System

Abstract

To provide a system that identifies an appropriate medical department based on a user's symptoms or medical condition and provides information.SOLUTION: A system according to the embodiment comprises a reception unit, an identification unit, and a provision unit. The reception unit receives input of a user's symptoms or medical condition. The identification unit identifies a type of medical department recommended to the user based on the information received by the reception unit. The provision unit provides information indicating the type of medical department identified by the identification unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem that it is difficult for users to identify an appropriate medical department based on their own symptoms and medical condition.

[0005] The system according to the embodiment aims to identify an appropriate medical department based on the user's symptoms and medical condition and provide information. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a determination unit, and a provision unit. The reception unit receives input of a user's symptoms or medical condition. The determination unit determines a type of medical department recommended for the user based on the information received by the reception unit. The provision unit provides information indicating the type of medical department determined by the determination unit. Effect of the Invention

[0007] The system according to the embodiment can identify an appropriate medical department based on the user's symptoms and medical condition and provide information. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The medical department recommendation system according to the embodiment of the present invention is a system that recommends an appropriate medical department and searches for and tells you about a hospital that meets the user's conditions simply by inputting the user's symptoms and symptoms. This medical department recommendation system allows the user to input their own symptoms and symptoms, analyzes the information, identifies a medical department to be recommended to the user, provides information on the identified medical department, and searches for and tells you about a hospital that meets the user's conditions. For example, when the user inputs information such as "I have a headache" or "I have a stomachache," this information is input into the system and analyzed. Next, the system analyzes the input information and identifies a medical department to be recommended to the user. For example, if the user has a headache, the system identifies an appropriate medical department, such as "neurology" for a headache, or "gastroenterology" for a stomachache. This analysis is performed by AI. Information on the identified medical department is provided to the user. For example, information such as "please visit a neurology department" or "please visit a gastroenterology department" is provided. Furthermore, the system searches for and tells you about a hospital that meets the user's conditions. For example, if the user inputs conditions such as "within walking distance of home," "good reviews," or "has special facilities," information on a hospital that meets the conditions is provided. This analysis is also performed by AI. This system allows users to visit an appropriate medical department based on their symptoms and medical condition, and also allows them to find a hospital that meets their requirements. For example, if you have a headache, information such as "visit a neurology department" is provided, along with information such as "here are neurology hospitals within walking distance of your home that have good reviews." This allows the medical department recommendation system to identify the appropriate medical department based on the user's symptoms and medical condition, and provide information.

[0029] The medical department recommendation system according to the embodiment includes a reception unit, a determination unit, and a provision unit. The reception unit receives input of a user's symptoms or medical condition. The user's symptoms or medical condition include, for example, a cold, a headache, a stomachache, and the like, but are not limited to such examples. The reception unit receives, for example, the symptoms or medical condition input by the user in text format. The reception unit can also receive the user's symptoms or medical condition using voice input. For example, the reception unit converts the user's voice into text data using a voice recognition technology. The determination unit identifies the type of medical department recommended to the user based on the information received by the reception unit. The determination unit, for example, analyzes the user's symptoms or medical condition using AI and identifies an appropriate medical department. For example, the determination unit uses a text generation AI (for example, LLM) to analyze the user's symptoms or medical condition and identifies an appropriate medical department. The determination unit can also analyze the user's symptoms or medical condition using a multimodal generation AI. For example, the determination unit uses an AI model that inputs the user's symptoms or medical condition and outputs an appropriate medical department. The providing unit provides information indicating the type of medical department identified by the identifying unit. The providing unit provides the user with information such as "Please visit a neurologist" or "Please visit a gastroenterologist." The providing unit also provides information on hospitals that meet the user's conditions. For example, the providing unit uses an AI model that inputs conditions such as "within walking distance of home," "good reviews," and "special facilities" and outputs information on hospitals that meet the conditions. As a result, the medical department recommendation system according to the embodiment can identify an appropriate medical department based on the user's symptoms and medical condition and provide information.

[0030] The reception unit receives input of the symptoms or medical condition of the user. The symptoms or medical condition of the user include, for example, a cold, a headache, a stomachache, and the like, but are not limited to such examples. The reception unit receives, for example, the symptoms or medical condition input by the user in text format. Specifically, the user can input the symptoms or medical condition into a dedicated input form using a smartphone or a personal computer. This input form is designed to be easily operated by the user, and includes a drop-down menu for selecting the symptoms or medical condition and a text box for free description. The reception unit can also receive the symptoms or medical condition of the user using voice input. For example, the reception unit converts the user's voice into text data using voice recognition technology. Voice recognition technology can recognize natural words spoken by the user with high accuracy and convert them into text data. This allows the user to easily input the symptoms or medical condition by voice without using a keyboard. Furthermore, the reception unit temporarily stores the user's input data so that it can be used for subsequent processing. For example, the data input by the user is stored in a cloud server and made accessible to the identification unit and the provision unit. This enables the reception unit to efficiently receive information about the user's symptoms and medical condition, enabling the processing of the entire system to proceed smoothly.

[0031] The identification unit identifies the type of medical department recommended to the user based on the information accepted by the reception unit. The identification unit, for example, uses AI to analyze the user's symptoms and medical condition and identifies the appropriate medical department. Specifically, the identification unit uses natural language processing technology to analyze text data input by the user. For example, a text generation AI (for example, LLM) is used to analyze the user's symptoms and medical condition and identify the appropriate medical department. The LLM has learned a large amount of medical data and can identify the optimal medical department from the user's input data with high accuracy. The identification unit can also analyze the user's symptoms and medical condition using a multimodal generation AI. The multimodal generation AI can analyze not only text data but also images and audio data, so it can identify the medical department by comprehensively judging the various information provided by the user. For example, if a user uploads a photo of the symptoms, the AI ​​analyzes the image and combines it with the text data to identify the medical department. The identification unit can also identify the medical department with higher accuracy by utilizing past medical data and statistical information. This enables the identification unit to quickly and accurately identify the most appropriate medical department based on the user's symptoms and medical condition.

[0032] The providing unit provides information indicating the type of medical department identified by the identifying unit. Specifically, the providing unit provides information such as "Please visit a neurologist" or "Please visit a gastroenterologist" to the user. The providing unit can use text messages or voice messages to provide information in a format that is easy for the user to understand. For example, a notification is sent to the user's smartphone to display information about the medical department. The providing unit also provides information about hospitals that meet the user's conditions. For example, the providing unit uses an AI model that inputs conditions such as "within walking distance of home," "good reviews," and "special facilities" and outputs information about hospitals that meet the conditions. The AI ​​model can search for the most suitable hospital based on the user's conditions and provide detailed information. For example, the AI ​​model provides information about the location of the hospital, opening hours, and specialists, and helps the user select an appropriate hospital. In addition, the providing unit can provide links and contact information for the user to make a medical appointment. This allows the user to not only obtain information about the medical department, but also to smoothly proceed with the procedure for actually receiving medical treatment. The providing unit supports the user's health management by providing information quickly and accurately, with maximum consideration given to the user's convenience.

[0033] The reception unit receives the user's conditions, and the provision unit can provide information on hospitals that meet the user's conditions among the types of medical departments identified by the identification unit. The user's conditions include, for example, age, sex, medical history, etc., but are not limited to such examples. The reception unit, for example, receives the conditions entered by the user in text format. The reception unit can also receive the user's conditions using voice input. For example, the reception unit converts the user's voice into text data using voice recognition technology. The provision unit provides information on hospitals that meet the user's conditions among the types of medical departments identified by the identification unit. For example, the provision unit provides information on hospitals that have good word-of-mouth reviews within walking distance of the user's home based on the user's conditions. The provision unit can also provide information on hospitals with special facilities. For example, the provision unit uses an AI model that takes the user's conditions as input and outputs information on hospitals that meet the conditions. This makes it possible to provide information on hospitals that meet the user's conditions. A part or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can use an AI model that takes a user's conditions as input and outputs information about hospitals that meet the conditions.

[0034] The reception unit can analyze the user's past symptoms and medical condition history and select an appropriate input method. For example, the reception unit automatically suggests similar symptoms based on the user's past symptoms and medical condition. The reception unit can also preferentially suggest frequently used input methods (voice, text, etc.) from the user's past input history. Furthermore, the reception unit can analyze the user's past symptoms and medical condition history and suggest an input method to be used in a specific time period. This improves input efficiency by selecting the optimal input method based on the past history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can use an AI model that inputs the user's past symptoms and medical condition history and outputs the optimal input method.

[0035] The reception unit can filter the symptoms and medical conditions based on the user's current health condition and lifestyle habits when inputting the symptoms and medical conditions. For example, the reception unit preferentially displays related symptoms and medical conditions based on the user's current health condition. The reception unit can also filter related symptoms and medical conditions taking into account the user's lifestyle habits (smoking, drinking, etc.). Furthermore, the reception unit can customize the information to be input based on the user's health condition and lifestyle habits. As a result, more relevant information can be provided by filtering information based on the user's health condition and lifestyle habits. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can use an AI model that inputs the user's health condition and lifestyle habits and outputs related symptoms and medical conditions.

[0036] The reception unit can preferentially input highly relevant information in consideration of the geographical location information of the user when inputting symptoms and medical conditions. For example, the reception unit preferentially inputs regional diseases and symptoms based on the user's current location. The reception unit can also preferentially input symptoms commonly seen at nearby medical institutions in consideration of the user's geographical location information. Furthermore, the reception unit can also preferentially input symptoms related to the climate and environment of the region based on the user's geographical location information. This makes it possible to respond to regional diseases and symptoms by considering the geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can use an AI model that uses the user's geographical location information as input and outputs related symptoms and medical conditions.

[0037] The reception unit can analyze the user's social media activity and input related information when inputting symptoms or medical conditions. The reception unit, for example, analyzes the user's social media posts and inputs information related to the user's recent physical condition. The reception unit can also input information related to stress and lifestyle habits from the user's social media activity. Furthermore, the reception unit can input related symptoms and medical conditions based on the content of the user's social media interactions. In this way, information related to the user's lifestyle habits and stress can be collected by analyzing social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can use an AI model that uses the user's social media activity as an input and outputs related information.

[0038] The identification unit can improve the accuracy of identification based on the interrelationships between symptoms and symptoms when identifying a medical department. For example, when multiple symptoms are related, the identification unit identifies a medical department by comprehensively considering them. The identification unit can also analyze the interrelationships between symptoms and symptoms and identify the most appropriate medical department. Furthermore, the identification unit can improve the accuracy of identification of a medical department based on the interrelationships between symptoms and symptoms. As a result, the accuracy of identification of a medical department is improved by considering the interrelationships between symptoms and symptoms. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can use an AI model that inputs the interrelationships between symptoms and symptoms and outputs a medical department.

[0039] The identification unit can identify the medical department based on the user's past medical history when identifying the medical department. The identification unit can identify the relevant medical department based on the user's past medical history, for example. The identification unit can also identify the medical department from the user's past medical history, taking into account symptoms that may recur. Furthermore, the identification unit can analyze the user's past medical history and identify the most appropriate medical department. This makes it possible to deal with symptoms that may recur by taking into account the past medical history. Some or all of the above-mentioned processing in the identification unit can be performed, for example, using AI, or can be performed without using AI. For example, the identification unit can use an AI model that inputs the user's past medical history and outputs the medical department.

[0040] The identification unit can identify the medical department based on the geographic distribution of symptoms and conditions when identifying the medical department. The identification unit, for example, identifies the medical department by taking into account diseases and symptoms specific to the region. The identification unit can also identify the most appropriate medical department based on the geographic distribution of symptoms and conditions. Furthermore, the identification unit can analyze geographic distribution information to improve the accuracy of identifying the medical department. This makes it possible to deal with diseases and symptoms specific to the region by taking the geographic distribution into account. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can use an AI model that inputs the geographic distribution of symptoms and conditions and outputs the medical department.

[0041] The identification unit can improve the accuracy of identification by referring to related literature when identifying a medical department. The identification unit can, for example, refer to the latest medical literature to improve the accuracy of identifying a medical department. The identification unit can also identify the most appropriate medical department based on related research papers. Furthermore, the identification unit can analyze medical literature and update the identification criteria for the medical department. As a result, the accuracy of identifying a medical department is improved by referring to related literature. A part or all of the above-mentioned processing in the identification unit can be performed, for example, using AI, or can be performed without using AI. For example, the identification unit can use an AI model that inputs related literature and outputs a medical department.

[0042] When providing information on the medical department, the providing unit can select the optimal information providing method by referring to the user's past medical history. For example, the providing unit preferentially provides related information based on the user's past medical history. The providing unit can also provide information on symptoms that may recur from the user's past medical history. Furthermore, the providing unit can analyze the user's past medical history and select the most appropriate information providing method. As a result, related information can be preferentially provided by referring to the past medical history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can use an AI model that inputs the user's past medical history and outputs the optimal information providing method.

[0043] The providing unit can customize the means of providing information based on the user's current health condition when providing information on the medical department. The providing unit, for example, preferentially provides related information based on the user's current health condition. The providing unit can also customize the means of providing information (text, voice, etc.) according to the user's health condition. Furthermore, the providing unit can also select the optimal information providing method in consideration of the user's health condition. As a result, more appropriate information can be provided by customizing the information providing means based on the current health condition. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can use an AI model that inputs the user's health condition and outputs the optimal information providing method.

[0044] The providing unit can select the optimal information providing method in consideration of the geographical location information of the user when providing information on the medical department. For example, the providing unit preferentially provides information on nearby medical institutions based on the user's current location. The providing unit can also provide medical information specific to the region in consideration of the geographical location information of the user. Furthermore, the providing unit can also select the optimal information providing method based on the geographical location information of the user. In this way, it is possible to provide medical information specific to the region by taking the geographical location information into consideration. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can use an AI model that inputs the geographical location information of the user and outputs the optimal information providing method.

[0045] The providing unit can analyze the user's social media activity and suggest a means of providing the information when providing information on the medical department. For example, the providing unit analyzes the user's social media posts and provides related medical information. The providing unit can also provide information on stress and lifestyle habits from the user's social media activity. Furthermore, the providing unit can also suggest an optimal information providing method based on the content of the user's social media interactions. In this way, information on the user's lifestyle habits and stress can be provided by analyzing social media activity. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can use an AI model that uses the user's social media activity as input and outputs the optimal information providing method.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The medical department recommendation system can further collect lifestyle data of the user and recommend medical departments based on this. For example, data can be collected from fitness trackers and smart watches that the user uses daily, and information such as the amount of exercise, sleep patterns, and heart rate can be analyzed. This makes it possible to recommend, for example, an "orthopedics" for symptoms that are likely to be caused by lack of exercise, and a "sleep clinic" for symptoms that are likely to be caused by lack of sleep. It can also collect data from the user's food record app and recommend a "nutritional guidance clinic" for symptoms that are likely to be caused by an imbalance in nutritional intake. This makes it possible to recommend medical departments with greater accuracy based on the user's lifestyle.

[0048] The medical specialty recommendation system can further analyze the user's genetic information and recommend a medical specialty based on this. For example, the system analyzes the genetic test results provided by the user, and recommends a related medical specialty if a particular genetic risk is high. For example, if a user has a high genetic risk of heart disease, a "cardiology department" can be recommended, and if a user has a high genetic risk of diabetes, an "endocrinology department" can be recommended. In addition, if a user has a high responsiveness to a particular drug based on genetic information, a medical specialty that specializes in drug prescriptions can be recommended. This makes it possible to recommend a more personalized medical specialty based on the user's genetic information.

[0049] The medical department recommendation system can further recommend medical departments taking into account the socio-economic background of the user. For example, the system recommends the most appropriate medical department by taking into account the user's income, type of insurance, and medical access in the area of ​​residence. Specifically, it can recommend cost-effective medical departments and public medical institutions to low-income users, and more specialized and expensive medical departments to high-income users. It can also preferentially recommend medical departments that are covered by insurance types. Furthermore, it can recommend medical departments that are available at nearby medical institutions by taking into account the medical access in the area of ​​residence. This makes it possible to recommend more appropriate medical departments based on the user's socio-economic background.

[0050] The medical department recommendation system can further recommend a medical department taking into account the user's occupational information. For example, if the user's occupation is mainly desk work, the system recommends a medical department that deals with symptoms that are likely to be caused by sitting for long periods of time. Specifically, if the user has lower back pain or stiff shoulders, the system can recommend "orthopedics," and if the user has eye strain, the system can recommend "ophthalmology." In addition, if the user's occupation is mainly physical labor, the system can recommend a medical department that deals with muscle and joint problems. Furthermore, if the user's occupation is in a stressful environment, the system can recommend a medical department that deals with stress-related symptoms. This makes it possible to recommend a more appropriate medical department based on the user's occupational information.

[0051] The medical department recommendation system can further recommend medical departments taking into account the user's hobbies and interests. For example, if the user enjoys sports, a medical department dealing with sports-related injuries and symptoms is recommended. Specifically, if the user has knee pain due to sports, "orthopedics" can be recommended, and if the user has respiratory problems due to sports, "respiratory medicine" can be recommended. In addition, if the user enjoys outdoor activities, a medical department dealing with symptoms such as insect bites and sunburn can be recommended. Furthermore, if the user is interested in music and art, a medical department related to stress relief and relaxation can be recommended. This makes it possible to recommend more appropriate medical departments based on the user's hobbies and interests.

[0052] The medical department recommendation system can further recommend a medical department taking into account the user's family history. For example, if the user's family has a history of a specific disease, the system recommends a medical department related to that disease. Specifically, if the user's family has a history of heart disease, the system can recommend a "cardiology department," and if the user's family has a history of diabetes, the system can recommend an "endocrinology department." In addition, if the user's family has a history of cancer, the system can recommend a medical department that performs cancer screening. Furthermore, if the user's family has a history of mental illness, the system can recommend a psychiatrist or psychosomatic medicine specialist. This makes it possible to recommend a more appropriate medical department based on the user's family history.

[0053] The process flow of the first embodiment will be briefly described below.

[0054] Step 1: The reception unit receives input of a user's symptoms or medical condition. Examples of the user's symptoms or medical condition include, but are not limited to, a cold, a headache, and a stomachache. The reception unit receives, for example, the symptoms or medical condition input by the user in a text format. The reception unit can also receive the user's symptoms or medical condition using a voice input. For example, the reception unit converts the user's voice into text data using a voice recognition technology. Step 2: The identification unit identifies the type of medical department recommended to the user based on the information received by the reception unit. The identification unit, for example, uses AI to analyze the user's symptoms and medical condition and identifies an appropriate medical department. For example, the identification unit uses a text generation AI (e.g., LLM) to analyze the user's symptoms and medical condition and identifies an appropriate medical department. The identification unit can also analyze the user's symptoms and medical condition using a multimodal generation AI. For example, the identification unit uses an AI model that inputs the user's symptoms and medical condition and outputs an appropriate medical department. Step 3: The providing unit provides information indicating the type of medical department identified by the identifying unit. For example, the providing unit provides the user with information such as "Please visit a neurologist" or "Please visit a gastroenterologist." The providing unit also provides information on hospitals that meet the user's conditions. For example, the providing unit uses an AI model that inputs conditions such as "within walking distance of home," "good reviews," and "has special facilities" and outputs information on hospitals that meet the conditions.

[0055] (Example 2) The medical department recommendation system according to the embodiment of the present invention is a system that recommends an appropriate medical department and searches for and tells you about a hospital that meets the user's conditions simply by inputting the user's symptoms and symptoms. This medical department recommendation system allows the user to input their own symptoms and symptoms, analyzes the information, identifies a medical department to be recommended to the user, provides information on the identified medical department, and searches for and tells you about a hospital that meets the user's conditions. For example, when the user inputs information such as "I have a headache" or "I have a stomachache," this information is input into the system and analyzed. Next, the system analyzes the input information and identifies a medical department to be recommended to the user. For example, if the user has a headache, the system identifies an appropriate medical department, such as "neurology" for a headache, or "gastroenterology" for a stomachache. This analysis is performed by AI. Information on the identified medical department is provided to the user. For example, information such as "please visit a neurology department" or "please visit a gastroenterology department" is provided. Furthermore, the system searches for and tells you about a hospital that meets the user's conditions. For example, if the user inputs conditions such as "within walking distance of home," "good reviews," or "has special facilities," information on a hospital that meets the conditions is provided. This analysis is also performed by AI. This system allows users to visit an appropriate medical department based on their symptoms and medical condition, and also allows them to find a hospital that meets their requirements. For example, if you have a headache, information such as "visit a neurology department" is provided, along with information such as "here are neurology hospitals within walking distance of your home that have good reviews." This allows the medical department recommendation system to identify the appropriate medical department based on the user's symptoms and medical condition, and provide information.

[0056] The medical department recommendation system according to the embodiment includes a reception unit, a determination unit, and a provision unit. The reception unit receives input of a user's symptoms or medical condition. The user's symptoms or medical condition include, for example, a cold, a headache, a stomachache, and the like, but are not limited to such examples. The reception unit receives, for example, the symptoms or medical condition input by the user in text format. The reception unit can also receive the user's symptoms or medical condition using voice input. For example, the reception unit converts the user's voice into text data using a voice recognition technology. The determination unit identifies the type of medical department recommended to the user based on the information received by the reception unit. The determination unit, for example, analyzes the user's symptoms or medical condition using AI and identifies an appropriate medical department. For example, the determination unit uses a text generation AI (for example, LLM) to analyze the user's symptoms or medical condition and identifies an appropriate medical department. The determination unit can also analyze the user's symptoms or medical condition using a multimodal generation AI. For example, the determination unit uses an AI model that inputs the user's symptoms or medical condition and outputs an appropriate medical department. The providing unit provides information indicating the type of medical department identified by the identifying unit. The providing unit provides the user with information such as "Please visit a neurologist" or "Please visit a gastroenterologist." The providing unit also provides information on hospitals that meet the user's conditions. For example, the providing unit uses an AI model that inputs conditions such as "within walking distance of home," "good reviews," and "special facilities" and outputs information on hospitals that meet the conditions. As a result, the medical department recommendation system according to the embodiment can identify an appropriate medical department based on the user's symptoms and medical condition and provide information.

[0057] The reception unit receives input of the symptoms or medical condition of the user. The symptoms or medical condition of the user include, for example, a cold, a headache, a stomachache, and the like, but are not limited to such examples. The reception unit receives, for example, the symptoms or medical condition input by the user in text format. Specifically, the user can input the symptoms or medical condition into a dedicated input form using a smartphone or a personal computer. This input form is designed to be easily operated by the user, and includes a drop-down menu for selecting the symptoms or medical condition and a text box for free description. The reception unit can also receive the symptoms or medical condition of the user using voice input. For example, the reception unit converts the user's voice into text data using voice recognition technology. Voice recognition technology can recognize natural words spoken by the user with high accuracy and convert them into text data. This allows the user to easily input the symptoms or medical condition by voice without using a keyboard. Furthermore, the reception unit temporarily stores the user's input data so that it can be used for subsequent processing. For example, the data input by the user is stored in a cloud server and made accessible to the identification unit and the provision unit. This enables the reception unit to efficiently receive information about the user's symptoms and medical condition, enabling the processing of the entire system to proceed smoothly.

[0058] The identification unit identifies the type of medical department recommended to the user based on the information accepted by the reception unit. The identification unit, for example, uses AI to analyze the user's symptoms and medical condition and identifies the appropriate medical department. Specifically, the identification unit uses natural language processing technology to analyze text data input by the user. For example, a text generation AI (for example, LLM) is used to analyze the user's symptoms and medical condition and identify the appropriate medical department. The LLM has learned a large amount of medical data and can identify the optimal medical department from the user's input data with high accuracy. The identification unit can also analyze the user's symptoms and medical condition using a multimodal generation AI. The multimodal generation AI can analyze not only text data but also images and audio data, so it can identify the medical department by comprehensively judging the various information provided by the user. For example, if a user uploads a photo of the symptoms, the AI ​​analyzes the image and combines it with the text data to identify the medical department. The identification unit can also identify the medical department with higher accuracy by utilizing past medical data and statistical information. This enables the identification unit to quickly and accurately identify the most appropriate medical department based on the user's symptoms and medical condition.

[0059] The providing unit provides information indicating the type of medical department identified by the identifying unit. Specifically, the providing unit provides information such as "Please visit a neurologist" or "Please visit a gastroenterologist" to the user. The providing unit can use text messages or voice messages to provide information in a format that is easy for the user to understand. For example, a notification is sent to the user's smartphone to display information about the medical department. The providing unit also provides information about hospitals that meet the user's conditions. For example, the providing unit uses an AI model that inputs conditions such as "within walking distance of home," "good reviews," and "special facilities" and outputs information about hospitals that meet the conditions. The AI ​​model can search for the most suitable hospital based on the user's conditions and provide detailed information. For example, the AI ​​model provides information about the location of the hospital, opening hours, and specialists, and helps the user select an appropriate hospital. In addition, the providing unit can provide links and contact information for the user to make a medical appointment. This allows the user to not only obtain information about the medical department, but also to smoothly proceed with the procedure for actually receiving medical treatment. The providing unit supports the user's health management by providing information quickly and accurately, with maximum consideration given to the user's convenience.

[0060] The reception unit receives the user's conditions, and the provision unit can provide information on hospitals that meet the user's conditions among the types of medical departments identified by the identification unit. The user's conditions include, for example, age, sex, medical history, etc., but are not limited to such examples. The reception unit, for example, receives the conditions entered by the user in text format. The reception unit can also receive the user's conditions using voice input. For example, the reception unit converts the user's voice into text data using voice recognition technology. The provision unit provides information on hospitals that meet the user's conditions among the types of medical departments identified by the identification unit. For example, the provision unit provides information on hospitals that have good word-of-mouth reviews within walking distance of the user's home based on the user's conditions. The provision unit can also provide information on hospitals with special facilities. For example, the provision unit uses an AI model that takes the user's conditions as input and outputs information on hospitals that meet the conditions. This makes it possible to provide information on hospitals that meet the user's conditions. A part or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can use an AI model that takes a user's conditions as input and outputs information about hospitals that meet the conditions.

[0061] The reception unit can estimate the user's emotion and adjust the input timing of the symptoms or medical condition based on the estimated emotion of the user. For example, when the user is feeling stressed, the reception unit delays the timing of prompting the user to input, so that the user can input in a relaxed state. In addition, when the user is relaxed, the reception unit can prompt the user to input immediately and collect information quickly. Furthermore, when the user is in a hurry, the reception unit can simplify the input and collect information in a short time. This allows more appropriate information to be collected by adjusting the input timing according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can use facial expression recognition technology or voice analysis technology to estimate the user's emotion.

[0062] The reception unit can analyze the user's past symptoms and medical condition history and select an appropriate input method. For example, the reception unit automatically suggests similar symptoms based on the user's past symptoms and medical condition. The reception unit can also preferentially suggest frequently used input methods (voice, text, etc.) from the user's past input history. Furthermore, the reception unit can analyze the user's past symptoms and medical condition history and suggest an input method to be used in a specific time period. This improves input efficiency by selecting the optimal input method based on the past history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can use an AI model that inputs the user's past symptoms and medical condition history and outputs the optimal input method.

[0063] The reception unit can filter the symptoms and medical conditions based on the user's current health condition and lifestyle habits when inputting the symptoms and medical conditions. For example, the reception unit preferentially displays related symptoms and medical conditions based on the user's current health condition. The reception unit can also filter related symptoms and medical conditions taking into account the user's lifestyle habits (smoking, drinking, etc.). Furthermore, the reception unit can customize the information to be input based on the user's health condition and lifestyle habits. As a result, more relevant information can be provided by filtering information based on the user's health condition and lifestyle habits. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can use an AI model that inputs the user's health condition and lifestyle habits and outputs related symptoms and medical conditions.

[0064] The reception unit can estimate the user's emotion and determine the priority of symptoms and medical conditions to be input based on the estimated user's emotion. For example, when the user feels anxious, the reception unit prompts the user to input serious symptoms with priority. In addition, when the user is relaxed, the reception unit can also prompt the user to input detailed symptoms and medical conditions with priority. Furthermore, when the user is in a hurry, the reception unit can also prompt the user to input only important symptoms with priority. In this way, important information can be collected with priority by determining the priority of input according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can use facial expression recognition technology or voice analysis technology to estimate the user's emotion.

[0065] The reception unit can preferentially input highly relevant information in consideration of the geographical location information of the user when inputting symptoms and medical conditions. For example, the reception unit preferentially inputs regional diseases and symptoms based on the user's current location. The reception unit can also preferentially input symptoms commonly seen at nearby medical institutions in consideration of the user's geographical location information. Furthermore, the reception unit can also preferentially input symptoms related to the climate and environment of the region based on the user's geographical location information. This makes it possible to respond to regional diseases and symptoms by considering the geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can use an AI model that uses the user's geographical location information as input and outputs related symptoms and medical conditions.

[0066] The reception unit can analyze the user's social media activity and input related information when inputting symptoms or medical conditions. The reception unit, for example, analyzes the user's social media posts and inputs information related to the user's recent physical condition. The reception unit can also input information related to stress and lifestyle habits from the user's social media activity. Furthermore, the reception unit can input related symptoms and medical conditions based on the content of the user's social media interactions. In this way, information related to the user's lifestyle habits and stress can be collected by analyzing social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can use an AI model that uses the user's social media activity as an input and outputs related information.

[0067] The identification unit can estimate the user's emotion and adjust the identification criteria of the medical department based on the estimated user's emotion. For example, when the user feels anxious, the identification unit uses more detailed identification criteria of the medical department. In addition, when the user is relaxed, the identification unit can also use general identification criteria of the medical department. Furthermore, when the user is in a hurry, the identification unit can also use simplified identification criteria of the medical department. In this way, by adjusting the identification criteria of the medical department according to the user's emotion, a more appropriate medical department can be identified. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the identification unit can use facial expression recognition technology or voice analysis technology to estimate the user's emotion.

[0068] The identification unit can improve the accuracy of identification based on the interrelationships between symptoms and symptoms when identifying a medical department. For example, when multiple symptoms are related, the identification unit identifies a medical department by comprehensively considering them. The identification unit can also analyze the interrelationships between symptoms and symptoms and identify the most appropriate medical department. Furthermore, the identification unit can improve the accuracy of identification of a medical department based on the interrelationships between symptoms and symptoms. As a result, the accuracy of identification of a medical department is improved by considering the interrelationships between symptoms and symptoms. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can use an AI model that inputs the interrelationships between symptoms and symptoms and outputs a medical department.

[0069] The identification unit can identify the medical department based on the user's past medical history when identifying the medical department. The identification unit can identify the relevant medical department based on the user's past medical history, for example. The identification unit can also identify the medical department from the user's past medical history, taking into account symptoms that may recur. Furthermore, the identification unit can analyze the user's past medical history and identify the most appropriate medical department. This makes it possible to deal with symptoms that may recur by taking into account the past medical history. Some or all of the above-mentioned processing in the identification unit can be performed, for example, using AI, or can be performed without using AI. For example, the identification unit can use an AI model that inputs the user's past medical history and outputs the medical department.

[0070] The identification unit can estimate the user's emotion and adjust the order of displaying the identified results of the medical department based on the estimated user's emotion. For example, when the user feels anxious, the identification unit displays the most relevant medical department first. In addition, when the user is relaxed, the identification unit can also display detailed medical department information sequentially. Furthermore, when the user is in a hurry, the identification unit can also display concise medical department information first. In this way, more appropriate information can be provided by adjusting the display order according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the identification unit can use facial expression recognition technology or voice analysis technology to estimate the user's emotion.

[0071] The identification unit can identify the medical department based on the geographic distribution of symptoms and conditions when identifying the medical department. The identification unit, for example, identifies the medical department by taking into account diseases and symptoms specific to the region. The identification unit can also identify the most appropriate medical department based on the geographic distribution of symptoms and conditions. Furthermore, the identification unit can analyze geographic distribution information to improve the accuracy of identifying the medical department. This makes it possible to deal with diseases and symptoms specific to the region by taking the geographic distribution into account. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can use an AI model that inputs the geographic distribution of symptoms and conditions and outputs the medical department.

[0072] The identification unit can improve the accuracy of identification by referring to related literature when identifying a medical department. The identification unit can, for example, refer to the latest medical literature to improve the accuracy of identifying a medical department. The identification unit can also identify the most appropriate medical department based on related research papers. Furthermore, the identification unit can analyze medical literature and update the identification criteria for the medical department. As a result, the accuracy of identifying a medical department is improved by referring to related literature. A part or all of the above-mentioned processing in the identification unit can be performed, for example, using AI, or can be performed without using AI. For example, the identification unit can use an AI model that inputs related literature and outputs a medical department.

[0073] The providing unit can estimate the user's emotion and adjust the information provision method of the medical department based on the estimated user's emotion. For example, when the user feels anxious, the providing unit provides a simple and highly visible information provision method. In addition, when the user is relaxed, the providing unit can also provide an information provision method including detailed information. Furthermore, when the user is in a hurry, the providing unit can also provide an information provision method that focuses on the main points. This allows more appropriate information to be provided by adjusting the information provision method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can use facial expression recognition technology or voice analysis technology to estimate the user's emotion.

[0074] When providing information on the medical department, the providing unit can select the optimal information providing method by referring to the user's past medical history. For example, the providing unit preferentially provides related information based on the user's past medical history. The providing unit can also provide information on symptoms that may recur from the user's past medical history. Furthermore, the providing unit can analyze the user's past medical history and select the most appropriate information providing method. As a result, related information can be preferentially provided by referring to the past medical history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can use an AI model that inputs the user's past medical history and outputs the optimal information providing method.

[0075] The providing unit can customize the means of providing information based on the user's current health condition when providing information on the medical department. The providing unit, for example, preferentially provides related information based on the user's current health condition. The providing unit can also customize the means of providing information (text, voice, etc.) according to the user's health condition. Furthermore, the providing unit can also select the optimal information providing method in consideration of the user's health condition. As a result, more appropriate information can be provided by customizing the information providing means based on the current health condition. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can use an AI model that inputs the user's health condition and outputs the optimal information providing method.

[0076] The providing unit can estimate the user's emotion and determine the priority of information provision of the medical department based on the estimated user's emotion. For example, when the user feels anxious, the providing unit provides the most relevant information preferentially. In addition, when the user is relaxed, the providing unit can also provide detailed information sequentially. Furthermore, when the user is in a hurry, the providing unit can also provide concise information preferentially. In this way, important information can be provided preferentially by determining the priority of information provision according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can use facial expression recognition technology or voice analysis technology to estimate the user's emotion.

[0077] The providing unit can select the optimal information providing method in consideration of the geographical location information of the user when providing information on the medical department. For example, the providing unit preferentially provides information on nearby medical institutions based on the user's current location. The providing unit can also provide medical information specific to the region in consideration of the geographical location information of the user. Furthermore, the providing unit can also select the optimal information providing method based on the geographical location information of the user. In this way, it is possible to provide medical information specific to the region by taking the geographical location information into consideration. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can use an AI model that inputs the geographical location information of the user and outputs the optimal information providing method.

[0078] The providing unit can analyze the user's social media activity and suggest a means of providing the information when providing information on the medical department. For example, the providing unit analyzes the user's social media posts and provides related medical information. The providing unit can also provide information on stress and lifestyle habits from the user's social media activity. Furthermore, the providing unit can also suggest an optimal information providing method based on the content of the user's social media interactions. In this way, information on the user's lifestyle habits and stress can be provided by analyzing social media activity. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can use an AI model that uses the user's social media activity as input and outputs the optimal information providing method.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The medical department recommendation system can further collect lifestyle data of the user and recommend medical departments based on this. For example, data can be collected from fitness trackers and smart watches that the user uses daily, and information such as the amount of exercise, sleep patterns, and heart rate can be analyzed. This makes it possible to recommend, for example, an "orthopedics" for symptoms that are likely to be caused by lack of exercise, and a "sleep clinic" for symptoms that are likely to be caused by lack of sleep. It can also collect data from the user's food record app and recommend a "nutritional guidance clinic" for symptoms that are likely to be caused by an imbalance in nutritional intake. This makes it possible to recommend medical departments with greater accuracy based on the user's lifestyle.

[0081] The medical specialty recommendation system can further analyze the user's genetic information and recommend a medical specialty based on this. For example, the system analyzes the genetic test results provided by the user, and recommends a related medical specialty if a particular genetic risk is high. For example, if a user has a high genetic risk of heart disease, a "cardiology department" can be recommended, and if a user has a high genetic risk of diabetes, an "endocrinology department" can be recommended. In addition, if a user has a high responsiveness to a particular drug based on genetic information, a medical specialty that specializes in drug prescriptions can be recommended. This makes it possible to recommend a more personalized medical specialty based on the user's genetic information.

[0082] The medical department recommendation system can further estimate the user's emotions and recommend a medical department based on the estimated user's emotions. For example, if the user is feeling stressed, a medical department that deals with stress-related symptoms is preferentially recommended. Specifically, for symptoms that are likely to be caused by stress, a "psychosomatic medicine department" or a "psychiatry department" can be recommended. In addition, if the user is feeling anxious, a medical department that provides counseling to reduce anxiety can be recommended. Furthermore, if the user is relaxed, a medical department related to preventive medicine can be recommended and advice for maintaining health can be provided. This makes it possible to recommend a more appropriate medical department based on the user's emotions.

[0083] The medical department recommendation system can further recommend medical departments taking into account the socio-economic background of the user. For example, the system recommends the most appropriate medical department by taking into account the user's income, type of insurance, and medical access in the area of ​​residence. Specifically, it can recommend cost-effective medical departments and public medical institutions to low-income users, and more specialized and expensive medical departments to high-income users. It can also preferentially recommend medical departments that are covered by insurance types. Furthermore, it can recommend medical departments that are available at nearby medical institutions by taking into account the medical access in the area of ​​residence. This makes it possible to recommend more appropriate medical departments based on the user's socio-economic background.

[0084] The medical department recommendation system can further estimate the user's emotions and customize the medical department recommendation results based on the estimated user's emotions. For example, if the user feels anxious, the medical department recommendation results are provided in a simple and easy-to-understand format. Also, if the user is relaxed, it is possible to provide a recommendation result including detailed information. Furthermore, if the user is in a hurry, it is possible to provide a concise recommendation result that focuses on the main points. In this way, by customizing the medical department recommendation results according to the user's emotions, more appropriate information can be provided. The estimation of emotions is realized, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI or a multimodal generation AI, but is not limited to such examples.

[0085] The medical department recommendation system can further recommend a medical department taking into account the user's occupational information. For example, if the user's occupation is mainly desk work, the system recommends a medical department that deals with symptoms that are likely to be caused by sitting for long periods of time. Specifically, if the user has lower back pain or stiff shoulders, the system can recommend "orthopedics," and if the user has eye strain, the system can recommend "ophthalmology." In addition, if the user's occupation is mainly physical labor, the system can recommend a medical department that deals with muscle and joint problems. Furthermore, if the user's occupation is in a stressful environment, the system can recommend a medical department that deals with stress-related symptoms. This makes it possible to recommend a more appropriate medical department based on the user's occupational information.

[0086] The medical department recommendation system can further estimate the user's emotions and explain the reason for recommending a medical department based on the estimated user's emotions. For example, if the user feels anxious, the recommendation reason is explained in detail to provide a sense of security. Also, if the user is relaxed, a concise explanation can be provided. Furthermore, if the user is in a hurry, a concise explanation that focuses on the main points can be provided. In this way, more appropriate information can be provided by explaining the reason for recommending a medical department according to the user's emotions. The estimation of emotions is realized, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI or a multimodal generation AI.

[0087] The medical department recommendation system can further recommend medical departments taking into account the user's hobbies and interests. For example, if the user enjoys sports, a medical department dealing with sports-related injuries and symptoms is recommended. Specifically, if the user has knee pain due to sports, "orthopedics" can be recommended, and if the user has respiratory problems due to sports, "respiratory medicine" can be recommended. In addition, if the user enjoys outdoor activities, a medical department dealing with symptoms such as insect bites and sunburn can be recommended. Furthermore, if the user is interested in music and art, a medical department related to stress relief and relaxation can be recommended. This makes it possible to recommend more appropriate medical departments based on the user's hobbies and interests.

[0088] The medical department recommendation system can further estimate the user's emotions and adjust the frequency of medical department recommendations based on the estimated user's emotions. For example, when the user feels anxious, medical departments can be recommended frequently to provide a sense of security. When the user is relaxed, medical departments can be recommended with the minimum frequency required. Furthermore, when the user is in a hurry, medical departments can be recommended quickly. This allows more appropriate information to be provided by adjusting the frequency of medical department recommendations according to the user's emotions. The estimation of emotions is realized, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI or a multimodal generation AI, but is not limited to such examples.

[0089] The medical department recommendation system can further recommend a medical department taking into account the user's family history. For example, if the user's family has a history of a specific disease, the system recommends a medical department related to that disease. Specifically, if the user's family has a history of heart disease, the system can recommend a "cardiology department," and if the user's family has a history of diabetes, the system can recommend an "endocrinology department." In addition, if the user's family has a history of cancer, the system can recommend a medical department that performs cancer screening. Furthermore, if the user's family has a history of mental illness, the system can recommend a psychiatrist or psychosomatic medicine specialist. This makes it possible to recommend a more appropriate medical department based on the user's family history.

[0090] The process flow of the second embodiment will be briefly described below.

[0091] Step 1: The reception unit receives input of a user's symptoms or medical condition. Examples of the user's symptoms or medical condition include, but are not limited to, a cold, a headache, and a stomachache. The reception unit receives, for example, the symptoms or medical condition input by the user in a text format. The reception unit can also receive the user's symptoms or medical condition using a voice input. For example, the reception unit converts the user's voice into text data using a voice recognition technology. Step 2: The identification unit identifies the type of medical department recommended to the user based on the information received by the reception unit. The identification unit, for example, uses AI to analyze the user's symptoms and medical condition and identifies an appropriate medical department. For example, the identification unit uses a text generation AI (e.g., LLM) to analyze the user's symptoms and medical condition and identifies an appropriate medical department. The identification unit can also analyze the user's symptoms and medical condition using a multimodal generation AI. For example, the identification unit uses an AI model that inputs the user's symptoms and medical condition and outputs an appropriate medical department. Step 3: The providing unit provides information indicating the type of medical department identified by the identifying unit. For example, the providing unit provides the user with information such as "Please visit a neurologist" or "Please visit a gastroenterologist." The providing unit also provides information on hospitals that meet the user's conditions. For example, the providing unit uses an AI model that inputs conditions such as "within walking distance of home," "good reviews," and "has special facilities" and outputs information on hospitals that meet the conditions.

[0092] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0093] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0094] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0095] For example, the reception unit can receive the symptoms and medical condition of the user via the reception device 38 of the smart device 14 or the communication I / F 26 of the data processing device 12. For example, the identification unit is realized by the identification processing unit 290 of the data processing device 12, and analyzes the symptoms and medical condition of the user using AI to identify an appropriate medical department. For example, the provision unit is realized by the output device 40 of the smart device 14 or the identification processing unit 290 of the data processing device 12, and provides the user with information on the identified medical department and information on hospitals that meet the conditions. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0099] 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.

[0100] 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 the voice according to instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0102] 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 performed in a secure state.

[0103] Fig. 4 shows an example of 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.

[0104] 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.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0107] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0108] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0109] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0110] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0111] For example, the reception unit can receive the user's symptoms and medical condition via the microphone 238 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. For example, the identification unit is realized by the identification processing unit 290 of the data processing device 12, and analyzes the user's symptoms and medical condition using AI to identify an appropriate medical department. For example, the provision unit is realized by the speaker 240 of the smart glasses 214 or the identification processing unit 290 of the data processing device 12, and provides the user with information on the identified medical department and information on hospitals that meet the conditions. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0115] 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.

[0116] 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 the voice according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0118] 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 performed in a secure state.

[0119] 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.

[0120] 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.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0122] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0123] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0124] 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 voice indicating a user input for 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.

[0125] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0126] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0127] For example, the reception unit can receive the symptoms and medical condition of the user via the microphone 238 of the headset type terminal 314 or the communication I / F 26 of the data processing device 12. For example, the identification unit is realized by the identification processing unit 290 of the data processing device 12, and analyzes the symptoms and medical condition of the user using AI and identifies an appropriate medical department. For example, the provision unit is realized by the speaker 240 of the headset type terminal 314 or the identification processing unit 290 of the data processing device 12, and provides the user with information on the identified medical department and information on hospitals that meet the conditions. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0131] 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.

[0132] 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 the voice according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0134] 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 performed in a secure state.

[0135] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0136] 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.

[0137] 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.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0139] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0140] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0141] 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 a voice indicating a user input for 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.

[0142] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0143] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0144] For example, the reception unit can receive the symptoms and medical condition of the user via the microphone 238 of the robot 414 or the communication I / F 26 of the data processing device 12. For example, the identification unit is realized by the identification processing unit 290 of the data processing device 12, and analyzes the symptoms and medical condition of the user using AI to identify an appropriate medical department. For example, the provision unit is realized by the speaker 240 of the robot 414 or the identification processing unit 290 of the data processing device 12, and provides the user with information on the identified medical department and information on hospitals that meet the conditions. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0145] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0146] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0147] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0148] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0149] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0150] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0151] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0152] 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.

[0153] 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 Universal Serial Bus (USB) 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.

[0154] In addition, 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 upon request from the data processing device 12.

[0155] 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.

[0156] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0157] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0158] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0159] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0160] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0161] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0162] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0163] (Appendix 1) A reception unit that receives an input of a user's symptoms or medical condition; An identification unit that identifies a type of medical department to be recommended to the user based on the information received by the reception unit; a providing unit that provides information indicating the type of medical department identified by the identifying unit. A system characterized by: (Appendix 2) The reception unit is Accepting a condition of the user; The providing unit is Providing information on hospitals that satisfy the user's conditions among the types of medical departments identified by the identification unit 2. The system of claim 1, (Appendix 3) The reception unit is Estimate the user's emotions, Adjusting the timing of symptom or medical condition input based on estimated user emotions 2. The system of claim 1, (Appendix 4) The reception unit is Analyze the user's past symptoms and medical history, Choose the right input method 2. The system of claim 1, (Appendix 5) The reception unit is When entering symptoms or illnesses, Filtering based on the user's current health and lifestyle 2. The system of claim 1, (Appendix 6) The reception unit is Estimate the user's emotions, Prioritize the symptoms and illnesses to be entered based on the estimated user emotions 2. The system of claim 1, (Appendix 7) The reception unit is When entering symptoms or illnesses, Prioritize relevant information based on the user's geographic location 2. The system of claim 1, (Appendix 8) The reception unit is When entering symptoms or illnesses, Analyze users' social media activity and input relevant information 2. The system of claim 1, (Appendix 9) The identification unit is Estimate the user's emotions, Adjusting department specific criteria based on estimated user sentiment 2. The system of claim 1, (Appendix 10) The identification unit is When identifying a medical specialty, Improve identification based on correlations of symptoms and conditions 2. The system of claim 1, (Appendix 11) The identification unit is When identifying a medical specialty, Identification based on the user's past medical history 2. The system of claim 1, (Appendix 12) The identification unit is Estimate the user's emotions, Adjust the order of displaying medical department results based on estimated user sentiment 2. The system of claim 1, (Appendix 13) The identification unit is When identifying a medical specialty, Identifying symptoms and conditions based on their geographic distribution 2. The system of claim 1, (Appendix 14) The identification unit is When identifying a medical specialty, Improve your identification by referring to related literature 2. The system of claim 1, (Appendix 15) The providing unit is Estimate the user's emotions, Adjusting the way information is provided to medical departments based on estimated user emotions 2. The system of claim 1, (Appendix 16) The providing unit is When providing information on medical departments, Select the optimal information delivery method by referring to the user's past medical history 2. The system of claim 1, (Appendix 17) The providing unit is When providing information on medical departments, Customize information delivery based on the user's current health status 2. The system of claim 1, (Appendix 18) The providing unit is Estimate the user's emotions, Decide the priority of providing information on medical departments based on estimated user emotions 2. The system of claim 1, (Appendix 19) The providing unit is When providing information on medical departments, Select the best way to provide information based on the user's geographic location 2. The system of claim 1, (Appendix 20) The providing unit is When providing information on medical departments, Analyze users' social media activity to suggest ways to provide information 2. The system of claim 1, [Explanation of symbols]

[0164] 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 an input of a user's symptoms or medical condition; An identification unit that identifies a type of medical department to be recommended to the user based on the information received by the reception unit; A providing unit that provides information indicating the type of medical department identified by the identifying unit. A system characterized by:

2. The reception unit is accepting a condition of the user; The providing unit is Providing information on hospitals that satisfy the user's conditions among the types of medical departments identified by the identification unit 2. The system of claim 1 .

3. The reception unit is Estimating the user's emotion; The timing of inputting symptoms or medical conditions is adjusted based on the estimated emotion of the user.

2. The system of claim 1 .

4. The reception unit is Analyzing the user's past symptoms and medical history; Choose the right input method 2. The system of claim 1 .

5. The reception unit is When entering symptoms or illnesses, Filtering based on the user's current health status and lifestyle habits 2. The system of claim 1 .

6. The reception unit is Estimating the user's emotion and determining a priority order of symptoms and / or conditions to be input based on the estimated emotion of the user.

2. The system of claim 1 .

7. The reception unit is When entering symptoms or illnesses, Prioritizing input of relevant information based on the user's geographic location information 2. The system of claim 1 .

8. The reception unit is When entering symptoms or illnesses, Analyze the user's social media activity and input relevant information 2. The system of claim 1 .

9. The identification unit is and estimating the user's emotion. and adjusting a medical department identification criterion based on the estimated user's emotion.

2. The system of claim 1 .

10. The identification unit is When identifying a medical specialty, Improve identification based on correlations of symptoms and conditions 2. The system of claim 1 .

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