes such as age, sex, and medical history. The reference unit can also access the latest medical literature to obtain information relevant to the diagnosis. The provision unit provides recommendations for treatments and prescriptions, as well as information on side effects, based on the information obtained by the reference unit. For example, the provision unit recommends appropriate treatments and medications for each listed diagnosis. For instance, it recommends antipyretics and cough suppressants for a cold diagnosis. The provision unit can also provide information on drug side effects. For example, it might present the risk of gastrointestinal disorders or allergic reactions as side effects of antipyretics. This allows the AI ​​medical diagnostic support assistant according to the embodiment to efficiently analyze the patient's symptoms and medical data and provide appropriate diagnoses and treatments. Some or all of the above-described processing in the provision unit may be performed using AI, or without AI.For example, the service provider can use an AI model that takes a list of recommended diagnoses and treatments as input and outputs recommendations for treatments and medications to make recommendations for treatments and medications.

[0030] The input unit inputs patient symptoms and medical data. Patient symptoms include, but are not limited to, fever, cough, and pain. Medical data includes, but are not limited to, medical records, test results, and image data. The input unit inputs, for example, patient symptoms in text format. The input unit can also retrieve medical records from the electronic medical record system. Furthermore, the input unit can retrieve test results from the medical institution's database. Specifically, the input unit can input symptoms self-reported by the patient as text data using natural language processing technology. For example, when a patient inputs symptoms using a smartphone or tablet, voice input is also possible, and it is converted to text using speech recognition technology. Data acquisition from the electronic medical record system is performed using medical data exchange standards such as HL7 and FHIR, and medical records and test results are automatically imported into the input unit. For image data, medical images in DICOM format can be directly imported into the input unit, thereby providing detailed medical image data such as X-ray images, MRI, and CT scans to the analysis unit. Furthermore, the input unit can acquire patient vital sign data (e.g., heart rate, blood pressure, body temperature, etc.) in real time from a wearable device. This allows for continuous monitoring of the patient's condition and immediate response if an abnormality is detected.

[0031] The analysis unit analyzes the data input by the input unit and lists diagnoses. The analysis unit analyzes the data using, for example, statistical analysis and machine learning algorithms. For example, the analysis unit lists diagnoses such as colds, influenza, and pneumonia based on symptoms such as fever and cough. The analysis unit can also estimate the patient's emotions and list diagnoses based on the estimated emotions. For example, the analysis unit estimates the patient's emotions using facial expression analysis and voice analysis. Specifically, the analysis unit uses statistical models and deep learning models based on symptom data to analyze the frequency and correlation of each symptom and list the most likely diagnosis. For example, if a combination of fever and cough is observed, it is determined that there is a high probability of a cold or influenza. The analysis unit also uses natural language processing technology to extract important keywords from the patient's symptom description and makes a diagnosis based on these. Furthermore, the analysis unit uses computer vision technology and voice analysis technology to estimate emotions from the patient's facial expressions and voice. For example, it analyzes the facial expressions of a patient when they speak to a camera and estimates the degree of stress and anxiety. Furthermore, voice analysis is used to estimate emotions from the tone and rhythm of the voice. This allows the analysis unit to perform a comprehensive diagnosis that also takes into account the patient's psychological state. In addition, the analysis unit can provide more accurate diagnoses by using machine learning models that have been trained on past medical and case data. For example, a predictive model based on past data can be used to calculate the probability of diagnosis when a specific combination of symptoms is observed, and the most likely diagnoses can be listed.

[0032] The reference unit searches medical databases for similar cases and medical literature based on the diagnoses listed by the analysis unit. For example, the reference unit prioritizes information on cases of other patients with similar attributes to the patient's. For instance, it searches for similar cases based on attributes such as age, sex, and medical history. The reference unit can also access the latest medical literature to obtain information relevant to the diagnosis. Specifically, it accesses medical databases and searches for case data related to the listed diagnosis. For example, if the patient is a 40-year-old male with fever and cough symptoms, it prioritizes accessing past case data of patients with similar attributes. This allows the reference unit to improve the accuracy of diagnoses based on similar cases. Furthermore, the reference unit accesses medical literature databases such as PubMed and the Cochrane Library to obtain the latest research findings and clinical trial results. This allows the reference unit to provide diagnostic information based on the latest medical knowledge. In addition, the reference unit can leverage expert networks within healthcare institutions to obtain expert opinions and advice. For example, if there are any uncertainties regarding a particular diagnosis, the reference system can consult with a specialist online to obtain expert opinions. This allows the reference system to provide more reliable diagnostic information.

[0033] The provider department provides recommendations for treatments and prescriptions, as well as information on side effects, based on the information obtained by the reference department. For example, the provider department recommends appropriate treatments and medications for each listed diagnosis. For example, for a diagnosis of the common cold, the provider department recommends antipyretics and cough suppressants. The provider department can also provide information on drug side effects. For example, the provider department may list the risk of gastrointestinal disorders and allergic reactions as side effects of antipyretics. Specifically, the provider department recommends treatments based on treatment guidelines and clinical protocols for each listed diagnosis. For example, for a diagnosis of the common cold, in addition to antipyretics and cough suppressants, it emphasizes the importance of sufficient rest and hydration. The provider department also considers the patient's medical history and allergy information in order to recommend treatments tailored to the patient's individual circumstances. For example, for a patient allergic to a particular drug, it recommends an alternative drug. Furthermore, the provider department provides detailed information on drug side effects. For example, it may list the risk of gastrointestinal disorders and allergic reactions as side effects of antipyretics and explain how to deal with these side effects if they occur. Furthermore, the service provider also provides information on drug interactions. For example, if there is a risk of increased side effects when certain drugs are used in combination, this information is provided to the patient, and they are warned. This allows the service provider to provide patients with comprehensive treatment information and support them in selecting appropriate treatment methods. In addition, the service provider can use AI to recommend treatments and drugs. For example, an AI model can be used that takes a list of diagnoses and treatment recommendations as input and outputs recommendations for treatments and drugs to recommend the most suitable treatments and drugs. This allows the service provider to make more accurate treatment recommendations.

[0034] The input unit can input data on the patient's symptoms, medical records, and test results. For example, the input unit can input the patient's symptoms in text format. The input unit can also retrieve medical records from an electronic medical record system. Furthermore, the input unit can retrieve test results from a medical institution's database. This allows for accurate input of the patient's symptoms, medical records, and test results. Some or all of the above-described processes in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the patient's symptoms in text format, input that data into a generating AI, and the generating AI can perform analysis.

[0035] The service provider can provide recommendations for treatments or prescriptions, as well as information on side effects, for each listed diagnosis. For example, for a diagnosis of a common cold, the service provider might recommend antipyretics or cough suppressants. The service provider can also provide information on drug side effects. For example, it might present the risk of gastrointestinal disorders or allergic reactions as side effects of antipyretics. This allows the service provider to provide appropriate treatment and drug recommendations, as well as information on side effects, based on the listed diagnoses. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can recommend treatments and drugs using an AI model that takes the listed diagnoses and treatment recommendations as input and outputs treatment and drug recommendations.

[0036] The reference unit can prioritize accessing information about other patient cases that have similar attributes to the patient's. For example, the reference unit can search for similar cases based on attributes such as age, sex, and medical history. The reference unit can also access the latest medical literature to obtain information relevant to the diagnosis. This allows for the priority access to highly relevant case information based on the patient's attributes. Some or all of the above processing in the reference unit may be performed using AI, for example, or without AI. For example, the reference unit can input patient attribute data into a generating AI and have the generating AI search for similar case information.

[0037] The input unit can perform filtering based on the patient's current living situation and medical history when inputting symptoms and medical data. For example, the input unit can input information about the patient's occupation and lifestyle. The input unit can also input the patient's past medical history and treatment history. This allows for the priority input of highly relevant data based on the patient's living situation and medical history. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input patient living situation data into a generating AI and have the generating AI perform filtering.

[0038] The input unit can analyze a patient's past medical records and select the optimal input method. For example, the input unit can automatically display frequently entered data items from past medical records. It can also suggest an appropriate input method (voice, text, etc.) for the patient based on past medical records. Furthermore, the input unit can analyze past medical records and provide templates to reduce the effort required for data entry. This allows the input unit to provide the optimal input method based on past medical records. Some or all of the above-described processes in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input past medical record data into a generating AI and have the generating AI select the optimal input method.

[0039] The input unit can prioritize inputting highly relevant data based on the patient's geographical location when inputting symptoms and medical data. For example, the input unit can prioritize inputting data related to region-specific diseases and symptoms based on the patient's place of residence. The input unit can also input information on nearby medical institutions based on the patient's current location. Furthermore, the input unit can input data related to environmental factors, taking into account the patient's geographical location. This allows for the priority input of highly relevant data based on the patient's geographical location. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the patient's geographical location data into a generating AI and have the generating AI prioritize inputting highly relevant data.

[0040] The analysis unit can adjust the level of detail in the diagnosis based on the severity of the symptoms when listing diagnoses. For example, the analysis unit can provide detailed diagnostic information for severe symptoms. It can also provide concise diagnostic information for mild symptoms. Furthermore, it can provide diagnostic information with an appropriate level of detail for moderate symptoms. This allows for the provision of diagnostic information with an appropriate level of detail according to the severity of the symptoms. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input symptom severity data into a generating AI and have the generating AI perform the adjustment of the level of detail in the diagnosis.

[0041] The analysis unit can apply different analysis algorithms depending on the symptom category when creating a diagnostic list. For example, the analysis unit applies a dedicated analysis algorithm for respiratory symptoms. It can also apply a different analysis algorithm for digestive symptoms. Furthermore, it can apply yet another analysis algorithm for neurological symptoms. This allows the optimal analysis algorithm to be applied according to the symptom category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input symptom category data into a generating AI and have the generating AI select the analysis algorithm to apply.

[0042] The reference unit can predict the current case by referring to past case data during the reference process. For example, the reference unit can predict the current case based on past case data. The reference unit can also analyze past case data and provide information relevant to the current case. Furthermore, the reference unit can supplement the diagnosis of the current case by referring to past case data. This allows the current case to be predicted based on past case data. Some or all of the above processing in the reference unit may be performed using AI, for example, or without AI. For example, the reference unit can input past case data into a generating AI and have the generating AI perform a prediction of the current case.

[0043] The reference unit can apply different reference methods to each symptom category during the referencing process. For example, the reference unit applies a dedicated reference method for respiratory symptoms. It can also apply a different reference method for digestive symptoms, and yet another for neurological symptoms. This allows for the application of the most appropriate reference method depending on the symptom category. Some or all of the above processing in the reference unit may be performed using AI, for example, or without AI. For instance, the reference unit can input symptom category data into a generating AI and have the generating AI select the appropriate reference method.

[0044] The service provider can analyze the patient's past treatment history at the time of service provision to recommend the most suitable treatment or medication. For example, the service provider can recommend treatments that have been effective based on past treatment history. The service provider can also analyze past treatment history and recommend treatments with fewer side effects. Furthermore, the service provider can recommend the most suitable medication by referring to past treatment history. This allows the service provider to recommend the most suitable treatment or medication based on past treatment history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past treatment history data into a generating AI and have the generating AI recommend the most suitable treatment or medication.

[0045] The service provider can customize treatment and medication recommendations based on the patient's current living situation at the time of delivery. For example, the service provider can customize treatment by considering the patient's work and home environment. It can also customize medication recommendations based on the patient's lifestyle (diet, exercise, etc.). Furthermore, the service provider can offer treatment options considering the patient's current living situation. This allows for the recommendation of the most suitable treatment and medication according to the patient's living situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input patient living situation data into a generating AI and have the generating AI perform the customization of treatment and medication.

[0046] The service provider can recommend the most suitable treatment and medication at the time of delivery, taking into account the patient's geographical location. For example, the service provider can recommend region-specific treatments based on the patient's place of residence. It can also recommend medications available at nearby medical institutions based on the patient's current location. Furthermore, the service provider can recommend treatments related to environmental factors, taking into account the patient's geographical location. This allows for the recommendation of the most suitable treatment and medication based on the patient's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the patient's geographical location data into a generating AI and have the generating AI recommend the most suitable treatment and medication.

[0047] The service provider can analyze the patient's social media activity at the time of service provision to recommend treatments and medications. For example, the service provider can extract health-related information from the patient's social media posts and recommend treatments. The service provider can also analyze the patient's social media activity and recommend treatments related to lifestyle habits. Furthermore, the service provider can recommend treatments related to stress and emotional states based on the patient's social media interactions. This allows for the recommendation of the most suitable treatments and medications based on the patient's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the patient's social media data into a generating AI and have the generating AI perform the recommendation of treatments and medications.

[0048] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0049] The analysis unit can analyze a patient's genetic information and list diagnoses based on genetic factors. For example, the analysis unit can obtain a patient's DNA sequencing data and list diseases associated with specific gene mutations. It can also prioritize listing diseases with a high genetic risk, taking family history into consideration. Furthermore, based on the genetic information, the analysis unit can propose the most appropriate treatment from a personalized medicine perspective. This enables more accurate diagnoses and treatment recommendations based on the patient's genetic information.

[0050] The service provider can recommend treatments and medications while considering the patient's diet and nutritional status. For example, the service provider can analyze the patient's dietary records and suggest a nutritionally balanced diet. Furthermore, if a patient is deficient in a particular nutrient, the service provider can recommend supplements to address that deficiency. In addition, the service provider can propose treatments, including dietary therapy, to comprehensively improve the patient's health. This allows for the recommendation of more effective treatments and medications based on the patient's diet and nutritional status.

[0051] The reference section can propose treatment methods, including exercise therapy, taking into account the patient's exercise habits. For example, the reference section can analyze the patient's exercise records and propose an appropriate exercise program. It can also refer to the effects of exercise therapy on specific diseases and propose the most suitable exercise therapy for the patient. Furthermore, the reference section can provide guidelines and precautions for implementing exercise therapy, supporting patients in safely performing the therapy. This makes it possible to propose more effective treatment methods based on the patient's exercise habits.

[0052] The analysis unit can analyze a patient's sleep patterns and list diagnoses related to sleep disorders. For example, the analysis unit can obtain a patient's sleep records and evaluate the quality and quantity of sleep. Furthermore, if the patient is at high risk of sleep disorders, the analysis unit can suggest treatments to mitigate that risk. In addition, based on sleep patterns, the analysis unit can provide advice on lifestyle improvements and environmental adjustments. This allows for more appropriate diagnoses and treatment suggestions based on the patient's sleep patterns.

[0053] The following briefly describes the processing flow for example form 1.

[0054] Step 1: The input section is used to input the patient's symptoms and medical data. Patient symptoms include fever, cough, pain, etc., and medical data includes medical records, test results, image data, etc. The input section can input patient symptoms in text format, retrieve medical records from the electronic medical record system, and retrieve test results from the medical institution's database. Step 2: The analysis unit analyzes the data entered by the input unit and lists diagnoses. The analysis unit analyzes the data using statistical analysis and machine learning algorithms, and for example, lists diagnoses such as colds, influenza, and pneumonia based on symptoms such as fever and cough. The analysis unit can also estimate the patient's emotions using facial expression analysis and voice analysis, and list diagnoses based on the estimated emotions. Step 3: The reference unit searches medical databases for similar cases and medical literature based on the diagnoses listed by the analysis unit. The reference unit prioritizes information on other patient cases with similar attributes to the patient's, searching for similar cases based on attributes such as age, sex, and medical history. The reference unit can also refer to the latest medical literature to obtain information relevant to the diagnosis. Step 4: The provider section provides treatment and prescription recommendations, as well as information on side effects, based on the information obtained by the reference section. The provider section recommends appropriate treatments and medications for each listed diagnosis, for example, recommending antipyretics and cough suppressants for a diagnosis of the common cold. The provider section also provides information on drug side effects, such as the risk of gastrointestinal problems and allergic reactions as side effects of antipyretics.

[0055] (Example of form 2) An AI assistant for medical diagnosis according to an embodiment of the present invention is a system that analyzes a patient's symptoms and medical data and lists possible diagnoses. This system takes the patient's symptoms and medical data as input, analyzes them, and lists diagnoses. Furthermore, based on the listed diagnoses, it refers to similar cases and the latest medical literature from a medical database. Finally, based on the obtained information, it provides recommendations for treatments and medications, as well as information on side effects. For example, data such as the patient's symptoms, medical records, and test results can be input. If the patient complains of symptoms such as fever or cough, that information is input. Past medical records and test results can also be input. Next, the input data is analyzed. The analysis unit lists possible diagnoses based on the input data. For example, from symptoms such as fever and cough, it can list diagnoses such as colds, influenza, and pneumonia. It can also estimate the patient's emotions and list diagnoses based on the estimated emotions. Furthermore, based on the listed diagnoses, it refers to similar cases and the latest medical literature from a medical database. The reference unit prioritizes referring to information related to the listed diagnoses. For example, it can prioritize referencing information about other patients with similar attributes to the patient's own. Finally, based on the information obtained, it provides recommendations for treatments and medications, as well as information on side effects. For each listed diagnosis, the system provides recommendations for treatments and prescriptions, as well as information on side effects. For example, for a diagnosis of the common cold, it can provide recommendations for appropriate medications and information on side effects. This allows for efficient analysis of the patient's symptoms and medical data, enabling the provision of appropriate diagnoses and treatments. Furthermore, filtering based on the patient's emotions and living situation can lead to more accurate diagnoses. For example, if a patient is experiencing stress, this information can be taken into consideration when making a diagnosis. As a result, the medical diagnostic support AI assistant can efficiently analyze the patient's symptoms and medical data, providing appropriate diagnoses and treatments.

[0056] The AI ​​assistant for medical diagnosis according to this embodiment comprises an input unit, an analysis unit, a reference unit, and a provision unit. The input unit inputs the patient's symptoms and medical data. Patient symptoms include, for example, fever, cough, and pain, but are not limited to such examples. Medical data includes, for example, medical records, test results, and image data, but are not limited to such examples. The input unit inputs, for example, the patient's symptoms in text format. The input unit can also obtain medical records from an electronic medical record system. Furthermore, the input unit can also obtain test results from a medical institution's database. The analysis unit analyzes the data input by the input unit and lists diagnoses. The analysis unit analyzes the data using, for example, statistical analysis or machine learning algorithms. For example, the analysis unit lists diagnoses such as a cold, influenza, and pneumonia based on symptoms such as fever and cough. The analysis unit can also estimate the patient's emotions and list diagnoses based on the estimated emotions. For example, the analysis unit estimates the patient's emotions using facial expression analysis or voice analysis. The reference unit searches medical databases for similar cases and medical literature based on the diagnoses listed by the analysis unit. For example, the reference unit prioritizes information on cases of other patients with similar attributes to the patient's. For instance, it searches for similar cases based on attributes such as age, sex, and medical history. The reference unit can also access the latest medical literature to obtain information relevant to the diagnosis. The provision unit provides recommendations for treatments and prescriptions, as well as information on side effects, based on the information obtained by the reference unit. For example, the provision unit recommends appropriate treatments and medications for each listed diagnosis. For instance, it recommends antipyretics and cough suppressants for a cold diagnosis. The provision unit can also provide information on drug side effects. For example, it might present the risk of gastrointestinal disorders or allergic reactions as side effects of antipyretics. This allows the AI ​​medical diagnostic support assistant according to the embodiment to efficiently analyze the patient's symptoms and medical data and provide appropriate diagnoses and treatments. Some or all of the above-described processing in the provision unit may be performed using AI, or without AI.For example, the service provider can use an AI model that takes a list of recommended diagnoses and treatments as input and outputs recommendations for treatments and medications to make recommendations for treatments and medications.

[0057] The input unit inputs patient symptoms and medical data. Patient symptoms include, but are not limited to, fever, cough, and pain. Medical data includes, but are not limited to, medical records, test results, and image data. The input unit inputs, for example, patient symptoms in text format. The input unit can also retrieve medical records from the electronic medical record system. Furthermore, the input unit can retrieve test results from the medical institution's database. Specifically, the input unit can input symptoms self-reported by the patient as text data using natural language processing technology. For example, when a patient inputs symptoms using a smartphone or tablet, voice input is also possible, and it is converted to text using speech recognition technology. Data acquisition from the electronic medical record system is performed using medical data exchange standards such as HL7 and FHIR, and medical records and test results are automatically imported into the input unit. For image data, medical images in DICOM format can be directly imported into the input unit, thereby providing detailed medical image data such as X-ray images, MRI, and CT scans to the analysis unit. Furthermore, the input unit can acquire patient vital sign data (e.g., heart rate, blood pressure, body temperature, etc.) in real time from a wearable device. This allows for continuous monitoring of the patient's condition and immediate response if an abnormality is detected.

[0058] The analysis unit analyzes the data input by the input unit and lists diagnoses. The analysis unit analyzes the data using, for example, statistical analysis and machine learning algorithms. For example, the analysis unit lists diagnoses such as colds, influenza, and pneumonia based on symptoms such as fever and cough. The analysis unit can also estimate the patient's emotions and list diagnoses based on the estimated emotions. For example, the analysis unit estimates the patient's emotions using facial expression analysis and voice analysis. Specifically, the analysis unit uses statistical models and deep learning models based on symptom data to analyze the frequency and correlation of each symptom and list the most likely diagnosis. For example, if a combination of fever and cough is observed, it is determined that there is a high probability of a cold or influenza. The analysis unit also uses natural language processing technology to extract important keywords from the patient's symptom description and makes a diagnosis based on these. Furthermore, the analysis unit uses computer vision technology and voice analysis technology to estimate emotions from the patient's facial expressions and voice. For example, it analyzes the facial expressions of a patient when they speak to a camera and estimates the degree of stress and anxiety. Furthermore, voice analysis is used to estimate emotions from the tone and rhythm of the voice. This allows the analysis unit to perform a comprehensive diagnosis that also takes into account the patient's psychological state. In addition, the analysis unit can provide more accurate diagnoses by using machine learning models that have been trained on past medical and case data. For example, a predictive model based on past data can be used to calculate the probability of diagnosis when a specific combination of symptoms is observed, and the most likely diagnoses can be listed.

[0059] The reference unit searches medical databases for similar cases and medical literature based on the diagnoses listed by the analysis unit. For example, the reference unit prioritizes information on cases of other patients with similar attributes to the patient's. For instance, it searches for similar cases based on attributes such as age, sex, and medical history. The reference unit can also access the latest medical literature to obtain information relevant to the diagnosis. Specifically, it accesses medical databases and searches for case data related to the listed diagnosis. For example, if the patient is a 40-year-old male with fever and cough symptoms, it prioritizes accessing past case data of patients with similar attributes. This allows the reference unit to improve the accuracy of diagnoses based on similar cases. Furthermore, the reference unit accesses medical literature databases such as PubMed and the Cochrane Library to obtain the latest research findings and clinical trial results. This allows the reference unit to provide diagnostic information based on the latest medical knowledge. In addition, the reference unit can leverage expert networks within healthcare institutions to obtain expert opinions and advice. For example, if there are any uncertainties regarding a particular diagnosis, the reference system can consult with a specialist online to obtain expert opinions. This allows the reference system to provide more reliable diagnostic information.

[0060] The provider department provides recommendations for treatments and prescriptions, as well as information on side effects, based on the information obtained by the reference department. For example, the provider department recommends appropriate treatments and medications for each listed diagnosis. For example, for a diagnosis of the common cold, the provider department recommends antipyretics and cough suppressants. The provider department can also provide information on drug side effects. For example, the provider department may list the risk of gastrointestinal disorders and allergic reactions as side effects of antipyretics. Specifically, the provider department recommends treatments based on treatment guidelines and clinical protocols for each listed diagnosis. For example, for a diagnosis of the common cold, in addition to antipyretics and cough suppressants, it emphasizes the importance of sufficient rest and hydration. The provider department also considers the patient's medical history and allergy information in order to recommend treatments tailored to the patient's individual circumstances. For example, for a patient allergic to a particular drug, it recommends an alternative drug. Furthermore, the provider department provides detailed information on drug side effects. For example, it may list the risk of gastrointestinal disorders and allergic reactions as side effects of antipyretics and explain how to deal with these side effects if they occur. Furthermore, the service provider also provides information on drug interactions. For example, if there is a risk of increased side effects when certain drugs are used in combination, this information is provided to the patient, and they are warned. This allows the service provider to provide patients with comprehensive treatment information and support them in selecting appropriate treatment methods. In addition, the service provider can use AI to recommend treatments and drugs. For example, an AI model can be used that takes a list of diagnoses and treatment recommendations as input and outputs recommendations for treatments and drugs to recommend the most suitable treatments and drugs. This allows the service provider to make more accurate treatment recommendations.

[0061] The input unit can input data on the patient's symptoms, medical records, and test results. For example, the input unit can input the patient's symptoms in text format. The input unit can also retrieve medical records from an electronic medical record system. Furthermore, the input unit can retrieve test results from a medical institution's database. This allows for accurate input of the patient's symptoms, medical records, and test results. Some or all of the above-described processes in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the patient's symptoms in text format, input that data into a generating AI, and the generating AI can perform analysis.

[0062] The service provider can provide recommendations for treatments or prescriptions, as well as information on side effects, for each listed diagnosis. For example, for a diagnosis of a common cold, the service provider might recommend antipyretics or cough suppressants. The service provider can also provide information on drug side effects. For example, it might present the risk of gastrointestinal disorders or allergic reactions as side effects of antipyretics. This allows the service provider to provide appropriate treatment and drug recommendations, as well as information on side effects, based on the listed diagnoses. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can recommend treatments and drugs using an AI model that takes the listed diagnoses and treatment recommendations as input and outputs treatment and drug recommendations.

[0063] The reference unit can prioritize accessing information about other patient cases that have similar attributes to the patient's. For example, the reference unit can search for similar cases based on attributes such as age, sex, and medical history. The reference unit can also access the latest medical literature to obtain information relevant to the diagnosis. This allows for the priority access to highly relevant case information based on the patient's attributes. Some or all of the above processing in the reference unit may be performed using AI, for example, or without AI. For example, the reference unit can input patient attribute data into a generating AI and have the generating AI search for similar case information.

[0064] The analysis unit can estimate the patient's emotions and list diagnoses based on the estimated emotions. The analysis unit estimates the patient's emotions using, for example, facial expression analysis or voice analysis. For example, the analysis unit acquires the patient's facial expression data with a camera and estimates the emotions using an emotion estimation algorithm. The analysis unit can also acquire the patient's voice data with a microphone and estimate the emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. This allows for the listing of more appropriate diagnoses based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the patient's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0065] The input unit can perform filtering based on the patient's current living situation and medical history when inputting symptoms and medical data. For example, the input unit can input information about the patient's occupation and lifestyle. The input unit can also input the patient's past medical history and treatment history. This allows for the priority input of highly relevant data based on the patient's living situation and medical history. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input patient living situation data into a generating AI and have the generating AI perform filtering.

[0066] The input unit can estimate the patient's emotions and adjust the timing of symptom and medical data input based on the estimated emotions. For example, if the patient is stressed, the input unit can prompt data input during a time when the patient can relax. The input unit can also request detailed data input if the patient is relaxed. Furthermore, if the patient is in a hurry, the input unit can provide a simplified input form. This allows data input to be performed at the optimal time according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using AI or not. For example, the input unit can input patient emotion data into the generative AI and have the generative AI adjust the timing of input.

[0067] The input unit can analyze a patient's past medical records and select the optimal input method. For example, the input unit can automatically display frequently entered data items from past medical records. It can also suggest an appropriate input method (voice, text, etc.) for the patient based on past medical records. Furthermore, the input unit can analyze past medical records and provide templates to reduce the effort required for data entry. This allows the input unit to provide the optimal input method based on past medical records. Some or all of the above-described processes in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input past medical record data into a generating AI and have the generating AI select the optimal input method.

[0068] The input unit can estimate the patient's emotions and determine the priority of the data to be input based on the estimated emotions. For example, if the patient is stressed, the input unit will prioritize inputting important data items. If the patient is relaxed, the input unit can also input detailed data items. Furthermore, if the patient is in a hurry, the input unit can input minimal data items. This allows for the priority of inputting important data according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using AI, for example, or not using AI. For example, the input unit can input patient emotion data into a generative AI and have the generative AI determine the priority of the data.

[0069] The input unit can prioritize inputting highly relevant data based on the patient's geographical location when inputting symptoms and medical data. For example, the input unit can prioritize inputting data related to region-specific diseases and symptoms based on the patient's place of residence. The input unit can also input information on nearby medical institutions based on the patient's current location. Furthermore, the input unit can input data related to environmental factors, taking into account the patient's geographical location. This allows for the priority input of highly relevant data based on the patient's geographical location. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the patient's geographical location data into a generating AI and have the generating AI prioritize inputting highly relevant data.

[0070] The analysis unit can estimate the patient's emotions and adjust the diagnostic listing method based on the estimated emotions. For example, if the patient is stressed, the analysis unit can provide a concise and easy-to-understand diagnostic list. If the patient is relaxed, the analysis unit can also provide a detailed diagnostic list. Furthermore, if the patient is in a hurry, the analysis unit can prioritize listing the most likely diagnoses. This allows for the provision of an optimal diagnostic list according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input patient emotion data into the generative AI and have the generative AI adjust the diagnostic listing method.

[0071] The analysis unit can adjust the level of detail in the diagnosis based on the severity of the symptoms when listing diagnoses. For example, the analysis unit can provide detailed diagnostic information for severe symptoms. It can also provide concise diagnostic information for mild symptoms. Furthermore, it can provide diagnostic information with an appropriate level of detail for moderate symptoms. This allows for the provision of diagnostic information with an appropriate level of detail according to the severity of the symptoms. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input symptom severity data into a generating AI and have the generating AI perform the adjustment of the level of detail in the diagnosis.

[0072] The analysis unit can apply different analysis algorithms depending on the symptom category when creating a diagnostic list. For example, the analysis unit applies a dedicated analysis algorithm for respiratory symptoms. It can also apply a different analysis algorithm for digestive symptoms. Furthermore, it can apply yet another analysis algorithm for neurological symptoms. This allows the optimal analysis algorithm to be applied according to the symptom category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input symptom category data into a generating AI and have the generating AI select the analysis algorithm to apply.

[0073] The reference unit can estimate the patient's emotions and adjust how the referenced information is displayed based on the estimated emotions. For example, if the patient is stressed, the reference unit can display concise and easily readable information. If the patient is relaxed, the reference unit can also display detailed information. Furthermore, if the patient is in a hurry, the reference unit can display concise information. This allows information to be provided in the most appropriate display method according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reference unit may be performed using AI, or not using AI. For example, the reference unit can input patient emotion data into the generative AI and have the generative AI adjust how the information is displayed.

[0074] The reference unit can predict the current case by referring to past case data during the reference process. For example, the reference unit can predict the current case based on past case data. The reference unit can also analyze past case data and provide information relevant to the current case. Furthermore, the reference unit can supplement the diagnosis of the current case by referring to past case data. This allows the current case to be predicted based on past case data. Some or all of the above processing in the reference unit may be performed using AI, for example, or without AI. For example, the reference unit can input past case data into a generating AI and have the generating AI perform a prediction of the current case.

[0075] The reference unit can apply different reference methods to each symptom category during the referencing process. For example, the reference unit applies a dedicated reference method for respiratory symptoms. It can also apply a different reference method for digestive symptoms, and yet another for neurological symptoms. This allows for the application of the most appropriate reference method depending on the symptom category. Some or all of the above processing in the reference unit may be performed using AI, for example, or without AI. For instance, the reference unit can input symptom category data into a generating AI and have the generating AI select the appropriate reference method.

[0076] The service provider can estimate the patient's emotions and adjust the treatment and medication recommendations based on the estimated emotions. For example, if the patient is stressed, the service provider may recommend a relaxing treatment. If the patient is relaxed, the service provider may also recommend a more detailed treatment. Furthermore, if the patient is in a hurry, the service provider may recommend a treatment that produces quick results. This allows for the recommendation of the most appropriate treatment and medication according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input patient emotion data into a generative AI and have the generative AI adjust the treatment and medication recommendations.

[0077] The service provider can analyze the patient's past treatment history at the time of service provision to recommend the most suitable treatment or medication. For example, the service provider can recommend treatments that have been effective based on past treatment history. The service provider can also analyze past treatment history and recommend treatments with fewer side effects. Furthermore, the service provider can recommend the most suitable medication by referring to past treatment history. This allows the service provider to recommend the most suitable treatment or medication based on past treatment history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past treatment history data into a generating AI and have the generating AI recommend the most suitable treatment or medication.

[0078] The service provider can customize treatment and medication recommendations based on the patient's current living situation at the time of delivery. For example, the service provider can customize treatment by considering the patient's work and home environment. It can also customize medication recommendations based on the patient's lifestyle (diet, exercise, etc.). Furthermore, the service provider can offer treatment options considering the patient's current living situation. This allows for the recommendation of the most suitable treatment and medication according to the patient's living situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input patient living situation data into a generating AI and have the generating AI perform the customization of treatment and medication.

[0079] The service provider can estimate the patient's emotions and prioritize treatments and medications based on the estimated emotions. For example, if the patient is stressed, the service provider will prioritize recommending treatments with relaxing effects. If the patient is relaxed, the service provider may also prioritize recommending more detailed treatments. Furthermore, if the patient is in a hurry, the service provider may prioritize recommending treatments that produce rapid results. This allows the service provider to determine the optimal priority of treatments and medications according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can input patient emotion data into a generative AI and have the generative AI determine the priority of treatments and medications.

[0080] The service provider can recommend the most suitable treatment and medication at the time of delivery, taking into account the patient's geographical location. For example, the service provider can recommend region-specific treatments based on the patient's place of residence. It can also recommend medications available at nearby medical institutions based on the patient's current location. Furthermore, the service provider can recommend treatments related to environmental factors, taking into account the patient's geographical location. This allows for the recommendation of the most suitable treatment and medication based on the patient's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the patient's geographical location data into a generating AI and have the generating AI recommend the most suitable treatment and medication.

[0081] The service provider can analyze the patient's social media activity at the time of service provision to recommend treatments and medications. For example, the service provider can extract health-related information from the patient's social media posts and recommend treatments. The service provider can also analyze the patient's social media activity and recommend treatments related to lifestyle habits. Furthermore, the service provider can recommend treatments related to stress and emotional states based on the patient's social media interactions. This allows for the recommendation of the most suitable treatments and medications based on the patient's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the patient's social media data into a generating AI and have the generating AI perform the recommendation of treatments and medications.

[0082] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0083] The analysis unit can analyze a patient's genetic information and list diagnoses based on genetic factors. For example, the analysis unit can obtain a patient's DNA sequencing data and list diseases associated with specific gene mutations. It can also prioritize listing diseases with a high genetic risk, taking family history into consideration. Furthermore, based on the genetic information, the analysis unit can propose the most appropriate treatment from a personalized medicine perspective. This enables more accurate diagnoses and treatment recommendations based on the patient's genetic information.

[0084] The service provider can recommend treatments and medications while considering the patient's diet and nutritional status. For example, the service provider can analyze the patient's dietary records and suggest a nutritionally balanced diet. Furthermore, if a patient is deficient in a particular nutrient, the service provider can recommend supplements to address that deficiency. In addition, the service provider can propose treatments, including dietary therapy, to comprehensively improve the patient's health. This allows for the recommendation of more effective treatments and medications based on the patient's diet and nutritional status.

[0085] The reference section can propose treatment methods, including exercise therapy, taking into account the patient's exercise habits. For example, the reference section can analyze the patient's exercise records and propose an appropriate exercise program. It can also refer to the effects of exercise therapy on specific diseases and propose the most suitable exercise therapy for the patient. Furthermore, the reference section can provide guidelines and precautions for implementing exercise therapy, supporting patients in safely performing the therapy. This makes it possible to propose more effective treatment methods based on the patient's exercise habits.

[0086] The analysis unit can analyze a patient's sleep patterns and list diagnoses related to sleep disorders. For example, the analysis unit can obtain a patient's sleep records and evaluate the quality and quantity of sleep. Furthermore, if the patient is at high risk of sleep disorders, the analysis unit can suggest treatments to mitigate that risk. In addition, based on sleep patterns, the analysis unit can provide advice on lifestyle improvements and environmental adjustments. This allows for more appropriate diagnoses and treatment suggestions based on the patient's sleep patterns.

[0087] The system can estimate the patient's emotions and adjust the explanation of treatment methods and drug side effects based on those estimates. For example, if the patient is feeling anxious, the system will explain side effects using gentle language. If the patient is relaxed, it can provide a more detailed explanation of side effects. Furthermore, if the patient is in a hurry, it can provide a concise explanation that gets straight to the point. This allows the system to optimize the explanation of side effects according to the patient's emotions, thereby deepening their understanding.

[0088] The analysis unit can estimate the patient's emotions and adjust the notification method of the diagnostic results based on the estimated emotions. For example, if the patient is stressed, the diagnostic results can be notified in stages. Conversely, if the patient is relaxed, detailed diagnostic results can be notified all at once. Furthermore, if the patient is in a hurry, the most important diagnostic results can be prioritized. This optimizes the notification method of the diagnostic results according to the patient's emotions, thereby reducing the burden on the patient.

[0089] The reference unit can estimate the patient's emotions and prioritize the information to be referenced based on those emotions. For example, if the patient is feeling anxious, information that provides reassurance will be displayed preferentially. If the patient is relaxed, detailed information can be displayed preferentially. Furthermore, if the patient is in a hurry, concise information can be displayed preferentially. This allows the system to provide the most appropriate information according to the patient's emotions.

[0090] The system can estimate the patient's emotions and adjust the order in which it presents treatment and medication options based on those emotions. For example, if the patient is stressed, it might first present relaxing treatments. If the patient is relaxed, it might first present detailed treatments. Furthermore, if the patient is in a hurry, it might first present treatments that produce quick results. This allows the system to present the most appropriate treatment and medication options according to the patient's emotions.

[0091] The analysis unit can estimate the patient's emotions and adjust the diagnostic listing method based on the estimated emotions. For example, if the patient is stressed, it can provide a concise and easy-to-understand diagnostic list. If the patient is relaxed, it can provide a more detailed diagnostic list. Furthermore, if the patient is in a hurry, it can prioritize listing the most likely diagnoses. This allows for the provision of an optimal diagnostic list tailored to the patient's emotions.

[0092] The system can estimate the patient's emotions and prioritize treatments and medications based on those estimates. For example, if a patient is stressed, it can prioritize recommending treatments with relaxing effects. If the patient is relaxed, it can also prioritize recommending more detailed treatments. Furthermore, if the patient is in a hurry, it can prioritize recommending treatments that produce rapid results. This allows the system to determine the optimal treatment and medication priorities according to the patient's emotions.

[0093] The following briefly describes the processing flow for example form 2.

[0094] Step 1: The input section is used to input the patient's symptoms and medical data. Patient symptoms include fever, cough, pain, etc., and medical data includes medical records, test results, image data, etc. The input section can input patient symptoms in text format, retrieve medical records from the electronic medical record system, and retrieve test results from the medical institution's database. Step 2: The analysis unit analyzes the data entered by the input unit and lists diagnoses. The analysis unit analyzes the data using statistical analysis and machine learning algorithms, and for example, lists diagnoses such as colds, influenza, and pneumonia based on symptoms such as fever and cough. The analysis unit can also estimate the patient's emotions using facial expression analysis and voice analysis, and list diagnoses based on the estimated emotions. Step 3: The reference unit searches medical databases for similar cases and medical literature based on the diagnoses listed by the analysis unit. The reference unit prioritizes information on other patient cases with similar attributes to the patient's, searching for similar cases based on attributes such as age, sex, and medical history. The reference unit can also refer to the latest medical literature to obtain information relevant to the diagnosis. Step 4: The provider section provides treatment and prescription recommendations, as well as information on side effects, based on the information obtained by the reference section. The provider section recommends appropriate treatments and medications for each listed diagnosis, for example, recommending antipyretics and cough suppressants for a diagnosis of the common cold. The provider section also provides information on drug side effects, such as the risk of gastrointestinal problems and allergic reactions as side effects of antipyretics.

[0095] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0096] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0097] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0098] For example, the input unit can input patient symptoms and medical data using the reception device 38 of the smart device 14. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12, which analyzes the input data and lists diagnoses. The reference unit is implemented by the identification processing unit 290 of the data processing device 12, which references similar cases and the latest medical literature from a medical database based on the listed diagnoses. The provision unit can provide information on treatment methods, drug recommendations, and side effects using the output device 40 of the smart device 14. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0099] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0100] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0101] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0102] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0103] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0105] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0106] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0107] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0108] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0109] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0110] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0113] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] For example, the input unit can input patient symptoms and medical data using the microphone 238 of the smart glasses 214. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12, which analyzes the input data and lists diagnoses. The reference unit is implemented by the identification processing unit 290 of the data processing device 12, which references similar cases and the latest medical literature from a medical database based on the listed diagnoses. The provision unit can provide information on treatment methods, drug recommendations, and side effects using the speaker 240 of the smart glasses 214. The correspondence between each unit and the device and control unit is not limited to the example described above, and various modifications are possible.

[0115] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0116] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0118] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

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

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

[0122] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0125] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0127] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0129] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] For example, the input unit can input patient symptoms and medical data using the microphone 238 of the headset terminal 314. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12, which analyzes the input data and lists diagnoses. The reference unit is implemented by the identification processing unit 290 of the data processing device 12, which references similar cases and the latest medical literature from a medical database based on the listed diagnoses. The provision unit can provide information on treatment methods, drug recommendations, and side effects using the display 343 of the headset terminal 314. The correspondence between each unit and the device and control unit is not limited to the example described above, and various modifications are possible.

[0131] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0132] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0134] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

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

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

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

[0138] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0139] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] For example, the input unit can input patient symptoms and medical data using the microphone 238 of the robot 414. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12, which analyzes the input data and lists diagnoses. The reference unit is implemented by the identification processing unit 290 of the data processing device 12, which references similar cases and the latest medical literature from a medical database based on the listed diagnoses. The provision unit can provide information on treatment methods, drug recommendations, and side effects using the speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0148] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0149] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0150] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0151] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0152] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0153] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0154] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0155] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0156] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0157] 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.

[0158] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0159] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0160] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0161] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0162] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0163] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0164] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0165] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0166] (Note 1) An input section for entering patient symptoms and medical data, An analysis unit analyzes the data input by the aforementioned input unit and lists the diagnoses, A reference unit that references similar cases and medical literature from a medical database based on the diagnoses listed by the aforementioned analysis unit, The system includes a providing unit that provides recommendations for treatment methods and prescriptions, as well as information on side effects, based on the information obtained by the aforementioned reference unit. A system characterized by the following features. (Note 2) The aforementioned input unit is Enter patient symptoms, medical records, and test results. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, For each listed diagnosis, we provide recommendations for treatment or prescriptions, as well as information on side effects. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reference section is, Prioritize referencing information about other patients with similar attributes to the patient's own characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, The system estimates the patient's emotions and lists diagnoses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned input unit is When entering symptoms and medical data, filtering is performed based on the patient's current lifestyle and medical history. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned input unit is The system estimates the patient's emotions and adjusts the timing of symptom and medical data entry based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned input unit is Analyze the patient's past medical records and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned input unit is The system estimates the patient's emotions and prioritizes the data to be entered based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned input unit is When entering symptoms and medical data, the system prioritizes inputting data that is highly relevant based on the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, We estimate the patient's emotions and adjust the diagnostic listing method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, When creating a diagnostic list, adjust the level of detail of the diagnosis based on the severity of the symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, When creating a diagnostic list, different analysis algorithms are applied depending on the category of symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reference section is, The system estimates the patient's emotions and adjusts how referenced information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reference section is, When referencing past case data, the system predicts the current case. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned reference section is, When referencing, apply different referencing methods for each symptom category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, The system estimates the patient's emotions and adjusts treatment and medication recommendations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing information, the system analyzes the patient's past treatment history to recommend the most suitable treatment and medication. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing information, treatment and medication recommendations are customized based on the patient's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, The system estimates the patient's emotions and determines the priority of treatments and medications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing information, the system recommends the most suitable treatment and medication, taking into account the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing information, the patient's social media activity is analyzed to recommend treatments and medications. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An input section for entering patient symptoms and medical data, An analysis unit analyzes the data input by the aforementioned input unit and lists the diagnoses, A reference unit that references similar cases and medical literature from a medical database based on the diagnoses listed by the aforementioned analysis unit, The system includes a providing unit that provides recommendations for treatment methods and prescriptions, as well as information on side effects, based on the information obtained by the aforementioned reference unit. A system characterized by the following features.

2. The aforementioned input unit is Enter patient symptoms, medical records, and test results. The system according to feature 1.

3. The aforementioned supply unit is, For each listed diagnosis, we provide recommendations for treatment or prescriptions, as well as information on side effects. The system according to feature 1.

4. The aforementioned reference section is, Prioritize referencing information about other patients with similar attributes to the patient's own characteristics. The system according to feature 1.

5. The aforementioned analysis unit, The system estimates the patient's emotions and lists diagnoses based on those estimated emotions. The system according to feature 1.

6. The aforementioned input unit is When entering symptoms and medical data, filtering is performed based on the patient's current lifestyle and medical history. The system according to feature 1.

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

8. The aforementioned input unit is Analyze the patient's past medical records and select the optimal input method. The system according to feature 1.

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