system

Abstract

The system according to the embodiment aims to allow users to easily consult about symptoms of poor health and details of illnesses, and to search for and make reservations at appropriate hospitals. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a search unit, and a reservation unit. The reception unit receives symptoms of poor health and details of illness from a user. The analysis unit analyzes the information received by the reception unit and determines the illness. The search unit searches for a hospital based on the results determined by the analysis unit. The reservation unit makes a reservation at a hospital found by the search unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making it difficult to easily consult about symptoms of poor health or the details of an illness, and to search for and make reservations at appropriate hospitals.

[0005] The system according to the embodiment aims to allow users to easily consult about symptoms of poor health and details of illnesses, and to search for and make reservations at appropriate hospitals. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a search unit, and a reservation unit. The reception unit receives symptoms of poor health and details of illness from a user. The analysis unit analyzes the information received by the reception unit and determines the illness. The search unit searches for a hospital based on the results determined by the analysis unit. The reservation unit makes a reservation at a hospital searched by the search unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily consult about symptoms of poor health or details of illness, and search for and make reservations at appropriate hospitals. [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) In a health consultation system according to an embodiment of the present invention, a user inputs their symptoms and illness details, which are analyzed by a generation AI, searched for hospitals, and made appointments. The health consultation system allows users to input their symptoms and illness details and upload detailed photos. The generation AI analyzes this information and determines possible illnesses. Based on the diagnosis results, an app is provided that recommends nearby hospitals and specialists that can treat the illness and even makes appointments. For example, a health consultation system allows users to input symptoms such as a sore throat or rash and upload photos of the rash. This information is input into the generation AI. The health consultation system then uses the generation AI to analyze the input information and determine possible illnesses. Based on the symptoms and photos, the generation AI utilizes past data and medical knowledge to identify the most likely illness. For example, the generation AI may determine the possibility of a cold or allergy based on a photo of a sore throat and rash. The health consultation system then recommends nearby hospitals and specialists that can treat the illness based on the results of the generation AI's determination. For example, if a cold is likely, the user is referred to an internal medicine or otolaryngology specialist. The health consultation system also allows users to make hospital appointments within the app. The user selects the hospital of their choice from the hospitals introduced and makes a reservation. This allows the health consultation system to easily consult about poor health and to quickly refer the patient to an appropriate medical institution. Furthermore, by utilizing generative AI, more accurate predictions and judgments become possible, improving the quality of medical care. This allows the health consultation system to easily consult about poor health and to quickly refer the patient to an appropriate medical institution. Furthermore, by utilizing generative AI, more accurate predictions and judgments become possible, improving the quality of medical care.

[0029] A health consultation system according to an embodiment includes a reception unit, an analysis unit, a search unit, and a reservation unit. The reception unit receives information about symptoms of poor health and details of illness from a user. Examples of symptoms of poor health and details of illness from a user include, but are not limited to, a cold, influenza, stomachache, etc. The reception unit receives, for example, information about symptoms of poor health and details of illness, as well as specific photos, entered by the user. For example, a user can enter symptoms such as a sore throat or a rash and upload a photo of the rash. The analysis unit uses a generation AI to analyze the information received by the reception unit and determine a possible illness. The analysis is performed, for example, using past data and medical knowledge, but is not limited to this example. For example, the generation AI identifies the most likely illness based on the symptoms and photos, using past data and medical knowledge. The search unit searches for a hospital based on the results determined by the analysis unit. For example, the search searches for hospitals and departments near the user's home that can provide the service, but is not limited to this example. For example, if a cold is likely, an internal medicine or otolaryngology department is searched for. The reservation unit makes an appointment at the hospital searched by the search unit. For example, the user may select a desired hospital and make a reservation, but this is not a limiting example. For example, the user may select a desired hospital from among the hospitals introduced to the user and make a reservation. In this way, the health consultation system according to the embodiment can accept and analyze the user's symptoms of poor health and details of the illness, search for a hospital, and make a reservation.

[0030] The reception unit can accept the symptoms of poor health, details of the illness, and photos entered by the user. The reception unit, for example, accepts the symptoms of poor health, details of the illness, and specific photos entered by the user. For example, the user can enter symptoms such as a sore throat or a rash and upload a photo of the rash. Examples of photos include, but are not limited to, photos of the affected area and photos of the medical certificate. This makes it possible to accept the symptoms of poor health, details of the illness, and specific photos entered by the user.

[0031] The analysis unit can determine a disease based on the received information and data and medical knowledge. The analysis unit, for example, determines a possible disease based on the received information by utilizing past data and medical knowledge. For example, the generative AI identifies the most likely disease based on symptoms and photos by utilizing past data and medical knowledge. Examples of data and medical knowledge include, but are not limited to, past medical data and medical papers. This makes it possible to determine a possible disease by utilizing past data and medical knowledge.

[0032] The search unit can search for hospitals and departments near the user's home based on the determination result. The search unit, for example, searches for hospitals and departments near the user's home that can treat the patient based on the determination result. For example, if there is a high possibility of a cold, an internal medicine department or an otolaryngology department is searched for. "Near the user's home" includes, for example, a range within a certain number of kilometers of the patient's home, but is not limited to such an example. This allows the user to search for hospitals and departments near the user's home that can treat the patient.

[0033] The reservation unit can select a hospital from the searched hospitals and make a reservation. The reservation unit, for example, selects a desired hospital from the searched hospitals and makes a reservation. For example, a user selects a desired hospital from the introduced hospitals and makes a reservation. Selection criteria for the desired hospital include, but are not limited to, medical specialty, reputation, distance, etc. This allows a desired hospital to be selected from the searched hospitals and make a reservation.

[0034] The reception unit can analyze the user's past history of poor health and select an input method. The reception unit, for example, analyzes the user's past history of poor health and selects the optimal input method. For example, symptoms and illness details that the user has frequently input in the past are automatically displayed as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest symptoms and illness details to be used in a specific time period based on the user's past history of poor health. Examples of past history of poor health include, but are not limited to, electronic medical records and self-reporting. This improves input efficiency by selecting the optimal input method based on the past history.

[0035] The reception unit can apply a filter based on the user's current living situation and areas of interest when inputting symptoms of poor health or details of an illness. For example, the reception unit performs filtering based on the user's current living situation and areas of interest when inputting symptoms of poor health or details of an illness. For example, when a user inputs their current living situation, related symptoms or details of an illness are preferentially displayed. The reception unit can also filter and display related symptoms or details of an illness based on the user's areas of interest. The reception unit can also omit unnecessary information during input based on the user's living situation and areas of interest to support efficient input. Examples of living situations and areas of interest include, but are not limited to, occupation, hobbies, and lifestyle habits. As a result, filtering based on the user's living situation and areas of interest can provide more relevant information.

[0036] The reception unit can select an input means according to the input method of the user when inputting symptoms of poor health or details of the illness. For example, when inputting symptoms of poor health or details of the illness, the reception unit selects the optimal input means according to the user's input method (voice, text, image, etc.). For example, if the user selects voice input, the symptoms or details of the illness are input using voice recognition technology. If the user selects text input, the symptoms or details of the illness can also be input using a keyboard or touch panel. If the user selects image input, the symptoms or details of the illness can also be uploaded and input using a camera. Input methods include, but are not limited to, voice input, text input, image input, etc., for example. By selecting the optimal input means according to the user's input method, input convenience is improved.

[0037] The reception unit can prioritize input of information when inputting symptoms of poor health or details of illness, taking into account the user's geographical location information. For example, when inputting symptoms of poor health or details of illness, the reception unit prioritizes input of highly relevant information, taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prompt the user to prioritize input of illnesses and symptoms that are prevalent in that area. Information on relevant medical institutions and specialists can also be provided based on the user's geographical location information. The reception unit can also prompt the user to prioritize input of symptoms and details of illness specific to the area, taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and address information. In this way, highly relevant information can be prioritized by taking into account the geographical location information.

[0038] The reception unit can analyze the user's social media activity and input the information when the user inputs symptoms of poor health or details of the illness. For example, when the user inputs symptoms of poor health or details of the illness, the reception unit analyzes the user's social media activity and inputs related information. For example, the reception unit can automatically input details of the symptoms or illness shared by the user on social media. It can also analyze the content of the user's social media posts and input details of the related symptoms or illness. It can also input details of the related symptoms or illness by referring to the activity of the user's friends on social media. Social media activity includes, for example, the content of posts, the number of followers, etc., but is not limited to these examples. In this way, it is possible to efficiently input related information by analyzing social media activity.

[0039] The reception unit can adjust the input method by reflecting the user's past feedback when inputting symptoms of poor health or details of an illness. For example, the reception unit customizes the input method by reflecting the user's past feedback when inputting symptoms of poor health or details of an illness. For example, the reception unit suggests an optimal input method based on feedback provided by the user in the past. It can also customize the input method by reflecting points that need improvement when inputting information based on the user's past feedback. It can also optimize the input interface by referring to the user's past feedback. Past feedback includes, for example, survey results, reviews, etc., but is not limited to such examples. In this way, the input method can be customized by reflecting past feedback.

[0040] The analysis unit can adjust the accuracy of the analysis based on the severity of the symptoms of poor health or the illness during the analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the severity of the symptoms of poor health or the illness during the analysis. For example, detailed analysis results can be provided for serious symptoms or illnesses. Also, concise analysis results can be provided for mild symptoms or illnesses. The level of detail of the analysis can be adjusted according to the severity of the symptoms or illness to provide appropriate information. The severity includes, but is not limited to, evaluation criteria such as the severity and urgency of the symptoms. In this way, appropriate information can be provided by adjusting the level of detail of the analysis according to the severity of the symptoms or illness.

[0041] The analysis unit can apply an analysis algorithm according to the category of the poor health symptom or illness during analysis. For example, the analysis unit applies different analysis algorithms according to the category of the poor health symptom or illness during analysis. For example, in the case of internal medicine symptoms or illnesses, an analysis algorithm specialized for internal medicine can be applied. Furthermore, in the case of dermatology symptoms or illnesses, an analysis algorithm specialized for dermatology can be applied. Furthermore, in the case of psychiatric symptoms or illnesses, an analysis algorithm specialized for psychiatry can be applied. Categories include, for example, internal medicine, surgery, dermatology, etc., but are not limited to these examples. In this way, by applying an analysis algorithm according to the category, the accuracy of the analysis is improved.

[0042] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the current analysis result is complemented based on the user's past analysis results. Patterns for improving the accuracy of the analysis can also be extracted from the user's past analysis results. The reliability of the current analysis result can also be improved by referring to the user's past analysis results. Past analysis results include, for example, diagnostic history, treatment results, etc., but are not limited to these examples. In this way, the accuracy of the analysis is improved by referring to the past analysis results.

[0043] The analysis unit can set analysis priorities based on the time of submission of the symptoms of poor health or illness during analysis. For example, the analysis unit determines analysis priorities based on the time of submission of the symptoms of poor health or illness during analysis. For example, the analysis unit prioritizes analysis of information on recently submitted symptoms or illnesses. Information submitted earlier can also be analyzed with a lower priority. Analysis priorities can also be dynamically adjusted based on the time of submission. The time of submission includes, but is not limited to, evaluation criteria such as the time elapsed since submission and urgency. This enables a rapid response by determining analysis priorities based on the time of submission.

[0044] The analysis unit can adjust the analysis order based on the relevance of symptoms of poor health and illnesses during analysis. The analysis unit, for example, adjusts the analysis order based on the relevance of symptoms of poor health and illnesses during analysis. For example, it prioritizes analysis of highly relevant symptoms and illnesses. It is also possible to postpone the analysis order of less relevant symptoms and illnesses. It is also possible to dynamically adjust the analysis order based on the relevance of symptoms and illnesses. Relevance includes, for example, evaluation criteria such as commonalities in symptoms and medical history, but is not limited to such examples. By adjusting the analysis order based on relevance, efficient analysis is possible.

[0045] The analysis unit can adjust the use of technical terms during analysis according to the user's level of expertise. For example, the analysis unit adjusts the use of technical terms during analysis according to the user's level of expertise. For example, if the user is a medical professional, the analysis unit can provide analysis results that make heavy use of technical terms. Also, if the user is a general public, the analysis unit can provide analysis results that are easy to understand and avoid technical terms. The method of expressing the analysis results can also be adjusted according to the user's level of expertise. Expertise levels include, but are not limited to, evaluation criteria such as medical professionals and general users. In this way, by adjusting the technical terms during analysis according to the level of expertise, analysis results suitable for the user can be provided.

[0046] The search unit can improve search accuracy by taking into account the interrelationships between symptoms of poor health and diseases during a search. For example, the search unit improves search accuracy by taking into account the interrelationships between symptoms of poor health and diseases during a search. For example, the search unit prioritizes searching for related hospitals by taking into account the interrelationships between symptoms and diseases. Search results can also be filtered based on the interrelationships between symptoms and diseases. The search unit can also analyze the interrelationships between symptoms and diseases and suggest the most suitable hospital. Interrelationships include, but are not limited to, evaluation criteria such as commonalities in symptoms and medical history. In this way, the search accuracy is improved by taking the interrelationships into account.

[0047] The search unit can perform a search taking into consideration the attribute information of the person who submitted the symptoms of poor health or the illness. For example, the search unit performs a search taking into consideration the attribute information of the person who submitted the symptoms of poor health or the illness. For example, an appropriate hospital can be searched for based on the age and gender of the person who submitted the symptoms of poor health or the illness. An appropriate hospital can also be searched for based on the living environment and occupation of the person who submitted the symptoms of poor health or the illness. An appropriate hospital can also be searched for based on the health condition and medical history of the person who submitted the symptoms of poor health or the illness. The attribute information includes, for example, but is not limited to, types such as age, gender, and occupation. In this way, a more appropriate hospital can be searched for by taking into consideration the attribute information of the person who submitted the symptoms of poor health or the illness.

[0048] The search unit can set search priorities based on the frequency of submission of symptoms of poor health or illnesses during a search. For example, the search unit weights the search based on the frequency of submission of symptoms of poor health or illnesses during a search. For example, weighting can be assigned to symptoms or illnesses that are submitted frequently to search for an appropriate hospital. Weighting can also be assigned to symptoms or illnesses that are submitted infrequently to search for an appropriate hospital. The priority of search results can also be adjusted based on the frequency of submission. The frequency of submission includes, but is not limited to, evaluation criteria such as the number of submissions and the period of submission. In this way, an appropriate hospital can be searched for by weighting the search based on the frequency of submission.

[0049] The search unit can perform a search taking into account the geographical distribution of symptoms of poor health and illnesses. For example, the search unit performs a search taking into account the geographical distribution of symptoms of poor health and illnesses. For example, the search unit may prioritize searching for nearby hospitals based on the user's current location. The search unit can also search for an appropriate hospital taking into account the geographical distribution of symptoms and illnesses. The search results can also be filtered based on the geographical distribution. The geographical distribution includes, but is not limited to, evaluation criteria such as the number of cases per region and the spread of infection. This allows the search for an appropriate hospital by taking into account the geographical distribution.

[0050] The search unit can improve search accuracy by referring to literature related to symptoms of poor health and diseases during a search. The search unit can improve search accuracy by, for example, referring to literature related to symptoms of poor health and diseases during a search. For example, the latest medical literature related to symptoms and diseases is referred to search for an appropriate hospital. Search results can also be filtered based on related literature. Related literature can also be analyzed to suggest the most appropriate hospital. Related literature includes, for example, types such as medical papers and research reports, but is not limited to such examples. In this way, by referring to related literature, search accuracy is improved.

[0051] The search unit can perform a search taking into account the market value of symptoms of poor health or illnesses when searching. For example, the search unit performs a search taking into account the market value of symptoms of poor health or illnesses when searching. For example, the search unit prioritizes searching for appropriate hospitals for symptoms or illnesses with high market value. It can also search for appropriate hospitals for symptoms or illnesses with low market value. It can also adjust the priority of search results based on market value. Market value includes, for example, evaluation criteria such as treatment costs and demand, but is not limited to such examples. In this way, it is possible to search for an appropriate hospital by taking market value into account.

[0052] The reservation unit can select a reservation method by analyzing the symptoms of poor health and past reservation history of illness when making a reservation. For example, the reservation unit selects the optimal reservation method by analyzing the symptoms of poor health and past reservation history of illness when making a reservation. For example, the reservation unit suggests the optimal reservation method based on reservation methods used by the user in the past. It can also suggest a reservation method that avoids crowds based on the user's past reservation history. It can also analyze the user's past reservation history and suggest the most efficient reservation method. The past reservation history includes specific details such as reservation date and time, reservation content, etc., but is not limited to these examples. In this way, the optimal reservation method can be selected by analyzing the past reservation history.

[0053] The reservation unit can adjust the reservation method based on the symptoms of poor health and the current living conditions of the illness when making a reservation. The reservation unit, for example, customizes the reservation method based on the symptoms of poor health and the current living conditions of the illness when making a reservation. For example, the reservation unit proposes the optimal reservation method taking into account the user's current living conditions. The reservation method can also be customized based on the user's living conditions. The reservation method can also be optimized by reflecting the user's living conditions. The current living conditions include, for example, specific details such as occupation and home environment, but are not limited to such examples. As a result, customizing the reservation method based on the current living conditions enables more appropriate reservations.

[0054] The reservation unit can improve the reservation method by reflecting user feedback at the time of reservation. The reservation unit, for example, improves the reservation method by reflecting user feedback at the time of reservation. For example, the reservation unit suggests the optimal reservation method based on the user's past feedback. It can also reflect and customize any points that need improvement at the time of reservation based on user feedback. It can also optimize the reservation interface by referring to user feedback. Feedback includes, for example, specific content such as survey results and reviews, but is not limited to such examples. In this way, the reservation method can be improved by reflecting feedback.

[0055] The reservation unit can select a reservation method by taking into consideration the symptoms of poor health and the geographical location information of the illness when making a reservation. For example, the reservation unit selects the optimal reservation method by taking into consideration the symptoms of poor health and the geographical location information of the illness when making a reservation. For example, the reservation unit prioritizes reservations at nearby hospitals based on the user's current location. The optimal reservation method can also be suggested based on the geographical location information. The reservation method can also be optimized by taking into consideration the geographical location information. Geographical location information includes, for example, specific acquisition methods such as GPS data and address information, but is not limited to these examples. In this way, the optimal reservation method can be selected by taking into consideration the geographical location information.

[0056] The reservation unit can suggest a reservation method by analyzing social media activity related to symptoms of poor health and illness at the time of reservation. For example, the reservation unit can suggest a reservation method by analyzing social media activity related to symptoms of poor health and illness at the time of reservation. For example, the reservation unit can analyze the user's social media activity and suggest the optimal reservation method. The reservation method can also be customized based on the content of posts on social media. The optimal reservation method can also be suggested by taking into account the activity of friends on social media. Social media activity includes specific content such as post content and number of followers, but is not limited to such examples. In this way, the optimal reservation method can be suggested by analyzing social media activity.

[0057] The reservation unit can adjust the reservation method by reflecting the symptoms of poor health and past feedback on illness when making a reservation. The reservation unit, for example, customizes the reservation method by reflecting the symptoms of poor health and past feedback on illness when making a reservation. For example, the reservation unit suggests the optimal reservation method based on the user's past feedback. It can also customize the reservation method by reflecting points that need improvement when making a reservation based on the user's past feedback. It can also optimize the reservation interface by referring to the user's past feedback. Past feedback includes specific content such as, for example, survey results and reviews, but is not limited to such examples. In this way, the reservation method can be customized by reflecting past feedback.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The health consultation system may further include a lifestyle analysis unit that analyzes the user's lifestyle. The lifestyle analysis unit collects data on the user's diet, exercise, sleep, etc., and provides it to the analysis unit. For example, the system may record the user's daily diet, exercise frequency, sleep duration, etc., and use this data to comprehensively evaluate the user's health condition. The lifestyle analysis unit may also provide preventative health advice based on the user's lifestyle. This allows the user to receive specific advice on maintaining health in their daily lives.

[0060] The analysis unit can complement the current analysis results by referring to the user's past medical history. For example, it can evaluate the relevance of the user's current symptoms based on the illnesses and treatments the user has been diagnosed with in the past. It can also extract specific patterns from the past medical history to improve the accuracy of the current analysis results. Furthermore, it can also improve the reliability of the current analysis results by referring to the user's past medical history. In this way, by utilizing the past medical history, the accuracy of the analysis can be improved, enabling more appropriate diagnoses.

[0061] The reservation unit can analyze the user's past reservation history and suggest the optimal reservation method. For example, it can suggest the optimal reservation method based on the reservation methods the user has used in the past. It can also suggest a reservation method that avoids congestion based on the user's past reservation history. Furthermore, it can analyze the user's past reservation history and suggest the most efficient reservation method. In this way, it is possible to select the optimal reservation method by analyzing the past reservation history.

[0062] The reception unit can filter input content based on the user's current living situation and areas of interest. For example, when a user inputs their current living situation, related symptoms and illnesses are preferentially displayed. The reception unit can also filter and display related symptoms and illnesses based on the user's areas of interest. Furthermore, it can omit unnecessary information during input based on the user's living situation and areas of interest, supporting efficient input. By filtering based on the user's living situation and areas of interest, more relevant information can be provided.

[0063] The search unit can improve search accuracy by taking into account the interrelationships between symptoms of poor health and illnesses. For example, it can prioritize searches for related hospitals by taking into account the interrelationships between symptoms and illnesses. It can also filter search results based on the interrelationships between symptoms and illnesses. It can also analyze the interrelationships between symptoms and illnesses and suggest the most suitable hospital. This improves search accuracy by taking into account the interrelationships.

[0064] The reception unit can analyze the user's social media activity and input related information. For example, it can automatically input details of symptoms or illnesses that the user has shared on social media. It can also analyze the content posted by the user on social media and input details of related symptoms or illnesses. It can also input details of related symptoms or illnesses by referring to the activities of the user's friends on social media. This allows related information to be input efficiently by analyzing social media activity.

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

[0066] Step 1: The reception unit receives the user's symptoms and illness details. The user can input symptoms such as cold, flu, stomachache, etc. and details of the illness, and upload specific photos. For example, it is possible to input symptoms such as a sore throat or a rash and upload a photo of the rash. Step 2: The analysis unit uses generative AI to analyze the information received by the reception unit and determine possible diseases. The analysis utilizes past data and medical knowledge to identify the most likely disease based on symptoms and photos. Step 3: The search unit searches for hospitals based on the results determined by the analysis unit. The search will look for hospitals and departments that can treat the patient near the patient's home, and if there is a high possibility of a cold, for example, it will search for internal medicine or otolaryngology departments. Step 4: The reservation unit makes a reservation at the hospital searched by the search unit. The user can select the desired hospital from the introduced hospitals and make a reservation.

[0067] (Example 2) In a health consultation system according to an embodiment of the present invention, a user inputs their symptoms and illness details, which are analyzed by a generation AI, searched for hospitals, and made appointments. The health consultation system allows users to input their symptoms and illness details and upload detailed photos. The generation AI analyzes this information and determines possible illnesses. Based on the diagnosis results, an app is provided that recommends nearby hospitals and specialists that can treat the illness and even makes appointments. For example, a health consultation system allows users to input symptoms such as a sore throat or rash and upload photos of the rash. This information is input into the generation AI. The health consultation system then uses the generation AI to analyze the input information and determine possible illnesses. Based on the symptoms and photos, the generation AI utilizes past data and medical knowledge to identify the most likely illness. For example, the generation AI may determine the possibility of a cold or allergy based on a photo of a sore throat and rash. The health consultation system then recommends nearby hospitals and specialists that can treat the illness based on the results of the generation AI's determination. For example, if a cold is likely, the user is referred to an internal medicine or otolaryngology specialist. The health consultation system also allows users to make hospital appointments within the app. The user selects the hospital of their choice from the hospitals introduced and makes a reservation. This allows the health consultation system to easily consult about poor health and to quickly refer the patient to an appropriate medical institution. Furthermore, by utilizing generative AI, more accurate predictions and judgments become possible, improving the quality of medical care. This allows the health consultation system to easily consult about poor health and to quickly refer the patient to an appropriate medical institution. Furthermore, by utilizing generative AI, more accurate predictions and judgments become possible, improving the quality of medical care.

[0068] A health consultation system according to an embodiment includes a reception unit, an analysis unit, a search unit, and a reservation unit. The reception unit receives information about symptoms of poor health and details of illness from a user. Examples of symptoms of poor health and details of illness from a user include, but are not limited to, a cold, influenza, stomachache, etc. The reception unit receives, for example, information about symptoms of poor health and details of illness, as well as specific photos, entered by the user. For example, a user can enter symptoms such as a sore throat or a rash and upload a photo of the rash. The analysis unit uses a generation AI to analyze the information received by the reception unit and determine a possible illness. The analysis is performed, for example, using past data and medical knowledge, but is not limited to this example. For example, the generation AI identifies the most likely illness based on the symptoms and photos, using past data and medical knowledge. The search unit searches for a hospital based on the results determined by the analysis unit. For example, the search searches for hospitals and departments near the user's home that can provide the service, but is not limited to this example. For example, if a cold is likely, an internal medicine or otolaryngology department is searched for. The reservation unit makes an appointment at the hospital searched by the search unit. For example, the user may select a desired hospital and make a reservation, but this is not a limiting example. For example, the user may select a desired hospital from among the hospitals introduced to the user and make a reservation. In this way, the health consultation system according to the embodiment can accept and analyze the user's symptoms of poor health and details of the illness, search for a hospital, and make a reservation.

[0069] The reception unit can accept the symptoms of poor health, details of the illness, and photos entered by the user. The reception unit, for example, accepts the symptoms of poor health, details of the illness, and specific photos entered by the user. For example, the user can enter symptoms such as a sore throat or a rash and upload a photo of the rash. Examples of photos include, but are not limited to, photos of the affected area and photos of the medical certificate. This makes it possible to accept the symptoms of poor health, details of the illness, and specific photos entered by the user.

[0070] The analysis unit can determine a disease based on the received information and data and medical knowledge. The analysis unit, for example, determines a possible disease based on the received information by utilizing past data and medical knowledge. For example, the generative AI identifies the most likely disease based on symptoms and photos by utilizing past data and medical knowledge. Examples of data and medical knowledge include, but are not limited to, past medical data and medical papers. This makes it possible to determine a possible disease by utilizing past data and medical knowledge.

[0071] The search unit can search for hospitals and departments near the user's home based on the determination result. The search unit, for example, searches for hospitals and departments near the user's home that can treat the patient based on the determination result. For example, if there is a high possibility of a cold, an internal medicine department or an otolaryngology department is searched for. "Near the user's home" includes, for example, a range within a certain number of kilometers of the patient's home, but is not limited to such an example. This allows the user to search for hospitals and departments near the user's home that can treat the patient.

[0072] The reservation unit can select a hospital from the searched hospitals and make a reservation. The reservation unit, for example, selects a desired hospital from the searched hospitals and makes a reservation. For example, a user selects a desired hospital from the introduced hospitals and makes a reservation. Selection criteria for the desired hospital include, but are not limited to, medical specialty, reputation, distance, etc. This allows a desired hospital to be selected from the searched hospitals and make a reservation.

[0073] The reception unit can estimate the user's emotions and adjust the input method for the symptoms of illness and the details of the illness based on the user's emotions. For example, the reception unit can estimate the user's emotions and adjust the input method for the symptoms of illness and the details of the illness based on the estimated user's emotions. For example, if the user is feeling anxious, a simple and intuitive interface can be provided to minimize the input steps. Furthermore, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to enable the user to quickly input the symptoms of illness and the details of the illness. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows for more appropriate input by adjusting the input method according to the user's emotions.

[0074] The reception unit can analyze the user's past history of poor health and select an input method. The reception unit, for example, analyzes the user's past history of poor health and selects the optimal input method. For example, symptoms and illness details that the user has frequently input in the past are automatically displayed as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest symptoms and illness details to be used in a specific time period based on the user's past history of poor health. Examples of past history of poor health include, but are not limited to, electronic medical records and self-reporting. This improves input efficiency by selecting the optimal input method based on the past history.

[0075] The reception unit can apply a filter based on the user's current living situation and areas of interest when inputting symptoms of poor health or details of an illness. For example, the reception unit performs filtering based on the user's current living situation and areas of interest when inputting symptoms of poor health or details of an illness. For example, when a user inputs their current living situation, related symptoms or details of an illness are preferentially displayed. The reception unit can also filter and display related symptoms or details of an illness based on the user's areas of interest. The reception unit can also omit unnecessary information during input based on the user's living situation and areas of interest to support efficient input. Examples of living situations and areas of interest include, but are not limited to, occupation, hobbies, and lifestyle habits. As a result, filtering based on the user's living situation and areas of interest can provide more relevant information.

[0076] The reception unit can select an input means according to the input method of the user when inputting symptoms of poor health or details of the illness. For example, when inputting symptoms of poor health or details of the illness, the reception unit selects the optimal input means according to the user's input method (voice, text, image, etc.). For example, if the user selects voice input, the symptoms or details of the illness are input using voice recognition technology. If the user selects text input, the symptoms or details of the illness can also be input using a keyboard or touch panel. If the user selects image input, the symptoms or details of the illness can also be uploaded and input using a camera. Input methods include, but are not limited to, voice input, text input, image input, etc., for example. By selecting the optimal input means according to the user's input method, input convenience is improved.

[0077] The reception unit can estimate the user's emotions and set a priority order for the symptoms of poor health and details of illness to be input based on the user's emotions. The reception unit, for example, estimates the user's emotions and determines a priority order for the symptoms of poor health and details of illness to be input based on the estimated user's emotions. For example, if the user is feeling anxious, the reception unit can prompt the user to input important symptoms and details of illness with priority. Also, if the user is relaxed, the reception unit can prompt the user to input detailed symptoms and details of illness with priority. Also, if the user is in a hurry, the reception unit can prompt the user to input the most important symptoms and details of illness with priority. The priority order can include, but is not limited to, setting criteria such as the severity and urgency of symptoms. As a result, by determining the priority order of input based on the user's emotions, important information can be input with priority.

[0078] The reception unit can prioritize input of information when inputting symptoms of poor health or details of illness, taking into account the user's geographical location information. For example, when inputting symptoms of poor health or details of illness, the reception unit prioritizes input of highly relevant information, taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prompt the user to prioritize input of illnesses and symptoms that are prevalent in that area. Information on relevant medical institutions and specialists can also be provided based on the user's geographical location information. The reception unit can also prompt the user to prioritize input of symptoms and details of illness specific to the area, taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and address information. In this way, highly relevant information can be prioritized by taking into account the geographical location information.

[0079] The reception unit can analyze the user's social media activity and input the information when the user inputs symptoms of poor health or details of the illness. For example, when the user inputs symptoms of poor health or details of the illness, the reception unit analyzes the user's social media activity and inputs related information. For example, the reception unit can automatically input details of the symptoms or illness shared by the user on social media. It can also analyze the content of the user's social media posts and input details of the related symptoms or illness. It can also input details of the related symptoms or illness by referring to the activity of the user's friends on social media. Social media activity includes, for example, the content of posts, the number of followers, etc., but is not limited to these examples. In this way, it is possible to efficiently input related information by analyzing social media activity.

[0080] The reception unit can adjust the input method by reflecting the user's past feedback when inputting symptoms of poor health or details of an illness. For example, the reception unit customizes the input method by reflecting the user's past feedback when inputting symptoms of poor health or details of an illness. For example, the reception unit suggests an optimal input method based on feedback provided by the user in the past. It can also customize the input method by reflecting points that need improvement when inputting information based on the user's past feedback. It can also optimize the input interface by referring to the user's past feedback. Past feedback includes, for example, survey results, reviews, etc., but is not limited to such examples. In this way, the input method can be customized by reflecting past feedback.

[0081] The analysis unit can estimate the user's emotions and adjust the analysis method based on the user's emotions. For example, the analysis unit estimates the user's emotions and adjusts the analysis presentation method based on the estimated user's emotions. For example, if the user is feeling anxious, a simple and easy-to-understand analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided. If the user is in a hurry, a summary analysis result can be provided. The analysis method includes, but is not limited to, the selection of an algorithm and the depth of the analysis. The emotion estimation is realized using, for example, an emotion engine or a generation AI with an emotion estimation function. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the analysis presentation method to be adjusted according to the user's emotions, thereby providing more appropriate analysis results.

[0082] The analysis unit can adjust the accuracy of the analysis based on the severity of the symptoms of poor health or the illness during the analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the severity of the symptoms of poor health or the illness during the analysis. For example, detailed analysis results can be provided for serious symptoms or illnesses. Also, concise analysis results can be provided for mild symptoms or illnesses. The level of detail of the analysis can be adjusted according to the severity of the symptoms or illness to provide appropriate information. The severity includes, but is not limited to, evaluation criteria such as the severity and urgency of the symptoms. In this way, appropriate information can be provided by adjusting the level of detail of the analysis according to the severity of the symptoms or illness.

[0083] The analysis unit can apply an analysis algorithm according to the category of the poor health symptom or illness during analysis. For example, the analysis unit applies different analysis algorithms according to the category of the poor health symptom or illness during analysis. For example, in the case of internal medicine symptoms or illnesses, an analysis algorithm specialized for internal medicine can be applied. Furthermore, in the case of dermatology symptoms or illnesses, an analysis algorithm specialized for dermatology can be applied. Furthermore, in the case of psychiatric symptoms or illnesses, an analysis algorithm specialized for psychiatry can be applied. Categories include, for example, internal medicine, surgery, dermatology, etc., but are not limited to these examples. In this way, by applying an analysis algorithm according to the category, the accuracy of the analysis is improved.

[0084] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the current analysis result is complemented based on the user's past analysis results. Patterns for improving the accuracy of the analysis can also be extracted from the user's past analysis results. The reliability of the current analysis result can also be improved by referring to the user's past analysis results. Past analysis results include, for example, diagnostic history, treatment results, etc., but are not limited to these examples. In this way, the accuracy of the analysis is improved by referring to the past analysis results.

[0085] The analysis unit can estimate the user's emotions and adjust the analysis time based on the user's emotions. For example, the analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. For example, if the user is feeling anxious, a short and concise analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided. If the user is in a hurry, a concise and quick analysis result can be provided. The analysis time can be adjusted based on, for example, but not limited to, the severity and urgency of the symptoms. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to, these examples. This allows the length of the analysis to be adjusted according to the user's emotions, thereby providing more appropriate analysis results.

[0086] The analysis unit can set analysis priorities based on the time of submission of the symptoms of poor health or illness during analysis. For example, the analysis unit determines analysis priorities based on the time of submission of the symptoms of poor health or illness during analysis. For example, the analysis unit prioritizes analysis of information on recently submitted symptoms or illnesses. Information submitted earlier can also be analyzed with a lower priority. Analysis priorities can also be dynamically adjusted based on the time of submission. The time of submission includes, but is not limited to, evaluation criteria such as the time elapsed since submission and urgency. This enables a rapid response by determining analysis priorities based on the time of submission.

[0087] The analysis unit can adjust the analysis order based on the relevance of symptoms of poor health and illnesses during analysis. The analysis unit, for example, adjusts the analysis order based on the relevance of symptoms of poor health and illnesses during analysis. For example, it prioritizes analysis of highly relevant symptoms and illnesses. It is also possible to postpone the analysis order of less relevant symptoms and illnesses. It is also possible to dynamically adjust the analysis order based on the relevance of symptoms and illnesses. Relevance includes, for example, evaluation criteria such as commonalities in symptoms and medical history, but is not limited to such examples. By adjusting the analysis order based on relevance, efficient analysis is possible.

[0088] The analysis unit can adjust the use of technical terms during analysis according to the user's level of expertise. For example, the analysis unit adjusts the use of technical terms during analysis according to the user's level of expertise. For example, if the user is a medical professional, the analysis unit can provide analysis results that make heavy use of technical terms. Also, if the user is a general public, the analysis unit can provide analysis results that are easy to understand and avoid technical terms. The method of expressing the analysis results can also be adjusted according to the user's level of expertise. Expertise levels include, but are not limited to, evaluation criteria such as medical professionals and general users. In this way, by adjusting the technical terms during analysis according to the level of expertise, analysis results suitable for the user can be provided.

[0089] The search unit can estimate the user's emotions and adjust search criteria based on the user's emotions. For example, the search unit estimates the user's emotions and adjusts the search criteria based on the estimated user's emotions. For example, if the user is feeling anxious, it can prioritize searching for reliable hospitals. Also, if the user is relaxed, it can provide a wide range of options. Also, if the user is in a hurry, it can prioritize searching for hospitals that can respond quickly. Search criteria include, but are not limited to, setting methods such as the severity and urgency of symptoms. 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. This allows the search criteria to be adjusted according to the user's emotions, thereby providing more appropriate search results.

[0090] The search unit can improve search accuracy by taking into account the interrelationships between symptoms of poor health and diseases during a search. For example, the search unit improves search accuracy by taking into account the interrelationships between symptoms of poor health and diseases during a search. For example, the search unit prioritizes searching for related hospitals by taking into account the interrelationships between symptoms and diseases. Search results can also be filtered based on the interrelationships between symptoms and diseases. The search unit can also analyze the interrelationships between symptoms and diseases and suggest the most suitable hospital. Interrelationships include, but are not limited to, evaluation criteria such as commonalities in symptoms and medical history. In this way, the search accuracy is improved by taking the interrelationships into account.

[0091] The search unit can perform a search taking into consideration the attribute information of the person who submitted the symptoms of poor health or the illness. For example, the search unit performs a search taking into consideration the attribute information of the person who submitted the symptoms of poor health or the illness. For example, an appropriate hospital can be searched for based on the age and gender of the person who submitted the symptoms of poor health or the illness. An appropriate hospital can also be searched for based on the living environment and occupation of the person who submitted the symptoms of poor health or the illness. An appropriate hospital can also be searched for based on the health condition and medical history of the person who submitted the symptoms of poor health or the illness. The attribute information includes, for example, but is not limited to, types such as age, gender, and occupation. In this way, a more appropriate hospital can be searched for by taking into consideration the attribute information of the person who submitted the symptoms of poor health or the illness.

[0092] The search unit can set search priorities based on the frequency of submission of symptoms of poor health or illnesses during a search. For example, the search unit weights the search based on the frequency of submission of symptoms of poor health or illnesses during a search. For example, weighting can be assigned to symptoms or illnesses that are submitted frequently to search for an appropriate hospital. Weighting can also be assigned to symptoms or illnesses that are submitted infrequently to search for an appropriate hospital. The priority of search results can also be adjusted based on the frequency of submission. The frequency of submission includes, but is not limited to, evaluation criteria such as the number of submissions and the period of submission. In this way, an appropriate hospital can be searched for by weighting the search based on the frequency of submission.

[0093] The search unit can estimate the user's emotions and adjust the display order of search results based on the user's emotions. For example, the search unit estimates the user's emotions and adjusts the display order of search results based on the estimated user's emotions. For example, if the user is feeling anxious, highly reliable hospitals can be displayed preferentially. Also, if the user is relaxed, a wide range of options can be provided. Also, if the user is in a hurry, hospitals that can respond quickly can be displayed preferentially. The display order can include, but is not limited to, set criteria such as the severity and urgency of the symptoms. 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. This allows for more appropriate search results to be provided by adjusting the display order of search results according to the user's emotions.

[0094] The search unit can perform a search taking into account the geographical distribution of symptoms of poor health and illnesses. For example, the search unit performs a search taking into account the geographical distribution of symptoms of poor health and illnesses. For example, the search unit may prioritize searching for nearby hospitals based on the user's current location. The search unit can also search for an appropriate hospital taking into account the geographical distribution of symptoms and illnesses. The search results can also be filtered based on the geographical distribution. The geographical distribution includes, but is not limited to, evaluation criteria such as the number of cases per region and the spread of infection. This allows the search for an appropriate hospital by taking into account the geographical distribution.

[0095] The search unit can improve search accuracy by referring to literature related to symptoms of poor health and diseases during a search. The search unit can improve search accuracy by, for example, referring to literature related to symptoms of poor health and diseases during a search. For example, the latest medical literature related to symptoms and diseases is referred to search for an appropriate hospital. Search results can also be filtered based on related literature. Related literature can also be analyzed to suggest the most appropriate hospital. Related literature includes, for example, types such as medical papers and research reports, but is not limited to such examples. In this way, by referring to related literature, search accuracy is improved.

[0096] The search unit can perform a search taking into account the market value of symptoms of poor health or illnesses when searching. For example, the search unit performs a search taking into account the market value of symptoms of poor health or illnesses when searching. For example, the search unit prioritizes searching for appropriate hospitals for symptoms or illnesses with high market value. It can also search for appropriate hospitals for symptoms or illnesses with low market value. It can also adjust the priority of search results based on market value. Market value includes, for example, evaluation criteria such as treatment costs and demand, but is not limited to such examples. In this way, it is possible to search for an appropriate hospital by taking market value into account.

[0097] The reservation unit can estimate a user's emotions and adjust the reservation method based on the user's emotions. For example, the reservation unit estimates a user's emotions and adjusts the reservation method based on the estimated user emotions. For example, if the user is feeling anxious, a simple and intuitive reservation interface can be provided. Also, if the user is relaxed, detailed reservation options can be provided. Also, if the user is in a hurry, the reservation can be completed quickly. Reservation methods include, but are not limited to, online reservations and telephone reservations. Emotion estimation is realized 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. This allows for more appropriate reservations by adjusting the reservation method according to the user's emotions.

[0098] The reservation unit can select a reservation method by analyzing the symptoms of poor health and past reservation history of illness when making a reservation. For example, the reservation unit selects the optimal reservation method by analyzing the symptoms of poor health and past reservation history of illness when making a reservation. For example, the reservation unit suggests the optimal reservation method based on reservation methods used by the user in the past. It can also suggest a reservation method that avoids crowds based on the user's past reservation history. It can also analyze the user's past reservation history and suggest the most efficient reservation method. The past reservation history includes specific details such as reservation date and time, reservation content, etc., but is not limited to these examples. In this way, the optimal reservation method can be selected by analyzing the past reservation history.

[0099] The reservation unit can adjust the reservation method based on the symptoms of poor health and the current living conditions of the illness when making a reservation. The reservation unit, for example, customizes the reservation method based on the symptoms of poor health and the current living conditions of the illness when making a reservation. For example, the reservation unit proposes the optimal reservation method taking into account the user's current living conditions. The reservation method can also be customized based on the user's living conditions. The reservation method can also be optimized by reflecting the user's living conditions. The current living conditions include, for example, specific details such as occupation and home environment, but are not limited to such examples. As a result, customizing the reservation method based on the current living conditions enables more appropriate reservations.

[0100] The reservation unit can improve the reservation method by reflecting user feedback at the time of reservation. The reservation unit, for example, improves the reservation method by reflecting user feedback at the time of reservation. For example, the reservation unit suggests the optimal reservation method based on the user's past feedback. It can also reflect and customize any points that need improvement at the time of reservation based on user feedback. It can also optimize the reservation interface by referring to user feedback. Feedback includes, for example, specific content such as survey results and reviews, but is not limited to such examples. In this way, the reservation method can be improved by reflecting feedback.

[0101] The reservation unit can estimate a user's emotions and prioritize reservations based on the user's emotions. For example, the reservation unit estimates a user's emotions and determines the priority of reservations based on the estimated user emotions. For example, if a user is feeling anxious, important reservations can be prioritized. Also, if a user is relaxed, detailed reservation options can be provided. Also, if a user is in a hurry, reservations can be completed quickly. Prioritization can include, but is not limited to, set criteria such as the severity of symptoms and urgency. 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. As a result, important reservations can be prioritized by determining the priority of reservations based on the user's emotions.

[0102] The reservation unit can select a reservation method by taking into consideration the symptoms of poor health and the geographical location information of the illness when making a reservation. For example, the reservation unit selects the optimal reservation method by taking into consideration the symptoms of poor health and the geographical location information of the illness when making a reservation. For example, the reservation unit prioritizes reservations at nearby hospitals based on the user's current location. The optimal reservation method can also be suggested based on the geographical location information. The reservation method can also be optimized by taking into consideration the geographical location information. Geographical location information includes, for example, specific acquisition methods such as GPS data and address information, but is not limited to these examples. In this way, the optimal reservation method can be selected by taking into consideration the geographical location information.

[0103] The reservation unit can suggest a reservation method by analyzing social media activity related to symptoms of poor health and illness at the time of reservation. For example, the reservation unit can suggest a reservation method by analyzing social media activity related to symptoms of poor health and illness at the time of reservation. For example, the reservation unit can analyze the user's social media activity and suggest the optimal reservation method. The reservation method can also be customized based on the content of posts on social media. The optimal reservation method can also be suggested by taking into account the activity of friends on social media. Social media activity includes specific content such as post content and number of followers, but is not limited to such examples. In this way, the optimal reservation method can be suggested by analyzing social media activity.

[0104] The reservation unit can adjust the reservation method by reflecting the symptoms of poor health and past feedback on illness when making a reservation. The reservation unit, for example, customizes the reservation method by reflecting the symptoms of poor health and past feedback on illness when making a reservation. For example, the reservation unit suggests the optimal reservation method based on the user's past feedback. It can also customize the reservation method by reflecting points that need improvement when making a reservation based on the user's past feedback. It can also optimize the reservation interface by referring to the user's past feedback. Past feedback includes specific content such as, for example, survey results and reviews, but is not limited to such examples. In this way, the reservation method can be customized by reflecting past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, search unit, and reservation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives the user's input of symptoms of poor health, details of the illness, and specific photos. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the received information using a generation AI to determine possible illnesses. The search unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and searches for hospitals based on the analysis results. The reservation unit is realized, for example, by the control unit 46A of the smart device 14 and makes a reservation at the searched hospital. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, search 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 the user's input of symptoms of poor health, details of the illness, and specific photos. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the received information using a generative AI to determine possible illnesses. The search unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and searches for hospitals based on the analysis results. The reservation unit is realized, for example, by the control unit 46A of the smart glasses 214 and makes a reservation at the searched hospital. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, analysis unit, search unit, and reservation unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset terminal 314 and receives the user's input of symptoms of poor health, details of the illness, and specific photos. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the received information using a generation AI to determine possible illnesses. The search unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and searches for hospitals based on the analysis results. The reservation unit is realized, for example, by the control unit 46A of the headset terminal 314 and makes a reservation at the searched hospital. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, search 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 the user's input of symptoms of poor health, details of the illness, and specific photos. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received information using a generative AI to determine possible illnesses. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and searches for hospitals based on the analysis results. The reservation unit is realized, for example, by the control unit 46A of the robot 414 and makes a reservation at the searched hospital.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The health consultation system may further include a lifestyle analysis unit that analyzes the user's lifestyle. The lifestyle analysis unit collects data on the user's diet, exercise, sleep, etc., and provides it to the analysis unit. For example, the system may record the user's daily diet, exercise frequency, sleep duration, etc., and use this data to comprehensively evaluate the user's health condition. The lifestyle analysis unit may also provide preventative health advice based on the user's lifestyle. This allows the user to receive specific advice on maintaining health in their daily lives.

[0107] The reception unit can estimate the user's emotions and customize the input interface based on the estimated emotions. For example, if the user is feeling stressed, a simple and intuitive interface can be provided to minimize input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow the user to quickly enter symptoms of illness or details of the illness. This allows the input interface to be adjusted according to the user's emotions, enabling more appropriate input.

[0108] The analysis unit can complement the current analysis results by referring to the user's past medical history. For example, it can evaluate the relevance of the user's current symptoms based on the illnesses and treatments the user has been diagnosed with in the past. It can also extract specific patterns from the past medical history to improve the accuracy of the current analysis results. Furthermore, it can also improve the reliability of the current analysis results by referring to the user's past medical history. In this way, by utilizing the past medical history, the accuracy of the analysis can be improved, enabling more appropriate diagnoses.

[0109] The search unit can estimate the user's emotions and adjust the display order of search results based on the estimated emotions. For example, if the user is feeling anxious, highly reliable hospitals can be displayed preferentially. Also, if the user is relaxed, a wide range of options can be provided. Furthermore, if the user is in a hurry, hospitals that can respond quickly can be displayed preferentially. In this way, by adjusting the display order of search results according to the user's emotions, more appropriate search results can be provided.

[0110] The reservation unit can analyze the user's past reservation history and suggest the optimal reservation method. For example, it can suggest the optimal reservation method based on the reservation methods the user has used in the past. It can also suggest a reservation method that avoids congestion based on the user's past reservation history. Furthermore, it can analyze the user's past reservation history and suggest the most efficient reservation method. In this way, it is possible to select the optimal reservation method by analyzing the past reservation history.

[0111] The reception unit can filter input content based on the user's current living situation and areas of interest. For example, when a user inputs their current living situation, related symptoms and illnesses are preferentially displayed. The reception unit can also filter and display related symptoms and illnesses based on the user's areas of interest. Furthermore, it can omit unnecessary information during input based on the user's living situation and areas of interest, supporting efficient input. By filtering based on the user's living situation and areas of interest, more relevant information can be provided.

[0112] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is feeling anxious, it can provide simple and easy-to-understand analysis results. If the user is relaxed, it can also provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide analysis results that focus on the main points. In this way, by adjusting the way the analysis is presented according to the user's emotions, it is possible to provide more appropriate analysis results.

[0113] The search unit can improve search accuracy by taking into account the interrelationships between symptoms of poor health and illnesses. For example, it can prioritize searches for related hospitals by taking into account the interrelationships between symptoms and illnesses. It can also filter search results based on the interrelationships between symptoms and illnesses. It can also analyze the interrelationships between symptoms and illnesses and suggest the most suitable hospital. This improves search accuracy by taking into account the interrelationships.

[0114] The reservation unit can estimate the user's emotions and set reservation priorities based on the estimated emotions. For example, if the user feels anxious, important reservations can be given priority. Also, if the user feels relaxed, detailed reservation options can be provided. Furthermore, if the user is in a hurry, reservations can be completed quickly. In this way, by determining reservation priorities according to the user's emotions, important reservations can be given priority.

[0115] The reception unit can analyze the user's social media activity and input related information. For example, it can automatically input details of symptoms or illnesses that the user has shared on social media. It can also analyze the content posted by the user on social media and input details of related symptoms or illnesses. It can also input details of related symptoms or illnesses by referring to the activities of the user's friends on social media. This allows related information to be input efficiently by analyzing social media activity.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The reception unit receives the user's symptoms and illness details. The user can input symptoms such as cold, flu, stomachache, etc. and details of the illness, and upload specific photos. For example, it is possible to input symptoms such as a sore throat or a rash and upload a photo of the rash. Step 2: The analysis unit uses generative AI to analyze the information received by the reception unit and determine possible diseases. The analysis utilizes past data and medical knowledge to identify the most likely disease based on symptoms and photos. Step 3: The search unit searches for hospitals based on the results determined by the analysis unit. The search will look for hospitals and departments that can treat the patient near the patient's home, and if there is a high possibility of a cold, for example, it will search for internal medicine or otolaryngology departments. Step 4: The reservation unit makes a reservation at the hospital searched by the search unit. The user can select the desired hospital from the introduced hospitals and make a reservation.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 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 symptoms of poor health and details of illness from a user; an analysis unit that analyzes the information received by the reception unit and determines a disease; a search unit that searches for hospitals based on the results determined by the analysis unit; a reservation unit for making a reservation at the hospital searched by the search unit; A system characterized by:

2. The reception unit Accepts user-entered information about symptoms and illnesses, as well as photos 2. The system of claim 1.

3. The analysis unit Based on the information received, a diagnosis is made based on data and medical knowledge.

2. The system of claim 1.

4. The search unit Based on the results, search for hospitals and departments near your home 2. The system of claim 1.

5. The reservation unit Select a hospital from the search results and make a reservation 2. The system of claim 1.

6. The reception unit Estimate the user's emotions and adjust the input method for symptoms of illness and details of illness based on the user's emotions 2. The system of claim 1.

7. The reception unit Analyze the user's past health history and select the input method 2. The system of claim 1.

8. The reception unit Apply filters based on the user's current lifestyle and interests when entering symptoms or illnesses 2. The system of claim 1.

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