System

Abstract

An object of a system according to an embodiment is to efficiently find a hospital suitable for symptoms of a user.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a collection unit, a generation unit, and a provision unit. The reception unit inputs a symptom. The analysis unit analyzes the symptom input by the reception unit. The collection unit collects hospital information. The generation unit generates a list of hospitals based on the information collected by the collection unit. The providing unit provides the list generated by the generating unit to the user.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 an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

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

[0004] Conventional technology has had the problem that it is difficult for users to find a hospital that is suitable for their symptoms.

[0005] The system according to the embodiment aims to efficiently find a hospital suitable for the user's symptoms. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a collection unit, a generation unit, and a provision unit. The reception unit inputs symptoms. The analysis unit analyzes the symptoms input by the reception unit. The collection unit collects information about hospitals. The generation unit generates a list of hospitals based on the information collected by the collection unit. The provision unit provides the list generated by the generation unit to a user. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently find a hospital suitable for the user's symptoms. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A hospital matching system according to an embodiment of the present invention uses a generation AI to match the most suitable hospital based on symptoms entered by a user. In the hospital matching system, a user inputs their symptoms, and a generation AI analyzes the symptoms and generates a list of hospitals that correspond to the symptoms. The generated list includes information such as each hospital's specialty and past treatment track record. Based on this list, the user can select the most suitable hospital for them. For example, in the hospital matching system, a user inputs their symptoms. For example, the user inputs information such as "I have a headache" or "I have a stomachache." This information is input to the generation AI. The hospital matching system then analyzes the input symptoms using the generation AI. The generation AI references a symptom database to identify hospitals that correspond to the input symptoms. For example, if a headache symptom is entered, the generation AI lists hospitals that specialize in treating headaches. The generated list includes information such as each hospital's specialty and past treatment track record. For example, if a hospital has a high track record in treating headaches, that information is displayed in the list. This allows the user to select the hospital that best suits their symptoms. The hospital matching system can also update the list of hospitals in real time according to the user's symptoms. For example, if a user inputs new symptoms, the generating AI will regenerate the list based on that information. This allows the system to always provide the most up-to-date information. This allows the hospital matching system to easily find the hospital that best suits the user's symptoms. For example, a user with a headache can receive more effective treatment by selecting a hospital that specializes in headache treatment. In addition, the generating AI lists hospitals based on their past treatment records, allowing the system to provide highly reliable information.

[0029] The hospital matching system according to the embodiment includes a reception unit, an analysis unit, a collection unit, a generation unit, and a provision unit. The reception unit allows a user to input their symptoms. The user can input specific symptoms or ailments, such as "I have a headache" or "I have a stomachache." The reception unit supports, for example, text input or voice input. The reception unit can also transmit the user's input to the generation AI. The analysis unit uses the generation AI to analyze the symptoms input by the reception unit. The generation AI refers to a symptom database and identifies a hospital corresponding to the input symptoms. For example, the generation AI analyzes the input symptoms using a text generation AI (e.g., LLM). The analysis unit can also extract and analyze important parts of the symptoms using the generation AI. The collection unit collects information about hospitals. The collection unit collects information about hospitals, such as hospital specialties and past treatment records. The collection unit can collect information from hospital websites and medical databases, for example. The collection unit can also automatically collect hospital information using the generation AI. The generation unit generates a list of hospitals based on the information collected by the collection unit. The generation unit generates a list of hospitals using, for example, a generation AI. The generation AI lists hospitals that are best suited to the user's symptoms based on the collected information. For example, the generation AI generates the list taking into account the hospital's areas of expertise and past treatment results. The provision unit provides the list generated by the generation unit to the user. The provision unit displays the list to the user, for example, through a web application or a mobile application. The provision unit can also adjust the display method of the list using the generation AI. For example, the provision unit selects the optimal display method depending on the user's input method (voice, text, image, etc.). This enables the hospital matching system according to the embodiment to efficiently match hospitals that are best suited to the user's symptoms.

[0030] The hospital matching system includes a rating collection unit that collects hospital ratings or word-of-mouth information. The rating collection unit collects hospital ratings and word-of-mouth information. The rating collection unit can collect, for example, online reviews and survey results. The rating collection unit can also automatically collect rating information using a generation AI. For example, the rating collection unit collects rating information from hospital websites and social media. By collecting hospital ratings and word-of-mouth information, reliable information can be provided to users. Some or all of the above-described processing in the rating collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the rating collection unit can input online reviews into the generation AI and cause the generation AI to collect rating information.

[0031] The hospital matching system includes a history input unit that inputs the user's past treatments and diagnosis results. The history input unit inputs the user's past treatments and diagnosis results. The history input unit can input, for example, medical records and test results. The history input unit can also automatically input past treatment information using a generation AI. For example, the history input unit acquires and inputs past treatment information from the user's medical database. This makes it possible to match a more appropriate hospital by taking into account the user's past treatments and diagnosis results. Some or all of the above-described processing in the history input unit may be performed, for example, using AI, or may be performed without using AI. For example, the history input unit can input the user's medical records into the generation AI and cause the generation AI to input past treatment information.

[0032] The hospital matching system includes a detail collection unit that collects information on hospital locations, clinic hours, and whether appointments are available. The detail collection unit collects information such as hospital locations, clinic hours, and whether appointments are available. The detail collection unit can collect, for example, hospital locations and clinic hours. The detail collection unit can also automatically collect detailed information using a generation AI. For example, the detail collection unit collects detailed information from hospital websites and medical databases. By collecting detailed hospital information, it is possible to provide highly convenient information to users. Some or all of the above-described processing in the detail collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detail collection unit can input hospital clinic hours into the generation AI and cause the generation AI to collect detailed information.

[0033] The evaluation collection unit can cooperate with the collection unit to collect hospital evaluation information, and the generation unit can generate a list based on that information. The evaluation collection unit cooperates with the collection unit to collect hospital evaluation information. For example, the evaluation collection unit generates a list based on the hospital evaluation information collected by the collection unit. The evaluation collection unit can also automatically collect evaluation information using a generation AI. For example, the evaluation collection unit collects evaluation information from hospital websites and social media, and the generation unit generates a list based on that information. In this way, by generating a list based on hospital evaluation information, reliable hospital information can be provided to users. Some or all of the above-mentioned processing in the evaluation collection unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation collection unit can input online reviews into the generation AI and cause the generation AI to collect evaluation information.

[0034] The history input unit cooperates with the reception unit to accept the user's past treatment information, and the analysis unit can perform analysis based on that information. The history input unit cooperates with the reception unit to accept the user's past treatment information. For example, the history input unit has the analysis unit perform analysis based on the user's medical records and test results accepted by the reception unit. The history input unit can also automatically input past treatment information using a generation AI. For example, the history input unit acquires past treatment information from the user's medical database, and the analysis unit performs analysis based on that information. This allows for analysis that takes the user's past treatment information into consideration, thereby enabling more appropriate hospital matching. Some or all of the above-mentioned processing in the history input unit may be performed using, for example, AI, or may be performed without using AI. For example, the history input unit can input the user's medical records into the generation AI and have the generation AI input past treatment information.

[0035] The reception unit can analyze the user's past symptom input history and suggest the optimal input method. The reception unit analyzes the user's past symptom input history and suggest the optimal input method. For example, the reception unit automatically displays symptoms that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest symptoms that will be input during a specific time period based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past symptom input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input data into a generation AI and have the generation AI suggest the optimal input method.

[0036] The reception unit can filter symptoms based on the user's current health condition and lifestyle habits when inputting the symptoms. The reception unit can filter symptoms based on the user's current health condition and lifestyle habits when inputting the symptoms. For example, the reception unit prioritizes input of related symptoms based on the user's current health condition. The reception unit can also filter related symptoms taking into account the user's lifestyle habits (smoking, drinking, etc.). The reception unit can also adjust the symptoms to be input based on the user's health data (heart rate, blood pressure, etc.). This enables more appropriate symptom input by filtering symptoms based on the user's health condition and lifestyle habits. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's health data to the generation AI and cause the generation AI to filter the symptoms.

[0037] The reception unit can select the optimal input means depending on the user's input method when inputting symptoms. The reception unit selects the optimal input means depending on the user's input method (voice, text, image, etc.) when inputting symptoms. For example, if the user selects voice input, the reception unit inputs symptoms using voice recognition technology. Also, if the user selects text input, the reception unit can support keyboard input. Also, if the user selects image input, the reception unit can analyze the symptoms using image recognition technology. In this way, by selecting the optimal input means depending on the user's input method, it is possible to provide an interface that is easy for the user to use. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select the optimal input means.

[0038] When inputting symptoms, the reception unit can prioritize inputting highly relevant symptoms by taking into account the user's geographical location information. When inputting symptoms, the reception unit prioritizes inputting highly relevant symptoms by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes inputting symptoms that are prevalent in that area. The reception unit can also input symptoms specific to that area based on the user's current location. The reception unit can also filter related symptoms based on the user's geographical location information. In this way, highly relevant symptoms can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location data to the generation AI and cause the generation AI to filter the symptoms.

[0039] The reception unit can analyze the user's social media activity and input related symptoms when the user inputs symptoms. The reception unit can analyze the user's social media activity and input related symptoms when the user inputs symptoms. For example, the reception unit can input related symptoms based on health information shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and input related symptoms. The reception unit can also input related symptoms by referring to the activities of the user's friends on social media. In this way, related symptoms can be input by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may 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 input related symptoms.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when inputting symptoms. The reception unit customizes the input method by reflecting the user's past feedback when inputting symptoms. For example, the reception unit adjusts the input interface based on feedback provided by the user in the past. The reception unit can also optimize the input procedure by reflecting the user's past feedback. The reception unit can also customize the input method based on the user's feedback. In this way, the input method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's feedback data to the generation AI and cause the generation AI to customize the input method.

[0041] The analysis unit can adjust the level of detail of the analysis based on the severity of the symptom during analysis. The analysis unit can adjust the level of detail of the analysis based on the severity of the symptom during analysis. For example, the analysis unit performs a detailed analysis when the symptom is serious. The analysis unit can also perform a simplified analysis when the symptom is minor. The analysis unit can also adjust the depth of the analysis according to the severity of the symptom. In this way, by adjusting the level of detail of the analysis based on the severity of the symptom, appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input symptom severity data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the symptom category during analysis. The analysis unit applies different analysis algorithms depending on the symptom category during analysis. For example, the analysis unit applies an analysis algorithm specialized for internal medicine to internal medicine symptoms. The analysis unit can also apply an analysis algorithm specialized for surgery to surgical symptoms. The analysis unit can also apply an analysis algorithm specialized for psychiatry to psychiatric symptoms. In this way, by applying different analysis algorithms depending on the symptom category, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input symptom category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit adjusts the current analysis based on the user's past analysis results. The analysis unit can also optimize the analysis algorithm by referring to the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by using the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] The analysis unit can determine the priority of analysis based on the time when the symptoms were submitted during analysis. The analysis unit determines the priority of analysis based on the time when the symptoms were submitted during analysis. For example, the analysis unit prioritizes the analysis of recently submitted symptoms. The analysis unit can also postpone the analysis of symptoms that were submitted earlier. The analysis unit can also adjust the order of analysis based on the time when the symptoms were submitted. In this way, by determining the priority of analysis based on the time when the symptoms were submitted, it is possible to perform the analysis in an appropriate order. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the symptoms were submitted to the generation AI and have the generation AI determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of symptoms during analysis. The analysis unit adjusts the order of analysis based on the relevance of symptoms during analysis. For example, the analysis unit prioritizes analysis of highly relevant symptoms. The analysis unit can also postpone analysis of less relevant symptoms. The analysis unit can also adjust the order of analysis based on the relevance of symptoms. In this way, by adjusting the order of analysis based on the relevance of symptoms, highly relevant symptoms can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input symptom relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. If the user does not have technical expertise, the analysis unit can provide analysis results in simple language. The analysis unit can also adjust the use of technical terms in the analysis according to the user's level of expertise. This makes it possible to provide analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI use technical terms.

[0047] The collection unit can adjust the level of detail of the collection based on the importance of the hospital at the time of collection. The collection unit adjusts the level of detail of the collection based on the importance of the hospital at the time of collection. For example, the collection unit collects important hospital information in detail. The collection unit can also collect less important hospital information in a simplified manner. The collection unit can also adjust the level of detail of the collection according to the importance of the hospital. As a result, appropriate information can be collected by adjusting the level of detail of the collection based on the importance of the hospital. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input hospital importance data to the generation AI and cause the generation AI to adjust the level of detail of the collection.

[0048] The collection unit can apply different collection algorithms depending on the hospital category during collection. The collection unit applies different collection algorithms depending on the hospital category during collection. For example, the collection unit applies a collection algorithm specialized for internal medicine to information on internal medicine hospitals. The collection unit can also apply a collection algorithm specialized for surgery to information on surgery hospitals. The collection unit can also apply a collection algorithm specialized for psychiatry to information on psychiatric hospitals. In this way, by applying different collection algorithms depending on the hospital category, more accurate information can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input hospital category data to the generation AI and cause the generation AI to apply the collection algorithm.

[0049] The collection unit can improve the accuracy of collection by referring to the user's past collection results when collecting data. The collection unit can improve the accuracy of collection by referring to the user's past collection results when collecting data. For example, the collection unit adjusts the current collection based on the user's past collection results. The collection unit can also optimize the collection algorithm by referring to the user's past collection results. The collection unit can also improve the accuracy of collection by using the user's past collection results. In this way, the accuracy of collection can be improved by referring to the user's past collection results. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past collection data into a generation AI and cause the generation AI to improve the accuracy of collection.

[0050] The collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of hospitals when collecting data. The collection unit prioritizes collecting highly relevant information by taking into account the geographical location information of hospitals when collecting data. For example, the collection unit prioritizes collecting hospital information close to the user's current location. The collection unit can also collect relevant hospital information based on the user's geographical location information. The collection unit can also collect optimal hospital information by taking into account the user's range of movement. In this way, highly relevant information can be prioritized by taking into account the geographical location information of hospitals. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input geographical location data of hospitals to the generation AI and cause the generation AI to collect information.

[0051] The collection unit can analyze the hospital's social media activities and collect related information at the time of collection. The collection unit can analyze the hospital's social media activities and collect related information at the time of collection. For example, the collection unit collects the hospital's social media ratings and reviews. The collection unit can also analyze the hospital's social media posts and collect related information. The collection unit can also refer to the hospital's social media activities to collect the latest information. In this way, related information can be collected by analyzing the hospital's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the hospital's social media data into a generation AI and cause the generation AI to collect information.

[0052] The collection unit can customize the collection method by reflecting the hospital's past feedback at the time of collection. The collection unit customizes the collection method by reflecting the hospital's past feedback at the time of collection. For example, the collection unit adjusts the collection interface based on the hospital's past feedback. The collection unit can also optimize the collection procedure by reflecting the hospital's past feedback. The collection unit can also customize the collection method based on the hospital's feedback. In this way, the collection method can be customized by reflecting the hospital's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the hospital's feedback data into the generation AI and cause the generation AI to customize the collection method.

[0053] The generation unit can adjust the level of detail of the list based on the importance of the hospital when generating the list. The generation unit adjusts the level of detail of the list based on the importance of the hospital when generating the list. For example, the generation unit includes important hospital information in the list in detail. The generation unit can also simplify hospital information of less importance and include it in the list. The generation unit can also adjust the level of detail of the list according to the importance of the hospital. In this way, an appropriate list can be provided by adjusting the level of detail of the list based on the importance of the hospital. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input hospital importance data into the generation AI and cause the generation AI to adjust the level of detail of the list.

[0054] The generation unit can apply different generation algorithms depending on the hospital category when generating the list. The generation unit applies different generation algorithms depending on the hospital category when generating the list. For example, the generation unit applies an internal medicine-specialized generation algorithm to information about internal medicine hospitals. The generation unit can also apply a surgery-specialized generation algorithm to information about surgery hospitals. The generation unit can also apply a psychiatry-specialized generation algorithm to information about psychiatric hospitals. In this way, by applying different generation algorithms depending on the hospital category, a more accurate list can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input hospital category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0055] When generating a list, the generation unit can improve the accuracy of generation by referring to the user's past list generation results. When generating a list, the generation unit can improve the accuracy of generation by referring to the user's past list generation results. For example, the generation unit adjusts the current list based on the user's past list generation results. The generation unit can also optimize the generation algorithm by referring to the user's past list generation results. The generation unit can also improve the accuracy of generation by using the user's past list generation results. In this way, the accuracy of generation can be improved by referring to the user's past list generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past list data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0056] When generating the list, the generation unit can determine the priority of the list based on the time of submission by the hospital. When generating the list, the generation unit determines the priority of the list based on the time of submission by the hospital. For example, the generation unit prioritizes the most recently submitted hospital information in the list. The generation unit can also postpone hospital information submitted earlier. The generation unit can also adjust the order of the list based on the time of submission. In this way, by determining the priority of the list based on the time of submission by the hospital, it is possible to provide the list in an appropriate order. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input hospital submission time data into the generation AI and cause the generation AI to determine the priority of the list.

[0057] The generation unit can adjust the order of the list based on the relevance of the hospitals when generating the list. The generation unit adjusts the order of the list based on the relevance of the hospitals when generating the list. For example, the generation unit prioritizes including highly relevant hospital information in the list. The generation unit can also postpone less relevant hospital information. The generation unit can also adjust the order of the list based on the relevance of the hospitals. In this way, by adjusting the order of the list based on the relevance of the hospitals, highly relevant hospital information can be prioritized and included in the list. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input hospital relevance data into the generation AI and cause the generation AI to adjust the order of the list.

[0058] When generating the list, the generation unit can adjust the use of technical terminology in the list according to the user's level of expertise. When generating the list, the generation unit can adjust the use of technical terminology in the list according to the user's level of expertise. For example, if the user has technical expertise, the generation unit uses a lot of technical terminology. Also, if the user does not have technical expertise, the generation unit can provide the list in simple language. Also, the generation unit can adjust the use of technical terminology in the list according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the list according to the user's level of expertise, a list that is easy for the user to understand can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0059] The providing unit can adjust the level of detail to be provided based on the importance of the hospital when providing the list. The providing unit adjusts the level of detail to be provided based on the importance of the hospital when providing the list. For example, the providing unit provides important hospital information in detail. The providing unit can also provide less important hospital information in a simplified form. The providing unit can also adjust the level of detail to be provided according to the importance of the hospital. As a result, appropriate information can be provided by adjusting the level of detail to be provided based on the importance of the hospital. Some or all of the above-described 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 hospital importance data to the generating AI and cause the generating AI to adjust the level of detail to be provided.

[0060] The providing unit can apply different providing algorithms depending on the hospital category when providing the list. The providing unit applies different providing algorithms depending on the hospital category when providing the list. For example, the providing unit applies an internal medicine-specialized providing algorithm to information on internal medicine hospitals. The providing unit can also apply a surgery-specialized providing algorithm to information on surgery hospitals. The providing unit can also apply a psychiatry-specialized providing algorithm to information on psychiatric hospitals. In this way, by applying different providing algorithms depending on the hospital category, more accurate information can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input hospital category data to the generating AI and cause the generating AI to apply the providing algorithm.

[0061] When providing a list, the providing unit can improve the accuracy of the list provision by referring to the user's past provision results. When providing a list, the providing unit can improve the accuracy of the list provision by referring to the user's past provision results. For example, the providing unit adjusts the current list provision based on the user's past provision results. The providing unit can also optimize the provision algorithm by referring to the user's past provision results. The providing unit can also improve the accuracy of the list provision by using the user's past provision results. In this way, the accuracy of the list provision can be improved by referring to the user's past provision results. Some or all of the above-described 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 user's past provision data into the generation AI and cause the generation AI to improve the accuracy of the list provision.

[0062] When providing the list, the providing unit can prioritize providing highly relevant information by taking into account the geographical location information of the hospital. When providing the list, the providing unit prioritizes providing highly relevant information by taking into account the geographical location information of the hospital. For example, the providing unit prioritizes providing hospital information close to the user's current location. The providing unit can also provide relevant hospital information based on the user's geographical location information. The providing unit can also provide optimal hospital information by taking into account the user's range of movement. In this way, by taking into account the geographical location information of the hospital, highly relevant information can be prioritized. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input geographical location data of hospitals to the generation AI and cause the generation AI to provide information.

[0063] When providing the list, the providing unit can analyze the hospital's social media activity and provide related information. When providing the list, the providing unit can analyze the hospital's social media activity and provide related information. For example, the providing unit provides the hospital's social media ratings and reviews. The providing unit can also analyze the content of the hospital's social media posts and provide related information. The providing unit can also provide the latest information by referring to the hospital's social media activity. In this way, related information can be provided by analyzing the hospital's social media activity. 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 hospital's social media data into the generation AI and cause the generation AI to provide the information.

[0064] The providing unit can customize the provision method by reflecting the hospital's past feedback when providing the list. The providing unit customizes the provision method by reflecting the hospital's past feedback when providing the list. For example, the providing unit adjusts the provision interface based on the hospital's past feedback. The providing unit can also optimize the provision procedure by reflecting the hospital's past feedback. The providing unit can also customize the provision method based on the hospital's feedback. In this way, the provision method can be customized by reflecting the hospital's past feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the hospital's feedback data into the generating AI and cause the generating AI to customize the provision method.

[0065] The evaluation collection unit can adjust the level of detail of the collection based on the importance of the hospital when collecting evaluation information. The evaluation collection unit adjusts the level of detail of the collection based on the importance of the hospital when collecting evaluation information. For example, the evaluation collection unit collects detailed evaluation information of important hospitals. The evaluation collection unit can also collect simplified evaluation information of less important hospitals. The evaluation collection unit can also adjust the level of detail of the collection according to the importance of the hospital. In this way, appropriate information can be collected by adjusting the level of detail of the collection based on the importance of the hospital. Some or all of the above-mentioned processing in the evaluation collection unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation collection unit can input hospital importance data to the generation AI and cause the generation AI to adjust the level of detail of the collection.

[0066] The evaluation collection unit can apply different collection algorithms depending on the hospital category when collecting evaluation information. The evaluation collection unit applies different collection algorithms depending on the hospital category when collecting evaluation information. For example, the evaluation collection unit applies a collection algorithm specialized for internal medicine to evaluation information of internal medicine hospitals. The evaluation collection unit can also apply a collection algorithm specialized for surgery to evaluation information of surgical hospitals. The evaluation collection unit can also apply a collection algorithm specialized for psychiatry to evaluation information of psychiatric hospitals. In this way, by applying different collection algorithms depending on the hospital category, more accurate information can be collected. Some or all of the above-mentioned processing in the evaluation collection unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation collection unit can input hospital category data to the generation AI and cause the generation AI to apply the collection algorithm.

[0067] When collecting evaluation information, the evaluation collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of hospitals. When collecting evaluation information, the evaluation collection unit prioritizes collecting highly relevant information by taking into account the geographical location information of hospitals. For example, the evaluation collection unit prioritizes collecting evaluation information of hospitals close to the user's current location. The evaluation collection unit can also collect evaluation information of related hospitals based on the user's geographical location information. The evaluation collection unit can also collect evaluation information of optimal hospitals by taking into account the user's range of movement. In this way, by taking into account the geographical location information of hospitals, highly relevant information can be preferentially collected. Some or all of the above-described processing in the evaluation collection unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation collection unit can input geographical location data of hospitals to the generation AI and cause the generation AI to collect information.

[0068] The evaluation collection unit can analyze the hospital's social media activities and collect related information when collecting evaluation information. The evaluation collection unit can analyze the hospital's social media activities and collect related information when collecting evaluation information. For example, the evaluation collection unit collects evaluations and reviews of the hospital on social media. The evaluation collection unit can also analyze the content of the hospital's social media posts and collect related information. The evaluation collection unit can also refer to the hospital's social media activities to collect the latest information. In this way, related information can be collected by analyzing the hospital's social media activities. Some or all of the above-mentioned processing in the evaluation collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation collection unit can input the hospital's social media data into the generation AI and cause the generation AI to collect information.

[0069] The history input unit can adjust the level of detail of the input based on the past treatment history when inputting the treatment history. The history input unit can adjust the level of detail of the input based on the past treatment history when inputting the treatment history. For example, the history input unit prompts the user to input detailed information based on the user's past treatment history. The history input unit can also prompt the user to input simplified information if the user has little past treatment history. The history input unit can also adjust the level of detail of the input according to the user's past treatment history. This allows appropriate information to be input by adjusting the level of detail of the input based on the past treatment history. Some or all of the above-described processing in the history input unit can be performed using, for example, AI, or can be performed without using AI. For example, the history input unit can input the user's past treatment history data to the generation AI and cause the generation AI to adjust the level of detail of the input.

[0070] The history input unit can apply different input algorithms depending on the treatment category when inputting the treatment history. The history input unit applies different input algorithms depending on the treatment category when inputting the treatment history. For example, the history input unit applies an input algorithm specialized for internal medicine to internal medicine treatment history. The history input unit can also apply an input algorithm specialized for surgery to surgical treatment history. The history input unit can also apply an input algorithm specialized for psychiatry to psychiatric treatment history. In this way, by applying different input algorithms depending on the treatment category, more accurate information can be input. Some or all of the above-mentioned processing in the history input unit may be performed using, for example, AI, or may be performed without using AI. For example, the history input unit can input treatment category data to the generation AI and cause the generation AI to apply the input algorithm.

[0071] When inputting a treatment history, the history input unit can prioritize inputting highly relevant information by taking into account the geographical location information of the treatment. When inputting a treatment history, the history input unit prioritizes inputting highly relevant information by taking into account the geographical location information of the treatment. For example, the history input unit prioritizes inputting treatment history close to the user's current location. The history input unit can also input related treatment history based on the user's geographical location information. The history input unit can also input optimal treatment history by taking into account the user's range of movement. In this way, highly relevant information can be prioritized by taking into account the geographical location information of the treatment. Some or all of the above-described processing in the history input unit may be performed using AI, for example, or may be performed without using AI. For example, the history input unit can input geographical location data of the treatment to a generation AI and cause the generation AI to input information.

[0072] The history input unit can analyze the social media activity of the treatment and input related information when inputting the treatment history. The history input unit can analyze the social media activity of the treatment and input related information when inputting the treatment history. For example, the history input unit can input related treatment history based on treatment information shared by the user on social media. The history input unit can also analyze the content of the user's social media posts and input related treatment history. The history input unit can also input related treatment history with reference to the activities of the user's friends on social media. In this way, related information can be input by analyzing the social media activity of the treatment. Some or all of the above-mentioned processing in the history input unit may be performed, for example, using AI or may be performed without using AI. For example, the history input unit can input social media data of the treatment to a generation AI and cause the generation AI to input information.

[0073] The detail collection unit can adjust the level of detail of the collection based on the importance of the hospital when collecting the detailed information. The detail collection unit adjusts the level of detail of the collection based on the importance of the hospital when collecting the detailed information. For example, the detail collection unit collects detailed information about important hospitals. The detail collection unit can also collect simplified detailed information about less important hospitals. The detail collection unit can also adjust the level of detail of the collection according to the importance of the hospital. In this way, appropriate information can be collected by adjusting the level of detail of the collection based on the importance of the hospital. Some or all of the above-mentioned processing in the detail collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detail collection unit can input hospital importance data to the generation AI and cause the generation AI to adjust the level of detail of the collection.

[0074] The detail collection unit can apply different collection algorithms depending on the hospital category when collecting detailed information. The detail collection unit applies different collection algorithms depending on the hospital category when collecting detailed information. For example, the detail collection unit applies a collection algorithm specialized for internal medicine to detailed information about internal medicine hospitals. The detail collection unit can also apply a collection algorithm specialized for surgery to detailed information about surgical hospitals. The detail collection unit can also apply a collection algorithm specialized for psychiatry to detailed information about psychiatric hospitals. In this way, by applying different collection algorithms depending on the hospital category, more accurate information can be collected. Some or all of the above-mentioned processing in the detail collection unit may be performed using AI, for example, or may be performed without using AI. For example, the detail collection unit can input hospital category data to the generation AI and cause the generation AI to apply the collection algorithm.

[0075] When collecting detailed information, the detail collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of the hospital. When collecting detailed information, the detail collection unit prioritizes collecting highly relevant information by taking into account the geographical location information of the hospital. For example, the detail collection unit prioritizes collecting detailed information of hospitals close to the user's current location. The detail collection unit can also collect detailed information of related hospitals based on the user's geographical location information. The detail collection unit can also collect detailed information of the most suitable hospital by taking into account the user's range of movement. In this way, by taking into account the geographical location information of the hospital, highly relevant information can be collected preferentially. Some or all of the above-described processing in the detail collection unit may be performed using AI, for example, or may be performed without using AI. For example, the detail collection unit can input geographical location data of hospitals to the generation AI and cause the generation AI to collect information.

[0076] The detail collection unit can analyze the hospital's social media activities and collect relevant information when collecting detailed information. The detail collection unit can analyze the hospital's social media activities and collect relevant information when collecting detailed information. For example, the detail collection unit collects the hospital's social media ratings and reviews. The detail collection unit can also analyze the hospital's social media posts and collect relevant information. The detail collection unit can also refer to the hospital's social media activities to collect the latest information. In this way, relevant information can be collected by analyzing the hospital's social media activities. Some or all of the above-mentioned processing in the detail collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detail collection unit can input the hospital's social media data into the generation AI and cause the generation AI to collect information.

[0077] The detail collection unit can customize the collection method by reflecting the hospital's past feedback when collecting detailed information. The detail collection unit customizes the collection method by reflecting the hospital's past feedback when collecting detailed information. For example, the detail collection unit adjusts the collection interface based on the hospital's past feedback. The detail collection unit can also optimize the collection procedure by reflecting the hospital's past feedback. The detail collection unit can also customize the collection method based on the hospital's feedback. In this way, the collection method can be customized by reflecting the hospital's past feedback. Some or all of the above-described processing in the detail collection unit may be performed using AI, for example, or may be performed without using AI. For example, the detail collection unit can input the hospital's feedback data to the generation AI and cause the generation AI to customize the collection method.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The reception unit can also analyze the user's input and provide appropriate advice based on the user's health condition. For example, the reception unit can suggest lifestyle improvements and preventive measures based on the symptoms entered by the user. The reception unit can also provide general health knowledge and information based on the information entered by the user. Furthermore, the reception unit can determine whether the symptoms entered by the user are urgent and encourage emergency response if necessary. This allows the user to quickly take appropriate measures for the symptoms.

[0080] When collecting hospital evaluation information, the collection unit may also collect profiles and qualification information of the hospital's specialists. For example, the collection unit may collect information such as the specialty, qualifications, and years of experience of the hospital's doctors. The collection unit may also collect information such as the doctors' research achievements and academic presentations. Furthermore, the collection unit may collect evaluations and word-of-mouth information about the doctors from their patients. This allows users to select a hospital based not only on the hospital itself but also on the information about the doctors in charge.

[0081] The history input unit can also predict future health risks based on the user's past medical history. For example, the history input unit can analyze the user's past medical history and predict future disease risks. The history input unit can also perform risk assessment taking into account information such as the user's lifestyle and family history. Furthermore, the history input unit can provide advice on preventive measures and health management based on the predicted risks. This allows the user to take measures against future health risks early.

[0082] The analysis unit can also optimize the analysis algorithm based on the user's past analysis results. For example, the analysis unit can refer to the user's past analysis results and reflect them in the current analysis. The analysis unit can also adjust the parameters of the analysis algorithm based on the past analysis results. Furthermore, the analysis unit can use the past analysis results to improve the accuracy of the analysis. In this way, by utilizing the user's past analysis results, it is possible to provide more accurate analysis results.

[0083] When collecting hospital evaluation information, the collection unit can also collect information about the hospital's equipment and facilities. For example, the collection unit collects information about the hospital's medical equipment and testing equipment. The collection unit can also collect information about the cleanliness and comfort of the hospital's facilities. Furthermore, the collection unit can collect information such as the hospital's accessibility and whether or not parking is available. This allows the user to select a hospital based on information about the hospital's equipment and facilities.

[0084] The history input unit can also predict future health risks based on the user's past medical history. For example, the history input unit can analyze the user's past medical history and predict future disease risks. The history input unit can also perform risk assessment taking into account information such as the user's lifestyle and family history. Furthermore, the history input unit can provide advice on preventive measures and health management based on the predicted risks. This allows the user to take measures against future health risks early.

[0085] The processing flow of the first embodiment will be briefly explained below.

[0086] Step 1: The reception unit allows the user to input their symptoms. For example, the user can input specific symptoms or ailments such as "I have a headache" or "I have a stomachache." The reception unit supports text input and voice input, and can also send the user's input to the generation AI. Step 2: The analysis unit uses the generation AI to analyze the symptoms entered by the reception unit. The generation AI refers to a database of symptoms and identifies hospitals that correspond to the entered symptoms. The analysis unit can also use the generation AI to extract and analyze important parts of the symptoms. Step 3: The collection unit collects hospital information. The collection unit collects information such as the hospital's areas of expertise and past treatment results, and can collect information from the hospital's website or medical database. The collection unit can also use generative AI to automatically collect hospital information. Step 4: The generator generates a list of hospitals based on the information collected by the collector. The generator uses a generation AI to generate a list of hospitals, and lists the hospitals that are best suited to the user's symptoms based on the collected information. The generator AI generates the list taking into account the hospital's areas of expertise and past treatment performance. Step 5: The provider provides the list generated by the generator to the user. The provider displays the list to the user through a web application or a mobile application, and can also adjust the display method of the list using the generation AI. The provider selects the optimal display method depending on the user's input method (voice, text, image, etc.).

[0087] (Example 2) A hospital matching system according to an embodiment of the present invention uses a generation AI to match the most suitable hospital based on symptoms entered by a user. In the hospital matching system, a user inputs their symptoms, and a generation AI analyzes the symptoms and generates a list of hospitals that correspond to the symptoms. The generated list includes information such as each hospital's specialty and past treatment track record. Based on this list, the user can select the most suitable hospital for them. For example, in the hospital matching system, a user inputs their symptoms. For example, the user inputs information such as "I have a headache" or "I have a stomachache." This information is input to the generation AI. The hospital matching system then analyzes the input symptoms using the generation AI. The generation AI references a symptom database to identify hospitals that correspond to the input symptoms. For example, if a headache symptom is entered, the generation AI lists hospitals that specialize in treating headaches. The generated list includes information such as each hospital's specialty and past treatment track record. For example, if a hospital has a high track record in treating headaches, that information is displayed in the list. This allows the user to select the hospital that best suits their symptoms. The hospital matching system can also update the list of hospitals in real time according to the user's symptoms. For example, if a user inputs new symptoms, the generating AI will regenerate the list based on that information. This allows the system to always provide the most up-to-date information. This allows the hospital matching system to easily find the hospital that best suits the user's symptoms. For example, a user with a headache can receive more effective treatment by selecting a hospital that specializes in headache treatment. In addition, the generating AI lists hospitals based on their past treatment records, allowing the system to provide highly reliable information.

[0088] The hospital matching system according to the embodiment includes a reception unit, an analysis unit, a collection unit, a generation unit, and a provision unit. The reception unit allows a user to input their symptoms. The user can input specific symptoms or ailments, such as "I have a headache" or "I have a stomachache." The reception unit supports, for example, text input or voice input. The reception unit can also transmit the user's input to the generation AI. The analysis unit uses the generation AI to analyze the symptoms input by the reception unit. The generation AI refers to a symptom database and identifies a hospital corresponding to the input symptoms. For example, the generation AI analyzes the input symptoms using a text generation AI (e.g., LLM). The analysis unit can also extract and analyze important parts of the symptoms using the generation AI. The collection unit collects information about hospitals. The collection unit collects information about hospitals, such as hospital specialties and past treatment records. The collection unit can collect information from hospital websites and medical databases, for example. The collection unit can also automatically collect hospital information using the generation AI. The generation unit generates a list of hospitals based on the information collected by the collection unit. The generation unit generates a list of hospitals using, for example, a generation AI. The generation AI lists hospitals that are best suited to the user's symptoms based on the collected information. For example, the generation AI generates the list taking into account the hospital's areas of expertise and past treatment results. The provision unit provides the list generated by the generation unit to the user. The provision unit displays the list to the user, for example, through a web application or a mobile application. The provision unit can also adjust the display method of the list using the generation AI. For example, the provision unit selects the optimal display method depending on the user's input method (voice, text, image, etc.). This enables the hospital matching system according to the embodiment to efficiently match hospitals that are best suited to the user's symptoms.

[0089] The hospital matching system includes a rating collection unit that collects hospital ratings or word-of-mouth information. The rating collection unit collects hospital ratings and word-of-mouth information. The rating collection unit can collect, for example, online reviews and survey results. The rating collection unit can also automatically collect rating information using a generation AI. For example, the rating collection unit collects rating information from hospital websites and social media. By collecting hospital ratings and word-of-mouth information, reliable information can be provided to users. Some or all of the above-described processing in the rating collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the rating collection unit can input online reviews into the generation AI and cause the generation AI to collect rating information.

[0090] The hospital matching system includes a history input unit that inputs the user's past treatments and diagnosis results. The history input unit inputs the user's past treatments and diagnosis results. The history input unit can input, for example, medical records and test results. The history input unit can also automatically input past treatment information using a generation AI. For example, the history input unit acquires and inputs past treatment information from the user's medical database. This makes it possible to match a more appropriate hospital by taking into account the user's past treatments and diagnosis results. Some or all of the above-described processing in the history input unit may be performed, for example, using AI, or may be performed without using AI. For example, the history input unit can input the user's medical records into the generation AI and cause the generation AI to input past treatment information.

[0091] The hospital matching system includes a detail collection unit that collects information on hospital locations, clinic hours, and whether appointments are available. The detail collection unit collects information such as hospital locations, clinic hours, and whether appointments are available. The detail collection unit can collect, for example, hospital locations and clinic hours. The detail collection unit can also automatically collect detailed information using a generation AI. For example, the detail collection unit collects detailed information from hospital websites and medical databases. By collecting detailed hospital information, it is possible to provide highly convenient information to users. Some or all of the above-described processing in the detail collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detail collection unit can input hospital clinic hours into the generation AI and cause the generation AI to collect detailed information.

[0092] The evaluation collection unit can cooperate with the collection unit to collect hospital evaluation information, and the generation unit can generate a list based on that information. The evaluation collection unit cooperates with the collection unit to collect hospital evaluation information. For example, the evaluation collection unit generates a list based on the hospital evaluation information collected by the collection unit. The evaluation collection unit can also automatically collect evaluation information using a generation AI. For example, the evaluation collection unit collects evaluation information from hospital websites and social media, and the generation unit generates a list based on that information. In this way, by generating a list based on hospital evaluation information, reliable hospital information can be provided to users. Some or all of the above-mentioned processing in the evaluation collection unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation collection unit can input online reviews into the generation AI and cause the generation AI to collect evaluation information.

[0093] The history input unit cooperates with the reception unit to accept the user's past treatment information, and the analysis unit can perform analysis based on that information. The history input unit cooperates with the reception unit to accept the user's past treatment information. For example, the history input unit has the analysis unit perform analysis based on the user's medical records and test results accepted by the reception unit. The history input unit can also automatically input past treatment information using a generation AI. For example, the history input unit acquires past treatment information from the user's medical database, and the analysis unit performs analysis based on that information. This allows for analysis that takes the user's past treatment information into consideration, thereby enabling more appropriate hospital matching. Some or all of the above-mentioned processing in the history input unit may be performed using, for example, AI, or may be performed without using AI. For example, the history input unit can input the user's medical records into the generation AI and have the generation AI input past treatment information.

[0094] The reception unit can estimate the user's emotion and adjust the symptom input method based on the estimated user emotion. The reception unit can estimate the user's emotion and adjust the symptom input method based on the estimated user emotion. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable quick symptom input. This allows for an easy-to-use interface to be provided by adjusting the symptom input method according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0095] The reception unit can analyze the user's past symptom input history and suggest the optimal input method. The reception unit analyzes the user's past symptom input history and suggest the optimal input method. For example, the reception unit automatically displays symptoms that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest symptoms that will be input during a specific time period based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past symptom input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input data into a generation AI and have the generation AI suggest the optimal input method.

[0096] The reception unit can filter symptoms based on the user's current health condition and lifestyle habits when inputting the symptoms. The reception unit can filter symptoms based on the user's current health condition and lifestyle habits when inputting the symptoms. For example, the reception unit prioritizes input of related symptoms based on the user's current health condition. The reception unit can also filter related symptoms taking into account the user's lifestyle habits (smoking, drinking, etc.). The reception unit can also adjust the symptoms to be input based on the user's health data (heart rate, blood pressure, etc.). This enables more appropriate symptom input by filtering symptoms based on the user's health condition and lifestyle habits. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's health data to the generation AI and cause the generation AI to filter the symptoms.

[0097] The reception unit can select the optimal input means depending on the user's input method when inputting symptoms. The reception unit selects the optimal input means depending on the user's input method (voice, text, image, etc.) when inputting symptoms. For example, if the user selects voice input, the reception unit inputs symptoms using voice recognition technology. Also, if the user selects text input, the reception unit can support keyboard input. Also, if the user selects image input, the reception unit can analyze the symptoms using image recognition technology. In this way, by selecting the optimal input means depending on the user's input method, it is possible to provide an interface that is easy for the user to use. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select the optimal input means.

[0098] The reception unit can estimate the user's emotions and determine the priority of symptoms to be input based on the estimated user emotions. The reception unit can estimate the user's emotions and determine the priority of symptoms to be input based on the estimated user emotions. For example, if the user is feeling anxious, the reception unit can prioritize input of serious symptoms. Also, if the user is relaxed, the reception unit can also input minor symptoms. Also, if the user is in a hurry, the reception unit can prioritize input of only major symptoms. This enables more appropriate symptom input by determining the priority of symptoms according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit can input the user's facial expression data into the generation AI and cause the generation AI to perform emotion estimation.

[0099] When inputting symptoms, the reception unit can prioritize inputting highly relevant symptoms by taking into account the user's geographical location information. When inputting symptoms, the reception unit prioritizes inputting highly relevant symptoms by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes inputting symptoms that are prevalent in that area. The reception unit can also input symptoms specific to that area based on the user's current location. The reception unit can also filter related symptoms based on the user's geographical location information. In this way, highly relevant symptoms can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location data to the generation AI and cause the generation AI to filter the symptoms.

[0100] The reception unit can analyze the user's social media activity and input related symptoms when the user inputs symptoms. The reception unit can analyze the user's social media activity and input related symptoms when the user inputs symptoms. For example, the reception unit can input related symptoms based on health information shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and input related symptoms. The reception unit can also input related symptoms by referring to the activities of the user's friends on social media. In this way, related symptoms can be input by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may 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 input related symptoms.

[0101] The reception unit can customize the input method by reflecting the user's past feedback when inputting symptoms. The reception unit customizes the input method by reflecting the user's past feedback when inputting symptoms. For example, the reception unit adjusts the input interface based on feedback provided by the user in the past. The reception unit can also optimize the input procedure by reflecting the user's past feedback. The reception unit can also customize the input method based on the user's feedback. In this way, the input method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's feedback data to the generation AI and cause the generation AI to customize the input method.

[0102] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is in a hurry, the analysis unit can provide a summary analysis result. By adjusting the presentation method of the analysis according to the user's emotions, it is possible to provide an analysis result that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0103] The analysis unit can adjust the level of detail of the analysis based on the severity of the symptom during analysis. The analysis unit can adjust the level of detail of the analysis based on the severity of the symptom during analysis. For example, the analysis unit performs a detailed analysis when the symptom is serious. The analysis unit can also perform a simplified analysis when the symptom is minor. The analysis unit can also adjust the depth of the analysis according to the severity of the symptom. In this way, by adjusting the level of detail of the analysis based on the severity of the symptom, appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input symptom severity data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0104] The analysis unit can apply different analysis algorithms depending on the symptom category during analysis. The analysis unit applies different analysis algorithms depending on the symptom category during analysis. For example, the analysis unit applies an analysis algorithm specialized for internal medicine to internal medicine symptoms. The analysis unit can also apply an analysis algorithm specialized for surgery to surgical symptoms. The analysis unit can also apply an analysis algorithm specialized for psychiatry to psychiatric symptoms. In this way, by applying different analysis algorithms depending on the symptom category, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input symptom category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0105] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit adjusts the current analysis based on the user's past analysis results. The analysis unit can also optimize the analysis algorithm by referring to the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by using the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0106] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, it is possible to provide an analysis result of an appropriate length for the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0107] The analysis unit can determine the priority of analysis based on the time when the symptoms were submitted during analysis. The analysis unit determines the priority of analysis based on the time when the symptoms were submitted during analysis. For example, the analysis unit prioritizes the analysis of recently submitted symptoms. The analysis unit can also postpone the analysis of symptoms that were submitted earlier. The analysis unit can also adjust the order of analysis based on the time when the symptoms were submitted. In this way, by determining the priority of analysis based on the time when the symptoms were submitted, it is possible to perform the analysis in an appropriate order. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the symptoms were submitted to the generation AI and have the generation AI determine the priority of analysis.

[0108] The analysis unit can adjust the order of analysis based on the relevance of symptoms during analysis. The analysis unit adjusts the order of analysis based on the relevance of symptoms during analysis. For example, the analysis unit prioritizes analysis of highly relevant symptoms. The analysis unit can also postpone analysis of less relevant symptoms. The analysis unit can also adjust the order of analysis based on the relevance of symptoms. In this way, by adjusting the order of analysis based on the relevance of symptoms, highly relevant symptoms can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input symptom relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0109] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. If the user does not have technical expertise, the analysis unit can provide analysis results in simple language. The analysis unit can also adjust the use of technical terms in the analysis according to the user's level of expertise. This makes it possible to provide analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI use technical terms.

[0110] The collection unit can estimate the user's emotions and adjust the hospital information collection method based on the estimated user emotions. The collection unit can estimate the user's emotions and adjust the hospital information collection method based on the estimated user emotions. For example, if the user is feeling anxious, the collection unit prioritizes collecting reliable hospital information. Also, if the user is relaxed, the collection unit can collect a wide range of hospital information. Also, if the user is in a hurry, the collection unit can prioritize hospital information that can be collected quickly. This allows the user to be provided with reliable information by adjusting the hospital information collection method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0111] The collection unit can adjust the level of detail of the collection based on the importance of the hospital at the time of collection. The collection unit adjusts the level of detail of the collection based on the importance of the hospital at the time of collection. For example, the collection unit collects important hospital information in detail. The collection unit can also collect less important hospital information in a simplified manner. The collection unit can also adjust the level of detail of the collection according to the importance of the hospital. As a result, appropriate information can be collected by adjusting the level of detail of the collection based on the importance of the hospital. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input hospital importance data to the generation AI and cause the generation AI to adjust the level of detail of the collection.

[0112] The collection unit can apply different collection algorithms depending on the hospital category during collection. The collection unit applies different collection algorithms depending on the hospital category during collection. For example, the collection unit applies a collection algorithm specialized for internal medicine to information on internal medicine hospitals. The collection unit can also apply a collection algorithm specialized for surgery to information on surgery hospitals. The collection unit can also apply a collection algorithm specialized for psychiatry to information on psychiatric hospitals. In this way, by applying different collection algorithms depending on the hospital category, more accurate information can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input hospital category data to the generation AI and cause the generation AI to apply the collection algorithm.

[0113] The collection unit can improve the accuracy of collection by referring to the user's past collection results when collecting data. The collection unit can improve the accuracy of collection by referring to the user's past collection results when collecting data. For example, the collection unit adjusts the current collection based on the user's past collection results. The collection unit can also optimize the collection algorithm by referring to the user's past collection results. The collection unit can also improve the accuracy of collection by using the user's past collection results. In this way, the accuracy of collection can be improved by referring to the user's past collection results. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past collection data into a generation AI and cause the generation AI to improve the accuracy of collection.

[0114] The collection unit can estimate the user's emotions and determine the priority of hospital information to be collected based on the estimated user emotions. The collection unit can estimate the user's emotions and determine the priority of hospital information to be collected based on the estimated user emotions. For example, if the user is feeling anxious, the collection unit can prioritize collecting reliable hospital information. Also, if the user is relaxed, the collection unit can collect a wide range of hospital information. Also, if the user is in a hurry, the collection unit can prioritize hospital information that can be collected quickly. This allows more appropriate information to be collected by determining the priority of hospital information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0115] The collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of hospitals when collecting data. The collection unit prioritizes collecting highly relevant information by taking into account the geographical location information of hospitals when collecting data. For example, the collection unit prioritizes collecting hospital information close to the user's current location. The collection unit can also collect relevant hospital information based on the user's geographical location information. The collection unit can also collect optimal hospital information by taking into account the user's range of movement. In this way, highly relevant information can be prioritized by taking into account the geographical location information of hospitals. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input geographical location data of hospitals to the generation AI and cause the generation AI to collect information.

[0116] The collection unit can analyze the hospital's social media activities and collect related information at the time of collection. The collection unit can analyze the hospital's social media activities and collect related information at the time of collection. For example, the collection unit collects the hospital's social media ratings and reviews. The collection unit can also analyze the hospital's social media posts and collect related information. The collection unit can also refer to the hospital's social media activities to collect the latest information. In this way, related information can be collected by analyzing the hospital's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the hospital's social media data into a generation AI and cause the generation AI to collect information.

[0117] The collection unit can customize the collection method by reflecting the hospital's past feedback at the time of collection. The collection unit customizes the collection method by reflecting the hospital's past feedback at the time of collection. For example, the collection unit adjusts the collection interface based on the hospital's past feedback. The collection unit can also optimize the collection procedure by reflecting the hospital's past feedback. The collection unit can also customize the collection method based on the hospital's feedback. In this way, the collection method can be customized by reflecting the hospital's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the hospital's feedback data into the generation AI and cause the generation AI to customize the collection method.

[0118] The generation unit can estimate the user's emotions and adjust the list generation method based on the estimated user emotions. The generation unit can estimate the user's emotions and adjust the list generation method based on the estimated user emotions. For example, the generation unit generates a detailed list when the user is relaxed. The generation unit can also generate a concise list when the user is in a hurry. The generation unit can also generate a visually stimulating list when the user is excited. This allows the list generation method to be adjusted according to the user's emotions, thereby providing a list appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0119] The generation unit can adjust the level of detail of the list based on the importance of the hospital when generating the list. The generation unit adjusts the level of detail of the list based on the importance of the hospital when generating the list. For example, the generation unit includes important hospital information in the list in detail. The generation unit can also simplify hospital information of less importance and include it in the list. The generation unit can also adjust the level of detail of the list according to the importance of the hospital. In this way, an appropriate list can be provided by adjusting the level of detail of the list based on the importance of the hospital. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input hospital importance data into the generation AI and cause the generation AI to adjust the level of detail of the list.

[0120] The generation unit can apply different generation algorithms depending on the hospital category when generating the list. The generation unit applies different generation algorithms depending on the hospital category when generating the list. For example, the generation unit applies an internal medicine-specialized generation algorithm to information about internal medicine hospitals. The generation unit can also apply a surgery-specialized generation algorithm to information about surgery hospitals. The generation unit can also apply a psychiatry-specialized generation algorithm to information about psychiatric hospitals. In this way, by applying different generation algorithms depending on the hospital category, a more accurate list can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input hospital category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0121] When generating a list, the generation unit can improve the accuracy of generation by referring to the user's past list generation results. When generating a list, the generation unit can improve the accuracy of generation by referring to the user's past list generation results. For example, the generation unit adjusts the current list based on the user's past list generation results. The generation unit can also optimize the generation algorithm by referring to the user's past list generation results. The generation unit can also improve the accuracy of generation by using the user's past list generation results. In this way, the accuracy of generation can be improved by referring to the user's past list generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past list data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0122] The generation unit can estimate the user's emotion and adjust the length of the list based on the estimated user emotion. The generation unit can estimate the user's emotion and adjust the length of the list based on the estimated user emotion. For example, if the user is in a hurry, the generation unit can generate a short and to-the-point list. If the user is relaxed, the generation unit can generate a detailed list. If the user is excited, the generation unit can generate a visually stimulating list. By adjusting the length of the list according to the user's emotion, a list of an appropriate length can be provided to the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0123] When generating the list, the generation unit can determine the priority of the list based on the time of submission by the hospital. When generating the list, the generation unit determines the priority of the list based on the time of submission by the hospital. For example, the generation unit prioritizes the most recently submitted hospital information in the list. The generation unit can also postpone hospital information submitted earlier. The generation unit can also adjust the order of the list based on the time of submission. In this way, by determining the priority of the list based on the time of submission by the hospital, it is possible to provide the list in an appropriate order. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input hospital submission time data into the generation AI and cause the generation AI to determine the priority of the list.

[0124] The generation unit can adjust the order of the list based on the relevance of the hospitals when generating the list. The generation unit adjusts the order of the list based on the relevance of the hospitals when generating the list. For example, the generation unit prioritizes including highly relevant hospital information in the list. The generation unit can also postpone less relevant hospital information. The generation unit can also adjust the order of the list based on the relevance of the hospitals. In this way, by adjusting the order of the list based on the relevance of the hospitals, highly relevant hospital information can be prioritized and included in the list. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input hospital relevance data into the generation AI and cause the generation AI to adjust the order of the list.

[0125] When generating the list, the generation unit can adjust the use of technical terminology in the list according to the user's level of expertise. When generating the list, the generation unit can adjust the use of technical terminology in the list according to the user's level of expertise. For example, if the user has technical expertise, the generation unit uses a lot of technical terminology. Also, if the user does not have technical expertise, the generation unit can provide the list in simple language. Also, the generation unit can adjust the use of technical terminology in the list according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the list according to the user's level of expertise, a list that is easy for the user to understand can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0126] The providing unit can estimate the user's emotions and adjust the method of providing the list based on the estimated user emotions. The providing unit can estimate the user's emotions and adjust the method of providing the list based on the estimated user emotions. For example, the providing unit can provide a detailed list when the user is relaxed. The providing unit can also provide a concise list when the user is in a hurry. The providing unit can also provide a visually stimulating list when the user is excited. This allows the list providing method to be adjusted according to the user's emotions, thereby providing an appropriate list for the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0127] The providing unit can adjust the level of detail to be provided based on the importance of the hospital when providing the list. The providing unit adjusts the level of detail to be provided based on the importance of the hospital when providing the list. For example, the providing unit provides important hospital information in detail. The providing unit can also provide less important hospital information in a simplified form. The providing unit can also adjust the level of detail to be provided according to the importance of the hospital. As a result, appropriate information can be provided by adjusting the level of detail to be provided based on the importance of the hospital. Some or all of the above-described 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 hospital importance data to the generating AI and cause the generating AI to adjust the level of detail to be provided.

[0128] The providing unit can apply different providing algorithms depending on the hospital category when providing the list. The providing unit applies different providing algorithms depending on the hospital category when providing the list. For example, the providing unit applies an internal medicine-specialized providing algorithm to information on internal medicine hospitals. The providing unit can also apply a surgery-specialized providing algorithm to information on surgery hospitals. The providing unit can also apply a psychiatry-specialized providing algorithm to information on psychiatric hospitals. In this way, by applying different providing algorithms depending on the hospital category, more accurate information can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input hospital category data to the generating AI and cause the generating AI to apply the providing algorithm.

[0129] When providing a list, the providing unit can improve the accuracy of the list provision by referring to the user's past provision results. When providing a list, the providing unit can improve the accuracy of the list provision by referring to the user's past provision results. For example, the providing unit adjusts the current list provision based on the user's past provision results. The providing unit can also optimize the provision algorithm by referring to the user's past provision results. The providing unit can also improve the accuracy of the list provision by using the user's past provision results. In this way, the accuracy of the list provision can be improved by referring to the user's past provision results. Some or all of the above-described 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 user's past provision data into the generation AI and cause the generation AI to improve the accuracy of the list provision.

[0130] The providing unit can estimate the user's emotions and determine the priority of the lists to be provided based on the estimated user emotions. The providing unit can estimate the user's emotions and determine the priority of the lists to be provided based on the estimated user emotions. For example, if the user is feeling anxious, the providing unit can prioritize providing reliable hospital information. Also, if the user is relaxed, the providing unit can provide a wide range of hospital information. Also, if the user is in a hurry, the providing unit can prioritize hospital information that can be provided quickly. This allows more appropriate information to be provided by determining the priority of the lists according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0131] When providing the list, the providing unit can prioritize providing highly relevant information by taking into account the geographical location information of the hospital. When providing the list, the providing unit prioritizes providing highly relevant information by taking into account the geographical location information of the hospital. For example, the providing unit prioritizes providing hospital information close to the user's current location. The providing unit can also provide relevant hospital information based on the user's geographical location information. The providing unit can also provide optimal hospital information by taking into account the user's range of movement. In this way, by taking into account the geographical location information of the hospital, highly relevant information can be prioritized. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input geographical location data of hospitals to the generation AI and cause the generation AI to provide information.

[0132] When providing the list, the providing unit can analyze the hospital's social media activity and provide related information. When providing the list, the providing unit can analyze the hospital's social media activity and provide related information. For example, the providing unit provides the hospital's social media ratings and reviews. The providing unit can also analyze the content of the hospital's social media posts and provide related information. The providing unit can also provide the latest information by referring to the hospital's social media activity. In this way, related information can be provided by analyzing the hospital's social media activity. 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 hospital's social media data into the generation AI and cause the generation AI to provide the information.

[0133] The providing unit can customize the provision method by reflecting the hospital's past feedback when providing the list. The providing unit customizes the provision method by reflecting the hospital's past feedback when providing the list. For example, the providing unit adjusts the provision interface based on the hospital's past feedback. The providing unit can also optimize the provision procedure by reflecting the hospital's past feedback. The providing unit can also customize the provision method based on the hospital's feedback. In this way, the provision method can be customized by reflecting the hospital's past feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the hospital's feedback data into the generating AI and cause the generating AI to customize the provision method.

[0134] The evaluation collection unit can estimate the user's emotions and adjust the evaluation information collection method based on the estimated user emotions. The evaluation collection unit can estimate the user's emotions and adjust the evaluation information collection method based on the estimated user emotions. For example, if the user is feeling anxious, the evaluation collection unit prioritizes collecting highly reliable evaluation information. Furthermore, if the user is relaxed, the evaluation collection unit can also collect a wide range of evaluation information. Furthermore, if the user is in a hurry, the evaluation collection unit can prioritize evaluation information that can be collected quickly. This allows the user to be provided with highly reliable information by adjusting the evaluation information collection method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation collection unit may be performed using AI, for example, or without AI. For example, the evaluation collection unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0135] The evaluation collection unit can adjust the level of detail of the collection based on the importance of the hospital when collecting evaluation information. The evaluation collection unit adjusts the level of detail of the collection based on the importance of the hospital when collecting evaluation information. For example, the evaluation collection unit collects detailed evaluation information of important hospitals. The evaluation collection unit can also collect simplified evaluation information of less important hospitals. The evaluation collection unit can also adjust the level of detail of the collection according to the importance of the hospital. In this way, appropriate information can be collected by adjusting the level of detail of the collection based on the importance of the hospital. Some or all of the above-mentioned processing in the evaluation collection unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation collection unit can input hospital importance data to the generation AI and cause the generation AI to adjust the level of detail of the collection.

[0136] The evaluation collection unit can apply different collection algorithms depending on the hospital category when collecting evaluation information. The evaluation collection unit applies different collection algorithms depending on the hospital category when collecting evaluation information. For example, the evaluation collection unit applies a collection algorithm specialized for internal medicine to evaluation information of internal medicine hospitals. The evaluation collection unit can also apply a collection algorithm specialized for surgery to evaluation information of surgical hospitals. The evaluation collection unit can also apply a collection algorithm specialized for psychiatry to evaluation information of psychiatric hospitals. In this way, by applying different collection algorithms depending on the hospital category, more accurate information can be collected. Some or all of the above-mentioned processing in the evaluation collection unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation collection unit can input hospital category data to the generation AI and cause the generation AI to apply the collection algorithm.

[0137] When collecting evaluation information, the evaluation collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of hospitals. When collecting evaluation information, the evaluation collection unit prioritizes collecting highly relevant information by taking into account the geographical location information of hospitals. For example, the evaluation collection unit prioritizes collecting evaluation information of hospitals close to the user's current location. The evaluation collection unit can also collect evaluation information of related hospitals based on the user's geographical location information. The evaluation collection unit can also collect evaluation information of optimal hospitals by taking into account the user's range of movement. In this way, by taking into account the geographical location information of hospitals, highly relevant information can be preferentially collected. Some or all of the above-described processing in the evaluation collection unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation collection unit can input geographical location data of hospitals to the generation AI and cause the generation AI to collect information.

[0138] The evaluation collection unit can analyze the hospital's social media activities and collect related information when collecting evaluation information. The evaluation collection unit can analyze the hospital's social media activities and collect related information when collecting evaluation information. For example, the evaluation collection unit collects evaluations and reviews of the hospital on social media. The evaluation collection unit can also analyze the content of the hospital's social media posts and collect related information. The evaluation collection unit can also refer to the hospital's social media activities to collect the latest information. In this way, related information can be collected by analyzing the hospital's social media activities. Some or all of the above-mentioned processing in the evaluation collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation collection unit can input the hospital's social media data into the generation AI and cause the generation AI to collect information.

[0139] The history input unit can estimate the user's emotions and adjust the treatment history input method based on the estimated user emotions. The history input unit can estimate the user's emotions and adjust the treatment history input method based on the estimated user emotions. For example, when the user is stressed, the history input unit provides a simple interface and minimizes input steps. Furthermore, when the user is relaxed, the history input unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the history input unit can prioritize voice input to enable quick treatment history input. This allows for an easy-to-use interface to be provided by adjusting the treatment history input method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the history input unit can be performed using AI, for example, or without AI. For example, the history input unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0140] The history input unit can adjust the level of detail of the input based on the past treatment history when inputting the treatment history. The history input unit can adjust the level of detail of the input based on the past treatment history when inputting the treatment history. For example, the history input unit prompts the user to input detailed information based on the user's past treatment history. The history input unit can also prompt the user to input simplified information if the user has little past treatment history. The history input unit can also adjust the level of detail of the input according to the user's past treatment history. This allows appropriate information to be input by adjusting the level of detail of the input based on the past treatment history. Some or all of the above-described processing in the history input unit can be performed using, for example, AI, or can be performed without using AI. For example, the history input unit can input the user's past treatment history data to the generation AI and cause the generation AI to adjust the level of detail of the input.

[0141] The history input unit can apply different input algorithms depending on the treatment category when inputting the treatment history. The history input unit applies different input algorithms depending on the treatment category when inputting the treatment history. For example, the history input unit applies an input algorithm specialized for internal medicine to internal medicine treatment history. The history input unit can also apply an input algorithm specialized for surgery to surgical treatment history. The history input unit can also apply an input algorithm specialized for psychiatry to psychiatric treatment history. In this way, by applying different input algorithms depending on the treatment category, more accurate information can be input. Some or all of the above-mentioned processing in the history input unit may be performed using, for example, AI, or may be performed without using AI. For example, the history input unit can input treatment category data to the generation AI and cause the generation AI to apply the input algorithm.

[0142] When inputting a treatment history, the history input unit can prioritize inputting highly relevant information by taking into account the geographical location information of the treatment. When inputting a treatment history, the history input unit prioritizes inputting highly relevant information by taking into account the geographical location information of the treatment. For example, the history input unit prioritizes inputting treatment history close to the user's current location. The history input unit can also input related treatment history based on the user's geographical location information. The history input unit can also input optimal treatment history by taking into account the user's range of movement. In this way, highly relevant information can be prioritized by taking into account the geographical location information of the treatment. Some or all of the above-described processing in the history input unit may be performed using AI, for example, or may be performed without using AI. For example, the history input unit can input geographical location data of the treatment to a generation AI and cause the generation AI to input information.

[0143] The history input unit can analyze the social media activity of the treatment and input related information when inputting the treatment history. The history input unit can analyze the social media activity of the treatment and input related information when inputting the treatment history. For example, the history input unit can input related treatment history based on treatment information shared by the user on social media. The history input unit can also analyze the content of the user's social media posts and input related treatment history. The history input unit can also input related treatment history with reference to the activities of the user's friends on social media. In this way, related information can be input by analyzing the social media activity of the treatment. Some or all of the above-mentioned processing in the history input unit may be performed, for example, using AI or may be performed without using AI. For example, the history input unit can input social media data of the treatment to a generation AI and cause the generation AI to input information.

[0144] The detail collection unit can estimate the user's emotion and adjust the detailed information collection method based on the estimated user emotion. The detail collection unit can estimate the user's emotion and adjust the detailed information collection method based on the estimated user emotion. For example, if the user is feeling anxious, the detail collection unit prioritizes collecting reliable detailed information. Also, if the user is relaxed, the detail collection unit can collect a wide range of detailed information. Also, if the user is in a hurry, the detail collection unit can prioritize detailed information that can be collected quickly. This allows the detailed information collection method to be adjusted according to the user's emotion, thereby providing the user with reliable information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the detail collection unit may be performed using AI, for example, or without AI. For example, the detail collection unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0145] The detail collection unit can adjust the level of detail of the collection based on the importance of the hospital when collecting the detailed information. The detail collection unit adjusts the level of detail of the collection based on the importance of the hospital when collecting the detailed information. For example, the detail collection unit collects detailed information about important hospitals. The detail collection unit can also collect simplified detailed information about less important hospitals. The detail collection unit can also adjust the level of detail of the collection according to the importance of the hospital. In this way, appropriate information can be collected by adjusting the level of detail of the collection based on the importance of the hospital. Some or all of the above-mentioned processing in the detail collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detail collection unit can input hospital importance data to the generation AI and cause the generation AI to adjust the level of detail of the collection.

[0146] The detail collection unit can apply different collection algorithms depending on the hospital category when collecting detailed information. The detail collection unit applies different collection algorithms depending on the hospital category when collecting detailed information. For example, the detail collection unit applies a collection algorithm specialized for internal medicine to detailed information about internal medicine hospitals. The detail collection unit can also apply a collection algorithm specialized for surgery to detailed information about surgical hospitals. The detail collection unit can also apply a collection algorithm specialized for psychiatry to detailed information about psychiatric hospitals. In this way, by applying different collection algorithms depending on the hospital category, more accurate information can be collected. Some or all of the above-mentioned processing in the detail collection unit may be performed using AI, for example, or may be performed without using AI. For example, the detail collection unit can input hospital category data to the generation AI and cause the generation AI to apply the collection algorithm.

[0147] When collecting detailed information, the detail collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of the hospital. When collecting detailed information, the detail collection unit prioritizes collecting highly relevant information by taking into account the geographical location information of the hospital. For example, the detail collection unit prioritizes collecting detailed information of hospitals close to the user's current location. The detail collection unit can also collect detailed information of related hospitals based on the user's geographical location information. The detail collection unit can also collect detailed information of the most suitable hospital by taking into account the user's range of movement. In this way, by taking into account the geographical location information of the hospital, highly relevant information can be collected preferentially. Some or all of the above-described processing in the detail collection unit may be performed using AI, for example, or may be performed without using AI. For example, the detail collection unit can input geographical location data of hospitals to the generation AI and cause the generation AI to collect information.

[0148] The detail collection unit can analyze the hospital's social media activities and collect relevant information when collecting detailed information. The detail collection unit can analyze the hospital's social media activities and collect relevant information when collecting detailed information. For example, the detail collection unit collects the hospital's social media ratings and reviews. The detail collection unit can also analyze the hospital's social media posts and collect relevant information. The detail collection unit can also refer to the hospital's social media activities to collect the latest information. In this way, relevant information can be collected by analyzing the hospital's social media activities. Some or all of the above-mentioned processing in the detail collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detail collection unit can input the hospital's social media data into the generation AI and cause the generation AI to collect information.

[0149] The detail collection unit can customize the collection method by reflecting the hospital's past feedback when collecting detailed information. The detail collection unit customizes the collection method by reflecting the hospital's past feedback when collecting detailed information. For example, the detail collection unit adjusts the collection interface based on the hospital's past feedback. The detail collection unit can also optimize the collection procedure by reflecting the hospital's past feedback. The detail collection unit can also customize the collection method based on the hospital's feedback. In this way, the collection method can be customized by reflecting the hospital's past feedback. Some or all of the above-described processing in the detail collection unit may be performed using AI, for example, or may be performed without using AI. For example, the detail collection unit can input the hospital's feedback data to the generation AI and cause the generation AI to customize the collection method. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, collection unit, generation unit, provision unit, evaluation collection unit, history input unit, and detail collection 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 provides an interface for inputting the user's symptoms. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the symptoms using a generation AI. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects hospital information. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a list of hospitals based on the collected information. The provision unit is realized by the control unit 46A of the smart device 14 and provides the generated list to the user. The evaluation collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects hospital ratings and word-of-mouth information. The history input unit is realized by the control unit 46A of the smart device 14 and inputs the user's past treatments and diagnostic results. The detail collection unit is realized by the specific processing unit 290 of the data processing device 12, and collects detailed information such as the location information of the hospital, the consultation hours, and whether or not an appointment can be made. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, collection unit, generation unit, provision unit, evaluation collection unit, history input unit, and detail collection 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 provides an interface for inputting the user's symptoms. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the symptoms using a generation AI. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects hospital information. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a list of hospitals based on the collected information. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides the generated list to the user. The evaluation collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects hospital ratings and word-of-mouth information. The history input unit is realized by the control unit 46A of the smart glasses 214 and inputs the user's past treatment and diagnosis results. The detail collection unit is realized by the specific processing unit 290 of the data processing device 12, and collects detailed information such as the location information of the hospital, the consultation hours, and whether or not an appointment can be made. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, analysis unit, collection unit, generation unit, provision unit, evaluation collection unit, history input unit, and detail collection 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 provides an interface for inputting the user's symptoms. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the symptoms using a generation AI. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects hospital information. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a list of hospitals based on the collected information. The provision unit is realized by the control unit 46A of the headset-type terminal 314 and provides the generated list to the user. The evaluation collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects hospital ratings and word-of-mouth information. The history input unit is realized by the control unit 46A of the headset-type terminal 314 and inputs the user's past treatments and diagnostic results. The detail collection unit is realized by the specific processing unit 290 of the data processing device 12, and collects detailed information such as the location information of the hospital, the consultation hours, and whether or not an appointment can be made. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, collection unit, generation unit, provision unit, evaluation collection unit, history input unit, and detail collection 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 provides an interface for inputting the user's symptoms. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the symptoms using a generation AI. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects hospital information. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a list of hospitals based on the collected information. The provision unit is realized by the control unit 46A of the robot 414 and provides the generated list to the user. The evaluation collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects hospital ratings and word-of-mouth information. The history input unit is realized by the control unit 46A of the robot 414 and inputs the user's past treatments and diagnostic results. The detail collection unit is realized by the specific processing unit 290 of the data processing device 12, and collects detailed information such as the location information of the hospital, the consultation hours, and whether or not an appointment can be made.

[0150] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0151] The reception unit can also analyze the user's input and provide appropriate advice based on the user's health condition. For example, the reception unit can suggest lifestyle improvements and preventive measures based on the symptoms entered by the user. The reception unit can also provide general health knowledge and information based on the information entered by the user. Furthermore, the reception unit can determine whether the symptoms entered by the user are urgent and encourage emergency response if necessary. This allows the user to quickly take appropriate measures for the symptoms.

[0152] The analysis unit can also estimate the user's emotions and adjust the way the analysis results are presented based on the estimated user's emotions. For example, if the user is feeling anxious, the analysis unit uses simple expressions that convey a sense of security. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can also provide concise analysis results that focus on the main points. In this way, by providing analysis results that correspond to the user's emotions, it is possible to provide information that is easy for the user to understand.

[0153] When collecting hospital evaluation information, the collection unit may also collect profiles and qualification information of the hospital's specialists. For example, the collection unit may collect information such as the specialty, qualifications, and years of experience of the hospital's doctors. The collection unit may also collect information such as the doctors' research achievements and academic presentations. Furthermore, the collection unit may collect evaluations and word-of-mouth information about the doctors from their patients. This allows users to select a hospital based not only on the hospital itself but also on the information about the doctors in charge.

[0154] The providing unit can also estimate the user's emotions and adjust the way in which the list is provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide a simple and visually easy-to-understand list. If the user is relaxed, the providing unit can also provide a list including detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide a concise list that focuses on the main points. In this way, by providing a list according to the user's emotions, it is possible to provide information that is easy for the user to use.

[0155] The history input unit can also predict future health risks based on the user's past medical history. For example, the history input unit can analyze the user's past medical history and predict future disease risks. The history input unit can also perform risk assessment taking into account information such as the user's lifestyle and family history. Furthermore, the history input unit can provide advice on preventive measures and health management based on the predicted risks. This allows the user to take measures against future health risks early.

[0156] The reception unit can also estimate the user's emotions and adjust the symptom input method based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Alternatively, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick symptom input. In this way, by adjusting the symptom input method according to the user's emotions, an easy-to-use interface can be provided for the user.

[0157] The analysis unit can also optimize the analysis algorithm based on the user's past analysis results. For example, the analysis unit can refer to the user's past analysis results and reflect them in the current analysis. The analysis unit can also adjust the parameters of the analysis algorithm based on the past analysis results. Furthermore, the analysis unit can use the past analysis results to improve the accuracy of the analysis. In this way, by utilizing the user's past analysis results, it is possible to provide more accurate analysis results.

[0158] When collecting hospital evaluation information, the collection unit can also collect information about the hospital's equipment and facilities. For example, the collection unit collects information about the hospital's medical equipment and testing equipment. The collection unit can also collect information about the cleanliness and comfort of the hospital's facilities. Furthermore, the collection unit can collect information such as the hospital's accessibility and whether or not parking is available. This allows the user to select a hospital based on information about the hospital's equipment and facilities.

[0159] The providing unit can also estimate the user's emotions and adjust the way in which the list is provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide a simple and visually easy-to-understand list. If the user is relaxed, the providing unit can also provide a list including detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide a concise list that focuses on the main points. In this way, by providing a list according to the user's emotions, it is possible to provide information that is easy for the user to use.

[0160] The history input unit can also predict future health risks based on the user's past medical history. For example, the history input unit can analyze the user's past medical history and predict future disease risks. The history input unit can also perform risk assessment taking into account information such as the user's lifestyle and family history. Furthermore, the history input unit can provide advice on preventive measures and health management based on the predicted risks. This allows the user to take measures against future health risks early.

[0161] The processing flow of the second embodiment will be briefly explained below.

[0162] Step 1: The reception unit allows the user to input their symptoms. For example, the user can input specific symptoms or ailments such as "I have a headache" or "I have a stomachache." The reception unit supports text input and voice input, and can also send the user's input to the generation AI. Step 2: The analysis unit uses the generation AI to analyze the symptoms entered by the reception unit. The generation AI refers to a database of symptoms and identifies hospitals that correspond to the entered symptoms. The analysis unit can also use the generation AI to extract and analyze important parts of the symptoms. Step 3: The collection unit collects hospital information. The collection unit collects information such as the hospital's areas of expertise and past treatment results, and can collect information from the hospital's website or medical database. The collection unit can also use generative AI to automatically collect hospital information. Step 4: The generator generates a list of hospitals based on the information collected by the collector. The generator uses a generation AI to generate a list of hospitals, and lists the hospitals that are best suited to the user's symptoms based on the collected information. The generator AI generates the list taking into account the hospital's areas of expertise and past treatment performance. Step 5: The provider provides the list generated by the generator to the user. The provider displays the list to the user through a web application or a mobile application, and can also adjust the display method of the list using the generation AI. The provider selects the optimal display method depending on the user's input method (voice, text, image, etc.).

[0163] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0165] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0168] 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.

[0169] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0170] 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.

[0171] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0172] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0173] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0174] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0175] 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.

[0176] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0177] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0178] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0179] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0180] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0181] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0182] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0183] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0184] 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.

[0185] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0186] 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.

[0187] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0188] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0189] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0190] 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.

[0191] 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.

[0192] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0193] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0194] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0195] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0196] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0197] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0198] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0199] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0200] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0201] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0202] 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.

[0203] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0204] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0205] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0206] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0207] 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.

[0208] 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.

[0209] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0210] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0211] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0212] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0213] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0214] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0215] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0216] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0217] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0218] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0219] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0220] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0221] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0222] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0223] 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.

[0224] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0225] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0226] 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.

[0227] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0228] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0229] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0230] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0231] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0232] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0233] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0234] [Explanation of symbols]

[0235] 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 section for inputting symptoms; an analysis unit that analyzes the symptoms input by the reception unit; a collection department that collects hospital information; a generating unit that generates a list of hospitals based on the information collected by the collecting unit; a providing unit that provides the list generated by the generating unit to a user; Equipped with A system characterized by:

2. Equipping a department that collects hospital reviews and word-of-mouth information 2. The system of claim 1.

3. Equipped with a history input section for inputting the user's past treatment and diagnosis results 2. The system of claim 1.

4. Equipped with a detailed collection unit that collects information on the location of the hospital, consultation hours, and availability of reservations 2. The system of claim 1.

5. The evaluation collection unit cooperates with the collection unit to collect evaluation information of hospitals, and the generation unit generates a list based on the information.

3. The system of claim 2.

6. The history input unit cooperates with the reception unit to receive the user's past treatment information, and the analysis unit performs analysis based on the information.

4. The system of claim 3.

7. The reception unit Inferring the user's emotions and adjusting the symptom input method based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyzes the user's symptom input history and suggests the optimal input method 2. The system of claim 1.

9. The reception unit When entering symptoms, filtering is performed based on the user's current health status and lifestyle habits.

2. The system of claim 1.

10. The reception unit When entering symptoms, select the most appropriate input method depending on the user's input method.

2. The system of claim 1.

Images