System

Abstract

An object of the system according to the embodiment is to allow a user to efficiently collect information on medical facilities and treatment options and make an optimal selection.SOLUTION: A system includes a reception part, an analysis part, a provision part, and a reservation part. The reception unit receives information on medical facilities and treatment options from a user. The analysis unit analyzes the information received by the reception unit and generates a list of related medical facilities and treatment options. The providing unit provides the user with the list generated by the analyzing unit. The reservation unit performs reservation and inquiry based on the list provided by the providing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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 technologies have had the problem of making it difficult for users to efficiently gather information about medical facilities and treatment options and make optimal choices.

[0005] The system according to the embodiment aims to enable users to efficiently collect information about medical facilities and treatment options and make optimal choices. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a reservation unit. The reception unit receives information about medical facilities and treatment options from a user. The analysis unit analyzes the information received by the reception unit and generates a list of relevant medical facilities and treatment options. The provision unit provides the list generated by the analysis unit to the user. The reservation unit makes reservations and inquiries based on the list provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to efficiently gather information about medical facilities and treatment options and make optimal choices. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A medical information provision system according to an embodiment of the present invention allows a user to easily obtain information about medical facilities and treatment options and make cost-effective choices. In the medical information provision system, a user inputs information about medical facilities and treatment options. AI analyzes the information to generate a list of relevant medical facilities and treatment options, and provides the list to the user. The user can make cost-effective choices based on this list. For example, in the medical information provision system, a user inputs information about treatments for a specific illness and nearby medical facilities. AI then analyzes the input information and generates a list of relevant medical facilities and treatment options. The generated list includes information about the cost and effectiveness of each option. This allows the user to compare the cost-effectiveness of each option. The user can make cost-effective choices based on the generated list. This increases awareness of medical costs and reduces wasteful spending. The medical information provision system allows a user to easily obtain information about medical facilities and treatment options and make cost-effective choices. For example, the user can easily obtain and compare information about treatments for a specific illness and nearby medical facilities. This increases awareness of medical costs and reduces wasteful spending.

[0029] A medical information provision system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a reservation unit. The reception unit receives information about medical facilities and treatment options from a user. Information input by the user includes, but is not limited to, a disease name, symptoms, and desired treatment. The reception unit receives, for example, the disease name and symptoms input by the user in text format. The reception unit can also receive information using voice input or image input. For example, when a user inputs a disease name or symptoms by voice, the information is converted into text data using voice recognition technology. The analysis unit analyzes the information received by the reception unit and generates a list of relevant medical facilities and treatment options. The analysis unit performs analysis using, for example, past data and statistical information. For example, the analysis is performed based on past patient data and treatment success rates. The analysis unit can also analyze information using AI. For example, AI generates a list of relevant medical facilities and treatment options based on the information input by the user. The provision unit provides the list generated by the analysis unit to the user. The provision unit provides the information via, for example, a website or an app. The provision unit can also provide information using email or a messaging app. For example, the provision unit makes a reservation or inquiry as a next step based on the treatment option selected by the user. The reservation unit makes a reservation or inquiry based on a list provided by the provision unit. The reservation unit makes a reservation, for example, through an online reservation system. The reservation unit can also make inquiries using telephone or email. For example, the reservation unit makes a reservation at a medical facility selected by the user. This allows the medical information provision system according to the embodiment to allow the user to easily obtain information about medical facilities and treatment options and make cost-effective choices. For example, the user can easily obtain and compare information about treatments for a specific illness and nearby medical facilities. This increases awareness of medical costs and reduces wasteful spending.

[0030] The analysis unit can perform analysis using past data and statistical information. The analysis unit performs analysis based on, for example, past patient data. For example, it collects past patient data and analyzes information related to treatment success rates and costs. The analysis unit can also perform analysis using statistical information. For example, it performs analysis based on statistical information related to treatment success rates and medical costs. The analysis unit can also perform analysis by combining past data and statistical information. For example, it performs analysis by combining past patient data and statistical information related to treatment success rates. By utilizing past data and statistical information, the accuracy of the analysis is improved. 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 past data and statistical information into AI and have the AI ​​perform the analysis.

[0031] The providing unit can provide information including costs, success rates of treatment, and patient reviews. The providing unit, for example, provides information on costs of treatment options. For example, it provides information on treatment costs, medical fees, and drug costs. The providing unit can also provide information on treatment success rates. For example, it provides information on survival rates and recurrence rates after treatment. The providing unit can also provide information on patient reviews. For example, it provides information on patient satisfaction surveys and reviews on review sites. This allows users to select the optimal treatment option based on costs, success rates of treatment, and patient reviews. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the costs and success rates of treatment options and patient reviews into AI and have the AI ​​provide the information.

[0032] The reservation unit can make reservations and inquiries based on the user's selection. The reservation unit can make reservations, for example, based on the treatment options selected by the user. For example, the reservation can be made through an online reservation system. The reservation unit can also make inquiries to the medical facility selected by the user. For example, the inquiry can be made by phone or email. This allows reservations and inquiries to be made smoothly based on the treatment options selected by the user. Some or all of the above-mentioned processing in the reservation unit can be performed using, for example, AI, or can be performed without using AI. For example, the reservation unit can input the treatment options selected by the user into AI and have the AI ​​make reservations and inquiries.

[0033] The reception unit can accept the disease name, symptoms, and desired treatment entered by the user. The reception unit, for example, accepts the disease name entered by the user in text format. For example, when the user enters a specific disease name, the reception unit accepts the input using a text box. The reception unit can also accept the symptoms entered by the user. For example, when the user enters a specific symptom, the reception unit accepts the input using a check box or a drop-down list. The reception unit can also accept the treatment desired by the user. For example, when the user selects the desired treatment, the reception unit accepts the input using a radio button or a slider. In this way, by entering the disease name, symptoms, and desired treatment, appropriate medical facilities and treatment options can be suggested. 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 disease name, symptoms, and desired treatment entered by the user into AI and have the AI ​​accept the information.

[0034] The provision unit can make a reservation or inquiry as the next step based on the treatment option selected by the user. The provision unit, for example, makes a reservation based on the treatment option selected by the user. For example, the reservation is made through an online reservation system. The provision unit can also make an inquiry to the medical facility selected by the user. For example, the inquiry is made by phone or email. This allows the next step to proceed smoothly based on the treatment option selected by the user. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the treatment option selected by the user into AI and have the AI ​​make the reservation or inquiry.

[0035] The reception unit can analyze the user's past input history and suggest the optimal input method. The reception unit can suggest the optimal input method based on the user's past input history, for example. For example, the reception unit can automatically display disease names and symptoms that the user 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. For example, if the user has used voice input in the past, the reception unit can preferentially suggest voice input. The reception unit can also predict and suggest disease names and symptoms to be used in a specific time period based on the user's past input history. For example, the reception unit can suggest the optimal input method based on the disease names and symptoms that the user input in a specific time period in the past. This improves input efficiency by suggesting the optimal input method based on the user's past input history. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without AI. For example, the reception unit can input the user's past input history into AI and have the AI ​​suggest the optimal input method.

[0036] When inputting a disease name or symptoms, the reception unit can filter the input content based on the user's current health condition. For example, the reception unit can prioritize displaying relevant disease names and symptoms based on the user's current health condition. For example, the reception unit can display relevant disease names and symptoms based on the user's diagnosis results or self-reporting. The reception unit can also omit unnecessary input items depending on the user's health condition. For example, if the user's health condition is good, the reception unit can omit input of detailed symptoms. The reception unit can also suggest appropriate input content based on the user's health condition. For example, the reception unit can suggest appropriate disease names and symptoms based on the user's health checkup data. This allows appropriate information to be provided by filtering the input content based on the user's current health condition. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's health condition data into AI and have the AI ​​filter the input content.

[0037] When a user inputs a disease name or symptoms, the reception unit can select the optimal input means according to the user's input method. For example, if the user selects voice input, the reception unit inputs the disease name or symptoms using voice recognition technology. For example, when the user inputs the disease name or symptoms by voice, voice recognition software automatically analyzes the voice and converts it into text data. Furthermore, if the user selects text input, the reception unit can also support keyboard input. For example, when the user inputs the disease name or symptoms using a keyboard, the reception unit accepts the input using a text box. Furthermore, if the user selects image input, the reception unit can also input the disease name or symptoms using image recognition technology. For example, when the user inputs the disease name or symptoms using an image, image recognition software automatically analyzes the image and converts it into text data. This improves input efficiency by providing the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's input method into AI and have the AI ​​select the optimal input means.

[0038] When inputting a disease name or symptoms, the reception unit can prioritize receiving highly relevant information by taking into account the user's geographical location information. The reception unit, for example, prioritizes displaying information related to nearby medical facilities based on the user's current location. For example, it displays information about nearby hospitals and clinics based on the user's current location. The reception unit can also prioritize displaying information related to diseases and symptoms specific to a region based on the user's geographical location information. For example, it displays information about diseases and symptoms that are prevalent in a particular region. The reception unit can also suggest optimal treatment options by taking into account the user's geographical location information. For example, it suggests optimal treatments and medical facilities based on the user's current location. This makes it possible to provide highly relevant information by taking the user's geographical location information into account. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location information into AI and have the AI ​​provide highly relevant information.

[0039] The reception unit can analyze the user's social media activity when entering a disease name or symptoms and receive related information. The reception unit can, for example, suggest related disease names or symptoms based on health information shared by the user on social media. For example, the reception unit can analyze health information posted by the user on social media and display related disease names or symptoms. The reception unit can also analyze the content of the user's social media posts and suggest related disease names or symptoms. For example, the reception unit can display related disease names or symptoms based on the content posted by the user on social media. The reception unit can also suggest related disease names or symptoms based on the activity of the user's friends on social media. For example, the reception unit can display related disease names or symptoms based on health information shared by the user's friends on social media. This makes it possible to provide related information by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's social media activity data into AI and have the AI ​​provide related information.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when inputting a disease name or symptoms. The reception unit, for example, suggests an optimal input method based on feedback previously provided by the user. For example, the reception unit suggests an optimal input method based on feedback regarding a disease name or symptoms previously input by the user. The reception unit can also customize the input interface by reflecting the user's past feedback. For example, the reception unit can adjust the layout and design of the input interface based on feedback previously provided by the user. The reception unit can also improve the input procedure based on the user's past feedback. For example, the reception unit can simplify the input procedure based on feedback previously provided by the user. In this way, the input method can be customized by reflecting the user's past feedback. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's past feedback into AI and have the AI ​​customize the input method.

[0041] The analysis unit can improve the accuracy of the analysis by referring to past data and statistical information during analysis. The analysis unit performs analysis based on, for example, past patient data. For example, it collects past patient data and analyzes information related to treatment success rates and costs. The analysis unit can also perform analysis by referring to statistical information. For example, it performs analysis based on statistical information related to treatment success rates and medical costs. The analysis unit can also perform analysis by combining past data and statistical information. For example, it performs analysis by combining past patient data and statistical information related to treatment success rates. By referring to past data and statistical information, the accuracy of the analysis is improved. 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 past data and statistical information into AI and have the AI ​​perform the analysis.

[0042] During analysis, the analysis unit can apply different analysis methods depending on the disease name or symptom category. For example, the analysis unit selects the optimal analysis method depending on the disease name or symptom category. For example, a specialized analysis method is applied to a specific disease or symptom. The analysis unit can also customize the analysis method based on the disease name or symptom category. For example, the analysis unit adjusts the analysis method depending on the disease name or symptom category. This improves the accuracy of the analysis by providing an analysis method depending on the disease name or symptom category. 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 disease name or symptom category into AI and have the AI ​​select the optimal analysis method.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, performs a current analysis based on the user's past analysis results. For example, the current analysis is performed based on the user's past diagnosis results and treatment effect data. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. For example, the analysis algorithm can be optimized based on the user's past analysis results. The analysis unit can also select an optimal analysis method based on the user's past analysis results. For example, the analysis unit can suggest an optimal analysis method based on the user's past analysis results. This improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-described 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 results into AI and have the AI ​​improve the accuracy of the analysis.

[0044] The analysis unit can perform the analysis while taking into account the geographic distribution of medical facilities and treatment options. The analysis unit, for example, proposes the optimal treatment option based on the geographic distribution of medical facilities. For example, the analysis unit proposes the optimal treatment method based on the number of medical facilities by region and the distribution of treatment options. The analysis unit can also provide analysis results by taking into account the geographic distribution of treatment options. For example, the analysis unit can preferentially display treatment options available in a specific region. The analysis unit can also propose the optimal medical facility based on the geographic distribution. For example, the analysis unit can propose the optimal medical facility based on the user's current location. This makes it possible to propose the optimal treatment option by taking into account the geographic distribution of medical facilities and treatment options. Some or all of the above-described 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 geographic distribution data of medical facilities and treatment options into AI and have the AI ​​perform the analysis.

[0045] During analysis, the analysis unit can improve the accuracy of the analysis by referring to related medical literature. The analysis unit, for example, complements the analysis results based on related medical literature. For example, the analysis results are complemented by referring to academic papers and medical guidelines. The analysis unit can also analyze the effects of treatment options by referring to medical literature. For example, the analysis is performed based on data on treatment effects described in medical literature. The analysis unit can also suggest optimal treatment options based on medical literature. For example, the optimal treatment method is suggested based on medical literature. In this way, the accuracy of the analysis is improved by referring to related medical literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevant medical literature data into AI and have the AI ​​perform the analysis.

[0046] The analysis unit can perform the analysis while taking into account the market value of medical facilities and treatment options. The analysis unit, for example, proposes the optimal treatment option based on the market value of the medical facility. For example, the analysis unit proposes the optimal treatment method based on the treatment cost and the balance between supply and demand. The analysis unit can also provide analysis results by taking into account the market value of the treatment option. For example, the analysis unit can preferentially display treatment options with high market value. The analysis unit can also propose the optimal medical facility based on market value. For example, the analysis unit proposes the optimal medical facility based on the treatment cost and the balance between supply and demand. This makes it possible to propose the optimal treatment option by taking into account the market value of the medical facility and treatment option. 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 market value data of medical facilities and treatment options into AI and have the AI ​​perform the analysis.

[0047] The providing unit can adjust the level of detail of the provided information based on the importance of the medical facility or treatment option when providing the information. The providing unit, for example, provides detailed information about medical facilities or treatment options with high importance. For example, it displays detailed information about treatment methods or medical facilities with high importance. The providing unit can also provide information with low importance in a concise manner. For example, it displays information about treatment methods or medical facilities with low importance in a concise manner. The providing unit can also adjust the level of detail of the information based on the importance. For example, it displays information with high importance in detail and information with low importance in a concise manner. In this way, by adjusting the level of detail of the information based on the importance of the medical facility or treatment option, it is possible to provide information that is important to the user. 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 importance data of medical facilities and treatment options into AI and cause the AI ​​to adjust the level of detail of the information.

[0048] The providing unit can apply different delivery algorithms depending on the category of the medical facility or the treatment option when providing the information. The providing unit, for example, selects the optimal delivery algorithm depending on the category of the medical facility. For example, the providing unit selects the optimal delivery algorithm depending on the category, such as hospital, clinic, or specialty clinic. The providing unit can also customize the delivery algorithm based on the category of the treatment option. For example, the delivery algorithm can be adjusted based on the category, such as treatment method, drug therapy, or surgery. The providing unit can also adjust the method of providing information depending on the category. For example, detailed information can be provided for certain categories, and concise information can be provided for other categories. In this way, optimal information can be provided by applying a delivery algorithm depending on the category of the medical facility or the treatment option. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input category data of the medical facility or the treatment option into AI and have the AI ​​apply the delivery algorithm.

[0049] The providing unit can improve the accuracy of the provided information by referring to the user's past provision results. The providing unit, for example, adjusts the current provided information based on the user's past provision results. For example, the current provided information is optimized based on data regarding treatment options and medical facilities selected by the user in the past. The providing unit can also improve the provision algorithm by referring to the user's past provision results. For example, the providing unit adjusts the provision algorithm based on the user's past selection data. The providing unit can also select an optimal provision method based on the user's past provision results. For example, the providing unit suggests an optimal information provision method based on the user's past selection data. This improves the accuracy of the provided information 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 result data into AI and cause the AI ​​to improve the accuracy of the provided information.

[0050] The providing unit can determine the priority of the provided information based on the submission date of the medical facility and the treatment option at the time of provision. For example, the providing unit can prioritize providing information that was submitted most recently. For example, information about the latest treatment methods and medical facilities can be displayed preferentially. The providing unit can also provide information that was submitted older in a concise manner. For example, information about older treatment methods and medical facilities can be displayed concisely. The providing unit can also adjust the priority of the information based on the submission date. For example, recently submitted information can be displayed at the top, and older information can be displayed at the bottom. In this way, the latest information can be provided by determining the priority of the information based on the submission date of the medical facility and the treatment option. Some or all of the above-described processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input data about the submission date of the medical facility and the treatment option into AI and have the AI ​​determine the priority of the information.

[0051] The providing unit can adjust the order of the provided information based on the relevance of medical facilities and treatment options when providing the information. The providing unit, for example, prioritizes providing highly relevant information. For example, it prioritizes displaying information about treatment methods and medical facilities related to the user's disease name or symptoms. The providing unit can also provide less relevant information in a concise manner. For example, it concisely displays information that is less relevant to the user's disease name or symptoms. The providing unit can also adjust the order of the information based on the relevance. For example, it can display more relevant information at the top and less relevant information at the bottom. In this way, by adjusting the order of the information based on the relevance of medical facilities and treatment options, it is possible to provide information that is more relevant to the user. 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 relevance data about medical facilities and treatment options into AI and have the AI ​​adjust the order of the information.

[0052] The providing unit can adjust the use of technical terminology in the provided information according to the user's level of expertise when providing the information. For example, the providing unit uses detailed technical terminology for users with a high level of expertise. For example, detailed medical terminology is used for medical professionals and experts. The providing unit can also provide explanations in simple terms for users with a low level of expertise. For example, information on treatment methods and medical facilities is provided in simple terms for general users. The providing unit can also adjust the use of technical terminology in the information based on the level of expertise. For example, the level of detail of the information and the use of technical terminology are adjusted according to the level of expertise. This improves understanding of the information by providing information according to the user's level of expertise. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's level of expertise data into AI and have the AI ​​execute the use of technical terminology in the information.

[0053] When making a reservation, the reservation unit can analyze the user's past reservation history and select the optimal reservation method. The reservation unit, for example, suggests the optimal reservation method based on the user's past reservation history. For example, the reservation unit suggests the optimal reservation method based on data regarding the reservation methods and reservation dates and times used by the user in the past. The reservation unit can also simplify the reservation procedure by referring to the user's past reservation history. For example, the reservation unit simplifies the reservation procedure based on the reservation methods used by the user in the past. The reservation unit can also suggest the optimal reservation date and time based on the user's past reservation history. For example, the reservation unit suggests the optimal reservation date and time based on the user's past reservation history. This improves reservation efficiency by providing the optimal reservation method based on the user's past reservation history. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's past reservation history data into AI and have the AI ​​select the optimal reservation method.

[0054] The reservation unit can propose a reservation date and time based on the user's current schedule when making a reservation. The reservation unit, for example, proposes the optimal reservation date and time based on the user's calendar information. For example, the reservation unit proposes the optimal reservation date and time based on the schedule in the user's calendar app or planner. The reservation unit can also adjust the reservation date and time by referring to the user's current schedule. For example, the reservation unit adjusts the reservation date and time based on the user's current schedule. The reservation unit can also propose the optimal reservation method based on the user's schedule. For example, the reservation unit proposes the optimal reservation method based on the user's schedule. This improves reservation efficiency by proposing the optimal reservation date and time based on the user's current schedule. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's schedule data into AI and have the AI ​​suggest the optimal reservation date and time.

[0055] The reservation unit can improve the reservation procedure by reflecting user feedback at the time of reservation. The reservation unit can improve the reservation procedure based on, for example, the user's past feedback. For example, the reservation unit can optimize the reservation procedure based on feedback provided by the user in the past. The reservation unit can also customize the reservation interface by reflecting user feedback. For example, the reservation unit can adjust the layout and design of the reservation interface based on feedback provided by the user in the past. The reservation unit can also simplify the reservation procedure based on user feedback. For example, the reservation procedure can be simplified based on feedback provided by the user in the past. In this way, the reservation procedure can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the reservation unit can be performed using, for example, AI, or can be performed without using AI. For example, the reservation unit can input user feedback data into AI and have the AI ​​improve the reservation procedure.

[0056] The reservation unit can select the optimal reservation method by taking into account the user's geographical location information when making a reservation. The reservation unit, for example, prioritizes reservations at nearby medical facilities based on the user's current location. For example, the reservation unit prioritizes reservations at nearby hospitals or clinics based on the user's current location. The reservation unit can also suggest the optimal reservation method based on the user's geographical location information. For example, the reservation unit can suggest the optimal reservation method based on the user's current location. The reservation unit can also adjust the reservation procedure by taking into account the user's geographical location information. For example, the reservation procedure can be adjusted based on the user's current location. In this way, the optimal reservation method can be provided by taking the user's geographical location information into account. Some or all of the above-mentioned processing in the reservation unit may be performed using, or without, AI. For example, the reservation unit can input the user's geographical location information data into AI and have the AI ​​select the optimal reservation method.

[0057] At the time of reservation, the reservation unit can analyze the user's social media activity and suggest a means for the reservation procedure. The reservation unit can, for example, suggest the optimal reservation method based on the user's social media activity. For example, the reservation unit can suggest the optimal reservation method based on health information shared by the user on social media. The reservation unit can also analyze the content of the user's social media posts and suggest a related reservation procedure. For example, the reservation unit can suggest a related reservation procedure based on the content posted by the user on social media. The reservation unit can also suggest the optimal reservation procedure based on the activity of the user's friends on social media. For example, the reservation unit can suggest the optimal reservation procedure based on health information shared by the user's friends on social media. In this way, the optimal reservation method can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the reservation unit can be performed using, for example, AI, or can be performed without using AI. For example, the reservation unit can input the user's social media activity data into AI and have the AI ​​suggest the optimal reservation method.

[0058] The reservation unit can customize the reservation procedure method by reflecting the user's past feedback when making a reservation. The reservation unit, for example, proposes an optimal reservation procedure based on the user's past feedback. For example, the reservation unit proposes an optimal reservation procedure based on feedback provided by the user in the past. The reservation unit can also customize the reservation interface by reflecting the user's feedback. For example, the reservation unit can adjust the layout and design of the reservation interface based on feedback provided by the user in the past. The reservation unit can also improve the reservation procedure based on the user's feedback. For example, the reservation unit can simplify the reservation procedure based on feedback provided by the user in the past. In this way, the reservation procedure method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reservation unit may be performed using, or without, AI. For example, the reservation unit can input the user's past feedback data into AI and have the AI ​​customize the reservation procedure.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The analysis unit can automatically collect relevant medical literature based on the user's past search history and use it in the analysis. For example, the latest medical literature related to the disease names and symptoms that the user has previously searched for can be collected and reflected in the analysis. Information on related treatments and medical facilities can also be collected based on the user's past search history. This improves the accuracy of the analysis by utilizing the user's past search history. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past search history data into AI and have the AI ​​collect relevant medical literature.

[0061] The provision unit can propose optimal treatment options based on the user's past selection history. For example, it can propose relevant treatment options based on the treatment methods and medical facilities selected by the user in the past. It can also determine the priority of treatment options based on the user's past selection history. This makes it possible to propose optimal treatment options by utilizing the user's past selection history. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the user's past selection history data into AI and have the AI ​​propose optimal treatment options.

[0062] The reservation unit can propose the optimal reservation date and time based on the user's current schedule. For example, it can automatically detect available time slots based on the user's calendar information and propose the optimal reservation date and time. It can also send reservation reminders based on the user's schedule. This improves reservation efficiency by taking the user's schedule into consideration. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's schedule data into AI and have the AI ​​propose the optimal reservation date and time.

[0063] The reception unit can suggest the most suitable medical facility based on the user's geographical location information. For example, it can preferentially display nearby medical facilities based on the user's current location. It can also provide information related to diseases and symptoms specific to the region based on the user's geographical location information. This makes it possible to suggest the most suitable medical facility by taking the user's geographical location information into consideration. Some or all of the above-mentioned 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 geographical location information data into AI and have the AI ​​suggest the most suitable medical facility.

[0064] The analysis unit can perform analysis taking into account the market value of medical facilities and treatment options. For example, it can propose the optimal treatment method based on the treatment cost and the balance between supply and demand. It can also propose the optimal medical facility based on the market value of the medical facility. This makes it possible to propose the optimal treatment option by taking into account the market value of the medical facility and treatment option. 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 market value data of medical facilities and treatment options into AI and have the AI ​​perform the analysis.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit accepts information about medical facilities and treatment options from the user. Information entered by the user includes the name of the disease, symptoms, and desired treatment. The reception unit can accept information in text format, voice input, or image input. For example, voice input can be converted into text data using voice recognition technology. Step 2: The analysis unit analyzes the information received by the reception unit and generates a list of relevant medical facilities and treatment options. The analysis unit uses past data, statistical information, and AI to perform the analysis. For example, analysis is performed based on past patient data and treatment success rates, and the AI ​​generates the list based on the information entered by the user. Step 3: The provider provides the list generated by the analyzer to the user. The provider can provide information via a website, app, email, or messaging app. For example, the provider can schedule an appointment or make an inquiry as a next step based on the treatment option selected by the user. Step 4: The reservation unit makes reservations or inquiries based on the list provided by the provider unit. The reservation unit can make reservations or inquiries using an online reservation system, telephone, or email. For example, the reservation is made at the medical facility selected by the user.

[0067] (Example 2) A medical information provision system according to an embodiment of the present invention allows a user to easily obtain information about medical facilities and treatment options and make cost-effective choices. In the medical information provision system, a user inputs information about medical facilities and treatment options. AI analyzes the information to generate a list of relevant medical facilities and treatment options, and provides the list to the user. The user can make cost-effective choices based on this list. For example, in the medical information provision system, a user inputs information about treatments for a specific illness and nearby medical facilities. AI then analyzes the input information and generates a list of relevant medical facilities and treatment options. The generated list includes information about the cost and effectiveness of each option. This allows the user to compare the cost-effectiveness of each option. The user can make cost-effective choices based on the generated list. This increases awareness of medical costs and reduces wasteful spending. The medical information provision system allows a user to easily obtain information about medical facilities and treatment options and make cost-effective choices. For example, the user can easily obtain and compare information about treatments for a specific illness and nearby medical facilities. This increases awareness of medical costs and reduces wasteful spending.

[0068] A medical information provision system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a reservation unit. The reception unit receives information about medical facilities and treatment options from a user. Information input by the user includes, but is not limited to, a disease name, symptoms, and desired treatment. The reception unit receives, for example, the disease name and symptoms input by the user in text format. The reception unit can also receive information using voice input or image input. For example, when a user inputs a disease name or symptoms by voice, the information is converted into text data using voice recognition technology. The analysis unit analyzes the information received by the reception unit and generates a list of relevant medical facilities and treatment options. The analysis unit performs analysis using, for example, past data and statistical information. For example, the analysis is performed based on past patient data and treatment success rates. The analysis unit can also analyze information using AI. For example, AI generates a list of relevant medical facilities and treatment options based on the information input by the user. The provision unit provides the list generated by the analysis unit to the user. The provision unit provides the information via, for example, a website or an app. The provision unit can also provide information using email or a messaging app. For example, the provision unit makes a reservation or inquiry as a next step based on the treatment option selected by the user. The reservation unit makes a reservation or inquiry based on a list provided by the provision unit. The reservation unit makes a reservation, for example, through an online reservation system. The reservation unit can also make inquiries using telephone or email. For example, the reservation unit makes a reservation at a medical facility selected by the user. This allows the medical information provision system according to the embodiment to allow the user to easily obtain information about medical facilities and treatment options and make cost-effective choices. For example, the user can easily obtain and compare information about treatments for a specific illness and nearby medical facilities. This increases awareness of medical costs and reduces wasteful spending.

[0069] The analysis unit can perform analysis using past data and statistical information. The analysis unit performs analysis based on, for example, past patient data. For example, it collects past patient data and analyzes information related to treatment success rates and costs. The analysis unit can also perform analysis using statistical information. For example, it performs analysis based on statistical information related to treatment success rates and medical costs. The analysis unit can also perform analysis by combining past data and statistical information. For example, it performs analysis by combining past patient data and statistical information related to treatment success rates. By utilizing past data and statistical information, the accuracy of the analysis is improved. 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 past data and statistical information into AI and have the AI ​​perform the analysis.

[0070] The providing unit can provide information including costs, success rates of treatment, and patient reviews. The providing unit, for example, provides information on costs of treatment options. For example, it provides information on treatment costs, medical fees, and drug costs. The providing unit can also provide information on treatment success rates. For example, it provides information on survival rates and recurrence rates after treatment. The providing unit can also provide information on patient reviews. For example, it provides information on patient satisfaction surveys and reviews on review sites. This allows users to select the optimal treatment option based on costs, success rates of treatment, and patient reviews. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the costs and success rates of treatment options and patient reviews into AI and have the AI ​​provide the information.

[0071] The reservation unit can make reservations and inquiries based on the user's selection. The reservation unit can make reservations, for example, based on the treatment options selected by the user. For example, the reservation can be made through an online reservation system. The reservation unit can also make inquiries to the medical facility selected by the user. For example, the inquiry can be made by phone or email. This allows reservations and inquiries to be made smoothly based on the treatment options selected by the user. Some or all of the above-mentioned processing in the reservation unit can be performed using, for example, AI, or can be performed without using AI. For example, the reservation unit can input the treatment options selected by the user into AI and have the AI ​​make reservations and inquiries.

[0072] The reception unit can accept the disease name, symptoms, and desired treatment entered by the user. The reception unit, for example, accepts the disease name entered by the user in text format. For example, when the user enters a specific disease name, the reception unit accepts the input using a text box. The reception unit can also accept the symptoms entered by the user. For example, when the user enters a specific symptom, the reception unit accepts the input using a check box or a drop-down list. The reception unit can also accept the treatment desired by the user. For example, when the user selects the desired treatment, the reception unit accepts the input using a radio button or a slider. In this way, by entering the disease name, symptoms, and desired treatment, appropriate medical facilities and treatment options can be suggested. 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 disease name, symptoms, and desired treatment entered by the user into AI and have the AI ​​accept the information.

[0073] The provision unit can make a reservation or inquiry as the next step based on the treatment option selected by the user. The provision unit, for example, makes a reservation based on the treatment option selected by the user. For example, the reservation is made through an online reservation system. The provision unit can also make an inquiry to the medical facility selected by the user. For example, the inquiry is made by phone or email. This allows the next step to proceed smoothly based on the treatment option selected by the user. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the treatment option selected by the user into AI and have the AI ​​make the reservation or inquiry.

[0074] The reception unit can estimate the user's emotion and adjust the display method of the input interface based on the estimated user emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expression. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit can analyze the tone and speed of the voice and calculate an emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on heart rate fluctuations. This improves the user's input experience by providing an interface that corresponds 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-mentioned processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0075] The reception unit can analyze the user's past input history and suggest the optimal input method. The reception unit can suggest the optimal input method based on the user's past input history, for example. For example, the reception unit can automatically display disease names and symptoms that the user 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. For example, if the user has used voice input in the past, the reception unit can preferentially suggest voice input. The reception unit can also predict and suggest disease names and symptoms to be used in a specific time period based on the user's past input history. For example, the reception unit can suggest the optimal input method based on the disease names and symptoms that the user input in a specific time period in the past. This improves input efficiency by suggesting the optimal input method based on the user's past input history. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without AI. For example, the reception unit can input the user's past input history into AI and have the AI ​​suggest the optimal input method.

[0076] When inputting a disease name or symptoms, the reception unit can filter the input content based on the user's current health condition. For example, the reception unit can prioritize displaying relevant disease names and symptoms based on the user's current health condition. For example, the reception unit can display relevant disease names and symptoms based on the user's diagnosis results or self-reporting. The reception unit can also omit unnecessary input items depending on the user's health condition. For example, if the user's health condition is good, the reception unit can omit input of detailed symptoms. The reception unit can also suggest appropriate input content based on the user's health condition. For example, the reception unit can suggest appropriate disease names and symptoms based on the user's health checkup data. This allows appropriate information to be provided by filtering the input content based on the user's current health condition. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's health condition data into AI and have the AI ​​filter the input content.

[0077] When a user inputs a disease name or symptoms, the reception unit can select the optimal input means according to the user's input method. For example, if the user selects voice input, the reception unit inputs the disease name or symptoms using voice recognition technology. For example, when the user inputs the disease name or symptoms by voice, voice recognition software automatically analyzes the voice and converts it into text data. Furthermore, if the user selects text input, the reception unit can also support keyboard input. For example, when the user inputs the disease name or symptoms using a keyboard, the reception unit accepts the input using a text box. Furthermore, if the user selects image input, the reception unit can also input the disease name or symptoms using image recognition technology. For example, when the user inputs the disease name or symptoms using an image, image recognition software automatically analyzes the image and converts it into text data. This improves input efficiency by providing the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's input method into AI and have the AI ​​select the optimal input means.

[0078] The reception unit can estimate the user's emotions and prioritize input content based on the estimated user emotions. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expression. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit can analyze the tone and speed of the voice and calculate an emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on heart rate fluctuations. This improves input efficiency by prioritizing input content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0079] When inputting a disease name or symptoms, the reception unit can prioritize receiving highly relevant information by taking into account the user's geographical location information. The reception unit, for example, prioritizes displaying information related to nearby medical facilities based on the user's current location. For example, it displays information about nearby hospitals and clinics based on the user's current location. The reception unit can also prioritize displaying information related to diseases and symptoms specific to a region based on the user's geographical location information. For example, it displays information about diseases and symptoms that are prevalent in a particular region. The reception unit can also suggest optimal treatment options by taking into account the user's geographical location information. For example, it suggests optimal treatments and medical facilities based on the user's current location. This makes it possible to provide highly relevant information by taking the user's geographical location information into account. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location information into AI and have the AI ​​provide highly relevant information.

[0080] The reception unit can analyze the user's social media activity when entering a disease name or symptoms and receive related information. The reception unit can, for example, suggest related disease names or symptoms based on health information shared by the user on social media. For example, the reception unit can analyze health information posted by the user on social media and display related disease names or symptoms. The reception unit can also analyze the content of the user's social media posts and suggest related disease names or symptoms. For example, the reception unit can display related disease names or symptoms based on the content posted by the user on social media. The reception unit can also suggest related disease names or symptoms based on the activity of the user's friends on social media. For example, the reception unit can display related disease names or symptoms based on health information shared by the user's friends on social media. This makes it possible to provide related information by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's social media activity data into AI and have the AI ​​provide related information.

[0081] The reception unit can customize the input method by reflecting the user's past feedback when inputting a disease name or symptoms. The reception unit, for example, suggests an optimal input method based on feedback previously provided by the user. For example, the reception unit suggests an optimal input method based on feedback regarding a disease name or symptoms previously input by the user. The reception unit can also customize the input interface by reflecting the user's past feedback. For example, the reception unit can adjust the layout and design of the input interface based on feedback previously provided by the user. The reception unit can also improve the input procedure based on the user's past feedback. For example, the reception unit can simplify the input procedure based on feedback previously provided by the user. In this way, the input method can be customized by reflecting the user's past feedback. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's past feedback into AI and have the AI ​​customize the input method.

[0082] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expressions. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice and calculate an emotion score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on heart rate fluctuations. This improves the accuracy of the analysis by providing an analysis algorithm 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, 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 analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0083] The analysis unit can improve the accuracy of the analysis by referring to past data and statistical information during analysis. The analysis unit performs analysis based on, for example, past patient data. For example, it collects past patient data and analyzes information related to treatment success rates and costs. The analysis unit can also perform analysis by referring to statistical information. For example, it performs analysis based on statistical information related to treatment success rates and medical costs. The analysis unit can also perform analysis by combining past data and statistical information. For example, it performs analysis by combining past patient data and statistical information related to treatment success rates. By referring to past data and statistical information, the accuracy of the analysis is improved. 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 past data and statistical information into AI and have the AI ​​perform the analysis.

[0084] During analysis, the analysis unit can apply different analysis methods depending on the disease name or symptom category. For example, the analysis unit selects the optimal analysis method depending on the disease name or symptom category. For example, a specialized analysis method is applied to a specific disease or symptom. The analysis unit can also customize the analysis method based on the disease name or symptom category. For example, the analysis unit adjusts the analysis method depending on the disease name or symptom category. This improves the accuracy of the analysis by providing an analysis method depending on the disease name or symptom category. 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 disease name or symptom category into AI and have the AI ​​select the optimal analysis method.

[0085] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, performs a current analysis based on the user's past analysis results. For example, the current analysis is performed based on the user's past diagnosis results and treatment effect data. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. For example, the analysis algorithm can be optimized based on the user's past analysis results. The analysis unit can also select an optimal analysis method based on the user's past analysis results. For example, the analysis unit can suggest an optimal analysis method based on the user's past analysis results. This improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-described 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 results into AI and have the AI ​​improve the accuracy of the analysis.

[0086] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expressions. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice and calculate an emotion score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on heart rate fluctuations. This improves the understanding of the analysis results by providing a display method that corresponds 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, 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 analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0087] The analysis unit can perform the analysis while taking into account the geographic distribution of medical facilities and treatment options. The analysis unit, for example, proposes the optimal treatment option based on the geographic distribution of medical facilities. For example, the analysis unit proposes the optimal treatment method based on the number of medical facilities by region and the distribution of treatment options. The analysis unit can also provide analysis results by taking into account the geographic distribution of treatment options. For example, the analysis unit can preferentially display treatment options available in a specific region. The analysis unit can also propose the optimal medical facility based on the geographic distribution. For example, the analysis unit can propose the optimal medical facility based on the user's current location. This makes it possible to propose the optimal treatment option by taking into account the geographic distribution of medical facilities and treatment options. Some or all of the above-described 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 geographic distribution data of medical facilities and treatment options into AI and have the AI ​​perform the analysis.

[0088] During analysis, the analysis unit can improve the accuracy of the analysis by referring to related medical literature. The analysis unit, for example, complements the analysis results based on related medical literature. For example, the analysis results are complemented by referring to academic papers and medical guidelines. The analysis unit can also analyze the effects of treatment options by referring to medical literature. For example, the analysis is performed based on data on treatment effects described in medical literature. The analysis unit can also suggest optimal treatment options based on medical literature. For example, the optimal treatment method is suggested based on medical literature. In this way, the accuracy of the analysis is improved by referring to related medical literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevant medical literature data into AI and have the AI ​​perform the analysis.

[0089] The analysis unit can perform the analysis while taking into account the market value of medical facilities and treatment options. The analysis unit, for example, proposes the optimal treatment option based on the market value of the medical facility. For example, the analysis unit proposes the optimal treatment method based on the treatment cost and the balance between supply and demand. The analysis unit can also provide analysis results by taking into account the market value of the treatment option. For example, the analysis unit can preferentially display treatment options with high market value. The analysis unit can also propose the optimal medical facility based on market value. For example, the analysis unit proposes the optimal medical facility based on the treatment cost and the balance between supply and demand. This makes it possible to propose the optimal treatment option by taking into account the market value of the medical facility and treatment option. 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 market value data of medical facilities and treatment options into AI and have the AI ​​perform the analysis.

[0090] The providing unit can estimate the user's emotion and adjust the presentation method of the information to be provided based on the estimated user's emotion. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expression. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the tone and speed of the voice can be analyzed to calculate an emotion score. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the emotion score can be calculated based on heart rate fluctuations. This improves the user's understanding of the information by providing an information presentation method that corresponds to the user's emotion. Emotion estimation is realized 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 these examples. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input image data of a user taken with a camera into the generating AI and have the generating AI estimate the user's emotions.

[0091] The providing unit can adjust the level of detail of the provided information based on the importance of the medical facility or treatment option when providing the information. The providing unit, for example, provides detailed information about medical facilities or treatment options with high importance. For example, it displays detailed information about treatment methods or medical facilities with high importance. The providing unit can also provide information with low importance in a concise manner. For example, it displays information about treatment methods or medical facilities with low importance in a concise manner. The providing unit can also adjust the level of detail of the information based on the importance. For example, it displays information with high importance in detail and information with low importance in a concise manner. In this way, by adjusting the level of detail of the information based on the importance of the medical facility or treatment option, it is possible to provide information that is important to the user. 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 importance data of medical facilities and treatment options into AI and cause the AI ​​to adjust the level of detail of the information.

[0092] The providing unit can apply different delivery algorithms depending on the category of the medical facility or the treatment option when providing the information. The providing unit, for example, selects the optimal delivery algorithm depending on the category of the medical facility. For example, the providing unit selects the optimal delivery algorithm depending on the category, such as hospital, clinic, or specialty clinic. The providing unit can also customize the delivery algorithm based on the category of the treatment option. For example, the delivery algorithm can be adjusted based on the category, such as treatment method, drug therapy, or surgery. The providing unit can also adjust the method of providing information depending on the category. For example, detailed information can be provided for certain categories, and concise information can be provided for other categories. In this way, optimal information can be provided by applying a delivery algorithm depending on the category of the medical facility or the treatment option. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input category data of the medical facility or the treatment option into AI and have the AI ​​apply the delivery algorithm.

[0093] The providing unit can improve the accuracy of the provided information by referring to the user's past provision results. The providing unit, for example, adjusts the current provided information based on the user's past provision results. For example, the current provided information is optimized based on data regarding treatment options and medical facilities selected by the user in the past. The providing unit can also improve the provision algorithm by referring to the user's past provision results. For example, the providing unit adjusts the provision algorithm based on the user's past selection data. The providing unit can also select an optimal provision method based on the user's past provision results. For example, the providing unit suggests an optimal information provision method based on the user's past selection data. This improves the accuracy of the provided information 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 result data into AI and cause the AI ​​to improve the accuracy of the provided information.

[0094] The providing unit can estimate the user's emotion and adjust the length of the information to be provided based on the estimated user's emotion. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expression. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the tone and speed of the voice can be analyzed to calculate an emotion score. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the emotion score can be calculated based on heart rate fluctuations. This improves the user's understanding of the information by providing the length of information according to the user's emotion. 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 these examples. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input image data of a user taken with a camera into the generating AI and have the generating AI estimate the user's emotions.

[0095] The providing unit can determine the priority of the provided information based on the submission date of the medical facility and the treatment option at the time of provision. For example, the providing unit can prioritize providing information that was submitted most recently. For example, information about the latest treatment methods and medical facilities can be displayed preferentially. The providing unit can also provide information that was submitted older in a concise manner. For example, information about older treatment methods and medical facilities can be displayed concisely. The providing unit can also adjust the priority of the information based on the submission date. For example, recently submitted information can be displayed at the top, and older information can be displayed at the bottom. In this way, the latest information can be provided by determining the priority of the information based on the submission date of the medical facility and the treatment option. Some or all of the above-described processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input data about the submission date of the medical facility and the treatment option into AI and have the AI ​​determine the priority of the information.

[0096] The providing unit can adjust the order of the provided information based on the relevance of medical facilities and treatment options when providing the information. The providing unit, for example, prioritizes providing highly relevant information. For example, it prioritizes displaying information about treatment methods and medical facilities related to the user's disease name or symptoms. The providing unit can also provide less relevant information in a concise manner. For example, it concisely displays information that is less relevant to the user's disease name or symptoms. The providing unit can also adjust the order of the information based on the relevance. For example, it can display more relevant information at the top and less relevant information at the bottom. In this way, by adjusting the order of the information based on the relevance of medical facilities and treatment options, it is possible to provide information that is more relevant to the user. 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 relevance data about medical facilities and treatment options into AI and have the AI ​​adjust the order of the information.

[0097] The providing unit can adjust the use of technical terminology in the provided information according to the user's level of expertise when providing the information. For example, the providing unit uses detailed technical terminology for users with a high level of expertise. For example, detailed medical terminology is used for medical professionals and experts. The providing unit can also provide explanations in simple terms for users with a low level of expertise. For example, information on treatment methods and medical facilities is provided in simple terms for general users. The providing unit can also adjust the use of technical terminology in the information based on the level of expertise. For example, the level of detail of the information and the use of technical terminology are adjusted according to the level of expertise. This improves understanding of the information by providing information according to the user's level of expertise. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's level of expertise data into AI and have the AI ​​execute the use of technical terminology in the information.

[0098] The reservation unit can estimate the user's emotions and adjust the reservation procedure method based on the estimated user emotions. For example, the reservation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the reservation unit calculates an emotion score based on changes in facial expressions. The reservation unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the reservation unit can analyze the tone and speed of the voice and calculate an emotion score. The reservation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the emotion score can be calculated based on heart rate fluctuations. This improves reservation efficiency by providing a reservation procedure that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 reservation unit can be performed using, for example, AI, or without AI. For example, the reservation unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0099] When making a reservation, the reservation unit can analyze the user's past reservation history and select the optimal reservation method. The reservation unit, for example, suggests the optimal reservation method based on the user's past reservation history. For example, the reservation unit suggests the optimal reservation method based on data regarding the reservation methods and reservation dates and times used by the user in the past. The reservation unit can also simplify the reservation procedure by referring to the user's past reservation history. For example, the reservation unit simplifies the reservation procedure based on the reservation methods used by the user in the past. The reservation unit can also suggest the optimal reservation date and time based on the user's past reservation history. For example, the reservation unit suggests the optimal reservation date and time based on the user's past reservation history. This improves reservation efficiency by providing the optimal reservation method based on the user's past reservation history. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's past reservation history data into AI and have the AI ​​select the optimal reservation method.

[0100] The reservation unit can propose a reservation date and time based on the user's current schedule when making a reservation. The reservation unit, for example, proposes the optimal reservation date and time based on the user's calendar information. For example, the reservation unit proposes the optimal reservation date and time based on the schedule in the user's calendar app or planner. The reservation unit can also adjust the reservation date and time by referring to the user's current schedule. For example, the reservation unit adjusts the reservation date and time based on the user's current schedule. The reservation unit can also propose the optimal reservation method based on the user's schedule. For example, the reservation unit proposes the optimal reservation method based on the user's schedule. This improves reservation efficiency by proposing the optimal reservation date and time based on the user's current schedule. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's schedule data into AI and have the AI ​​suggest the optimal reservation date and time.

[0101] The reservation unit can improve the reservation procedure by reflecting user feedback at the time of reservation. The reservation unit can improve the reservation procedure based on, for example, the user's past feedback. For example, the reservation unit can optimize the reservation procedure based on feedback provided by the user in the past. The reservation unit can also customize the reservation interface by reflecting user feedback. For example, the reservation unit can adjust the layout and design of the reservation interface based on feedback provided by the user in the past. The reservation unit can also simplify the reservation procedure based on user feedback. For example, the reservation procedure can be simplified based on feedback provided by the user in the past. In this way, the reservation procedure can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the reservation unit can be performed using, for example, AI, or can be performed without using AI. For example, the reservation unit can input user feedback data into AI and have the AI ​​improve the reservation procedure.

[0102] The reservation unit can estimate a user's emotions and determine the priority of reservation procedures based on the estimated user emotions. For example, the reservation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reservation unit calculates an emotion score based on changes in facial expression. The reservation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reservation unit can analyze the tone and speed of the voice and calculate an emotion score. The reservation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the emotion score can be calculated based on heart rate fluctuations. This improves reservation efficiency by providing a priority of reservation procedures according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using 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 reservation unit can be performed using, for example, AI, or without AI. For example, the reservation unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0103] The reservation unit can select the optimal reservation method by taking into account the user's geographical location information when making a reservation. The reservation unit, for example, prioritizes reservations at nearby medical facilities based on the user's current location. For example, the reservation unit prioritizes reservations at nearby hospitals or clinics based on the user's current location. The reservation unit can also suggest the optimal reservation method based on the user's geographical location information. For example, the reservation unit can suggest the optimal reservation method based on the user's current location. The reservation unit can also adjust the reservation procedure by taking into account the user's geographical location information. For example, the reservation procedure can be adjusted based on the user's current location. In this way, the optimal reservation method can be provided by taking the user's geographical location information into account. Some or all of the above-mentioned processing in the reservation unit may be performed using, or without, AI. For example, the reservation unit can input the user's geographical location information data into AI and have the AI ​​select the optimal reservation method.

[0104] At the time of reservation, the reservation unit can analyze the user's social media activity and suggest a means for the reservation procedure. The reservation unit can, for example, suggest the optimal reservation method based on the user's social media activity. For example, the reservation unit can suggest the optimal reservation method based on health information shared by the user on social media. The reservation unit can also analyze the content of the user's social media posts and suggest a related reservation procedure. For example, the reservation unit can suggest a related reservation procedure based on the content posted by the user on social media. The reservation unit can also suggest the optimal reservation procedure based on the activity of the user's friends on social media. For example, the reservation unit can suggest the optimal reservation procedure based on health information shared by the user's friends on social media. In this way, the optimal reservation method can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the reservation unit can be performed using, for example, AI, or can be performed without using AI. For example, the reservation unit can input the user's social media activity data into AI and have the AI ​​suggest the optimal reservation method.

[0105] The reservation unit can customize the reservation procedure method by reflecting the user's past feedback when making a reservation. The reservation unit, for example, proposes an optimal reservation procedure based on the user's past feedback. For example, the reservation unit proposes an optimal reservation procedure based on feedback provided by the user in the past. The reservation unit can also customize the reservation interface by reflecting the user's feedback. For example, the reservation unit can adjust the layout and design of the reservation interface based on feedback provided by the user in the past. The reservation unit can also improve the reservation procedure based on the user's feedback. For example, the reservation unit can simplify the reservation procedure based on feedback provided by the user in the past. In this way, the reservation procedure method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reservation unit may be performed using, or without, AI. For example, the reservation unit can input the user's past feedback data into AI and have the AI ​​customize the reservation procedure. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, and reservation unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives information about medical facilities and treatment options from a user. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information received by the reception unit and generates a list of relevant medical facilities and treatment options. The provision unit is implemented, for example, by the output device 40 of the smart device 14 and provides the list generated by the analysis unit to the user. The reservation unit is implemented, for example, by the control unit 46A of the smart device 14 and makes reservations or inquiries based on the list provided by the provision unit. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, and reservation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives information about medical facilities and treatment options from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information received by the reception unit and generates a list of relevant medical facilities and treatment options. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the list generated by the analysis unit to the user. The reservation unit is realized, for example, by the control unit 46A of the smart glasses 214 and makes reservations or inquiries based on the list provided by the provision unit. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and reservation 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 microphone 238 of the headset-type terminal 314 and receives information about medical facilities and treatment options from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information received by the reception unit and generates a list of relevant medical facilities and treatment options. The provision unit is realized, for example, by the speaker 240 of the headset-type terminal 314 and provides the list generated by the analysis unit to the user. The reservation unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and makes reservations or inquiries based on the list provided by the provision unit. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, analysis unit, provision unit, and reservation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives information about medical facilities and treatment options from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information received by the reception unit and generates a list of relevant medical facilities and treatment options. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the list generated by the analysis unit to the user. The reservation unit is realized, for example, by the control unit 46A of the robot 414 and makes reservations or inquiries based on the list provided by the provision unit.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis results can be displayed concisely, while if the user is feeling relaxed, detailed information can be provided. Furthermore, if the user is feeling anxious, the analysis results can be presented using expressions that give a sense of security. This improves the user's understanding of the analysis results by providing a display method for the analysis results that corresponds to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input image data of the user captured by a camera into the generation AI and have the generation AI estimate the user's emotions.

[0108] The providing unit can estimate the user's emotions and adjust the way the information is presented based on the estimated user's emotions. For example, if the user is depressed, the information can be presented using positive expressions, and if the user is excited, the information can be presented using calm expressions. Furthermore, if the user is confused, the information can be presented using concise and clear expressions. This improves the user's understanding of the information by providing an information presentation method that corresponds to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input image data of the user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0109] The reservation unit can estimate the user's emotions and adjust the reservation procedure based on the estimated user emotions. For example, if the user is nervous, a simple and intuitive reservation procedure can be provided, while if the user is relaxed, a detailed reservation procedure can be provided. Furthermore, if the user is in a hurry, a quick reservation procedure can be provided. This improves reservation efficiency by providing a reservation procedure that corresponds 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 reservation unit can be performed using, for example, AI, or without AI. For example, the reservation unit can input image data of the user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0110] The reception unit can estimate the user's emotions and adjust the display method of the input interface based on the estimated user emotions. For example, if the user is feeling stressed, a simple and intuitive interface can be provided, while if the user is relaxed, a detailed interface can be provided. Furthermore, if the user is feeling anxious, an interface can be provided with a design that gives a sense of security. This improves the user's input experience by providing an interface that corresponds to the user's emotions. The 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 reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input image data of the user captured by a camera into the generation AI and have the generation AI estimate the user's emotions.

[0111] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is feeling stressed, the analysis algorithm can be simplified, and if the user is relaxed, a detailed analysis can be performed. Furthermore, if the user is feeling anxious, an analysis result that provides a sense of security can be provided. This improves the accuracy of the analysis by providing an analysis algorithm tailored to the user's emotions. The 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 analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input image data of the user captured by a camera into the generation AI and have the generation AI estimate the user's emotions.

[0112] The analysis unit can automatically collect relevant medical literature based on the user's past search history and use it in the analysis. For example, the latest medical literature related to the disease names and symptoms that the user has previously searched for can be collected and reflected in the analysis. Information on related treatments and medical facilities can also be collected based on the user's past search history. This improves the accuracy of the analysis by utilizing the user's past search history. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past search history data into AI and have the AI ​​collect relevant medical literature.

[0113] The provision unit can propose optimal treatment options based on the user's past selection history. For example, it can propose relevant treatment options based on the treatment methods and medical facilities selected by the user in the past. It can also determine the priority of treatment options based on the user's past selection history. This makes it possible to propose optimal treatment options by utilizing the user's past selection history. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the user's past selection history data into AI and have the AI ​​propose optimal treatment options.

[0114] The reservation unit can propose the optimal reservation date and time based on the user's current schedule. For example, it can automatically detect available time slots based on the user's calendar information and propose the optimal reservation date and time. It can also send reservation reminders based on the user's schedule. This improves reservation efficiency by taking the user's schedule into consideration. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's schedule data into AI and have the AI ​​propose the optimal reservation date and time.

[0115] The reception unit can suggest the most suitable medical facility based on the user's geographical location information. For example, it can preferentially display nearby medical facilities based on the user's current location. It can also provide information related to diseases and symptoms specific to the region based on the user's geographical location information. This makes it possible to suggest the most suitable medical facility by taking the user's geographical location information into consideration. Some or all of the above-mentioned 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 geographical location information data into AI and have the AI ​​suggest the most suitable medical facility.

[0116] The analysis unit can perform analysis taking into account the market value of medical facilities and treatment options. For example, it can propose the optimal treatment method based on the treatment cost and the balance between supply and demand. It can also propose the optimal medical facility based on the market value of the medical facility. This makes it possible to propose the optimal treatment option by taking into account the market value of the medical facility and treatment option. 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 market value data of medical facilities and treatment options into AI and have the AI ​​perform the analysis.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The reception unit accepts information about medical facilities and treatment options from the user. Information entered by the user includes the name of the disease, symptoms, and desired treatment. The reception unit can accept information in text format, voice input, or image input. For example, voice input can be converted into text data using voice recognition technology. Step 2: The analysis unit analyzes the information received by the reception unit and generates a list of relevant medical facilities and treatment options. The analysis unit uses past data, statistical information, and AI to perform the analysis. For example, analysis is performed based on past patient data and treatment success rates, and the AI ​​generates the list based on the information entered by the user. Step 3: The provider provides the list generated by the analyzer to the user. The provider can provide information via a website, app, email, or messaging app. For example, the provider can schedule an appointment or make an inquiry as a next step based on the treatment option selected by the user. Step 4: The reservation unit makes reservations or inquiries based on the list provided by the provider unit. The reservation unit can make reservations or inquiries using an online reservation system, telephone, or email. For example, the reservation is made at the medical facility selected by the user.

[0119] 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.

[0120] 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.

[0121] 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.

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

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

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

[0126] The 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.

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

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

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

[0130] Fig. 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.

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

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

[0133] In the 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.

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

[0135] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0137] 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.

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

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

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

[0142] The 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.

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

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

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

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives information about medical facilities and treatment options from a user; an analysis unit that analyzes the information received by the reception unit and generates a list of relevant medical facilities and treatment options; a providing unit that provides a user with the list generated by the analysis unit; a reservation unit that makes reservations and inquiries based on the list provided by the provision unit; A system characterized by:

2. The analysis unit Conduct analysis using past data and statistical information The system of claim 1 .

3. The providing unit Providing information including costs, treatment success rates, and patient reviews The system of claim 1 .

4. The reservation unit Make reservations or inquiries based on user selections The system of claim 1 .

5. The reception unit Accepts user input of disease name, symptoms, and desired treatment The system of claim 1 .

6. The providing unit Next steps include booking an appointment or making an inquiry based on the treatment option selected by the user The system of claim 1 .

7. The reception unit The system estimates the user's emotions and adjusts the display method of the input interface based on the estimated user emotions. The system of claim 1 .

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

Images