System

Abstract

To provide a system that recommends appropriate clinics according to symptoms and body parts.SOLUTION: A system according to the embodiment comprises a reception unit, an analysis unit, a recommendation unit, and a provision unit. The reception unit receives input of body parts or symptoms. The analysis unit analyzes the information received by the reception unit. The recommendation unit recommends an appropriate clinic based on the information analyzed by the analysis unit. The provision unit provides information on the clinic recommended by the recommendation 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] With conventional technology, it was difficult to find an appropriate clinic based on symptoms and area of ​​the body.

[0005] The system according to the embodiment aims to recommend an appropriate clinic according to symptoms and body part. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a recommendation unit, and a provision unit. The reception unit receives input of a body part or a symptom. The analysis unit analyzes the information received by the reception unit. The recommendation unit recommends an appropriate clinic based on the information analyzed by the analysis unit. The provision unit provides information on the clinic recommended by the recommendation unit. Effect of the Invention

[0007] The system according to the embodiment can recommend an appropriate clinic depending on symptoms and body part. [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 clinic recommendation system according to the embodiment of the present invention is a system that guides users to recommended clinics when they input a part or symptom when they are sick or injured. When a user inputs a part or symptom such as "this part hurts like this," the system analyzes the input information and recommends the most suitable clinic. At this time, the system makes the recommendation taking into consideration the specialty and past treatment results of each clinic. For example, in the case of orthopedics, a clinic that is good at specific parts or symptoms is recommended. This allows the user to easily find the clinic that is best suited to their symptoms. For example, when a user inputs "my right knee hurts," this information is input into the system. Next, the system analyzes the input information and recommends an orthopedic clinic that is good at right knee pain. At this time, the system takes into consideration the specialty and past treatment results of each clinic. The system provides the user with information on the recommended clinic. For example, the system provides information such as the name, address, telephone number, and opening hours of the clinic. This allows the user to easily find the clinic that is best suited to their symptoms. This mechanism allows the user to easily find the clinic that is best suited to their symptoms, making it easier for them to visit the clinic. For example, in the case of orthopedics, by recommending a clinic that is good at specific parts or symptoms, the user can receive the treatment that is best suited to their symptoms. This enables the clinic recommendation system to quickly and accurately recommend a clinic that is best suited to the user's symptoms.

[0029] The clinic recommendation system according to the embodiment includes a reception unit, an analysis unit, a recommendation unit, and a provision unit. The reception unit receives a site and symptoms input by a user. For example, when a user inputs "my right knee hurts," the reception unit receives this information. The analysis unit analyzes the information received by the reception unit. For example, the analysis unit performs an analysis to identify an optimal clinic based on the input site and symptoms. The analysis unit analyzes the text input by the user using, for example, natural language processing technology to identify the site and symptoms. The recommendation unit recommends an optimal clinic based on the information analyzed by the analysis unit. For example, the recommendation unit recommends a clinic optimal for the user's symptoms, taking into account the specialty and past treatment results of each clinic. The recommendation unit, for example, refers to a database of clinics and identifies a clinic optimal for the user's symptoms. The provision unit provides the user with information on the clinic recommended by the recommendation unit. For example, the provision unit provides the user with information such as the name, address, telephone number, and consultation hours of the clinic. The provision unit, for example, displays the clinic information on the user's device. As a result, the clinic recommendation system according to the embodiment can quickly and accurately recommend a clinic that is optimal for the user's symptoms. Some or all of the above-mentioned processes in the analysis unit, recommendation unit, and provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input text entered by the user to the generation AI and cause the generation AI to identify the body part and symptoms. The recommendation unit can input a clinic database to the generation AI and cause the generation AI to identify the optimal clinic. The provision unit can input clinic information to the generation AI and cause the generation AI to generate information to be provided to the user.

[0030] The reception unit receives the body part and symptoms input by the user. For example, when the user inputs "my right knee hurts," the reception unit receives this information. The reception unit has a user interface designed to receive the information input by the user accurately. For example, the reception unit is equipped with not only a text input field, but also multiple choice questions and a voice input function, allowing the user to easily input information. The voice input function converts the user's speech into text using voice recognition technology and transmits it to the reception unit. Furthermore, the reception unit has a mechanism to prevent input errors by temporarily storing the information input by the user and displaying a confirmation screen before transmitting the information to the analysis unit. For example, after the user inputs "my right knee hurts," the confirmation screen displays "pain in the right knee," and the information is transmitted to the analysis unit when the user presses the confirmation button. In addition, the reception unit refers to the user's past input history, and can quickly respond based on past information if the same symptom recurs. This allows the reception unit to accurately and quickly receive the information input by the user and smoothly hand it over to the next process.

[0031] The analysis unit analyzes the information received by the reception unit. For example, the analysis unit performs an analysis to identify the optimal clinic based on the inputted part and symptoms. The analysis unit analyzes the text input by the user using, for example, natural language processing technology to identify the part and symptoms. Specifically, the analysis unit uses natural language processing technology to extract keywords from the text input by the user and identify the symptoms and part based on these keywords. For example, from the input "my right knee hurts", the keywords "right knee" and "pain" are extracted and analysis is performed based on this. Furthermore, the analysis unit can use the generation AI to perform a more advanced analysis of the text input by the user. The generation AI has learned a large amount of medical data and can identify symptoms and parts from the user's input with high accuracy. For example, the generation AI can identify specific symptoms such as inflammation of the knee joint and damage to the ligaments from the input "my right knee hurts". This allows the analysis unit to quickly and accurately analyze the information input by the user and hand it over to the next recommendation unit.

[0032] The recommendation unit recommends an optimal clinic based on the information analyzed by the analysis unit. For example, the recommendation unit recommends a clinic optimal for the user's symptoms, taking into consideration the specialty and past treatment record of each clinic. The recommendation unit, for example, refers to a clinic database to identify a clinic optimal for the user's symptoms. Specifically, the clinic database includes information such as each clinic's specialty, treatment record, and patient evaluation, and identifies the optimal clinic based on this information. Furthermore, the recommendation unit can analyze the clinic database using a generation AI to identify the optimal clinic. The generation AI has learned past treatment data and patient evaluation, and can identify the clinic optimal for the user's symptoms with high accuracy. For example, the generation AI can identify and recommend a clinic that has a track record of treating similar symptoms in the past based on the user's symptoms. This allows the recommendation unit to quickly and accurately recommend a clinic optimal for the user's symptoms.

[0033] The providing unit provides the user with information on the clinic recommended by the recommendation unit. For example, the providing unit provides the user with information such as the name, address, telephone number, and consultation hours of the clinic. For example, the providing unit displays the clinic information on the user's device. Specifically, the providing unit displays the clinic information on the user's smartphone or computer, allowing the user to easily access the clinic. Furthermore, the providing unit can cooperate with the clinic's reservation system to allow the user to make a reservation directly. For example, the providing unit displays a link to the clinic's reservation page, and the user can click to access the reservation page directly. The providing unit can also display the nearest clinic based on the user's location information. This allows the user to easily find the nearest clinic from his / her current location. Furthermore, the providing unit can collect user feedback and continuously update the clinic information. For example, the user can input an evaluation after visiting a clinic, thereby providing useful information for other users. This allows the providing unit to provide the user with quick and accurate information and support the selection of a clinic.

[0034] The analysis unit may include a history unit based on the user's past medical history or evaluation. The analysis unit improves the accuracy of the analysis, for example, based on the user's past medical history. For example, the analysis unit refers to the user's past medical records and analyzes the association with the current symptoms. The analysis unit can also improve the accuracy of the analysis, based on the user's past medical evaluation. For example, the analysis unit refers to the evaluation of the treatment the user has received in the past and identifies the optimal treatment method for the current symptoms. This improves the accuracy of the analysis by taking into account the user's past medical history and evaluation. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's past medical history into the generation AI and cause the generation AI to execute the improvement of the accuracy of the analysis.

[0035] The recommendation unit may include a collection unit that collects information about the clinic. The recommendation unit may include, for example, a collection unit for collecting information about the clinic. The collection unit may collect, for example, the clinic's medical department, doctor's profile, and facility information. For example, the collection unit may collect information from the clinic's website or database. The collection unit may also collect evaluations of the clinic and patient feedback. For example, the collection unit may collect online review sites and patient questionnaire results. By collecting the clinic's information, the accuracy of the recommendation is improved. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit may input the clinic's information to the generation AI and cause the generation AI to collect the information.

[0036] The providing unit can provide information on the name, address, telephone number, and consultation hours of the clinic. The providing unit provides information such as the name, address, telephone number, and consultation hours of the clinic to the user. For example, the providing unit acquires information from the clinic's database and displays it on the user's device. The providing unit can also update the clinic's consultation hours and reservation status in real time and provide it to the user. For example, the providing unit cooperates with the clinic's system to acquire the latest consultation hours and reservation status. This makes it easier for the user to select an appropriate clinic by providing detailed information about the clinic. Some 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 input clinic information to the generating AI and cause the generating AI to provide the information.

[0037] The reception unit can analyze the user's past input history and propose the optimal input method. The reception unit, for example, analyzes the user's past input history and proposes the optimal input method. For example, the reception unit automatically displays the body parts and symptoms that the user frequently inputs in the past as candidates. The reception unit can also preferentially propose the input method (voice, text, etc.) that the user used in the past. Furthermore, the reception unit can predict and propose the body parts and symptoms to be used in a specific time period from the user's past input history. As a result, the input work is made more efficient by proposing the optimal input method based on the past input history. 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 input the user's past input data to the generation AI and cause the generation AI to execute the proposal of the optimal input method.

[0038] The reception unit can complement the input contents based on the user's current health condition and lifestyle habits when inputting the body part and symptoms. The reception unit, for example, automatically complements the relevant body part and symptoms based on the user's current health condition. For example, the reception unit complements the relevant body part and symptoms by referring to the user's health checkup results. The reception unit can also complement the input contents by taking into account the user's lifestyle habits (exercise frequency, dietary content, etc.). For example, the reception unit complements the relevant body part and symptoms based on the results of a lifestyle questionnaire of the user. Furthermore, the reception unit can complement the input contents by referring to the user's past health checkup results. As a result, more accurate information is input by complementing the input contents 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, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's health checkup data to the generation AI and cause the generation AI to execute the completion of the input contents.

[0039] The reception unit can preferentially input highly relevant information in consideration of the geographical location information of the user when inputting the part or symptom. The reception unit, for example, can preferentially input parts or symptoms related to nearby clinics based on the user's current location. The reception unit can also preferentially input information related to region-specific diseases and symptoms based on the user's geographical location information. Furthermore, the reception unit can also preferentially input parts or symptoms related to the nearest medical institution in consideration of the user's geographical location information. In this way, highly relevant information is preferentially input by considering the user's geographical location information. 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 input the user's location information data to the generation AI and cause the generation AI to execute the preferential input of highly relevant information.

[0040] The reception unit can analyze the user's social media activity when inputting the body part or symptoms, and input related information. The reception unit can, for example, analyze the user's social media posts, and input information related to the user's recent health condition or symptoms. The reception unit can also preferentially input information related to a specific body part or symptom from the user's social media activity. Furthermore, the reception unit can also input related body parts and symptoms with reference to posts by the user's friends on social media. In this way, related information is input by analyzing the user's social media activity. A part or all of the above-mentioned processing in the reception unit can be performed, for example, using AI, or can be performed without using AI. For example, the reception unit can input the user's social media data to the generation AI, and cause the generation AI to execute input of related information.

[0041] The analysis unit can improve the accuracy of the analysis by taking into account the user's past medical history and evaluations during the analysis. The analysis unit, for example, analyzes related parts and symptoms based on the user's past medical history. The analysis unit can also improve the accuracy of the analysis by referring to the user's past medical evaluations. Furthermore, the analysis unit can provide optimal analysis results by combining the user's past medical history and evaluations. This improves the accuracy of the analysis by taking into account the user's past medical history and evaluations. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's past medical data into the generation AI and cause the generation AI to execute improvement of the accuracy of the analysis.

[0042] The analysis unit can improve the accuracy of the analysis by taking into account detailed information on the site and symptoms during the analysis. The analysis unit improves the accuracy of the analysis, for example, based on detailed information on the site and symptoms input by the user. The analysis unit can also analyze information on related clinics by taking into account detailed information on the site and symptoms. Furthermore, the analysis unit can analyze an optimal treatment method based on detailed information on the site and symptoms. This improves the accuracy of the analysis by taking into account detailed information on the site and symptoms. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input detailed information on the site and symptoms to the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0043] The analysis unit can perform the analysis while taking into consideration the geographical distribution of clinics. For example, the analysis unit analyzes the optimal clinic based on the geographical distribution of clinics. The analysis unit can also preferentially analyze nearby clinics based on the user's current location. Furthermore, the analysis unit can analyze the optimal treatment method while taking into consideration the geographical distribution of clinics. This makes it possible to analyze the optimal clinic by taking into consideration the geographical distribution of clinics. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input geographical data of clinics to the generation AI and cause the generation AI to execute the analysis.

[0044] The analysis unit can improve the accuracy of the analysis by referring to related medical literature during the analysis. The analysis unit can improve the accuracy of the analysis, for example, based on related medical literature. The analysis unit can also analyze the optimal treatment method by referring to medical literature. Furthermore, the analysis unit can analyze information on related clinics based on medical literature. As a result, the accuracy of the analysis is improved by referring to related medical literature. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can input medical literature data to the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0045] The recommendation unit can improve the accuracy of the recommendation by considering the specialty and past treatment results of the clinic when making a recommendation. The recommendation unit recommends an optimal clinic based on, for example, the specialty of the clinic. The recommendation unit can also improve the accuracy of the recommendation by referring to the past treatment results of the clinic. Furthermore, the recommendation unit can recommend an optimal clinic by combining the specialty and past treatment results of the clinic. As a result, the accuracy of the recommendation is improved by considering the specialty and past treatment results of the clinic. A part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI or may be performed without using AI. For example, the recommendation unit can input the specialty and treatment results data of the clinic to the generation AI and cause the generation AI to improve the accuracy of the recommendation.

[0046] The recommendation unit can recommend the most suitable clinic by taking into consideration the user's past medical history and evaluations at the time of recommendation. The recommendation unit recommends the most suitable clinic based on the user's past medical history, for example. The recommendation unit can also improve the accuracy of the recommendation by referring to the user's past medical evaluations. Furthermore, the recommendation unit can recommend the most suitable clinic by combining the user's past medical history and evaluations. In this way, the most suitable clinic can be recommended by taking into consideration the user's past medical history and evaluations. A part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit can input the user's past medical data into the generation AI and cause the generation AI to recommend the most suitable clinic.

[0047] The recommendation unit can recommend the most suitable clinic in consideration of the geographical distribution of clinics when making a recommendation. The recommendation unit recommends the most suitable clinic based on, for example, the geographical distribution of clinics. The recommendation unit can also preferentially recommend nearby clinics based on the user's current location. Furthermore, the recommendation unit can also recommend the most suitable treatment method in consideration of the geographical distribution of clinics. This makes it possible to recommend the most suitable clinic for the user by taking into account the geographical distribution of clinics. A part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI or may be performed without using AI. For example, the recommendation unit can input geographical data of clinics to the generation AI and cause the generation AI to recommend the most suitable clinic.

[0048] The recommendation unit can improve the accuracy of the recommendation by referring to related medical literature when making a recommendation. The recommendation unit can improve the accuracy of the recommendation, for example, based on related medical literature. The recommendation unit can also recommend an optimal treatment method by referring to medical literature. Furthermore, the recommendation unit can also recommend information on related clinics based on medical literature. As a result, the accuracy of the recommendation is improved by referring to related medical literature. A part or all of the above-mentioned processing in the recommendation unit can be performed, for example, using AI, or can be performed without using AI. For example, the recommendation unit can input medical literature data to the generation AI and cause the generation AI to improve the accuracy of the recommendation.

[0049] The providing unit can provide optimal information by considering the user's past medical history and evaluation at the time of providing. The providing unit provides information of related clinics based on the user's past medical history, for example. The providing unit can also provide optimal clinic information by referring to the user's past medical evaluation. Furthermore, the providing unit can provide optimal information by combining the user's past medical history and evaluation. In this way, optimal information is provided by considering the user's past medical history and evaluation. 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 input the user's past medical data to the generating AI and cause the generating AI to provide optimal information.

[0050] The providing unit can update the clinic's consultation hours and reservation status in real time at the time of providing. For example, the providing unit updates the clinic's consultation hours in real time and provides it to the user. The providing unit can also update the clinic's reservation status in real time and provide it to the user. Furthermore, the providing unit can combine the clinic's consultation hours and reservation status to provide optimal information. As a result, the latest information is provided by updating the clinic's consultation hours and reservation status in real time. Some 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 input the clinic's consultation hours and reservation status data to the generating AI and cause the generating AI to perform real-time updates.

[0051] The providing unit can provide optimal clinic information by considering the geographical location information of the user at the time of providing. For example, the providing unit provides nearby clinic information with priority based on the user's current location. The providing unit can also provide clinic information related to region-specific diseases and symptoms based on the user's geographical location information. Furthermore, the providing unit can also provide information on the nearest medical institution by considering the user's geographical location information. In this way, optimal clinic information is provided by considering the user's geographical location information. 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 input the user's location information data to the generating AI and cause the generating AI to provide optimal clinic information.

[0052] The providing unit can provide related clinic information by analyzing the user's social media activity at the time of providing. The providing unit can, for example, analyze the user's social media posts and provide clinic information related to recent health conditions and symptoms. The providing unit can also provide clinic information related to specific parts or symptoms from the user's social media activities. Furthermore, the providing unit can also provide related clinic information by referring to posts by the user's friends on social media. In this way, related clinic information is provided by analyzing the user's social media activities. A part or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or can be performed without using AI. For example, the providing unit can input the user's social media data to the generating AI and cause the generating AI to provide related clinic information.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The reception unit can automatically search for related medical literature based on the contents input by the user and provide it to the user. For example, if the user inputs "my right knee hurts," the reception unit searches for the latest medical literature on right knee pain and provides the user with a summary of the literature. The reception unit can also provide literature on treatments and preventive measures related to the symptoms input by the user. Furthermore, the reception unit can also provide information on the progress of related medical research and clinical trials based on the contents input by the user. This allows the user to obtain the latest medical information on his or her symptoms and use it as a reference for receiving more appropriate treatment.

[0055] The analysis unit can provide relevant health advice based on the user's input. For example, if the user inputs "my right knee hurts," the analysis unit can suggest stretching and exercise methods for the right knee pain. The analysis unit can also provide advice on improvements to diet and lifestyle habits based on the user's symptoms. Furthermore, the analysis unit can provide preventive measures and points of attention related to the user's symptoms. This allows the user to know specific measures for their symptoms, which can be useful for health management in daily life.

[0056] The recommendation unit can recommend a related online medical consultation service based on the user's input. For example, if the user inputs "my right knee hurts," the recommendation unit recommends a clinic that provides an online medical consultation service for the pain in the right knee. The recommendation unit can also recommend an online consultation service by a specialist based on the user's symptoms. Furthermore, the recommendation unit can also recommend online health seminars and webinars related to the user's symptoms. This allows the user to receive professional medical consultation at home, helping them to receive appropriate treatment quickly.

[0057] The providing unit can recommend a related health app based on the user's input. For example, if the user inputs "my right knee hurts," the providing unit recommends an app that supports exercises and rehabilitation for the pain in the right knee. The providing unit can also recommend an app that supports dietary management and lifestyle improvement based on the user's symptoms. Furthermore, the providing unit can recommend a health monitoring app related to the user's symptoms. This allows the user to more effectively manage their health in daily life by using an app appropriate for their own symptoms.

[0058] The reception unit can recommend relevant health communities and support groups based on the user's input. For example, if the user inputs "my right knee hurts," the reception unit can recommend online forums and support groups related to right knee pain. The reception unit can also recommend local health events and workshops based on the user's symptoms. Furthermore, the reception unit can recommend lectures and seminars by experts related to the user's symptoms. This allows the user to share information about their symptoms and have an opportunity to interact with other people.

[0059] The process flow of the first embodiment will be briefly described below.

[0060] Step 1: The reception unit receives the part and symptoms input by the user. For example, if the user inputs "my right knee hurts," the reception unit receives this information. Step 2: The analysis unit analyzes the information received by the reception unit. For example, the analysis unit performs an analysis to identify the most suitable clinic based on the input body part and symptoms. The analysis unit uses natural language processing technology to analyze the text input by the user and identify the body part and symptoms. Step 3: The recommendation unit recommends the most suitable clinic based on the information analyzed by the analysis unit. For example, the recommendation unit recommends the most suitable clinic for the user's symptoms, taking into account each clinic's specialty and past treatment results. The recommendation unit refers to a database of clinics and identifies the most suitable clinic for the user's symptoms. Step 4: The providing unit provides the user with information about the clinic recommended by the recommending unit. For example, the providing unit provides the user with information such as the name, address, telephone number, and opening hours of the clinic. The providing unit displays the clinic information on the user's device.

[0061] (Example 2) The clinic recommendation system according to the embodiment of the present invention is a system that guides users to recommended clinics when they input a part or symptom when they are sick or injured. When a user inputs a part or symptom such as "this part hurts like this," the system analyzes the input information and recommends the most suitable clinic. At this time, the system makes the recommendation taking into consideration the specialty and past treatment results of each clinic. For example, in the case of orthopedics, a clinic that is good at specific parts or symptoms is recommended. This allows the user to easily find the clinic that is best suited to their symptoms. For example, when a user inputs "my right knee hurts," this information is input into the system. Next, the system analyzes the input information and recommends an orthopedic clinic that is good at right knee pain. At this time, the system takes into consideration the specialty and past treatment results of each clinic. The system provides the user with information on the recommended clinic. For example, the system provides information such as the name, address, telephone number, and opening hours of the clinic. This allows the user to easily find the clinic that is best suited to their symptoms. This mechanism allows the user to easily find the clinic that is best suited to their symptoms, making it easier for them to visit the clinic. For example, in the case of orthopedics, by recommending a clinic that is good at specific parts or symptoms, the user can receive the treatment that is best suited to their symptoms. This enables the clinic recommendation system to quickly and accurately recommend a clinic that is best suited to the user's symptoms.

[0062] The clinic recommendation system according to the embodiment includes a reception unit, an analysis unit, a recommendation unit, and a provision unit. The reception unit receives a site and symptoms input by a user. For example, when a user inputs "my right knee hurts," the reception unit receives this information. The analysis unit analyzes the information received by the reception unit. For example, the analysis unit performs an analysis to identify an optimal clinic based on the input site and symptoms. The analysis unit analyzes the text input by the user using, for example, natural language processing technology to identify the site and symptoms. The recommendation unit recommends an optimal clinic based on the information analyzed by the analysis unit. For example, the recommendation unit recommends a clinic optimal for the user's symptoms, taking into account the specialty and past treatment results of each clinic. The recommendation unit, for example, refers to a database of clinics and identifies a clinic optimal for the user's symptoms. The provision unit provides the user with information on the clinic recommended by the recommendation unit. For example, the provision unit provides the user with information such as the name, address, telephone number, and consultation hours of the clinic. The provision unit, for example, displays the clinic information on the user's device. As a result, the clinic recommendation system according to the embodiment can quickly and accurately recommend a clinic that is optimal for the user's symptoms. Some or all of the above-mentioned processes in the analysis unit, recommendation unit, and provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input text entered by the user to the generation AI and cause the generation AI to identify the body part and symptoms. The recommendation unit can input a clinic database to the generation AI and cause the generation AI to identify the optimal clinic. The provision unit can input clinic information to the generation AI and cause the generation AI to generate information to be provided to the user.

[0063] The reception unit receives the body part and symptoms input by the user. For example, when the user inputs "my right knee hurts," the reception unit receives this information. The reception unit has a user interface designed to receive the information input by the user accurately. For example, the reception unit is equipped with not only a text input field, but also multiple choice questions and a voice input function, allowing the user to easily input information. The voice input function converts the user's speech into text using voice recognition technology and transmits it to the reception unit. Furthermore, the reception unit has a mechanism to prevent input errors by temporarily storing the information input by the user and displaying a confirmation screen before transmitting the information to the analysis unit. For example, after the user inputs "my right knee hurts," the confirmation screen displays "pain in the right knee," and the information is transmitted to the analysis unit when the user presses the confirmation button. In addition, the reception unit refers to the user's past input history, and can quickly respond based on past information if the same symptom recurs. This allows the reception unit to accurately and quickly receive the information input by the user and smoothly hand it over to the next process.

[0064] The analysis unit analyzes the information received by the reception unit. For example, the analysis unit performs an analysis to identify the optimal clinic based on the inputted part and symptoms. The analysis unit analyzes the text input by the user using, for example, natural language processing technology to identify the part and symptoms. Specifically, the analysis unit uses natural language processing technology to extract keywords from the text input by the user and identify the symptoms and part based on these keywords. For example, from the input "my right knee hurts", the keywords "right knee" and "pain" are extracted and analysis is performed based on this. Furthermore, the analysis unit can use the generation AI to perform a more advanced analysis of the text input by the user. The generation AI has learned a large amount of medical data and can identify symptoms and parts from the user's input with high accuracy. For example, the generation AI can identify specific symptoms such as inflammation of the knee joint and damage to the ligaments from the input "my right knee hurts". This allows the analysis unit to quickly and accurately analyze the information input by the user and hand it over to the next recommendation unit.

[0065] The recommendation unit recommends an optimal clinic based on the information analyzed by the analysis unit. For example, the recommendation unit recommends a clinic optimal for the user's symptoms, taking into consideration the specialty and past treatment record of each clinic. The recommendation unit, for example, refers to a clinic database to identify a clinic optimal for the user's symptoms. Specifically, the clinic database includes information such as each clinic's specialty, treatment record, and patient evaluation, and identifies the optimal clinic based on this information. Furthermore, the recommendation unit can analyze the clinic database using a generation AI to identify the optimal clinic. The generation AI has learned past treatment data and patient evaluation, and can identify the clinic optimal for the user's symptoms with high accuracy. For example, the generation AI can identify and recommend a clinic that has a track record of treating similar symptoms in the past based on the user's symptoms. This allows the recommendation unit to quickly and accurately recommend a clinic optimal for the user's symptoms.

[0066] The providing unit provides the user with information on the clinic recommended by the recommendation unit. For example, the providing unit provides the user with information such as the name, address, telephone number, and consultation hours of the clinic. For example, the providing unit displays the clinic information on the user's device. Specifically, the providing unit displays the clinic information on the user's smartphone or computer, allowing the user to easily access the clinic. Furthermore, the providing unit can cooperate with the clinic's reservation system to allow the user to make a reservation directly. For example, the providing unit displays a link to the clinic's reservation page, and the user can click to access the reservation page directly. The providing unit can also display the nearest clinic based on the user's location information. This allows the user to easily find the nearest clinic from his / her current location. Furthermore, the providing unit can collect user feedback and continuously update the clinic information. For example, the user can input an evaluation after visiting a clinic, thereby providing useful information for other users. This allows the providing unit to provide the user with quick and accurate information and support the selection of a clinic.

[0067] The analysis unit may include a history unit based on the user's past medical history or evaluation. The analysis unit improves the accuracy of the analysis, for example, based on the user's past medical history. For example, the analysis unit refers to the user's past medical records and analyzes the association with the current symptoms. The analysis unit can also improve the accuracy of the analysis, based on the user's past medical evaluation. For example, the analysis unit refers to the evaluation of the treatment the user has received in the past and identifies the optimal treatment method for the current symptoms. This improves the accuracy of the analysis by taking into account the user's past medical history and evaluation. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's past medical history into the generation AI and cause the generation AI to execute the improvement of the accuracy of the analysis.

[0068] The recommendation unit may include a collection unit that collects information about the clinic. The recommendation unit may include, for example, a collection unit for collecting information about the clinic. The collection unit may collect, for example, the clinic's medical department, doctor's profile, and facility information. For example, the collection unit may collect information from the clinic's website or database. The collection unit may also collect evaluations of the clinic and patient feedback. For example, the collection unit may collect online review sites and patient questionnaire results. By collecting the clinic's information, the accuracy of the recommendation is improved. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit may input the clinic's information to the generation AI and cause the generation AI to collect the information.

[0069] The providing unit can provide information on the name, address, telephone number, and consultation hours of the clinic. The providing unit provides information such as the name, address, telephone number, and consultation hours of the clinic to the user. For example, the providing unit acquires information from the clinic's database and displays it on the user's device. The providing unit can also update the clinic's consultation hours and reservation status in real time and provide it to the user. For example, the providing unit cooperates with the clinic's system to acquire the latest consultation hours and reservation status. This makes it easier for the user to select an appropriate clinic by providing detailed information about the clinic. Some 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 input clinic information to the generating AI and cause the generating AI to provide the information.

[0070] The reception unit can estimate the user's emotion and adjust the display method of the input interface based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the display method of the input interface based on the estimated emotion. For example, when the user feels anxious, the reception unit provides an interface with a calm color scheme to reduce visual stress. Also, when the user is relaxed, the reception unit provides an interface with a bright color scheme to make the input work enjoyable. Furthermore, when the user is in a hurry, the reception unit provides an interface with a simple and high visibility to enable the input work to be performed quickly. As a result, the input work becomes comfortable by providing an interface according to the user's emotion. The estimation of the 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 reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the emotion estimation.

[0071] The reception unit can analyze the user's past input history and propose the optimal input method. The reception unit, for example, analyzes the user's past input history and proposes the optimal input method. For example, the reception unit automatically displays the body parts and symptoms that the user frequently inputs in the past as candidates. The reception unit can also preferentially propose the input method (voice, text, etc.) that the user used in the past. Furthermore, the reception unit can predict and propose the body parts and symptoms to be used in a specific time period from the user's past input history. As a result, the input work is made more efficient by proposing the optimal input method based on the past input history. 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 input the user's past input data to the generation AI and cause the generation AI to execute the proposal of the optimal input method.

[0072] The reception unit can complement the input contents based on the user's current health condition and lifestyle habits when inputting the body part and symptoms. The reception unit, for example, automatically complements the relevant body part and symptoms based on the user's current health condition. For example, the reception unit complements the relevant body part and symptoms by referring to the user's health checkup results. The reception unit can also complement the input contents by taking into account the user's lifestyle habits (exercise frequency, dietary content, etc.). For example, the reception unit complements the relevant body part and symptoms based on the results of a lifestyle questionnaire of the user. Furthermore, the reception unit can complement the input contents by referring to the user's past health checkup results. As a result, more accurate information is input by complementing the input contents 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, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's health checkup data to the generation AI and cause the generation AI to execute the completion of the input contents.

[0073] The reception unit can estimate the user's emotion and determine the priority of the input contents based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and determines the priority of the input contents based on the estimated emotion. For example, when the user feels anxious, the reception unit prioritizes the input of important parts and symptoms. Also, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit prioritizes the input of the most important parts and symptoms. As a result, important information is preferentially input by determining the priority of the input contents according to the user's emotion. The estimation of the 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 reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0074] The reception unit can preferentially input highly relevant information in consideration of the geographical location information of the user when inputting the part or symptom. The reception unit, for example, can preferentially input parts or symptoms related to nearby clinics based on the user's current location. The reception unit can also preferentially input information related to region-specific diseases and symptoms based on the user's geographical location information. Furthermore, the reception unit can also preferentially input parts or symptoms related to the nearest medical institution in consideration of the user's geographical location information. In this way, highly relevant information is preferentially input by considering the user's geographical location information. 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 input the user's location information data to the generation AI and cause the generation AI to execute the preferential input of highly relevant information.

[0075] The reception unit can analyze the user's social media activity when inputting the body part or symptoms, and input related information. The reception unit can, for example, analyze the user's social media posts, and input information related to the user's recent health condition or symptoms. The reception unit can also preferentially input information related to a specific body part or symptom from the user's social media activity. Furthermore, the reception unit can also input related body parts and symptoms with reference to posts by the user's friends on social media. In this way, related information is input by analyzing the user's social media activity. A part or all of the above-mentioned processing in the reception unit can be performed, for example, using AI, or can be performed without using AI. For example, the reception unit can input the user's social media data to the generation AI, and cause the generation AI to execute input of related information.

[0076] The analysis unit can estimate the user's emotion and adjust the analysis algorithm based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the analysis algorithm based on the estimated emotion. For example, when the user feels anxious, the analysis unit adjusts the analysis algorithm to provide results more quickly. Also, when the user is relaxed, the analysis unit can perform detailed analysis and provide more information. Furthermore, when the user is in a hurry, the analysis unit prioritizes analysis of important information and provides results quickly. This improves the accuracy of the analysis result by adjusting the analysis algorithm 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 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 analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the analysis algorithm.

[0077] The analysis unit can improve the accuracy of the analysis by taking into account the user's past medical history and evaluations during the analysis. The analysis unit, for example, analyzes related parts and symptoms based on the user's past medical history. The analysis unit can also improve the accuracy of the analysis by referring to the user's past medical evaluations. Furthermore, the analysis unit can provide optimal analysis results by combining the user's past medical history and evaluations. This improves the accuracy of the analysis by taking into account the user's past medical history and evaluations. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's past medical data into the generation AI and cause the generation AI to execute improvement of the accuracy of the analysis.

[0078] The analysis unit can improve the accuracy of the analysis by taking into account detailed information on the site and symptoms during the analysis. The analysis unit improves the accuracy of the analysis, for example, based on detailed information on the site and symptoms input by the user. The analysis unit can also analyze information on related clinics by taking into account detailed information on the site and symptoms. Furthermore, the analysis unit can analyze an optimal treatment method based on detailed information on the site and symptoms. This improves the accuracy of the analysis by taking into account detailed information on the site and symptoms. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input detailed information on the site and symptoms to the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0079] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the display method of the analysis result based on the estimated emotion. For example, when the user feels anxious, the analysis unit provides a simple and highly visible display method. Also, when the user is relaxed, the analysis unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the analysis unit provides a display method that focuses on the main points. This makes it easier to see the analysis result by providing a display 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 analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method.

[0080] The analysis unit can perform the analysis while taking into consideration the geographical distribution of clinics. For example, the analysis unit analyzes the optimal clinic based on the geographical distribution of clinics. The analysis unit can also preferentially analyze nearby clinics based on the user's current location. Furthermore, the analysis unit can analyze the optimal treatment method while taking into consideration the geographical distribution of clinics. This makes it possible to analyze the optimal clinic by taking into consideration the geographical distribution of clinics. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input geographical data of clinics to the generation AI and cause the generation AI to execute the analysis.

[0081] The analysis unit can improve the accuracy of the analysis by referring to related medical literature during the analysis. The analysis unit can improve the accuracy of the analysis, for example, based on related medical literature. The analysis unit can also analyze the optimal treatment method by referring to medical literature. Furthermore, the analysis unit can analyze information on related clinics based on medical literature. As a result, the accuracy of the analysis is improved by referring to related medical literature. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can input medical literature data to the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0082] The recommendation unit can estimate the user's emotion and adjust the recommendation expression method based on the estimated user's emotion. The recommendation unit, for example, estimates the user's emotion and adjusts the recommendation expression method based on the estimated emotion. For example, when the user feels anxious, the recommendation unit provides a simple and highly visible recommendation method. Also, when the user is relaxed, the recommendation unit can provide a recommendation method including detailed information. Furthermore, when the user is in a hurry, the recommendation unit provides a recommendation method that covers the main points. As a result, by providing a recommendation method according to the user's emotion, the recommendation result becomes easier to see. 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 recommendation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recommendation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the recommendation expression method.

[0083] The recommendation unit can improve the accuracy of the recommendation by considering the specialty and past treatment results of the clinic when making a recommendation. The recommendation unit recommends an optimal clinic based on, for example, the specialty of the clinic. The recommendation unit can also improve the accuracy of the recommendation by referring to the past treatment results of the clinic. Furthermore, the recommendation unit can recommend an optimal clinic by combining the specialty and past treatment results of the clinic. As a result, the accuracy of the recommendation is improved by considering the specialty and past treatment results of the clinic. A part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI or may be performed without using AI. For example, the recommendation unit can input the specialty and treatment results data of the clinic to the generation AI and cause the generation AI to improve the accuracy of the recommendation.

[0084] The recommendation unit can recommend the most suitable clinic by taking into consideration the user's past medical history and evaluations at the time of recommendation. The recommendation unit recommends the most suitable clinic based on the user's past medical history, for example. The recommendation unit can also improve the accuracy of the recommendation by referring to the user's past medical evaluations. Furthermore, the recommendation unit can recommend the most suitable clinic by combining the user's past medical history and evaluations. In this way, the most suitable clinic can be recommended by taking into consideration the user's past medical history and evaluations. A part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit can input the user's past medical data into the generation AI and cause the generation AI to recommend the most suitable clinic.

[0085] The recommendation unit can estimate the user's emotion and determine the priority of recommendations based on the estimated user's emotion. The recommendation unit, for example, estimates the user's emotion and determines the priority of recommendations based on the estimated emotion. For example, if the user feels anxious, the recommendation unit prioritizes recommendations of important clinics. Also, if the user is relaxed, the recommendation unit can prioritize recommendations of clinics including detailed information. Furthermore, if the user is in a hurry, the recommendation unit prioritizes recommendations of the most important clinics. In this way, important information is provided preferentially by determining the priority of recommendations 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 recommendation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recommendation unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of recommendations.

[0086] The recommendation unit can recommend the most suitable clinic in consideration of the geographical distribution of clinics when making a recommendation. The recommendation unit recommends the most suitable clinic based on, for example, the geographical distribution of clinics. The recommendation unit can also preferentially recommend nearby clinics based on the user's current location. Furthermore, the recommendation unit can also recommend the most suitable treatment method in consideration of the geographical distribution of clinics. This makes it possible to recommend the most suitable clinic for the user by taking into account the geographical distribution of clinics. A part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI or may be performed without using AI. For example, the recommendation unit can input geographical data of clinics to the generation AI and cause the generation AI to recommend the most suitable clinic.

[0087] The recommendation unit can improve the accuracy of the recommendation by referring to related medical literature when making a recommendation. The recommendation unit can improve the accuracy of the recommendation, for example, based on related medical literature. The recommendation unit can also recommend an optimal treatment method by referring to medical literature. Furthermore, the recommendation unit can also recommend information on related clinics based on medical literature. As a result, the accuracy of the recommendation is improved by referring to related medical literature. A part or all of the above-mentioned processing in the recommendation unit can be performed, for example, using AI, or can be performed without using AI. For example, the recommendation unit can input medical literature data to the generation AI and cause the generation AI to improve the accuracy of the recommendation.

[0088] The providing unit can estimate the user's emotion and adjust the display method of the clinic information based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the display method of the clinic information based on the estimated emotion. For example, when the user feels anxious, the providing unit provides a simple and highly visible display method. Also, when the user is relaxed, the providing unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the providing unit provides a display method that focuses on the main points. This makes it easier to see the clinic information by providing a display method 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 providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method.

[0089] The providing unit can provide optimal information by considering the user's past medical history and evaluation at the time of providing. The providing unit provides information of related clinics based on the user's past medical history, for example. The providing unit can also provide optimal clinic information by referring to the user's past medical evaluation. Furthermore, the providing unit can provide optimal information by combining the user's past medical history and evaluation. In this way, optimal information is provided by considering the user's past medical history and evaluation. 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 input the user's past medical data to the generating AI and cause the generating AI to provide optimal information.

[0090] The providing unit can update the clinic's consultation hours and reservation status in real time at the time of providing. For example, the providing unit updates the clinic's consultation hours in real time and provides it to the user. The providing unit can also update the clinic's reservation status in real time and provide it to the user. Furthermore, the providing unit can combine the clinic's consultation hours and reservation status to provide optimal information. As a result, the latest information is provided by updating the clinic's consultation hours and reservation status in real time. Some 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 input the clinic's consultation hours and reservation status data to the generating AI and cause the generating AI to perform real-time updates.

[0091] The providing unit can estimate the user's emotion and determine the priority of the clinic information based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and determines the priority of the clinic information based on the estimated emotion. For example, when the user feels anxious, the providing unit provides important clinic information with priority. Also, when the user is relaxed, the providing unit can provide clinic information including detailed information with priority. Furthermore, when the user is in a hurry, the providing unit provides the most important clinic information with priority. As a result, important information is provided with priority by determining the priority of the clinic information 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 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 input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority.

[0092] The providing unit can provide optimal clinic information by considering the geographical location information of the user at the time of providing. For example, the providing unit provides nearby clinic information with priority based on the user's current location. The providing unit can also provide clinic information related to region-specific diseases and symptoms based on the user's geographical location information. Furthermore, the providing unit can also provide information on the nearest medical institution by considering the user's geographical location information. In this way, optimal clinic information is provided by considering the user's geographical location information. 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 input the user's location information data to the generating AI and cause the generating AI to provide optimal clinic information.

[0093] The providing unit can provide related clinic information by analyzing the user's social media activity at the time of providing. The providing unit can, for example, analyze the user's social media posts and provide clinic information related to recent health conditions and symptoms. The providing unit can also provide clinic information related to specific parts or symptoms from the user's social media activities. Furthermore, the providing unit can also provide related clinic information by referring to posts by the user's friends on social media. In this way, related clinic information is provided by analyzing the user's social media activities. A part or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or can be performed without using AI. For example, the providing unit can input the user's social media data to the generating AI and cause the generating AI to provide related clinic information.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The reception unit can automatically search for related medical literature based on the contents input by the user and provide it to the user. For example, if the user inputs "my right knee hurts," the reception unit searches for the latest medical literature on right knee pain and provides the user with a summary of the literature. The reception unit can also provide literature on treatments and preventive measures related to the symptoms input by the user. Furthermore, the reception unit can also provide information on the progress of related medical research and clinical trials based on the contents input by the user. This allows the user to obtain the latest medical information on his or her symptoms and use it as a reference for receiving more appropriate treatment.

[0096] The analysis unit can provide relevant health advice based on the user's input. For example, if the user inputs "my right knee hurts," the analysis unit can suggest stretching and exercise methods for the right knee pain. The analysis unit can also provide advice on improvements to diet and lifestyle habits based on the user's symptoms. Furthermore, the analysis unit can provide preventive measures and points of attention related to the user's symptoms. This allows the user to know specific measures for their symptoms, which can be useful for health management in daily life.

[0097] The recommendation unit can recommend a related online medical consultation service based on the user's input. For example, if the user inputs "my right knee hurts," the recommendation unit recommends a clinic that provides an online medical consultation service for the pain in the right knee. The recommendation unit can also recommend an online consultation service by a specialist based on the user's symptoms. Furthermore, the recommendation unit can also recommend online health seminars and webinars related to the user's symptoms. This allows the user to receive professional medical consultation at home, helping them to receive appropriate treatment quickly.

[0098] The providing unit can recommend a related health app based on the user's input. For example, if the user inputs "my right knee hurts," the providing unit recommends an app that supports exercises and rehabilitation for the pain in the right knee. The providing unit can also recommend an app that supports dietary management and lifestyle improvement based on the user's symptoms. Furthermore, the providing unit can recommend a health monitoring app related to the user's symptoms. This allows the user to more effectively manage their health in daily life by using an app appropriate for their own symptoms.

[0099] The reception unit can estimate the user's emotions and adjust the confirmation method of the input contents based on the estimated emotions. For example, when the user feels anxious, the reception unit carefully confirms the input contents to make the user feel at ease. When the user is relaxed, the reception unit provides a simple confirmation method to smoothly proceed with the input work. When the user is in a hurry, the reception unit confirms only the most important input contents to allow the user to proceed to the next step quickly. In this way, by providing a confirmation method according to the user's emotions, the input work becomes more comfortable.

[0100] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated emotions. For example, if the user feels anxious, the analysis unit carefully explains the analysis results to reassure the user. If the user feels relaxed, the analysis unit provides a concise notification method to enable the user to quickly check the results. If the user is in a hurry, the analysis unit notifies the user of only the most important analysis results so that the user can quickly proceed to the next step. In this way, by providing a notification method according to the user's emotions, the analysis results can be received more smoothly.

[0101] The recommendation unit can estimate the user's emotion and adjust the level of detail of the recommendation content based on the estimated emotion. For example, if the user feels anxious, the recommendation unit provides detailed information to make the user feel at ease. If the user is relaxed, the recommendation unit provides concise information to allow the user to make a quick selection. If the user is in a hurry, the recommendation unit provides only the most important information to allow the user to proceed to the next step quickly. In this way, the recommendation content according to the user's emotion can be provided to allow the user to make a more appropriate selection.

[0102] The providing unit can estimate the user's emotions and adjust the display order of the clinic information based on the estimated emotions. For example, when the user feels anxious, the providing unit can preferentially display reliable clinic information, so that the user can feel at ease. Also, when the user is relaxed, the providing unit can preferentially display clinic information based on the user's interests. Furthermore, when the user is in a hurry, the providing unit preferentially displays the most important clinic information, so that the user can quickly proceed to the next step. In this way, by providing a display order according to the user's emotions, the user can select a more appropriate clinic.

[0103] The providing unit can estimate the user's emotions and adjust the display format of the clinic information based on the estimated emotions. For example, when the user feels anxious, the providing unit can provide a simple and highly visible display format to make the user feel at ease. When the user feels relaxed, the providing unit can provide a display format including detailed information. Furthermore, when the user is in a hurry, the providing unit can provide a display format that focuses on the main points to allow the user to proceed to the next step quickly. In this way, by providing a display format according to the user's emotions, the clinic information becomes easier to view.

[0104] The reception unit can recommend relevant health communities and support groups based on the user's input. For example, if the user inputs "my right knee hurts," the reception unit can recommend online forums and support groups related to right knee pain. The reception unit can also recommend local health events and workshops based on the user's symptoms. Furthermore, the reception unit can recommend lectures and seminars by experts related to the user's symptoms. This allows the user to share information about their symptoms and have an opportunity to interact with other people.

[0105] The process flow of the second embodiment will be briefly described below.

[0106] Step 1: The reception unit receives the part and symptoms input by the user. For example, if the user inputs "my right knee hurts," the reception unit receives this information. Step 2: The analysis unit analyzes the information received by the reception unit. For example, the analysis unit performs an analysis to identify the most suitable clinic based on the input body part and symptoms. The analysis unit uses natural language processing technology to analyze the text input by the user and identify the body part and symptoms. Step 3: The recommendation unit recommends the most suitable clinic based on the information analyzed by the analysis unit. For example, the recommendation unit recommends the most suitable clinic for the user's symptoms, taking into account each clinic's specialty and past treatment results. The recommendation unit refers to a database of clinics and identifies the most suitable clinic for the user's symptoms. Step 4: The providing unit provides the user with information about the clinic recommended by the recommending unit. For example, the providing unit provides the user with information such as the name, address, telephone number, and opening hours of the clinic. The providing unit displays the clinic information on the user's device.

[0107] 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.

[0108] 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 also 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.

[0109] 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.

[0110] Each of the multiple elements including the above-mentioned reception unit, analysis unit, recommendation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives the part and symptoms input by the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information received by the reception unit. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends an optimal clinic based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the user with information on the recommended clinic. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements including the above-mentioned reception unit, analysis unit, recommendation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives the part and symptoms input by the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information received by the reception unit. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends an optimal clinic based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the user with information on the recommended clinic. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements including the above-mentioned reception unit, analysis unit, recommendation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and receives the body part and symptoms input by the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the information received by the reception unit. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and recommends an optimal clinic based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the user with information on the recommended clinic. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements including the above-mentioned reception unit, analysis unit, recommendation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives the body part and symptoms input by the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information received by the reception unit. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends an optimal clinic based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the user with information on the recommended clinic. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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 on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] (Appendix 1) A reception unit that receives an input of a body part or a symptom; an analysis unit that analyzes the information received by the reception unit; A recommendation unit that recommends an appropriate clinic based on the information analyzed by the analysis unit; A provision unit that provides information on the clinic recommended by the recommendation unit. A system characterized by: (Appendix 2) The system described in Appendix 1, characterized in that the analysis unit includes a history unit based on the user's past medical history or evaluation. (Appendix 3) The recommendation unit, Equipping a collection department to collect clinic information 2. The system of claim 1, (Appendix 4) The system described in Appendix 1, characterized in that the providing unit provides information on the clinic's name, address, telephone number, and business hours. (Appendix 5) The reception unit is To estimate a user's emotion and adjust a display method of an input interface based on the estimated user's emotion. 2. The system of claim 1, (Appendix 6) The reception unit is Analyze the user's past input history and suggest the optimal input method 2. The system of claim 1, (Appendix 7) The reception unit is When inputting the area or symptoms, the input is completed based on the user's current health condition and lifestyle habits. 2. The system of claim 1, (Appendix 8) The reception unit is Infer user emotions and prioritize input content based on the inferred user emotions 2. The system of claim 1, (Appendix 9) The reception unit is When inputting the area or symptoms, the system takes into account the user's geographic location information and prioritizes input of the most relevant information. 2. The system of claim 1, (Appendix 10) The reception unit is When inputting the area and symptoms, the system analyzes the user's social media activity and prompts them to input related information. 2. The system of claim 1, (Appendix 11) The analysis unit is Inferring user emotions and adjusting analysis algorithms based on the inferred user emotions 2. The system of claim 1, (Appendix 12) The analysis unit is During analysis, the accuracy of the analysis is improved by taking into account the user's past medical history and evaluations. 2. The system of claim 1, (Appendix 13) The analysis unit is Improve the accuracy of analysis by taking into account detailed information about the area and symptoms during analysis. 2. The system of claim 1, (Appendix 14) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 15) The analysis unit is The geographic distribution of clinics was taken into account during the analysis. 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, refer to relevant medical literature to improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 17) The recommendation unit, Estimate user emotions and adjust the way recommendations are presented based on the estimated user emotions 2. The system of claim 1, (Appendix 18) The recommendation unit, When making recommendations, we will improve the accuracy of recommendations by taking into account the clinic's areas of expertise and past treatment results. 2. The system of claim 1, (Appendix 19) The recommendation unit, When making recommendations, the system takes into account the user's past medical history and evaluations to recommend the most suitable clinic. 2. The system of claim 1, (Appendix 20) The recommendation unit, Estimate user emotions and prioritize recommendations based on the estimated user emotions 2. The system of claim 1, (Appendix 21) The recommendation unit, When making recommendations, the geographical distribution of clinics is taken into account to recommend the most suitable clinic. 2. The system of claim 1, (Appendix 22) The recommendation unit, When making recommendations, refer to relevant medical literature to improve the accuracy of recommendations 2. The system of claim 1, (Appendix 23) The providing unit is To estimate a user's emotion and adjust a display method of clinic information based on the estimated user's emotion. 2. The system of claim 1, (Appendix 24) The providing unit is When providing information, the system provides optimal information by taking into account the user's past medical history and evaluations. 2. The system of claim 1, (Appendix 25) The providing unit is When provided, clinic hours and reservation status are updated in real time 2. The system of claim 1, (Appendix 26) The providing unit is To estimate a user's emotion and determine a priority of clinic information based on the estimated user's emotion. 2. The system of claim 1, (Appendix 27) The providing unit is When providing information, the system takes into account the user's geographic location to provide optimal clinic information. 2. The system of claim 1, (Appendix 28) The providing unit is When provided, analyze your social media activity to provide you with relevant clinic information 2. The system of claim 1, [Explanation of symbols]

[0179] 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 body part or a symptom; an analysis unit that analyzes the information received by the reception unit; A recommendation unit that recommends an appropriate clinic based on the information analyzed by the analysis unit; A provision unit that provides information on the clinic recommended by the recommendation unit. A system characterized by:

2. The analysis unit is A history section based on the user's past medical history or evaluation 2. The system of claim 1 .

3. The recommendation unit, Equipping a collection department to collect clinic information 2. The system of claim 1 .

4. The providing unit is Provide the clinic's name, address, phone number, and opening hours.

2. The system of claim 1 .

5. The reception unit is To estimate a user's emotion and adjust a display method of an input interface based on the estimated user's emotion.

2. The system of claim 1 .

6. The reception unit is Analyze the user's past input history and suggest the optimal input method 2. The system of claim 1 .

7. The reception unit is When inputting the area or symptoms, the input is completed based on the user's current health condition and lifestyle habits.

2. The system of claim 1 .

8. The reception unit is Infer user emotions and prioritize input content based on the inferred user emotions 2. The system of claim 1 .

9. The reception unit is When inputting the area or symptoms, the system takes into account the user's geographic location information and prioritizes input of the most relevant information.

2. The system of claim 1 .

10. The reception unit is When inputting the area and symptoms, the system analyzes the user's social media activity and prompts them to input related information.

2. The system of claim 1 .

11. The analysis unit is Inferring user emotions and adjusting analysis algorithms based on the inferred user emotions 2. The system of claim 1 .

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