system

Abstract

The system according to the embodiment aims to introduce a patient to an appropriate hospital based on the patient's symptoms and make an appointment. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a prediction unit, a referral unit, and a reservation unit. The reception unit receives input of symptoms. The analysis unit analyzes the information received by the reception unit. The prediction unit predicts a disease based on the information analyzed by the analysis unit. The referral unit refers a patient to a hospital based on the disease predicted by the prediction unit. The reservation unit makes a reservation at the hospital referred to by the referral unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the process of finding an appropriate hospital based on symptoms and making an appointment is cumbersome, and there is room for improvement.

[0005] The system according to the embodiment aims to introduce a patient to an appropriate hospital based on the patient's symptoms and make an appointment. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a prediction unit, a referral unit, and a reservation unit. The reception unit receives input of symptoms. The analysis unit analyzes the information received by the reception unit. The prediction unit predicts a disease based on the information analyzed by the analysis unit. The referral unit refers a patient to a hospital based on the disease predicted by the prediction unit. The reservation unit makes a reservation at the hospital referred by the referral unit. [Effects of the Invention]

[0007] The system according to the embodiment can introduce a patient to an appropriate hospital based on the patient's symptoms and make an appointment. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A medical consultation system according to an embodiment of the present invention allows users to easily consult about any concerns, even minor illnesses. This medical consultation system uses a generative AI to perform predictions, hospital searches, and even reservations. Customers upload their symptoms, illness details, and detailed photos of their concerns. The generative AI identifies possible illnesses. Based on the diagnosis, the app recommends nearby hospitals and specialists that can treat the illness and even assists with reservations. Hospital ratings are also displayed, allowing customers to select a hospital. For example, a customer opens the app and inputs the symptoms and illness they are concerned about. For example, they can input specific symptoms such as "persistent headache" or "red spots on the skin." They can also upload photos of the symptoms, if necessary. This information is sent to the generative AI. The generative AI then analyzes the input information and predicts possible illnesses. Based on past medical data and case studies, the generative AI determines the most likely illness from the input symptoms and photos. For example, for a symptom of "persistent headache," the generative AI suggests possibilities such as "migraine" or "tension headache." Based on the generative AI's prediction, the app then recommends nearby hospitals and specialists that can treat the illness. The app also displays hospital ratings to help customers choose a hospital. For example, in the case of "migraine," hospitals with neurology and headache specialist clinics will be displayed. Finally, the customer makes a reservation at the hospital they selected through the app. The app connects with the hospital's reservation system to check availability and confirm the reservation. This allows customers to make hospital reservations without any hassle. This service allows customers to easily consult about their health problems and quickly visit an appropriate medical institution. In addition, the generative AI's predictive judgment allows customers to receive appropriate treatment early, making health management more efficient. As a result, the medical consultation system allows customers to easily consult about their health problems and quickly visit an appropriate medical institution.

[0029] A medical consultation system according to an embodiment includes a reception unit, an analysis unit, a prediction unit, a referral unit, and a reservation unit. The reception unit receives a customer's symptoms, illness details, and specific photos of the illness. Symptoms input by the customer include, but are not limited to, headache, fever, and skin abnormalities. The reception unit provides an interface for the customer to open an app and input their symptoms. The reception unit also includes a function for the customer to upload photos of their symptoms. For example, the customer can take photos of their symptoms using a smartphone camera and upload them to the app. The analysis unit analyzes the information received by the reception unit using generative AI. The analysis unit analyzes the input symptoms and photos based on, for example, past medical data and case studies. For example, the analysis unit references a past medical database to search for cases matching the input symptoms. The analysis unit can also analyze the uploaded photos using image analysis technology. For example, the analysis unit analyzes photos of skin abnormalities to identify the type of abnormality. The prediction unit predicts the most likely disease based on the information analyzed by the analysis unit. The prediction unit predicts diseases from the analyzed information, for example, using generative AI. For example, the prediction unit suggests the possibility of migraine, tension headache, etc. based on the input symptoms. The prediction unit can also identify the type of skin abnormality based on the analyzed photo. For example, the prediction unit analyzes a photo of a skin abnormality and suggests the possibility of eczema, allergic reaction, etc. The referral unit introduces hospitals and departments near the customer's home that can treat the disease based on the disease predicted by the prediction unit. For example, the referral unit displays hospital rating information to help the customer select a hospital. For example, the referral unit displays hospitals with neurology or headache specialty outpatient clinics. The referral unit can also link with hospital reservation systems to check availability and make reservations. The reservation unit makes reservations at hospitals introduced by the referral unit. For example, the reservation unit link with hospital reservation systems to check availability and confirm the reservation. For example, the reservation unit accesses the reservation system of the hospital selected by the customer, checks availability, and makes the reservation.As a result, the medical consultation system according to the embodiment allows customers to easily consult about poor health and quickly visit an appropriate medical institution. Some or all of the above-described processes in the reception unit, analysis unit, prediction unit, referral unit, and reservation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit sends symptoms entered by the customer to the generation AI, and the analysis unit analyzes the symptoms using the generation AI. The prediction unit predicts illness using the generation AI, and the referral unit refers the customer to a hospital using the generation AI. The reservation unit can make a hospital reservation using the generation AI.

[0030] The reception unit can analyze the customer's past symptom input history and provide an appropriate input interface. For example, the reception unit can automatically display symptoms that the customer has frequently input in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the customer has used in the past. For example, the reception unit can predict and suggest symptoms to be used during a specific time period based on the customer's past input history. This improves input efficiency by providing an optimal interface based on the past input history. Some or all of the above-mentioned processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the customer's past input data into the generation AI and have the generation AI suggest an optimal input interface.

[0031] When inputting symptoms, the reception unit can filter the input content based on the customer's current health condition and lifestyle habits. For example, the reception unit can prioritize input of related symptoms based on the customer's current health condition. The reception unit can also prioritize input of related symptoms taking into account the customer's lifestyle habits (smoking, drinking, etc.). For example, the reception unit can prioritize input of related symptoms based on the customer's medical history. This allows more accurate information to be obtained by providing input content that is appropriate for the customer's health condition and lifestyle habits. Some or all of the above-mentioned processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the customer's health condition data into the generation AI and have the generation AI filter the input content.

[0032] When inputting symptoms, the reception unit can prioritize input of highly relevant symptoms based on the customer's geographical location information. For example, the reception unit can prioritize input of symptoms related to diseases specific to the region based on the customer's current location. The reception unit can also prioritize input of symptoms related to infectious diseases at the customer's travel destination based on information about the customer's travel destination. For example, the reception unit can prioritize input of symptoms related to environmental factors based on environmental information about the customer's place of residence. This makes it possible to respond to diseases specific to the region by providing input content based on the geographical location information. Some or all of the above-mentioned processing in the reception unit can be performed using or without the generation AI. For example, the reception unit can input the customer's geographical location information to the generation AI and cause the generation AI to suggest highly relevant symptoms.

[0033] When entering symptoms, the reception unit can analyze the customer's social media activity and input related symptoms. For example, the reception unit can analyze the customer's social media posts and input symptoms related to the posts. The reception unit can also input related symptoms based on the customer's health-related search history on social media. For example, the reception unit can analyze posts from the customer's followers and friends on social media and input related symptoms. This allows for more relevant information to be obtained by providing input content based on social media activity. Some or all of the above-described processing by the reception unit can be performed using or without the generation AI. For example, the reception unit can input the customer's social media data into the generation AI and cause the generation AI to suggest related symptoms.

[0034] During analysis, the analysis unit can adjust the analysis algorithm by referring to past medical data and cases. The analysis unit, for example, optimizes the analysis algorithm based on past medical data. The analysis unit can also optimize the analysis algorithm based on past case data. For example, the analysis unit optimizes the analysis algorithm by referring to a medical database. By optimizing the analysis algorithm based on past data, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input past medical data into the generation AI and have the generation AI optimize the analysis algorithm.

[0035] During analysis, the analysis unit can apply different analysis methods to different symptom categories. For example, the analysis unit applies a specific analysis method to respiratory symptoms. The analysis unit can also apply a different analysis method to digestive symptoms. For example, the analysis unit applies an even different analysis method to skin symptoms. This improves the accuracy of analysis by applying an analysis method according to the symptom category. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input symptom category data into the generation AI and cause the generation AI to apply different analysis methods.

[0036] During analysis, the analysis unit can determine the priority of analysis based on the time of symptom submission. For example, the analysis unit prioritizes analysis of recently submitted symptoms. The analysis unit can also postpone analysis of symptoms submitted earlier. For example, the analysis unit dynamically adjusts the priority of analysis based on the time of submission. This enables rapid analysis by setting the priority based on the time of submission. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the time of symptom submission into the generation AI and have the generation AI determine the priority of analysis.

[0037] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the symptoms. For example, the analysis unit performs the analysis by referring to the latest medical literature related to the symptoms. The analysis unit can also perform the analysis by referring to past research papers related to the symptoms. For example, the analysis unit performs the analysis by referring to specialized books related to the symptoms. By doing so, the accuracy of the analysis is improved by referring to the related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input literature data related to the symptoms into the generation AI and have the generation AI improve the accuracy of the analysis.

[0038] The prediction unit can adjust the level of detail of the prediction based on the importance of the analyzed information during prediction. For example, the prediction unit makes a detailed prediction based on information with high importance. The prediction unit can also make a concise prediction based on information with low importance. For example, the prediction unit dynamically adjusts the level of detail of the prediction according to the importance. This allows for a more appropriate prediction result to be obtained by providing a prediction level of detail according to the importance of the information. Some or all of the above-described processing in the prediction unit may be performed using or without the generation AI. For example, the prediction unit can input importance data of the analyzed information into the generation AI and cause the generation AI to adjust the level of detail of the prediction.

[0039] The prediction unit can apply different prediction algorithms depending on the disease category when making a prediction. For example, the prediction unit applies a specific prediction algorithm to respiratory system diseases. The prediction unit can also apply a different prediction algorithm to digestive system diseases. For example, the prediction unit applies an even different prediction algorithm to skin diseases. This improves prediction accuracy by applying a prediction algorithm depending on the disease category. Some or all of the above-mentioned processing in the prediction unit may be performed using or without the generation AI. For example, the prediction unit can input disease category data into the generation AI and cause the generation AI to apply different prediction algorithms.

[0040] During prediction, the prediction unit can determine the priority of predictions based on the submission time of the analyzed information. For example, the prediction unit prioritizes predictions for recently submitted information. The prediction unit can also postpone information submitted earlier. For example, the prediction unit dynamically adjusts the priority of predictions based on the submission time. This enables rapid predictions by setting priorities based on the submission time. Some or all of the above-described processing in the prediction unit may be performed using or without the generation AI. For example, the prediction unit can input submission time data for the analyzed information into the generation AI and have the generation AI determine the priority of predictions.

[0041] The prediction unit can adjust the order of prediction results based on the relevance of the analyzed information during prediction. For example, the prediction unit prioritizes predicting highly relevant analyzed information. The prediction unit can also postpone less relevant analyzed information. For example, the prediction unit dynamically adjusts the order of prediction results based on the relevance of the analyzed information. This allows for more appropriate prediction results to be obtained by providing an order of prediction results based on the relevance of the information. Some or all of the above-described processing in the prediction unit may be performed using or without the generation AI. For example, the prediction unit can input relevance data of the analyzed information into the generation AI and cause the generation AI to adjust the order of the prediction results.

[0042] The referral unit can adjust the level of detail of the referral based on the predicted importance of the disease at the time of referral. For example, the referral unit provides detailed hospital information for a disease with a high importance. The referral unit can also provide concise hospital information for a disease with a low importance. For example, the referral unit dynamically adjusts the level of detail of the referral according to the importance. This enables more appropriate hospital referral by providing detailed referrals according to the importance of the disease. Some or all of the above-mentioned processing in the referral unit may be performed using or without the generation AI. For example, the referral unit can input predicted disease importance data into the generation AI and cause the generation AI to adjust the level of detail of the referral.

[0043] The referral unit can apply different referral algorithms depending on the hospital category when making a referral. For example, the referral unit applies a specific referral algorithm to a general hospital. The referral unit can also apply a different referral algorithm to a specialized hospital. For example, the referral unit applies an even different referral algorithm to a clinic. This improves referral accuracy by applying a referral algorithm depending on the hospital category. Some or all of the above-mentioned processing in the referral unit may be performed using or without the generation AI. For example, the referral unit can input hospital category data into the generation AI and cause the generation AI to apply different referral algorithms.

[0044] At the time of referral, the referral unit can introduce an appropriate hospital based on the geographical distribution of hospitals. For example, the referral unit can introduce the hospital closest to the customer's current location. The referral unit can also introduce hospitals along the customer's commute route. For example, the referral unit can introduce hospitals that are easily accessible from the customer's home. In this way, by providing hospital referrals based on geographical distribution, the optimal hospital can be introduced for the customer. Some or all of the above-described processing in the referral unit may be performed using or without the generation AI. For example, the referral unit can input customer geographical distribution data into the generation AI and have the generation AI perform the referral of an appropriate hospital.

[0045] The referral unit can improve the accuracy of referrals by referring to literature related to the hospital when making a referral. For example, the referral unit makes the referral by referring to the latest medical literature related to the hospital. The referral unit can also make the referral by referring to past research papers related to the hospital. For example, the referral unit makes the referral by referring to specialized books related to the hospital. By doing so, the referral accuracy is improved by referring to the related literature. Some or all of the above-described processing in the referral unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the referral unit can input literature data related to the hospital into the generation AI and cause the generation AI to improve the accuracy of the referral.

[0046] When making a reservation, the reservation unit can select an appropriate reservation method by referring to past data of the hospital's reservation system. The reservation unit, for example, can suggest an optimal reservation time based on past reservation data. The reservation unit can also suggest a reservation method that avoids congestion based on past reservation data. For example, the reservation unit can suggest the most efficient reservation method based on past reservation data. This improves reservation efficiency by providing the optimal reservation method based on past data. Some or all of the above-mentioned processing in the reservation unit may be performed using or without using the generation AI. For example, the reservation unit can input past data from the hospital's reservation system into the generation AI and have the generation AI select an appropriate reservation method.

[0047] The reservation unit can apply different reservation methods to different hospital categories when making reservations. For example, the reservation unit applies a specific reservation method to general hospitals. The reservation unit can also apply a different reservation method to specialized hospitals. For example, the reservation unit applies an even different reservation method to clinics. This improves reservation accuracy by applying a reservation method according to the hospital category. Some or all of the above-mentioned processing in the reservation unit may be performed using or without the generation AI. For example, the reservation unit can input hospital category data into the generation AI and have the generation AI apply different reservation methods.

[0048] When making a reservation, the reservation unit can select an appropriate reservation method based on the geographic distribution of hospitals. For example, the reservation unit may preferentially suggest reservations at hospitals closest to the customer's current location. The reservation unit can also suggest reservations at hospitals along the customer's commute route. For example, the reservation unit may suggest reservations at hospitals that are easily accessible from the customer's home. By providing a reservation method based on geographic distribution, it is possible to make the optimal reservation for the customer. Some or all of the above-described processing in the reservation unit may be performed using or without the generation AI. For example, the reservation unit may input hospital geographic distribution data into the generation AI and have the generation AI select an appropriate reservation method.

[0049] The reservation unit can improve the accuracy of reservations by referring to literature related to the hospital when making a reservation. The reservation unit, for example, makes a reservation by referring to the latest medical literature related to the hospital. The reservation unit can also make a reservation by referring to past research papers related to the hospital. For example, the reservation unit makes a reservation by referring to specialized books related to the hospital. By referring to related literature, reservation accuracy is improved. Some or all of the above-mentioned processing in the reservation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reservation unit can input literature data related to the hospital into the generation AI and have the generation AI improve reservation accuracy.

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

[0051] The reception unit can monitor the customer's health condition and automatically prompt the customer to enter symptoms if an abnormality is detected. For example, it can collect data such as heart rate, blood pressure, and body temperature from wearable devices such as smartwatches and fitness trackers, and send a notification via the app if an abnormal value is detected. The reception unit can also prioritize the input of related symptoms based on the customer's health condition. For example, if the customer's heart rate is abnormally high, the reception unit will prompt the customer to enter heart-related symptoms. This allows the customer to enter appropriate symptoms according to their health condition.

[0052] The prediction unit can improve the accuracy of disease predictions based on the customer's lifestyle data. For example, it collects data on the customer's diet, exercise, sleep, etc. and incorporates this data into the analysis. The prediction unit can also assess the risk of specific diseases based on the customer's lifestyle and reflect this in the prediction results. For example, a customer who has a smoking habit may be assessed as having a higher risk of lung disease. This makes it possible to make more accurate disease predictions using lifestyle data.

[0053] The reservation department can analyze a customer's past reservation history and suggest the optimal reservation method. For example, it can prioritize reservation methods (online, telephone, etc.) that have been frequently used in the past. The reservation department can also analyze past reservation history to see if reservations tend to be concentrated during certain time periods or days of the week, and suggest the optimal reservation time. Furthermore, it can also suggest reservation methods that avoid overcrowding based on past reservation history. This makes it possible to make efficient reservations by utilizing past data.

[0054] During analysis, the analysis unit can apply different analysis methods to different symptom categories. For example, a specific analysis method can be applied to respiratory symptoms. Another analysis method can be applied to digestive symptoms. Furthermore, a different analysis method can be applied to skin symptoms. In this way, analysis accuracy can be improved by applying an analysis method according to the symptom category.

[0055] When making a referral, the referral department can introduce an appropriate hospital based on the geographical distribution of hospitals. For example, it can introduce the hospital closest to the customer's current location. It can also introduce hospitals along the customer's commute route. It can also introduce hospitals that are easily accessible from the customer's home. In this way, by providing hospital referrals based on geographical distribution, it is possible to introduce the optimal hospital for the customer.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The reception desk accepts the symptoms of the customer's health condition, the details of the illness, and specific photos. Symptoms that customers enter include, for example, headache, fever, and skin abnormalities. The reception desk provides an interface for customers to open the app and enter their symptoms, and also has a function for uploading photos of the symptoms. For example, customers can use their smartphone camera to take photos of their symptoms and upload them to the app. Step 2: The analysis unit uses the generative AI to analyze the information received by the reception unit. The analysis unit analyzes the input symptoms and photos based on past medical data and cases. For example, it can refer to a past medical database to search for cases that match the input symptoms, and it can also analyze the uploaded photos using image analysis technology. For example, it can analyze photos of skin abnormalities and identify the type of abnormality. Step 3: The prediction unit predicts the most likely disease based on the information analyzed by the analysis unit. The prediction unit uses generative AI to predict diseases from the analyzed information. For example, based on the input symptoms, it can present the possibility of migraine or tension headache, and based on the analyzed photo, it can also identify the type of skin abnormality. For example, it can analyze a photo of a skin abnormality and present the possibility of eczema or an allergic reaction. Step 4: The referral unit introduces nearby hospitals and departments that can treat the illness based on the disease predicted by the prediction unit. The referral unit displays hospital ratings to help customers choose a hospital. For example, it can display hospitals with neurology or headache specialist clinics, and link with the hospital's reservation system to check availability and make a reservation. Step 5: The reservation department makes a reservation at the hospital referred by the referral department. The reservation department connects with the hospital's reservation system, checks availability, and confirms the reservation. For example, it accesses the reservation system of the hospital selected by the customer, checks availability, and makes the reservation.

[0058] (Example 2) A medical consultation system according to an embodiment of the present invention allows users to easily consult about any concerns, even minor illnesses. This medical consultation system uses a generative AI to perform predictions, hospital searches, and even reservations. Customers upload their symptoms, illness details, and detailed photos of their concerns. The generative AI identifies possible illnesses. Based on the diagnosis, the app recommends nearby hospitals and specialists that can treat the illness and even assists with reservations. Hospital ratings are also displayed, allowing customers to select a hospital. For example, a customer opens the app and inputs the symptoms and illness they are concerned about. For example, they can input specific symptoms such as "persistent headache" or "red spots on the skin." They can also upload photos of the symptoms, if necessary. This information is sent to the generative AI. The generative AI then analyzes the input information and predicts possible illnesses. Based on past medical data and case studies, the generative AI determines the most likely illness from the input symptoms and photos. For example, for a symptom of "persistent headache," the generative AI suggests possibilities such as "migraine" or "tension headache." Based on the generative AI's prediction, the app then recommends nearby hospitals and specialists that can treat the illness. The app also displays hospital ratings to help customers choose a hospital. For example, in the case of "migraine," hospitals with neurology and headache specialist clinics will be displayed. Finally, the customer makes a reservation at the hospital they selected through the app. The app connects with the hospital's reservation system to check availability and confirm the reservation. This allows customers to make hospital reservations without any hassle. This service allows customers to easily consult about their health problems and quickly visit an appropriate medical institution. In addition, the generative AI's predictive judgment allows customers to receive appropriate treatment early, making health management more efficient. As a result, the medical consultation system allows customers to easily consult about their health problems and quickly visit an appropriate medical institution.

[0059] A medical consultation system according to an embodiment includes a reception unit, an analysis unit, a prediction unit, a referral unit, and a reservation unit. The reception unit receives a customer's symptoms, illness details, and specific photos of the illness. Symptoms input by the customer include, but are not limited to, headache, fever, and skin abnormalities. The reception unit provides an interface for the customer to open an app and input their symptoms. The reception unit also includes a function for the customer to upload photos of their symptoms. For example, the customer can take photos of their symptoms using a smartphone camera and upload them to the app. The analysis unit analyzes the information received by the reception unit using generative AI. The analysis unit analyzes the input symptoms and photos based on, for example, past medical data and case studies. For example, the analysis unit references a past medical database to search for cases matching the input symptoms. The analysis unit can also analyze the uploaded photos using image analysis technology. For example, the analysis unit analyzes photos of skin abnormalities to identify the type of abnormality. The prediction unit predicts the most likely disease based on the information analyzed by the analysis unit. The prediction unit predicts diseases from the analyzed information, for example, using generative AI. For example, the prediction unit suggests the possibility of migraine, tension headache, etc. based on the input symptoms. The prediction unit can also identify the type of skin abnormality based on the analyzed photo. For example, the prediction unit analyzes a photo of a skin abnormality and suggests the possibility of eczema, allergic reaction, etc. The referral unit introduces hospitals and departments near the customer's home that can treat the disease based on the disease predicted by the prediction unit. For example, the referral unit displays hospital rating information to help the customer select a hospital. For example, the referral unit displays hospitals with neurology or headache specialty outpatient clinics. The referral unit can also link with hospital reservation systems to check availability and make reservations. The reservation unit makes reservations at hospitals introduced by the referral unit. For example, the reservation unit link with hospital reservation systems to check availability and confirm the reservation. For example, the reservation unit accesses the reservation system of the hospital selected by the customer, checks availability, and makes the reservation.As a result, the medical consultation system according to the embodiment allows customers to easily consult about poor health and quickly visit an appropriate medical institution. Some or all of the above-described processes in the reception unit, analysis unit, prediction unit, referral unit, and reservation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit sends symptoms entered by the customer to the generation AI, and the analysis unit analyzes the symptoms using the generation AI. The prediction unit predicts illness using the generation AI, and the referral unit refers the customer to a hospital using the generation AI. The reservation unit can make a hospital reservation using the generation AI.

[0060] The reception unit can estimate the customer's emotions and adjust the symptom input method based on the estimated customer emotions. For example, if the customer is feeling anxious, the reception unit can provide a simple and intuitive interface and minimize input steps. Furthermore, if the customer is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. For example, if the customer is in a hurry, the reception unit can prioritize voice input to allow the customer to quickly input symptoms. This allows for more appropriate symptom input by providing an input method that suits the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 reception unit can be performed using the generation AI, or without the generation AI. For example, the reception unit can input the customer's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0061] The reception unit can analyze the customer's past symptom input history and provide an appropriate input interface. For example, the reception unit can automatically display symptoms that the customer has frequently input in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the customer has used in the past. For example, the reception unit can predict and suggest symptoms to be used during a specific time period based on the customer's past input history. This improves input efficiency by providing an optimal interface based on the past input history. Some or all of the above-mentioned processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the customer's past input data into the generation AI and have the generation AI suggest an optimal input interface.

[0062] When inputting symptoms, the reception unit can filter the input content based on the customer's current health condition and lifestyle habits. For example, the reception unit can prioritize input of related symptoms based on the customer's current health condition. The reception unit can also prioritize input of related symptoms taking into account the customer's lifestyle habits (smoking, drinking, etc.). For example, the reception unit can prioritize input of related symptoms based on the customer's medical history. This allows more accurate information to be obtained by providing input content that is appropriate for the customer's health condition and lifestyle habits. Some or all of the above-mentioned processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the customer's health condition data into the generation AI and have the generation AI filter the input content.

[0063] The reception unit can estimate the customer's emotions and prioritize the input symptoms based on the estimated customer emotions. For example, if the customer is feeling anxious, the reception unit can prioritize inputting serious symptoms. Furthermore, if the customer is relaxed, the reception unit can also prioritize inputting detailed symptoms. For example, if the customer is in a hurry, the reception unit can prioritize inputting major symptoms. This allows for priority setting based on the customer's emotions, allowing for priority input of important symptoms. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can 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 reception unit can be performed using the generation AI, or can be performed without the generation AI. For example, the reception unit can input customer facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0064] When inputting symptoms, the reception unit can prioritize input of highly relevant symptoms based on the customer's geographical location information. For example, the reception unit can prioritize input of symptoms related to diseases specific to the region based on the customer's current location. The reception unit can also prioritize input of symptoms related to infectious diseases at the customer's travel destination based on information about the customer's travel destination. For example, the reception unit can prioritize input of symptoms related to environmental factors based on environmental information about the customer's place of residence. This makes it possible to respond to diseases specific to the region by providing input content based on the geographical location information. Some or all of the above-mentioned processing in the reception unit can be performed using or without the generation AI. For example, the reception unit can input the customer's geographical location information to the generation AI and cause the generation AI to suggest highly relevant symptoms.

[0065] When entering symptoms, the reception unit can analyze the customer's social media activity and input related symptoms. For example, the reception unit can analyze the customer's social media posts and input symptoms related to the posts. The reception unit can also input related symptoms based on the customer's health-related search history on social media. For example, the reception unit can analyze posts from the customer's followers and friends on social media and input related symptoms. This allows for more relevant information to be obtained by providing input content based on social media activity. Some or all of the above-described processing by the reception unit can be performed using or without the generation AI. For example, the reception unit can input the customer's social media data into the generation AI and cause the generation AI to suggest related symptoms.

[0066] The analysis unit can estimate the customer's emotions and adjust the accuracy of the analysis based on the estimated customer emotions. For example, if the customer is feeling anxious, the analysis unit can increase the accuracy of the analysis to provide detailed results. The analysis unit can also adjust the accuracy of the analysis to provide concise results if the customer is relaxed. For example, if the customer is in a hurry, the analysis unit can adjust the accuracy of the analysis to provide quick results. This provides analysis accuracy according to the customer's emotions, resulting in more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 analysis unit can be performed using the generation AI, or can be performed without the generation AI. For example, the analysis unit can input customer facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0067] During analysis, the analysis unit can adjust the analysis algorithm by referring to past medical data and cases. The analysis unit, for example, optimizes the analysis algorithm based on past medical data. The analysis unit can also optimize the analysis algorithm based on past case data. For example, the analysis unit optimizes the analysis algorithm by referring to a medical database. By optimizing the analysis algorithm based on past data, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input past medical data into the generation AI and have the generation AI optimize the analysis algorithm.

[0068] During analysis, the analysis unit can apply different analysis methods to different symptom categories. For example, the analysis unit applies a specific analysis method to respiratory symptoms. The analysis unit can also apply a different analysis method to digestive symptoms. For example, the analysis unit applies an even different analysis method to skin symptoms. This improves the accuracy of analysis by applying an analysis method according to the symptom category. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input symptom category data into the generation AI and cause the generation AI to apply different analysis methods.

[0069] The analysis unit can estimate the customer's emotions and adjust the display method of the analysis results based on the estimated customer emotions. For example, if the customer is feeling anxious, the analysis unit can display detailed analysis results. Furthermore, if the customer is relaxed, the analysis unit can display concise analysis results. For example, if the customer is in a hurry, the analysis unit can display analysis results that focus on the main points. This provides a display method that corresponds to the customer's emotions, resulting in analysis results that are easier to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can 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 analysis unit can be performed using the generation AI, or can be performed without the generation AI. For example, the analysis unit can input customer facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0070] During analysis, the analysis unit can determine the priority of analysis based on the time of symptom submission. For example, the analysis unit prioritizes analysis of recently submitted symptoms. The analysis unit can also postpone analysis of symptoms submitted earlier. For example, the analysis unit dynamically adjusts the priority of analysis based on the time of submission. This enables rapid analysis by setting the priority based on the time of submission. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the time of symptom submission into the generation AI and have the generation AI determine the priority of analysis.

[0071] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the symptoms. For example, the analysis unit performs the analysis by referring to the latest medical literature related to the symptoms. The analysis unit can also perform the analysis by referring to past research papers related to the symptoms. For example, the analysis unit performs the analysis by referring to specialized books related to the symptoms. By doing so, the accuracy of the analysis is improved by referring to the related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input literature data related to the symptoms into the generation AI and have the generation AI improve the accuracy of the analysis.

[0072] The prediction unit can estimate the customer's emotions and adjust the way the prediction result is expressed based on the estimated customer emotions. For example, if the customer is feeling anxious, the prediction unit can provide a detailed prediction result. Furthermore, if the customer is relaxed, the prediction unit can provide a concise prediction result. For example, if the customer is in a hurry, the prediction unit can provide a prediction result that focuses on the main points. This allows for a more understandable prediction result by providing an expression method that matches the customer's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 prediction unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the prediction unit can input customer facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0073] The prediction unit can adjust the level of detail of the prediction based on the importance of the analyzed information during prediction. For example, the prediction unit makes a detailed prediction based on information with high importance. The prediction unit can also make a concise prediction based on information with low importance. For example, the prediction unit dynamically adjusts the level of detail of the prediction according to the importance. This allows for a more appropriate prediction result to be obtained by providing a prediction level of detail according to the importance of the information. Some or all of the above-described processing in the prediction unit may be performed using or without the generation AI. For example, the prediction unit can input importance data of the analyzed information into the generation AI and cause the generation AI to adjust the level of detail of the prediction.

[0074] The prediction unit can apply different prediction algorithms depending on the disease category when making a prediction. For example, the prediction unit applies a specific prediction algorithm to respiratory system diseases. The prediction unit can also apply a different prediction algorithm to digestive system diseases. For example, the prediction unit applies an even different prediction algorithm to skin diseases. This improves prediction accuracy by applying a prediction algorithm depending on the disease category. Some or all of the above-mentioned processing in the prediction unit may be performed using or without the generation AI. For example, the prediction unit can input disease category data into the generation AI and cause the generation AI to apply different prediction algorithms.

[0075] The prediction unit can estimate the customer's emotions and adjust the length of the prediction result based on the estimated customer emotions. For example, if the customer is feeling anxious, the prediction unit can provide a detailed prediction result. Furthermore, if the customer is relaxed, the prediction unit can provide a concise prediction result. For example, if the customer is in a hurry, the prediction unit can provide a prediction result that focuses on the main points. This provides a prediction result that is easier to understand by providing a length of the prediction result according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 prediction unit can be performed using the generation AI, or can be performed without the generation AI. For example, the prediction unit can input customer facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0076] During prediction, the prediction unit can determine the priority of predictions based on the submission time of the analyzed information. For example, the prediction unit prioritizes predictions for recently submitted information. The prediction unit can also postpone information submitted earlier. For example, the prediction unit dynamically adjusts the priority of predictions based on the submission time. This enables rapid predictions by setting priorities based on the submission time. Some or all of the above-described processing in the prediction unit may be performed using or without the generation AI. For example, the prediction unit can input submission time data for the analyzed information into the generation AI and have the generation AI determine the priority of predictions.

[0077] The prediction unit can adjust the order of prediction results based on the relevance of the analyzed information during prediction. For example, the prediction unit prioritizes predicting highly relevant analyzed information. The prediction unit can also postpone less relevant analyzed information. For example, the prediction unit dynamically adjusts the order of prediction results based on the relevance of the analyzed information. This allows for more appropriate prediction results to be obtained by providing an order of prediction results based on the relevance of the information. Some or all of the above-described processing in the prediction unit may be performed using or without the generation AI. For example, the prediction unit can input relevance data of the analyzed information into the generation AI and cause the generation AI to adjust the order of the prediction results.

[0078] The referral unit can estimate the customer's emotions and adjust the hospital referral method based on the estimated customer emotions. For example, if the customer is feeling anxious, the referral unit can provide detailed hospital information. Furthermore, if the customer is relaxed, the referral unit can provide concise hospital information. For example, if the customer is in a hurry, the referral unit can provide hospital information that focuses on the key points. This allows for a more appropriate hospital referral method based on the customer's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the referral unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the referral unit can input customer facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0079] The referral unit can adjust the level of detail of the referral based on the predicted importance of the disease at the time of referral. For example, the referral unit provides detailed hospital information for a disease with a high importance. The referral unit can also provide concise hospital information for a disease with a low importance. For example, the referral unit dynamically adjusts the level of detail of the referral according to the importance. This enables more appropriate hospital referral by providing detailed referrals according to the importance of the disease. Some or all of the above-mentioned processing in the referral unit may be performed using or without the generation AI. For example, the referral unit can input predicted disease importance data into the generation AI and cause the generation AI to adjust the level of detail of the referral.

[0080] The referral unit can apply different referral algorithms depending on the hospital category when making a referral. For example, the referral unit applies a specific referral algorithm to a general hospital. The referral unit can also apply a different referral algorithm to a specialized hospital. For example, the referral unit applies an even different referral algorithm to a clinic. This improves referral accuracy by applying a referral algorithm depending on the hospital category. Some or all of the above-mentioned processing in the referral unit may be performed using or without the generation AI. For example, the referral unit can input hospital category data into the generation AI and cause the generation AI to apply different referral algorithms.

[0081] The referral unit can estimate the customer's emotions and determine the priority of hospital referrals based on the estimated customer emotions. For example, if the customer is feeling anxious, the referral unit can prioritize referring the customer to a hospital with a high rating. Furthermore, if the customer is relaxed, the referral unit can prioritize referring the customer to a nearby hospital. For example, if the customer is in a hurry, the referral unit can prioritize referring the customer to a hospital where it is easy to make an appointment. This allows for more appropriate hospital referrals by setting priorities according to the customer's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can 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 referral unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the referral unit can input the customer's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0082] At the time of referral, the referral unit can introduce an appropriate hospital based on the geographical distribution of hospitals. For example, the referral unit can introduce the hospital closest to the customer's current location. The referral unit can also introduce hospitals along the customer's commute route. For example, the referral unit can introduce hospitals that are easily accessible from the customer's home. In this way, by providing hospital referrals based on geographical distribution, the optimal hospital can be introduced for the customer. Some or all of the above-described processing in the referral unit may be performed using or without the generation AI. For example, the referral unit can input customer geographical distribution data into the generation AI and have the generation AI perform the referral of an appropriate hospital.

[0083] The referral unit can improve the accuracy of referrals by referring to literature related to the hospital when making a referral. For example, the referral unit makes the referral by referring to the latest medical literature related to the hospital. The referral unit can also make the referral by referring to past research papers related to the hospital. For example, the referral unit makes the referral by referring to specialized books related to the hospital. By doing so, the referral accuracy is improved by referring to the related literature. Some or all of the above-described processing in the referral unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the referral unit can input literature data related to the hospital into the generation AI and cause the generation AI to improve the accuracy of the referral.

[0084] The reservation unit can estimate a customer's emotions and adjust the reservation method based on the estimated customer emotions. For example, if a customer is feeling anxious, the reservation unit can provide a simple and intuitive reservation interface. Furthermore, if a customer is relaxed, the reservation unit can provide detailed reservation options and suggest customizable reservation methods. For example, if a customer is in a hurry, the reservation unit can prioritize voice input to enable a quick reservation. This allows for more appropriate reservations by providing a reservation method that corresponds to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 reservation unit can be performed using the generation AI, or can be performed without the generation AI. For example, the reservation unit can input customer facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0085] When making a reservation, the reservation unit can select an appropriate reservation method by referring to past data of the hospital's reservation system. The reservation unit, for example, can suggest an optimal reservation time based on past reservation data. The reservation unit can also suggest a reservation method that avoids congestion based on past reservation data. For example, the reservation unit can suggest the most efficient reservation method based on past reservation data. This improves reservation efficiency by providing the optimal reservation method based on past data. Some or all of the above-mentioned processing in the reservation unit may be performed using or without using the generation AI. For example, the reservation unit can input past data from the hospital's reservation system into the generation AI and have the generation AI select an appropriate reservation method.

[0086] The reservation unit can apply different reservation methods to different hospital categories when making reservations. For example, the reservation unit applies a specific reservation method to general hospitals. The reservation unit can also apply a different reservation method to specialized hospitals. For example, the reservation unit applies an even different reservation method to clinics. This improves reservation accuracy by applying a reservation method according to the hospital category. Some or all of the above-mentioned processing in the reservation unit may be performed using or without the generation AI. For example, the reservation unit can input hospital category data into the generation AI and have the generation AI apply different reservation methods.

[0087] The reservation unit can estimate a customer's emotions and prioritize reservations based on the estimated customer emotions. For example, if a customer is feeling anxious, the reservation unit can prioritize reservations for earlier time slots. Furthermore, if a customer is feeling relaxed, the reservation unit can also suggest reservations that suit the customer's convenience. For example, if a customer is in a hurry, the reservation unit can suggest the earliest available reservation time slot. This allows for more appropriate reservations by setting priorities according to the customer's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can 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 reservation unit can be performed using the generation AI, or can be performed without the generation AI. For example, the reservation unit can input customer facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0088] When making a reservation, the reservation unit can select an appropriate reservation method based on the geographic distribution of hospitals. For example, the reservation unit may preferentially suggest reservations at hospitals closest to the customer's current location. The reservation unit can also suggest reservations at hospitals along the customer's commute route. For example, the reservation unit may suggest reservations at hospitals that are easily accessible from the customer's home. By providing a reservation method based on geographic distribution, it is possible to make the optimal reservation for the customer. Some or all of the above-described processing in the reservation unit may be performed using or without the generation AI. For example, the reservation unit may input hospital geographic distribution data into the generation AI and have the generation AI select an appropriate reservation method.

[0089] The reservation unit can improve the accuracy of reservations by referring to literature related to the hospital when making a reservation. The reservation unit, for example, makes a reservation by referring to the latest medical literature related to the hospital. The reservation unit can also make a reservation by referring to past research papers related to the hospital. For example, the reservation unit makes a reservation by referring to specialized books related to the hospital. By referring to related literature, reservation accuracy is improved. Some or all of the above-mentioned processing in the reservation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reservation unit can input literature data related to the hospital into the generation AI and have the generation AI improve reservation accuracy. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, analysis unit, prediction unit, referral unit, and reservation unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, allowing the customer to take a photo of their symptoms using a smartphone camera and upload it to the app. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input symptoms and photos based on past medical data and case histories. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and predicts a disease from the analyzed information. The referral unit is implemented, for example, by the control unit 46A of the smart device 14, and refers the customer to a hospital based on the predicted disease. The reservation unit is implemented, for example, by the control unit 46A of the smart device 14, and makes a reservation in cooperation with a hospital's reservation system. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, prediction unit, referral unit, and reservation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and allows a customer to take a photo of their symptoms using the smart glasses 214 and upload it to the app. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input symptoms and photos based on past medical data and case histories. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts a disease from the analyzed information. The referral unit is realized, for example, by the control unit 46A of the smart glasses 214, and refers the customer to a hospital based on the predicted disease. The reservation unit is realized, for example, by the control unit 46A of the smart glasses 214, and makes a reservation in cooperation with a hospital's reservation system. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, analysis unit, prediction unit, referral unit, and reservation unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset terminal 314, and allows a customer to take a photo of their symptoms using the headset terminal 314 and upload it to the app. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input symptoms and photos based on past medical data and case histories. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts a disease from the analyzed information. The referral unit is realized, for example, by the control unit 46A of the headset terminal 314, and refers the customer to a hospital based on the predicted disease. The reservation unit is realized, for example, by the control unit 46A of the headset terminal 314, and makes a reservation in cooperation with a hospital's reservation system. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, prediction unit, referral unit, and reservation unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and allows the customer to use the robot 414 to take a photo of their symptoms and upload it to the app. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input symptoms and photos based on past medical data and case histories. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts a disease from the analyzed information. The referral unit is realized, for example, by the control unit 46A of the robot 414, and refers the customer to a hospital based on the predicted disease. The reservation unit is realized, for example, by the control unit 46A of the robot 414, and makes a reservation in cooperation with a hospital's reservation system.

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

[0091] The reception unit can monitor the customer's health condition and automatically prompt the customer to enter symptoms if an abnormality is detected. For example, it can collect data such as heart rate, blood pressure, and body temperature from wearable devices such as smartwatches and fitness trackers, and send a notification via the app if an abnormal value is detected. The reception unit can also prioritize the input of related symptoms based on the customer's health condition. For example, if the customer's heart rate is abnormally high, the reception unit will prompt the customer to enter heart-related symptoms. This allows the customer to enter appropriate symptoms according to their health condition.

[0092] The analysis unit can estimate the customer's emotions and adjust the display method of the analysis results based on the estimated customer emotions. For example, if the customer is feeling anxious, detailed analysis results can be displayed, providing additional information to reassure the customer. Alternatively, if the customer is relaxed, concise analysis results can be displayed, providing only the minimum amount of information necessary. Furthermore, if the customer is in a hurry, analysis results that focus on the main points can be displayed, allowing the customer to understand quickly. In this way, by providing a display method that suits the customer's emotions, analysis results that are easier to understand can be obtained.

[0093] The prediction unit can improve the accuracy of disease predictions based on the customer's lifestyle data. For example, it collects data on the customer's diet, exercise, sleep, etc. and incorporates this data into the analysis. The prediction unit can also assess the risk of specific diseases based on the customer's lifestyle and reflect this in the prediction results. For example, a customer who has a smoking habit may be assessed as having a higher risk of lung disease. This makes it possible to make more accurate disease predictions using lifestyle data.

[0094] The referral unit can estimate the customer's emotions and adjust the hospital referral method based on the estimated customer emotions. For example, if the customer is feeling anxious, detailed hospital information can be provided and additional information can be displayed to give the customer a sense of security. If the customer is relaxed, concise hospital information can be provided and only the minimum necessary information can be displayed. Furthermore, if the customer is in a hurry, hospital information that focuses on the main points can be provided to enable quick understanding. This makes it possible to provide a more appropriate hospital referral method that suits the customer's emotions.

[0095] The reservation department can analyze a customer's past reservation history and suggest the optimal reservation method. For example, it can prioritize reservation methods (online, telephone, etc.) that have been frequently used in the past. The reservation department can also analyze past reservation history to see if reservations tend to be concentrated during certain time periods or days of the week, and suggest the optimal reservation time. Furthermore, it can also suggest reservation methods that avoid overcrowding based on past reservation history. This makes it possible to make efficient reservations by utilizing past data.

[0096] The reception unit can estimate the customer's emotions and determine the priority of the input symptoms based on the estimated customer emotions. For example, if the customer is feeling anxious, the reception unit can prompt the customer to input serious symptoms first. If the customer is relaxed, the reception unit can prompt the customer to input detailed symptoms first. If the customer is in a hurry, the reception unit can prompt the customer to input major symptoms first. In this way, by setting priorities according to the customer's emotions, it is possible to input important symptoms first.

[0097] During analysis, the analysis unit can apply different analysis methods to different symptom categories. For example, a specific analysis method can be applied to respiratory symptoms. Another analysis method can be applied to digestive symptoms. Furthermore, a different analysis method can be applied to skin symptoms. In this way, analysis accuracy can be improved by applying an analysis method according to the symptom category.

[0098] The prediction unit can estimate the customer's emotions and adjust the way in which the prediction results are expressed based on the estimated customer emotions. For example, if the customer is feeling anxious, a detailed prediction result can be provided. If the customer is relaxed, a concise prediction result can be provided. Furthermore, if the customer is in a hurry, a prediction result that focuses on the main points can be provided. In this way, by providing a way of expression that suits the customer's emotions, it is possible to obtain prediction results that are easier to understand.

[0099] When making a referral, the referral department can introduce an appropriate hospital based on the geographical distribution of hospitals. For example, it can introduce the hospital closest to the customer's current location. It can also introduce hospitals along the customer's commute route. It can also introduce hospitals that are easily accessible from the customer's home. In this way, by providing hospital referrals based on geographical distribution, it is possible to introduce the optimal hospital for the customer.

[0100] The reservation unit can estimate the customer's emotions and determine the priority of reservations based on the estimated customer emotions. For example, if the customer is feeling anxious, it can prioritize reservations for earlier time slots. Also, if the customer is relaxed, it can suggest reservations that suit the customer's convenience. Furthermore, if the customer is in a hurry, it can suggest the earliest available time slot. This allows for more appropriate reservations by setting priorities according to the customer's emotions.

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

[0102] Step 1: The reception desk accepts the symptoms of the customer's health condition, the details of the illness, and specific photos. Symptoms that customers enter include, for example, headache, fever, and skin abnormalities. The reception desk provides an interface for customers to open the app and enter their symptoms, and also has a function for uploading photos of the symptoms. For example, customers can use their smartphone camera to take photos of their symptoms and upload them to the app. Step 2: The analysis unit uses the generative AI to analyze the information received by the reception unit. The analysis unit analyzes the input symptoms and photos based on past medical data and cases. For example, it can refer to a past medical database to search for cases that match the input symptoms, and it can also analyze the uploaded photos using image analysis technology. For example, it can analyze photos of skin abnormalities and identify the type of abnormality. Step 3: The prediction unit predicts the most likely disease based on the information analyzed by the analysis unit. The prediction unit uses generative AI to predict diseases from the analyzed information. For example, based on the input symptoms, it can present the possibility of migraine or tension headache, and based on the analyzed photo, it can also identify the type of skin abnormality. For example, it can analyze a photo of a skin abnormality and present the possibility of eczema or an allergic reaction. Step 4: The referral unit introduces nearby hospitals and departments that can treat the illness based on the disease predicted by the prediction unit. The referral unit displays hospital ratings to help customers choose a hospital. For example, it can display hospitals with neurology or headache specialist clinics, and link with the hospital's reservation system to check availability and make a reservation. Step 5: The reservation department makes a reservation at the hospital referred by the referral department. The reservation department connects with the hospital's reservation system, checks availability, and confirms the reservation. For example, it accesses the reservation system of the hospital selected by the customer, checks availability, and makes the reservation.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0156] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0166] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of symptoms; an analysis unit that analyzes the information received by the reception unit; a prediction unit that predicts a disease based on the information analyzed by the analysis unit; a referral unit that introduces a hospital based on the disease predicted by the prediction unit; a reservation unit that makes a reservation at the hospital introduced by the introduction unit; A system comprising:

2. The reception unit Infer customer sentiment and adjust symptom entry methods based on the inferred sentiment 2. The system of claim 1.

3. The reception unit Analyze the customer's past symptom input history and provide an appropriate input interface 2. The system of claim 1.

4. The reception unit When entering symptoms, filter the input based on the customer's current health and lifestyle habits 2. The system of claim 1.

5. The reception unit Estimate customer sentiment and prioritize input symptoms based on the estimated sentiment 2. The system of claim 1.

6. The reception unit When entering symptoms, prioritize relevant symptoms based on the customer's geographic location 2. The system of claim 1.

7. The reception unit When entering symptoms, analyze the customer's social media activity and prompt them to enter related symptoms.

2. The system of claim 1.

8. The analysis unit Estimate customer sentiment and adjust analysis accuracy based on estimated customer sentiment 2. The system of claim 1.

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