system

Abstract

The system according to this embodiment aims to recommend the optimal diagnosis and treatment method based on the patient's symptoms and medical history, and to suggest making an appointment at a medical institution. [Solution] The system according to the embodiment comprises an analysis unit, a recommendation unit, and a reservation suggestion unit. The analysis unit analyzes the patient's symptoms and medical history. The recommendation unit recommends appropriate diagnoses and treatments based on the results analyzed by the analysis unit. The reservation suggestion unit proposes reservations at medical institutions based on the diagnoses and treatments recommended by the recommendation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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, it cannot be said that optimal diagnosis and treatment methods are sufficiently recommended based on patients' symptoms and medical histories, and further, reservation of medical institutions is proposed, and there is room for improvement.

[0005] [[ID=�9]]The system according to the embodiment aims to recommend an optimal diagnosis and treatment method based on patients' symptoms and medical histories, and propose a reservation of a medical institution.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a recommendation unit, and a reservation suggestion unit. The analysis unit analyzes the patient's symptoms and medical history. The recommendation unit recommends appropriate diagnoses and treatments based on the results analyzed by the analysis unit. The reservation suggestion unit proposes reservations at medical institutions based on the diagnoses and treatments recommended by the recommendation unit. [Effects of the Invention]

[0007] The system according to this embodiment can recommend the optimal diagnosis and treatment method based on the patient's symptoms and medical history, and can also suggest making an appointment at a medical institution. [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 applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The medical assistant agent system according to an embodiment of the present invention is a system that analyzes a patient's symptoms and medical history and recommends the optimal diagnosis and treatment method. By analyzing the patient's symptoms and medical history and recommending the optimal diagnosis and treatment method, this medical assistant agent system improves the accuracy of diagnosis, reduces patient waiting times, and improves treatment effectiveness. Furthermore, the medical assistant agent system links with local medical information and suggests appointments at medical institutions that suit the patient's convenience. For example, the medical assistant agent system utilizes a cloud-based medical information system to achieve real-time appointment system integration. This allows patients to receive optimal medical care even with busy schedules. In addition, the medical assistant agent system has a user-friendly interface and caters to a wide range of patients of all ages. For healthcare providers such as medical institutions, clinics, and hospitals, it reduces the risk of delayed diagnosis and inappropriate treatment, and eliminates the inconvenience of inaccessible medical care. The medical assistant agent system uses deep learning for symptom analysis, natural language processing (NLP) for patient information processing, and cloud-based real-time data synchronization to provide efficient and accessible medical services. This aims to improve patient health management and build a healthier future. This allows the medical assistant agent system to analyze the patient's symptoms and medical history, recommend the optimal diagnosis and treatment, and suggest appointments at medical facilities, thereby enabling efficient medical services.

[0029] The medical assistant agent system according to this embodiment comprises an analysis unit, a recommendation unit, and a reservation suggestion unit. The analysis unit analyzes the patient's symptoms and medical history. Patient symptoms include, but are not limited to, fever, cough, and headache. Medical history includes, but is not limited to, past diagnoses, treatment history, and allergy information. The analysis unit identifies the cause of the symptoms by, for example, recording the patient's symptoms in detail and comparing them with past medical history. The recommendation unit recommends appropriate diagnoses and treatments based on the results analyzed by the analysis unit. Diagnoses and treatments include, but are not limited to, the accuracy of the diagnosis and the effectiveness of the treatment. The recommendation unit presents, for example, the most suitable diagnostic method for the patient's symptoms and proposes a treatment method. The recommendation unit can use natural language processing (NLP) technology to analyze the patient's symptoms and medical history and recommend the optimal diagnosis and treatment. The reservation suggestion unit proposes an appointment at a medical institution based on the diagnosis and treatment recommended by the recommendation unit. The reservation suggestion includes, but is not limited to, appointment priority and timing of the suggestion. The appointment suggestion unit, for example, suggests the most suitable medical institution for the patient's needs. This allows the medical assistant agent system, according to the embodiment, to analyze the patient's symptoms and medical history, recommend the optimal diagnosis and treatment, and suggest appointments at medical institutions, thereby enabling efficient medical services.

[0030] The analysis department analyzes patients' symptoms and medical history. Patient symptoms include, but are not limited to, fever, cough, and headache. Specifically, it meticulously records symptom data entered by the patient and compares it with their past medical history. Medical history includes, but is not limited to, past diagnoses, treatment history, and allergy information. The analysis department integrates this data and utilizes AI technology to identify the cause of symptoms. For example, it uses machine learning algorithms to extract patterns from past data and compare them with current symptoms to identify potential diseases and health risks. Furthermore, the analysis department has the capability to monitor the progression of patients' symptoms in real time and issue immediate alerts if abnormalities are detected. This allows for continuous monitoring of the patient's health status and early, appropriate action.

[0031] The recommendation department recommends appropriate diagnoses and treatments based on the results analyzed by the analysis department. These recommendations include, but are not limited to, the accuracy of the diagnosis and the effectiveness of the treatment. The recommendation department can use natural language processing (NLP) technology to analyze a patient's symptoms and medical history to recommend the optimal diagnosis and treatment. Specifically, it uses NLP technology to analyze the patient's symptoms as text data and compare it with a medical database to suggest the most suitable diagnostic method. For example, if a patient has a fever and cough, NLP technology is used to identify diseases associated with these symptoms and suggest the most suitable diagnostic method. Furthermore, in suggesting treatments, the recommendation department evaluates the effectiveness and side effects of treatments based on past treatment data and proposes the most suitable treatment. In addition, the recommendation department can provide personalized diagnoses and treatments according to the patient's individual needs and circumstances. For example, if a patient has a specific allergy, the recommendation department will suggest a treatment that takes that allergy into consideration. This allows the recommendation department to provide patients with the best possible medical services and maximize the effectiveness of their treatment.

[0032] The Appointment Proposal Department suggests appointments at medical institutions based on diagnoses and treatments recommended by the Recommendation Department. These suggestions may include, but are not limited to, prioritizing appointments and suggesting timing. Specifically, it suggests the most suitable medical institution based on the patient's needs and urgency. For example, if a patient exhibits urgent symptoms, the Appointment Proposal Department will prioritize suggesting the medical institution with the earliest available appointment time. It can also flexibly adjust the date, time, and location of the appointment to suit the patient's convenience. Furthermore, the Appointment Proposal Department has the ability to check medical institution availability in real time and suggest the most suitable appointment. This allows patients to efficiently book appointments and minimize waiting times. Additionally, the Appointment Proposal Department can manage the patient's appointment history and suggest appointments for regular health checkups and follow-ups. This continuously supports the patient's health management and contributes to the promotion of preventive medicine.

[0033] The Cloud Integration Department utilizes a cloud-based medical information system. This system includes, but is not limited to, the cloud services used and the data synchronization methods. For example, the Cloud Integration Department uses cloud services to synchronize patient medical information in real time. By utilizing the cloud-based medical information system, the Cloud Integration Department can achieve real-time data synchronization. Some or all of the above-described processes in the Cloud Integration Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Cloud Integration Department can use cloud services to input patient medical information into a generative AI, which can then perform data synchronization.

[0034] The reservation integration unit performs real-time reservation system integration. Real-time reservation system integration includes, but is not limited to, the technologies used and data synchronization methods. For example, the reservation integration unit synchronizes patient reservation information with medical institutions in real time and makes optimal reservation suggestions. By performing real-time reservation system integration, the reservation integration unit can make reservation suggestions that are tailored to the patient's convenience. Some or all of the above processing in the reservation integration unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the reservation integration unit can input the real-time reservation system integration into a generation AI, and the generation AI can synchronize the reservation information.

[0035] The interface unit provides a user-friendly interface. A user-friendly interface includes, but is not limited to, the results of usability testing and design principles. For example, the interface unit provides an intuitive interface that allows patients to easily operate it. By providing a user-friendly interface, the interface unit can accommodate patients of a wide range of ages. Some or all of the above-described processes in the interface unit may be performed, for example, using generative AI, or without generative AI. For example, the interface unit can input a user-friendly interface design into a generative AI, which can then optimize the interface.

[0036] The analysis unit performs symptom analysis using deep learning. Deep learning includes, but is not limited to, the algorithms used and the types of training data. The analysis unit uses, for example, a deep learning algorithm to analyze the patient's symptoms in detail. This improves the accuracy of symptom analysis by using deep learning. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input patient symptom data into a generative AI, which can then perform symptom analysis.

[0037] The recommendation department processes patient information using natural language processing (NLP). NLP includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the recommendation department uses NLP techniques to analyze a patient's symptoms and medical history and recommend the most suitable diagnosis and treatment. This improves the accuracy of patient information processing through the use of NLP. Some or all of the processing described above in the recommendation department may be performed using, for example, generative AI, or without generative AI. For example, the recommendation department can input patient symptom and medical history data into a generative AI, which can then process the information.

[0038] The analysis unit collects lifestyle data from patients and incorporates it into symptom analysis. Lifestyle data includes, but is not limited to, diet, exercise, and sleep. For example, the analysis unit can collect patients' sleep patterns and incorporate them into symptom analysis. The analysis unit can also collect patients' dietary history and incorporate it into symptom analysis. The analysis unit can also collect patients' exercise history and incorporate it into symptom analysis. By collecting patients' lifestyle data and incorporating it into symptom analysis, a more accurate diagnosis becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input patients' lifestyle data into a generative AI, which can then perform symptom analysis.

[0039] The analysis unit performs symptom analysis based on the patient's genetic information. Genetic information includes, but is not limited to, genetic test results and family history. For example, the analysis unit collects the patient's family history and performs symptom analysis considering genetic risk. The analysis unit may also collect the patient's genetic test results and incorporate them into the symptom analysis. The analysis unit may also assess the patient's risk of genetic disease and incorporate it into the symptom analysis. This allows for a diagnosis that reflects genetic risk by considering the patient's genetic information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the patient's genetic information into a generative AI, which can then perform symptom analysis.

[0040] The analysis unit collects the patient's geographical environment data and incorporates it into the symptom analysis. Geographical environment data includes, but is not limited to, place of residence, climate, and environmental pollution. For example, the analysis unit can collect the patient's living environment data and incorporate it into the symptom analysis. The analysis unit can also collect the patient's work environment data and incorporate it into the symptom analysis. The analysis unit can also collect the patient's commute route data and incorporate it into the symptom analysis. By collecting the patient's geographical environment data and incorporating it into the symptom analysis, a more accurate diagnosis becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the patient's geographical environment data into a generative AI, which can then perform the symptom analysis.

[0041] The analysis unit analyzes the patient's dietary history and incorporates it into the symptom analysis. The dietary history includes, but is not limited to, the content of meals, calorie intake, and meal frequency. For example, the analysis unit can collect the patient's dietary content and incorporate it into the symptom analysis. The analysis unit can also collect the patient's meal frequency and incorporate it into the symptom analysis. This allows for a more accurate diagnosis by analyzing the patient's dietary history and incorporating it into the symptom analysis. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the patient's dietary history data into a generative AI, which can then perform the symptom analysis.

[0042] The recommendation department improves the accuracy of diagnostic and treatment recommendations by referring to past treatment outcome data. Past treatment outcome data includes, but is not limited to, treatment success rates and the presence or absence of side effects. The recommendation department can, for example, collect past treatment outcome data to improve the accuracy of diagnostic and treatment recommendations. The recommendation department can also analyze past treatment outcome data to recommend the optimal diagnosis and treatment. The recommendation department can also refer to past treatment outcome data and adjust the recommendations for diagnosis and treatment. This improves the accuracy of diagnostic and treatment recommendations by referring to past treatment outcome data. Some or all of the above processing in the recommendation department may be performed using, for example, a generative AI, or not using a generative AI. For example, the recommendation department can input past treatment outcome data into a generative AI, which can then analyze the data.

[0043] The recommendation department recommends diagnoses and treatments based on the patient's allergy information. Allergy information includes, but is not limited to, the type of allergen and the severity of the allergic reaction. For example, the recommendation department collects the patient's allergy information and recommends diagnoses and treatments. The recommendation department can also analyze the patient's allergy information and recommend the most appropriate diagnosis and treatment. The recommendation department can also adjust the recommendations for diagnosis and treatment, taking the patient's allergy information into consideration. This allows for the provision of treatments that avoid allergy risks by considering the patient's allergy information. Some or all of the above processing in the recommendation department may be performed, for example, using a generative AI, or without a generative AI. For example, the recommendation department can input the patient's allergy information into a generative AI, which can then analyze the data.

[0044] The recommendation department recommends diagnoses and treatments based on the patient's occupational information. Occupational information includes, but is not limited to, the type of occupation, working hours, and risk of occupational diseases. The recommendation department may, for example, collect the patient's occupational information and recommend diagnoses and treatments. The recommendation department may also analyze the patient's occupational information and recommend the most suitable diagnosis and treatment. The recommendation department may also adjust the recommendations for diagnosis and treatments considering the patient's occupational information. This allows for the provision of occupation-appropriate treatments by considering the patient's occupational information. Some or all of the above processing in the recommendation department may be performed using, for example, generative AI, or not using generative AI. For example, the recommendation department may input the patient's occupational information into a generative AI, which can then analyze the data.

[0045] The recommendation department analyzes the patient's exercise history and recommends a diagnosis and treatment. Exercise history includes, but is not limited to, the type, frequency, and intensity of exercise. For example, the recommendation department collects the patient's exercise history and recommends a diagnosis and treatment. The recommendation department can also analyze the patient's exercise history and recommend the most suitable diagnosis and treatment. The recommendation department can also adjust the recommendations for diagnosis and treatment, taking the patient's exercise history into consideration. This allows for the provision of treatment tailored to the patient's exercise habits by analyzing their exercise history. Some or all of the above processing in the recommendation department may be performed, for example, using generative AI, or without generative AI. For example, the recommendation department can input the patient's exercise history data into a generative AI, which can then analyze the data.

[0046] The appointment suggestion unit analyzes the patient's past appointment history and selects the optimal appointment suggestion method. Past appointment history includes, but is not limited to, the success rate of appointments and the frequency of cancellations. The appointment suggestion unit can, for example, collect the patient's past appointment history and select the optimal appointment suggestion method. The appointment suggestion unit can also analyze the patient's past appointment history and suggest the optimal appointment suggestion method. The appointment suggestion unit can also refer to the patient's past appointment history and adjust the appointment suggestion method. In this way, by analyzing the patient's past appointment history, the optimal appointment suggestion method can be provided. Some or all of the above processing in the appointment suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the appointment suggestion unit can input the patient's past appointment history data into a generative AI, which can then analyze the data.

[0047] The appointment suggestion unit makes appointment suggestions based on the patient's medical history. Medical history includes, but is not limited to, the frequency of visits and past medical treatments. The appointment suggestion unit can, for example, collect the patient's medical history and make the most suitable appointment suggestion. The appointment suggestion unit can also analyze the patient's medical history and make the most suitable appointment suggestion. The appointment suggestion unit can also adjust the content of the appointment suggestion considering the patient's medical history. This makes it possible to make the most suitable appointment suggestion by considering the patient's medical history. Some or all of the above processing in the appointment suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the appointment suggestion unit can input the patient's medical history data into a generative AI, which can then analyze the data.

[0048] The reservation suggestion unit makes reservation suggestions considering the patient's transportation information. Transportation information includes, but is not limited to, the mode of transport used, travel time, and transportation costs. The reservation suggestion unit can, for example, collect the patient's transportation information and make the optimal reservation suggestion. The reservation suggestion unit can also analyze the patient's transportation information and make the optimal reservation suggestion. The reservation suggestion unit can also adjust the content of the reservation suggestion considering the patient's transportation information. This makes it possible to make the optimal reservation suggestion by considering the patient's transportation information. Some or all of the above processing in the reservation suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reservation suggestion unit can input the patient's transportation information into a generative AI, which can then analyze the data.

[0049] The reservation suggestion unit makes reservation suggestions considering the patient's family structure information. Family structure information includes, but is not limited to, the number of family members and their health status. The reservation suggestion unit can, for example, collect the patient's family structure information and make the most suitable reservation suggestion. The reservation suggestion unit can also analyze the patient's family structure information and make the most suitable reservation suggestion. The reservation suggestion unit can also adjust the content of the reservation suggestion considering the patient's family structure information. This makes it possible to make the most suitable reservation suggestion by considering the patient's family structure information. Some or all of the above processing in the reservation suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reservation suggestion unit can input the patient's family structure information into a generative AI, which can then analyze the data.

[0050] The Cloud Integration Unit analyzes the data update frequency of medical institutions and optimizes cloud integration. Data update frequency includes, but is not limited to, data update intervals and update timing. For example, the Cloud Integration Unit collects data update frequencies from medical institutions and optimizes cloud integration. The Cloud Integration Unit can also analyze the data update frequency of medical institutions and determine the optimal data synchronization timing. The Cloud Integration Unit can also adjust the data synchronization timing for cloud integration, taking into account the data update frequency of medical institutions. This allows for optimization of cloud integration by analyzing the data update frequency of medical institutions. Some or all of the above processing in the Cloud Integration Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Cloud Integration Unit can input the data update frequency of medical institutions into a generative AI, which can then analyze the data.

[0051] The Cloud Integration Unit performs cloud integration while considering the geographical distribution of medical institutions. This geographical distribution includes, but is not limited to, factors such as the location and accessibility of medical institutions. The Cloud Integration Unit can, for example, collect data on the geographical distribution of medical institutions and perform cloud integration. It can also analyze the geographical distribution of medical institutions and perform optimal cloud integration. The Cloud Integration Unit can also adjust the data synchronization timing for cloud integration while considering the geographical distribution of medical institutions. This enables optimal cloud integration by considering the geographical distribution of medical institutions. Some or all of the above-described processes in the Cloud Integration Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Cloud Integration Unit can input geographical distribution data of medical institutions into a generative AI, which can then analyze the data.

[0052] The reservation integration unit analyzes the reservation status of medical institutions in real time to improve the accuracy of reservation integration. Reservation status includes, but is not limited to, reservation availability and cancellation rates. The reservation integration unit can, for example, collect the reservation status of medical institutions in real time to improve the accuracy of reservation integration. The reservation integration unit can also analyze the reservation status of medical institutions in real time to perform optimal reservation integration. The reservation integration unit can also adjust the timing of reservation integration by considering the reservation status of medical institutions in real time. This improves the accuracy of reservation integration by analyzing the reservation status of medical institutions in real time. Some or all of the above processing in the reservation integration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reservation integration unit can input the reservation status data of medical institutions into a generative AI, which can then analyze the data.

[0053] The appointment integration unit integrates appointments based on the patient's frequency of visits. This frequency includes, but is not limited to, the number of visits and the interval between visits. The appointment integration unit can, for example, collect patient visit frequency data and perform optimal appointment integration. It can also analyze patient visit frequency data and perform optimal appointment integration. Furthermore, the appointment integration unit can adjust the timing of appointment integration, taking patient visit frequency into consideration. This allows for optimal appointment integration by considering patient visit frequency. Some or all of the above-described processes in the appointment integration unit may be performed using, for example, a generative AI, or without one. For example, the appointment integration unit can input patient visit frequency data into a generative AI, which can then analyze the data.

[0054] The interface unit customizes the interface design according to the patient's age group. The interface design includes, but is not limited to, age-appropriate design criteria and customization methods. For example, the interface unit might increase font size and simplify operation for older users. For younger users, it might make the design more colorful and intuitive. For middle-aged users, it might adjust the amount of information and simplify operation. This customization of the interface design according to the patient's age group improves usability. Some or all of the above processing in the interface unit may be performed using, for example, a generative AI, or without one. For example, the interface unit can input patient age group data into a generative AI, which can then customize the design.

[0055] The interface unit optimizes the interface based on the patient's device information. This device information includes, but is not limited to, the type of device used and its performance. For example, if the patient is using a smartphone, the interface unit provides an interface optimized for the screen size. If the patient is using a tablet, the interface unit can also provide an interface optimized for a larger screen. If the patient is using a personal computer, the interface unit can also provide an interface that prioritizes usability. This allows for the provision of an optimal interface by considering the patient's device information. Some or all of the above processing in the interface unit may be performed using, for example, a generative AI, or without a generative AI. For example, the interface unit can input the patient's device information into a generative AI, which can then optimize the interface.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The medical assistant agent system can also collect patient lifestyle data and incorporate it into symptom analysis. For example, it can collect and incorporate the patient's sleep patterns, dietary history, and exercise history. By collecting and incorporating patient lifestyle data into symptom analysis, a more accurate diagnosis becomes possible.

[0058] The medical assistant agent system can further analyze symptoms based on the patient's genetic information. For example, it can collect the patient's family history and analyze symptoms while considering genetic risks. It can also collect the patient's genetic test results and incorporate them into the symptom analysis. Furthermore, it can assess the patient's risk of genetic diseases and incorporate that into the symptom analysis. This allows for diagnoses that reflect genetic risks by considering the patient's genetic information.

[0059] The medical assistant agent system can also collect and incorporate patients' geographical environment data into symptom analysis. For example, it can collect and incorporate data on the patient's living environment. It can also collect and incorporate data on the patient's work environment. Furthermore, it can collect and incorporate data on the patient's commute route. By collecting and incorporating patients' geographical environment data into symptom analysis, a more accurate diagnosis becomes possible.

[0060] The medical assistant agent system can further analyze a patient's dietary history and incorporate it into the symptom analysis. For example, it can collect information on the patient's meals and incorporate it into the symptom analysis. It can also collect information on the patient's meal frequency and incorporate it into the symptom analysis. Furthermore, it can collect information on the patient's eating patterns and incorporate them into the symptom analysis. By analyzing the patient's dietary history and incorporating it into the symptom analysis, a more accurate diagnosis becomes possible.

[0061] The medical assistant agent system can further recommend diagnoses and treatments based on the patient's occupational information. For example, it can collect the patient's occupational information and recommend diagnoses and treatments. It can also analyze the patient's occupational information and recommend the most suitable diagnoses and treatments. Furthermore, it can adjust the recommendations for diagnoses and treatments to take the patient's occupational information into consideration. This allows for the provision of occupation-appropriate treatments by considering the patient's occupation.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The analysis department analyzes the patient's symptoms and medical history. Patient symptoms include fever, cough, headache, etc., and medical history includes past diagnoses, treatment history, allergy information, etc. The analysis department records the patient's symptoms in detail and compares them with past medical history to identify the cause of the symptoms. Step 2: The recommendation department recommends appropriate diagnoses and treatments based on the results analyzed by the analysis department. These recommendations include the accuracy of the diagnosis and the effectiveness of the treatment. The recommendation department presents the most suitable diagnostic method for the patient's symptoms and proposes a treatment plan. Furthermore, the recommendation department can use natural language processing (NLP) technology to analyze the patient's symptoms and medical history to recommend the optimal diagnosis and treatment plan. Step 3: The Appointment Proposal Department suggests appointments at medical institutions based on the diagnosis and treatment recommended by the Recommendation Department. These suggestions include appointment priorities and timing. The Appointment Proposal Department proposes the most suitable medical institution to accommodate the patient's needs.

[0064] (Example of form 2) The medical assistant agent system according to an embodiment of the present invention is a system that analyzes a patient's symptoms and medical history and recommends the optimal diagnosis and treatment method. By analyzing the patient's symptoms and medical history and recommending the optimal diagnosis and treatment method, this medical assistant agent system improves the accuracy of diagnosis, reduces patient waiting times, and improves treatment effectiveness. Furthermore, the medical assistant agent system links with local medical information and suggests appointments at medical institutions that suit the patient's convenience. For example, the medical assistant agent system utilizes a cloud-based medical information system to achieve real-time appointment system integration. This allows patients to receive optimal medical care even with busy schedules. In addition, the medical assistant agent system has a user-friendly interface and caters to a wide range of patients of all ages. For healthcare providers such as medical institutions, clinics, and hospitals, it reduces the risk of delayed diagnosis and inappropriate treatment, and eliminates the inconvenience of inaccessible medical care. The medical assistant agent system uses deep learning for symptom analysis, natural language processing (NLP) for patient information processing, and cloud-based real-time data synchronization to provide efficient and accessible medical services. This aims to improve patient health management and build a healthier future. This allows the medical assistant agent system to analyze the patient's symptoms and medical history, recommend the optimal diagnosis and treatment, and suggest appointments at medical facilities, thereby enabling efficient medical services.

[0065] The medical assistant agent system according to this embodiment comprises an analysis unit, a recommendation unit, and a reservation suggestion unit. The analysis unit analyzes the patient's symptoms and medical history. Patient symptoms include, but are not limited to, fever, cough, and headache. Medical history includes, but is not limited to, past diagnoses, treatment history, and allergy information. The analysis unit identifies the cause of the symptoms by, for example, recording the patient's symptoms in detail and comparing them with past medical history. The recommendation unit recommends appropriate diagnoses and treatments based on the results analyzed by the analysis unit. Diagnoses and treatments include, but are not limited to, the accuracy of the diagnosis and the effectiveness of the treatment. The recommendation unit presents, for example, the most suitable diagnostic method for the patient's symptoms and proposes a treatment method. The recommendation unit can use natural language processing (NLP) technology to analyze the patient's symptoms and medical history and recommend the optimal diagnosis and treatment. The reservation suggestion unit proposes an appointment at a medical institution based on the diagnosis and treatment recommended by the recommendation unit. The reservation suggestion includes, but is not limited to, appointment priority and timing of the suggestion. The appointment suggestion unit, for example, suggests the most suitable medical institution for the patient's needs. This allows the medical assistant agent system, according to the embodiment, to analyze the patient's symptoms and medical history, recommend the optimal diagnosis and treatment, and suggest appointments at medical institutions, thereby enabling efficient medical services.

[0066] The analysis department analyzes patients' symptoms and medical history. Patient symptoms include, but are not limited to, fever, cough, and headache. Specifically, it meticulously records symptom data entered by the patient and compares it with their past medical history. Medical history includes, but is not limited to, past diagnoses, treatment history, and allergy information. The analysis department integrates this data and utilizes AI technology to identify the cause of symptoms. For example, it uses machine learning algorithms to extract patterns from past data and compare them with current symptoms to identify potential diseases and health risks. Furthermore, the analysis department has the capability to monitor the progression of patients' symptoms in real time and issue immediate alerts if abnormalities are detected. This allows for continuous monitoring of the patient's health status and early, appropriate action.

[0067] The recommendation department recommends appropriate diagnoses and treatments based on the results analyzed by the analysis department. These recommendations include, but are not limited to, the accuracy of the diagnosis and the effectiveness of the treatment. The recommendation department can use natural language processing (NLP) technology to analyze a patient's symptoms and medical history to recommend the optimal diagnosis and treatment. Specifically, it uses NLP technology to analyze the patient's symptoms as text data and compare it with a medical database to suggest the most suitable diagnostic method. For example, if a patient has a fever and cough, NLP technology is used to identify diseases associated with these symptoms and suggest the most suitable diagnostic method. Furthermore, in suggesting treatments, the recommendation department evaluates the effectiveness and side effects of treatments based on past treatment data and proposes the most suitable treatment. In addition, the recommendation department can provide personalized diagnoses and treatments according to the patient's individual needs and circumstances. For example, if a patient has a specific allergy, the recommendation department will suggest a treatment that takes that allergy into consideration. This allows the recommendation department to provide patients with the best possible medical services and maximize the effectiveness of their treatment.

[0068] The Appointment Proposal Department suggests appointments at medical institutions based on diagnoses and treatments recommended by the Recommendation Department. These suggestions may include, but are not limited to, prioritizing appointments and suggesting timing. Specifically, it suggests the most suitable medical institution based on the patient's needs and urgency. For example, if a patient exhibits urgent symptoms, the Appointment Proposal Department will prioritize suggesting the medical institution with the earliest available appointment time. It can also flexibly adjust the date, time, and location of the appointment to suit the patient's convenience. Furthermore, the Appointment Proposal Department has the ability to check medical institution availability in real time and suggest the most suitable appointment. This allows patients to efficiently book appointments and minimize waiting times. Additionally, the Appointment Proposal Department can manage the patient's appointment history and suggest appointments for regular health checkups and follow-ups. This continuously supports the patient's health management and contributes to the promotion of preventive medicine.

[0069] The Cloud Integration Department utilizes a cloud-based medical information system. This system includes, but is not limited to, the cloud services used and the data synchronization methods. For example, the Cloud Integration Department uses cloud services to synchronize patient medical information in real time. By utilizing the cloud-based medical information system, the Cloud Integration Department can achieve real-time data synchronization. Some or all of the above-described processes in the Cloud Integration Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Cloud Integration Department can use cloud services to input patient medical information into a generative AI, which can then perform data synchronization.

[0070] The reservation integration unit performs real-time reservation system integration. Real-time reservation system integration includes, but is not limited to, the technologies used and data synchronization methods. For example, the reservation integration unit synchronizes patient reservation information with medical institutions in real time and makes optimal reservation suggestions. By performing real-time reservation system integration, the reservation integration unit can make reservation suggestions that are tailored to the patient's convenience. Some or all of the above processing in the reservation integration unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the reservation integration unit can input the real-time reservation system integration into a generation AI, and the generation AI can synchronize the reservation information.

[0071] The interface unit provides a user-friendly interface. A user-friendly interface includes, but is not limited to, the results of usability testing and design principles. For example, the interface unit provides an intuitive interface that allows patients to easily operate it. By providing a user-friendly interface, the interface unit can accommodate patients of a wide range of ages. Some or all of the above-described processes in the interface unit may be performed, for example, using generative AI, or without generative AI. For example, the interface unit can input a user-friendly interface design into a generative AI, which can then optimize the interface.

[0072] The analysis unit performs symptom analysis using deep learning. Deep learning includes, but is not limited to, the algorithms used and the types of training data. The analysis unit uses, for example, a deep learning algorithm to analyze the patient's symptoms in detail. This improves the accuracy of symptom analysis by using deep learning. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input patient symptom data into a generative AI, which can then perform symptom analysis.

[0073] The recommendation department processes patient information using natural language processing (NLP). NLP includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the recommendation department uses NLP techniques to analyze a patient's symptoms and medical history and recommend the most suitable diagnosis and treatment. This improves the accuracy of patient information processing through the use of NLP. Some or all of the processing described above in the recommendation department may be performed using, for example, generative AI, or without generative AI. For example, the recommendation department can input patient symptom and medical history data into a generative AI, which can then process the information.

[0074] The analysis unit estimates the patient's emotions and adjusts the accuracy of the symptom analysis based on the estimated emotions. Patient emotions include, but are not limited to, facial recognition, voice analysis, and questionnaire results. For example, if the patient is feeling anxious, the analysis unit collects more detailed data to improve the accuracy of the symptom analysis using AI. If the patient is relaxed, the analysis unit can also adjust the accuracy of the symptom analysis and make a quicker diagnosis. If the patient is stressed, the analysis unit can ask additional questions to improve the accuracy of the symptom analysis using AI. This allows for a more accurate diagnosis by adjusting the accuracy of the symptom analysis based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not. For example, the analysis unit can input patient emotion data into a generative AI, which can then perform emotion estimation.

[0075] The analysis unit collects lifestyle data from patients and incorporates it into symptom analysis. Lifestyle data includes, but is not limited to, diet, exercise, and sleep. For example, the analysis unit can collect patients' sleep patterns and incorporate them into symptom analysis. The analysis unit can also collect patients' dietary history and incorporate it into symptom analysis. The analysis unit can also collect patients' exercise history and incorporate it into symptom analysis. By collecting patients' lifestyle data and incorporating it into symptom analysis, a more accurate diagnosis becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input patients' lifestyle data into a generative AI, which can then perform symptom analysis.

[0076] The analysis unit performs symptom analysis based on the patient's genetic information. Genetic information includes, but is not limited to, genetic test results and family history. For example, the analysis unit collects the patient's family history and performs symptom analysis considering genetic risk. The analysis unit may also collect the patient's genetic test results and incorporate them into the symptom analysis. The analysis unit may also assess the patient's risk of genetic disease and incorporate it into the symptom analysis. This allows for a diagnosis that reflects genetic risk by considering the patient's genetic information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the patient's genetic information into a generative AI, which can then perform symptom analysis.

[0077] The analysis unit estimates the patient's emotions and determines the priority of symptom analysis based on the estimated emotions. Prioritization of symptom analysis includes, but is not limited to, the severity of symptoms and the urgency of the patient's condition. For example, if the patient is feeling anxious, the AI ​​may prioritize symptom analysis to enable a rapid diagnosis. If the patient is relaxed, the AI ​​may adjust the priority of symptom analysis to prioritize the diagnosis of other patients. If the patient is stressed, the AI ​​may also prioritize symptom analysis to collect additional data. This enables a rapid diagnosis by prioritizing symptom analysis based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, 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, a generative AI, or not. For example, the analysis unit can input patient emotion data into a generative AI, which can then perform emotion estimation.

[0078] The analysis unit collects the patient's geographical environment data and incorporates it into the symptom analysis. Geographical environment data includes, but is not limited to, place of residence, climate, and environmental pollution. For example, the analysis unit can collect the patient's living environment data and incorporate it into the symptom analysis. The analysis unit can also collect the patient's work environment data and incorporate it into the symptom analysis. The analysis unit can also collect the patient's commute route data and incorporate it into the symptom analysis. By collecting the patient's geographical environment data and incorporating it into the symptom analysis, a more accurate diagnosis becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the patient's geographical environment data into a generative AI, which can then perform the symptom analysis.

[0079] The analysis unit analyzes the patient's dietary history and incorporates it into the symptom analysis. The dietary history includes, but is not limited to, the content of meals, calorie intake, and meal frequency. For example, the analysis unit can collect the patient's dietary content and incorporate it into the symptom analysis. The analysis unit can also collect the patient's meal frequency and incorporate it into the symptom analysis. This allows for a more accurate diagnosis by analyzing the patient's dietary history and incorporating it into the symptom analysis. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the patient's dietary history data into a generative AI, which can then perform the symptom analysis.

[0080] The recommendation system estimates the patient's emotions and adjusts the diagnostic and treatment recommendations based on the estimated emotions. These recommendations may include, but are not limited to, adjustments based on the patient's emotions and criteria for recommendations. For example, if the patient is feeling anxious, the AI ​​may provide a detailed explanation of the diagnostic and treatment recommendations. If the patient is relaxed, the AI ​​may provide a concise explanation. If the patient is stressed, the AI ​​may provide a detailed explanation of the diagnostic and treatment recommendations and offer additional support. This allows for the provision of more appropriate treatments by adjusting the diagnostic and treatment recommendations based on the patient's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the recommendation system may be performed using, for example, generative AI, or without generative AI. For example, the recommendation department can input patient emotional data into a generating AI, which can then perform emotion estimation.

[0081] The recommendation department improves the accuracy of diagnostic and treatment recommendations by referring to past treatment outcome data. Past treatment outcome data includes, but is not limited to, treatment success rates and the presence or absence of side effects. The recommendation department can, for example, collect past treatment outcome data to improve the accuracy of diagnostic and treatment recommendations. The recommendation department can also analyze past treatment outcome data to recommend the optimal diagnosis and treatment. The recommendation department can also refer to past treatment outcome data and adjust the recommendations for diagnosis and treatment. This improves the accuracy of diagnostic and treatment recommendations by referring to past treatment outcome data. Some or all of the above processing in the recommendation department may be performed using, for example, a generative AI, or not using a generative AI. For example, the recommendation department can input past treatment outcome data into a generative AI, which can then analyze the data.

[0082] The recommendation department recommends diagnoses and treatments based on the patient's allergy information. Allergy information includes, but is not limited to, the type of allergen and the severity of the allergic reaction. For example, the recommendation department collects the patient's allergy information and recommends diagnoses and treatments. The recommendation department can also analyze the patient's allergy information and recommend the most appropriate diagnosis and treatment. The recommendation department can also adjust the recommendations for diagnosis and treatment, taking the patient's allergy information into consideration. This allows for the provision of treatments that avoid allergy risks by considering the patient's allergy information. Some or all of the above processing in the recommendation department may be performed, for example, using a generative AI, or without a generative AI. For example, the recommendation department can input the patient's allergy information into a generative AI, which can then analyze the data.

[0083] The recommendation system estimates the patient's emotions and prioritizes diagnoses and treatments based on the estimated emotions. Prioritization of diagnoses and treatments includes, but is not limited to, methods for determining priorities based on the patient's emotions and criteria for urgency. For example, if a patient is feeling anxious, the AI ​​in the recommendation system will increase the priority of diagnoses and treatments and provide a quicker response. If a patient is relaxed, the AI ​​in the recommendation system may adjust the priority of diagnoses and treatments, prioritizing other patients. If a patient is stressed, the AI ​​in the recommendation system may increase the priority of diagnoses and treatments and provide additional support. This enables a quicker response by prioritizing diagnoses and treatments based on the patient's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation system may be performed, for example, using generative AI or not using generative AI. For example, the recommendation department can input patient emotional data into a generating AI, which can then perform emotion estimation.

[0084] The recommendation department recommends diagnoses and treatments based on the patient's occupational information. Occupational information includes, but is not limited to, the type of occupation, working hours, and risk of occupational diseases. The recommendation department may, for example, collect the patient's occupational information and recommend diagnoses and treatments. The recommendation department may also analyze the patient's occupational information and recommend the most suitable diagnosis and treatment. The recommendation department may also adjust the recommendations for diagnosis and treatments considering the patient's occupational information. This allows for the provision of occupation-appropriate treatments by considering the patient's occupational information. Some or all of the above processing in the recommendation department may be performed using, for example, generative AI, or not using generative AI. For example, the recommendation department may input the patient's occupational information into a generative AI, which can then analyze the data.

[0085] The recommendation department analyzes the patient's exercise history and recommends a diagnosis and treatment. Exercise history includes, but is not limited to, the type, frequency, and intensity of exercise. For example, the recommendation department collects the patient's exercise history and recommends a diagnosis and treatment. The recommendation department can also analyze the patient's exercise history and recommend the most suitable diagnosis and treatment. The recommendation department can also adjust the recommendations for diagnosis and treatment, taking the patient's exercise history into consideration. This allows for the provision of treatment tailored to the patient's exercise habits by analyzing their exercise history. Some or all of the above processing in the recommendation department may be performed, for example, using generative AI, or without generative AI. For example, the recommendation department can input the patient's exercise history data into a generative AI, which can then analyze the data.

[0086] The appointment suggestion unit estimates the patient's emotions and adjusts the timing of appointment suggestions based on those emotions. The timing of appointment suggestions includes, but is not limited to, the method of adjusting the timing based on the patient's emotions and the frequency of suggestions. For example, if the patient is feeling anxious, the AI ​​in the appointment suggestion unit may advance the timing of the appointment suggestion to provide a quicker response. If the patient is relaxed, the AI ​​in the appointment suggestion unit may also adjust the timing of the appointment suggestion to prioritize other patients' appointments. If the patient is feeling stressed, the AI ​​in the appointment suggestion unit may also advance the timing of the appointment suggestion to provide additional support. This allows for a quicker response by adjusting the timing of appointment suggestions based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the appointment suggestion unit may be performed using, for example, generative AI, or not using generative AI. For example, the reservation suggestion unit can input patient emotion data into a generating AI, which can then perform emotion estimation.

[0087] The appointment suggestion unit analyzes the patient's past appointment history and selects the optimal appointment suggestion method. Past appointment history includes, but is not limited to, the success rate of appointments and the frequency of cancellations. The appointment suggestion unit can, for example, collect the patient's past appointment history and select the optimal appointment suggestion method. The appointment suggestion unit can also analyze the patient's past appointment history and suggest the optimal appointment suggestion method. The appointment suggestion unit can also refer to the patient's past appointment history and adjust the appointment suggestion method. In this way, by analyzing the patient's past appointment history, the optimal appointment suggestion method can be provided. Some or all of the above processing in the appointment suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the appointment suggestion unit can input the patient's past appointment history data into a generative AI, which can then analyze the data.

[0088] The appointment suggestion unit makes appointment suggestions based on the patient's medical history. Medical history includes, but is not limited to, the frequency of visits and past medical treatments. The appointment suggestion unit can, for example, collect the patient's medical history and make the most suitable appointment suggestion. The appointment suggestion unit can also analyze the patient's medical history and make the most suitable appointment suggestion. The appointment suggestion unit can also adjust the content of the appointment suggestion considering the patient's medical history. This makes it possible to make the most suitable appointment suggestion by considering the patient's medical history. Some or all of the above processing in the appointment suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the appointment suggestion unit can input the patient's medical history data into a generative AI, which can then analyze the data.

[0089] The appointment suggestion unit estimates the patient's emotions and determines the priority of appointment suggestions based on the estimated emotions. Prioritization of appointment suggestions includes, but is not limited to, methods for determining priority based on patient emotions and criteria for urgency. For example, if a patient is feeling anxious, the AI ​​in the appointment suggestion unit will increase the priority of the appointment suggestion and provide a quicker response. If a patient is relaxed, the AI ​​in the appointment suggestion unit may adjust the priority of the appointment suggestion to prioritize other patients' appointments. If a patient is feeling stressed, the AI ​​in the appointment suggestion unit may increase the priority of the appointment suggestion and provide additional support. This enables a quicker response by prioritizing appointment suggestions based on patient emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the appointment suggestion unit may be performed, for example, using generative AI or not using generative AI. For example, the reservation suggestion unit can input patient emotion data into a generating AI, which can then perform emotion estimation.

[0090] The reservation suggestion unit makes reservation suggestions considering the patient's transportation information. Transportation information includes, but is not limited to, the mode of transport used, travel time, and transportation costs. The reservation suggestion unit can, for example, collect the patient's transportation information and make the optimal reservation suggestion. The reservation suggestion unit can also analyze the patient's transportation information and make the optimal reservation suggestion. The reservation suggestion unit can also adjust the content of the reservation suggestion considering the patient's transportation information. This makes it possible to make the optimal reservation suggestion by considering the patient's transportation information. Some or all of the above processing in the reservation suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reservation suggestion unit can input the patient's transportation information into a generative AI, which can then analyze the data.

[0091] The reservation suggestion unit makes reservation suggestions considering the patient's family structure information. Family structure information includes, but is not limited to, the number of family members and their health status. The reservation suggestion unit can, for example, collect the patient's family structure information and make the most suitable reservation suggestion. The reservation suggestion unit can also analyze the patient's family structure information and make the most suitable reservation suggestion. The reservation suggestion unit can also adjust the content of the reservation suggestion considering the patient's family structure information. This makes it possible to make the most suitable reservation suggestion by considering the patient's family structure information. Some or all of the above processing in the reservation suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reservation suggestion unit can input the patient's family structure information into a generative AI, which can then analyze the data.

[0092] The cloud integration unit estimates the patient's emotions and adjusts the timing of cloud integration data synchronization based on the estimated emotions. The timing of cloud integration data synchronization includes, but is not limited to, the method of adjusting the synchronization timing based on the patient's emotions and the frequency of synchronization. For example, if the patient is feeling anxious, the cloud integration unit may accelerate the cloud integration data synchronization timing to provide a quick response. If the patient is relaxed, the cloud integration unit may also adjust the cloud integration data synchronization timing to prioritize data synchronization for other patients. If the patient is stressed, the cloud integration unit may also accelerate the cloud integration data synchronization timing to provide additional support. This allows for a quick response by adjusting the cloud integration data synchronization timing based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the cloud integration unit may be performed using, for example, generative AI, or without generative AI. For example, the cloud integration unit can input patient emotion data into a generating AI, which can then perform emotion estimation.

[0093] The Cloud Integration Unit analyzes the data update frequency of medical institutions and optimizes cloud integration. Data update frequency includes, but is not limited to, data update intervals and update timing. For example, the Cloud Integration Unit collects data update frequencies from medical institutions and optimizes cloud integration. The Cloud Integration Unit can also analyze the data update frequency of medical institutions and determine the optimal data synchronization timing. The Cloud Integration Unit can also adjust the data synchronization timing for cloud integration, taking into account the data update frequency of medical institutions. This allows for optimization of cloud integration by analyzing the data update frequency of medical institutions. Some or all of the above processing in the Cloud Integration Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Cloud Integration Unit can input the data update frequency of medical institutions into a generative AI, which can then analyze the data.

[0094] The cloud integration unit estimates the patient's emotions and determines the priority of cloud integration data based on the estimated patient emotions. This data priority includes, but is not limited to, the method for determining priority based on patient emotions and the importance of the data. For example, if a patient is feeling anxious, the cloud integration unit will increase the priority of the cloud integration data to provide a quicker response. If a patient is relaxed, the cloud integration unit may also adjust the data priority to prioritize other patients' data. If a patient is feeling stressed, the cloud integration unit may increase the data priority to provide additional support. This enables a quicker response by determining the data priority of cloud integration based on patient emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the cloud integration unit may be performed using, for example, generative AI, or not. For example, the cloud integration unit can input patient emotion data into a generative AI, which can then perform emotion estimation.

[0095] The Cloud Integration Unit performs cloud integration while considering the geographical distribution of medical institutions. This geographical distribution includes, but is not limited to, factors such as the location and accessibility of medical institutions. The Cloud Integration Unit can, for example, collect data on the geographical distribution of medical institutions and perform cloud integration. It can also analyze the geographical distribution of medical institutions and perform optimal cloud integration. The Cloud Integration Unit can also adjust the data synchronization timing for cloud integration while considering the geographical distribution of medical institutions. This enables optimal cloud integration by considering the geographical distribution of medical institutions. Some or all of the above-described processes in the Cloud Integration Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Cloud Integration Unit can input geographical distribution data of medical institutions into a generative AI, which can then analyze the data.

[0096] The appointment integration unit estimates the patient's emotions and adjusts the timing of appointment integration based on the estimated emotions. The timing of appointment integration includes, but is not limited to, the method of adjusting the timing based on the patient's emotions and the frequency of integration. For example, if the patient is feeling anxious, the appointment integration unit may advance the timing of appointment integration to provide a quick response. If the patient is relaxed, the appointment integration unit may also adjust the timing of appointment integration to prioritize other patients' appointments. If the patient is feeling stressed, the appointment integration unit may also advance the timing of appointment integration to provide additional support. This allows for a quick response by adjusting the timing of appointment integration based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the appointment integration unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the appointment integration unit can input patient emotion data into a generative AI, which can perform emotion estimation.

[0097] The reservation integration unit analyzes the reservation status of medical institutions in real time to improve the accuracy of reservation integration. Reservation status includes, but is not limited to, reservation availability and cancellation rates. The reservation integration unit can, for example, collect the reservation status of medical institutions in real time to improve the accuracy of reservation integration. The reservation integration unit can also analyze the reservation status of medical institutions in real time to perform optimal reservation integration. The reservation integration unit can also adjust the timing of reservation integration by considering the reservation status of medical institutions in real time. This improves the accuracy of reservation integration by analyzing the reservation status of medical institutions in real time. Some or all of the above processing in the reservation integration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reservation integration unit can input the reservation status data of medical institutions into a generative AI, which can then analyze the data.

[0098] The appointment integration unit estimates the patient's emotions and determines the priority of appointment integration based on the estimated emotions. Prioritization of appointment integration includes, but is not limited to, methods for determining priority based on patient emotions and criteria for urgency. For example, if a patient is feeling anxious, the appointment integration unit may increase the priority of the appointment integration and provide a quicker response. If a patient is relaxed, the appointment integration unit may adjust the priority of the appointment integration and prioritize other patients' appointments. If a patient is feeling stressed, the appointment integration unit may increase the priority of the appointment integration and provide additional support. This enables a quicker response by determining the priority of appointment integration based on patient emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the appointment integration unit may be performed using, for example, generative AI, or without generative AI. For example, the appointment integration unit can input patient emotion data into a generative AI, which can then perform emotion estimation.

[0099] The appointment integration unit integrates appointments based on the patient's frequency of visits. This frequency includes, but is not limited to, the number of visits and the interval between visits. The appointment integration unit can, for example, collect patient visit frequency data and perform optimal appointment integration. It can also analyze patient visit frequency data and perform optimal appointment integration. Furthermore, the appointment integration unit can adjust the timing of appointment integration, taking patient visit frequency into consideration. This allows for optimal appointment integration by considering patient visit frequency. Some or all of the above-described processes in the appointment integration unit may be performed using, for example, a generative AI, or without one. For example, the appointment integration unit can input patient visit frequency data into a generative AI, which can then analyze the data.

[0100] The interface unit estimates the patient's emotions and adjusts the interface display method based on the estimated emotions. The interface display method includes, but is not limited to, methods for adjusting the display method based on the patient's emotions and the timing of the display. For example, if the patient is feeling anxious, the interface unit simplifies the interface display method to improve visibility. If the patient is relaxed, the interface unit can also make the interface display method more detailed to increase the amount of information. If the patient is feeling stressed, the interface unit can also simplify the interface display method to improve visibility. This improves visibility by adjusting the interface display method 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, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the interface unit may be performed using, for example, a generative AI, or without a generative AI. For example, the interface unit can input patient emotion data into a generative AI, which can then perform emotion estimation.

[0101] The interface unit customizes the interface design according to the patient's age group. The interface design includes, but is not limited to, age-appropriate design criteria and customization methods. For example, the interface unit might increase font size and simplify operation for older users. For younger users, it might make the design more colorful and intuitive. For middle-aged users, it might adjust the amount of information and simplify operation. This customization of the interface design according to the patient's age group improves usability. Some or all of the above processing in the interface unit may be performed using, for example, a generative AI, or without one. For example, the interface unit can input patient age group data into a generative AI, which can then customize the design.

[0102] The interface unit estimates the patient's emotions and adjusts the interface's operating procedures based on the estimated emotions. These operating procedures include, but are not limited to, methods for adjusting the procedures based on the patient's emotions and ease of operation. For example, if the patient is feeling anxious, the interface unit may simplify the operating procedures to enable quick operation. If the patient is relaxed, the interface unit may also make the operating procedures more detailed and provide more information. If the patient is stressed, the interface unit may also simplify the operating procedures to enable quick operation. This improves usability by adjusting the interface's operating procedures based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the interface unit may be performed using, for example, a generative AI, or without a generative AI. For example, the interface unit can input patient emotion data into a generative AI, which can then perform emotion estimation.

[0103] The interface unit optimizes the interface based on the patient's device information. This device information includes, but is not limited to, the type of device used and its performance. For example, if the patient is using a smartphone, the interface unit provides an interface optimized for the screen size. If the patient is using a tablet, the interface unit can also provide an interface optimized for a larger screen. If the patient is using a personal computer, the interface unit can also provide an interface that prioritizes usability. This allows for the provision of an optimal interface by considering the patient's device information. Some or all of the above processing in the interface unit may be performed using, for example, a generative AI, or without a generative AI. For example, the interface unit can input the patient's device information into a generative AI, which can then optimize the interface.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The medical assistant agent system can further estimate the patient's emotions and adjust the diagnostic and treatment recommendations based on those emotions. For example, if the patient is feeling anxious, the system can provide a detailed explanation of the diagnosis and treatment to increase the patient's sense of security. If the patient is relaxed, the system can provide a concise explanation and respond quickly. Also, if the patient is feeling stressed, the system can provide additional support to reduce the patient's stress. In this way, by adjusting the diagnostic and treatment recommendations based on the patient's emotions, more appropriate treatment can be provided.

[0106] The cloud integration unit can further estimate the patient's emotions and adjust the timing of cloud data synchronization based on those emotions. For example, if a patient is feeling anxious, the cloud data synchronization timing can be accelerated to enable a quicker response. If a patient is relaxed, the cloud data synchronization timing can be adjusted to prioritize data synchronization for other patients. Also, if a patient is feeling stressed, the cloud data synchronization timing can be accelerated to provide additional support. In this way, adjusting the cloud data synchronization timing based on the patient's emotions enables a quicker response.

[0107] The appointment consolidation unit can further estimate the patient's emotions and adjust the timing of appointment consolidation based on those emotions. For example, if a patient is feeling anxious, the appointment consolidation can be accelerated to allow for a quicker response. If a patient is relaxed, the timing of the appointment consolidation can be adjusted to prioritize other patients' appointments. Also, if a patient is feeling stressed, the appointment consolidation can be accelerated to provide additional support. In this way, by adjusting the timing of appointment consolidation based on the patient's emotions, a quicker response becomes possible.

[0108] The interface unit can further estimate the patient's emotions and adjust the interface display method based on the estimated emotions. For example, if the patient is feeling anxious, the interface display method can be simplified to improve visibility. If the patient is relaxed, the interface display method can be made more detailed to increase the amount of information. Also, if the patient is feeling stressed, the interface display method can be simplified to improve visibility. In this way, visibility is improved by adjusting the interface display method based on the patient's emotions.

[0109] The interface unit can further estimate the patient's emotions and adjust the interface's operating procedures based on those emotions. For example, if the patient is feeling anxious, the operating procedures can be simplified to allow for quick operation. If the patient is relaxed, the operating procedures can be made more detailed to increase the amount of information provided. Similarly, if the patient is feeling stressed, the operating procedures can be simplified to allow for quick operation. In this way, adjusting the interface's operating procedures based on the patient's emotions improves usability.

[0110] The medical assistant agent system can also collect patient lifestyle data and incorporate it into symptom analysis. For example, it can collect and incorporate the patient's sleep patterns, dietary history, and exercise history. By collecting and incorporating patient lifestyle data into symptom analysis, a more accurate diagnosis becomes possible.

[0111] The medical assistant agent system can further analyze symptoms based on the patient's genetic information. For example, it can collect the patient's family history and analyze symptoms while considering genetic risks. It can also collect the patient's genetic test results and incorporate them into the symptom analysis. Furthermore, it can assess the patient's risk of genetic diseases and incorporate that into the symptom analysis. This allows for diagnoses that reflect genetic risks by considering the patient's genetic information.

[0112] The medical assistant agent system can also collect and incorporate patients' geographical environment data into symptom analysis. For example, it can collect and incorporate data on the patient's living environment. It can also collect and incorporate data on the patient's work environment. Furthermore, it can collect and incorporate data on the patient's commute route. By collecting and incorporating patients' geographical environment data into symptom analysis, a more accurate diagnosis becomes possible.

[0113] The medical assistant agent system can further analyze a patient's dietary history and incorporate it into the symptom analysis. For example, it can collect information on the patient's meals and incorporate it into the symptom analysis. It can also collect information on the patient's meal frequency and incorporate it into the symptom analysis. Furthermore, it can collect information on the patient's eating patterns and incorporate them into the symptom analysis. By analyzing the patient's dietary history and incorporating it into the symptom analysis, a more accurate diagnosis becomes possible.

[0114] The medical assistant agent system can further recommend diagnoses and treatments based on the patient's occupational information. For example, it can collect the patient's occupational information and recommend diagnoses and treatments. It can also analyze the patient's occupational information and recommend the most suitable diagnoses and treatments. Furthermore, it can adjust the recommendations for diagnoses and treatments to take the patient's occupational information into consideration. This allows for the provision of occupation-appropriate treatments by considering the patient's occupation.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The analysis department analyzes the patient's symptoms and medical history. Patient symptoms include fever, cough, headache, etc., and medical history includes past diagnoses, treatment history, allergy information, etc. The analysis department records the patient's symptoms in detail and compares them with past medical history to identify the cause of the symptoms. Step 2: The recommendation department recommends appropriate diagnoses and treatments based on the results analyzed by the analysis department. These recommendations include the accuracy of the diagnosis and the effectiveness of the treatment. The recommendation department presents the most suitable diagnostic method for the patient's symptoms and proposes a treatment plan. Furthermore, the recommendation department can use natural language processing (NLP) technology to analyze the patient's symptoms and medical history to recommend the optimal diagnosis and treatment plan. Step 3: The Appointment Proposal Department suggests appointments at medical institutions based on the diagnosis and treatment recommended by the Recommendation Department. These suggestions include appointment priorities and timing. The Appointment Proposal Department proposes the most suitable medical institution to accommodate the patient's needs.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the analysis unit, recommendation unit, reservation proposal unit, cloud integration unit, reservation integration unit, and interface unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 and analyzes the patient's symptoms and medical history. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends the optimal diagnosis and treatment method. The reservation proposal unit is implemented by the control unit 46A of the smart device 14 and proposes reservations at medical institutions. The cloud integration unit is implemented by the specific processing unit 290 of the data processing unit 12 and utilizes a cloud-based medical information system. The reservation integration unit is implemented by the control unit 46A of the smart device 14 and performs real-time reservation system integration. The interface unit is implemented by the control unit 46A of the smart device 14 and provides a user-friendly interface. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the analysis unit, recommendation unit, reservation suggestion unit, cloud integration unit, reservation integration unit, and interface unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214 and analyzes the patient's symptoms and medical history. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends the optimal diagnosis and treatment method. The reservation suggestion unit is implemented by the control unit 46A of the smart glasses 214 and suggests reservations at medical institutions. The cloud integration unit is implemented by the specific processing unit 290 of the data processing unit 12 and utilizes a cloud-based medical information system. The reservation integration unit is implemented by the control unit 46A of the smart glasses 214 and performs real-time reservation system integration. The interface unit is implemented by the control unit 46A of the smart glasses 214 and provides a user-friendly interface. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the analysis unit, recommendation unit, reservation proposal unit, cloud integration unit, reservation integration unit, and interface unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 and analyzes the patient's symptoms and medical history. The recommendation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and recommends the optimal diagnosis and treatment method. The reservation proposal unit is implemented by, for example, the control unit 46A of the headset terminal 314 and proposes reservations at medical institutions. The cloud integration unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and utilizes a cloud-based medical information system. The reservation integration unit is implemented by, for example, the control unit 46A of the headset terminal 314 and performs real-time reservation system integration. The interface unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides a user-friendly interface. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Each of the multiple elements described above, including the analysis unit, recommendation unit, reservation proposal unit, cloud integration unit, reservation integration unit, and interface unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414 and analyzes the patient's symptoms and medical history. The recommendation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and recommends the optimal diagnosis and treatment method. The reservation proposal unit is implemented by, for example, the control unit 46A of the robot 414 and proposes reservations at medical institutions. The cloud integration unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and utilizes a cloud-based medical information system. The reservation integration unit is implemented by, for example, the control unit 46A of the robot 414 and performs real-time reservation system integration. The interface unit is implemented by, for example, the control unit 46A of the robot 414 and provides a user-friendly interface. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] (Note 1) The analysis department analyzes patients' symptoms and medical history, Based on the results of the analysis conducted by the aforementioned analysis department, a recommendation department recommends appropriate diagnoses and treatments. The system includes a reservation suggestion unit that proposes appointments at medical institutions based on diagnoses and treatments recommended by the aforementioned recommendation unit. A system characterized by the following features. (Note 2) It has a cloud integration department that utilizes cloud-based medical information systems. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a reservation integration unit that performs real-time reservation system integration. The system described in Appendix 1, characterized by the features described herein. (Note 4) It features an interface section that provides an easy-to-use interface. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is Perform symptom analysis using deep learning. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned recommendation department, Processing patient information using NLP The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is The system estimates the patient's emotions and adjusts the accuracy of the symptom analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is Collect lifestyle data from patients and use it to analyze their symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is Symptom analysis is performed based on the patient's genetic information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is The system estimates the patient's emotions and prioritizes symptom analysis based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is Collect geographical and environmental data of patients and incorporate it into symptom analysis. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is Analyze the patient's dietary history and incorporate it into the symptom analysis. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned recommendation department, The system estimates the patient's emotions and adjusts diagnostic and treatment recommendations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned recommendation department, By referring to past treatment outcome data, we improve the accuracy of diagnosis and treatment recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned recommendation department, Based on the patient's allergy information, we recommend a diagnosis and treatment plan. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned recommendation department, The system estimates the patient's emotions and prioritizes diagnosis and treatment based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned recommendation department, Recommend diagnoses and treatments based on the patient's occupational information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned recommendation department, We analyze the patient's exercise history and recommend a diagnosis and treatment plan. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned reservation proposal unit, The system estimates the patient's emotions and adjusts the timing of appointment suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reservation proposal unit, Analyze the patient's past appointment history and select the most suitable appointment suggestion method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reservation proposal unit, Appointment suggestions are made based on the patient's medical history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reservation proposal unit, The system estimates the patient's emotions and prioritizes appointment suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reservation proposal unit, We make appointment suggestions based on the patient's transportation information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reservation proposal unit, Appointment suggestions are made based on the patient's family structure information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned cloud integration unit is It estimates the patient's emotions and adjusts the timing of cloud-based data synchronization based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned cloud integration unit is Analyze the data update frequency of medical institutions and optimize cloud integration. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned cloud integration unit is The system estimates the patient's emotions and determines the data priority for cloud integration based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned cloud integration unit is Cloud integration based on the geographical distribution of medical institutions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reservation integration unit, The system estimates the patient's emotions and adjusts the timing of appointment consolidation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned reservation integration unit, We analyze the appointment status of medical institutions in real time to improve the accuracy of appointment consolidation. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned reservation integration unit, The system estimates the patient's emotions and determines the priority of appointment consolidation based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned reservation integration unit, Appointments are consolidated based on the patient's frequency of visits. The system described in Appendix 1, characterized by the features described herein. (Note 33) The interface unit is The system estimates the patient's emotions and adjusts the interface display based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The interface unit is Customize the interface design according to the patient's age group. The system described in Appendix 1, characterized by the features described herein. (Note 35) The interface unit is The system estimates the patient's emotions and adjusts the interface's operation procedures based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The interface unit is Optimize the interface based on the patient's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0189] 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. The analysis department analyzes patients' symptoms and medical history, Based on the results of the analysis conducted by the aforementioned analysis department, a recommendation department recommends appropriate diagnoses and treatments. The system includes a reservation suggestion unit that proposes appointments at medical institutions based on diagnoses and treatments recommended by the aforementioned recommendation unit. A system characterized by the following features.

2. It has a cloud integration department that utilizes cloud-based medical information systems. The system according to feature 1.

3. It includes a reservation integration unit that performs real-time reservation system integration. The system according to feature 1.

4. It features an interface section that provides an easy-to-use interface. The system according to feature 1.

5. The aforementioned analysis unit is Perform symptom analysis using deep learning. The system according to feature 1.

6. The aforementioned recommendation department, Processing patient information using NLP The system according to feature 1.

7. The aforementioned analysis unit is The system estimates the patient's emotions and adjusts the accuracy of the symptom analysis based on the estimated emotions. The system according to feature 1.

8. The aforementioned analysis unit is Collect lifestyle data from patients and use it to analyze their symptoms. The system according to feature 1.

9. The aforementioned analysis unit is Symptom analysis is performed based on the patient's genetic information. The system according to feature 1.

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