system

Abstract

The system according to this embodiment aims to quickly identify and guide users to the nearest hospital in the event of a sudden illness. [Solution] The system according to the embodiment comprises a reception unit, a search unit, a identification unit, and a provision unit. The reception unit receives input of symptoms of a sudden illness. The search unit searches for hospitals based on the symptoms received by the reception unit. The identification unit identifies the user's current location from among the hospitals found by the search unit. The provision unit provides information on the nearest hospital from the current location identified by the identification unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to quickly identify and guide the nearest hospital in case of an emergency.

[0005] The system according to the embodiment aims to quickly identify and guide the nearest hospital in case of an emergency.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a search unit, a identification unit, and a provision unit. The reception unit receives input of symptoms of a sudden illness. The search unit searches for hospitals based on the symptoms received by the reception unit. The identification unit identifies the user's current location from among the hospitals found by the search unit. The provision unit provides information on the nearest hospital from the current location identified by the identification unit. [Effects of the Invention]

[0007] The system according to this embodiment can quickly identify and guide the user to the nearest hospital in the event of a sudden illness. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The hospital search system according to an embodiment of the present invention is a system that uses AI to search for hospitals in the event of a sudden illness and tells the user which hospital is closest to their current location. In this hospital search system, the user inputs the symptoms of a sudden illness, the AI ​​searches for an appropriate hospital based on those symptoms, identifies the user's current location, and provides information on the nearest hospital. For example, if the user inputs symptoms such as "chest pain" or "headache," this information is input into the AI. Next, the AI ​​analyzes the input symptoms and searches for an appropriate hospital. The AI ​​identifies hospitals with specialized medical departments based on the symptoms. For example, for the symptom "chest pain," it searches for hospitals specializing in cardiology. Furthermore, the AI ​​identifies the user's current location. The current location is obtained using the GPS function of the user's smartphone. This provides the AI ​​with the user's location information. Finally, the AI ​​identifies the hospital closest to the user's current location and provides that information. For example, if the user inputs "chest pain" and their current location is identified, information on the nearest hospital specializing in cardiology will be provided. This allows the user to quickly access an appropriate medical institution. This mechanism ensures the safety of the user by allowing them to quickly find an appropriate medical institution in the event of a sudden illness. For example, even outside of normal clinic hours, such as at night or on holidays, AI can search for the nearest hospital and provide it to the user, enabling a quick response. This allows hospital search systems to quickly find appropriate medical facilities in times of emergency.

[0029] The hospital search system according to this embodiment comprises a reception unit, a search unit, a selection unit, and a provision unit. The reception unit accepts input of symptoms of a sudden illness. Symptoms of a sudden illness include, but are not limited to, heart attacks, strokes, and allergic reactions. The reception unit accepts input of symptoms such as "I have a chest ache" or "I have a headache." The reception unit can also provide multiple input methods, such as voice input and text input. For example, the reception unit can use voice recognition technology to convert the symptoms dictated by the user into text data. The search unit searches for hospitals based on the symptoms accepted by the reception unit. The search unit identifies hospitals with specialized departments based on the symptoms. For example, the search unit searches for hospitals specializing in cardiology for the symptom "I have a chest ache." The search unit can also optimize search results by considering the hospital's specialization and equipment information. For example, the search unit suggests the most suitable hospital based on the hospital's medical departments and types of medical equipment. The selection unit identifies the user's current location from among the hospitals found by the search unit. The identification unit obtains the user's current location, for example, by using the GPS function of the user's smartphone. This provides the identification unit with the user's location information. The identification unit can also improve the accuracy of the location information by using Wi-Fi location information and cellular base station information in combination. For example, the identification unit combines GPS data and Wi-Fi location information to determine a more accurate current location. The provision unit provides information on the nearest hospital from the current location determined by the identification unit. For example, the provision unit provides information such as the address, phone number, and medical department of the nearest hospital from the user's current location. The provision unit can also suggest the most suitable hospital, taking into account the hospital's congestion level and operating hours. For example, the provision unit suggests a hospital to avoid congestion based on real-time congestion data. As a result, the hospital search system according to this embodiment can quickly find an appropriate medical institution in case of an emergency. Some or all of the above processing in the provision unit may be performed using AI, for example, or without AI. For example, the provision unit can provide hospital information using an AI model that takes hospital information as input and outputs the most suitable hospital.

[0030] The hospital search system includes a congestion status acquisition unit that acquires the congestion status of hospitals. The congestion status acquisition unit acquires the congestion status of hospitals. The congestion status includes, but is not limited to, real-time data, historical data, and predictive data. For example, the congestion status acquisition unit acquires real-time congestion status data of hospitals. The congestion status acquisition unit can also predict the current congestion status based on historical congestion data. For example, the congestion status acquisition unit analyzes historical congestion data and suggests the least congested time slots. The congestion status acquisition unit can also suggest routes that avoid congestion. For example, based on historical congestion data, the congestion status acquisition unit suggests hospitals that avoid congestion. This allows users to select a hospital while considering the congestion status. Some or all of the above processing in the congestion status acquisition unit may be performed using, for example, AI, or not using AI. For example, the congestion status acquisition unit can acquire congestion status using an AI model that takes hospital congestion status data as input and outputs the optimal hospital.

[0031] The hospital search system includes a consultation time acquisition unit that obtains consultation hours. The consultation time acquisition unit obtains the consultation hours of hospitals. Consultation hours include, but are not limited to, the start time, end time, and days when the hospital is closed. For example, the consultation time acquisition unit obtains the start and end times of hospital consultations. The consultation time acquisition unit can also obtain days when the hospital is closed. For example, based on the hospital's consultation time data, the consultation time acquisition unit suggests the time slot with the shortest consultation time. The consultation time acquisition unit can also predict the current consultation time based on past consultation time data. For example, the consultation time acquisition unit analyzes past consultation time data and suggests the most efficient consultation time. This allows users to select a hospital considering consultation time. Some or all of the above processing in the consultation time acquisition unit may be performed using, for example, AI, or not using AI. For example, the consultation time acquisition unit can obtain consultation hours using an AI model that takes hospital consultation time data as input and outputs the optimal consultation time.

[0032] The hospital search system includes a detailed information input section that accepts detailed symptom information. This detailed information includes, but is not limited to, the type of symptom, the time of onset, and the severity of the symptom. For example, the detailed information input section accepts the user entering details such as "I have a chest ache" or "I have a headache." The detailed information input section can also provide multiple input methods, such as voice input and text input. For example, the detailed information input section can use voice recognition technology to convert the user's verbalized symptom details into text data, enabling detailed symptom input. Some or all of the above processing in the detailed information input section may be performed using, for example, AI, or not. For example, the detailed information input section can accept detailed symptom information using an AI model that takes the user's detailed symptom data as input and outputs the most suitable hospital.

[0033] The hospital search system includes a location information technology unit that uses multiple location information technologies in combination. The location information technology unit uses multiple location information technologies, including, but not limited to, GPS, Wi-Fi location information, and cellular base station information. For example, the location information technology unit combines GPS data and Wi-Fi location information to determine a more accurate current location. The location information technology unit can also improve the accuracy of location information by using cellular base station information in combination. For example, the location information technology unit combines GPS data and cellular base station information to determine the current location, thereby improving the accuracy of location information. Some or all of the above-described processing in the location information technology unit may be performed using, for example, AI, or without AI. For example, the location information technology unit can use a location information technology in combination with an AI model that takes multiple location information data as input and outputs the optimal current location.

[0034] The reception desk can analyze the user's past symptom input history and suggest the optimal input method. For example, the reception desk can automatically display symptoms that the user has frequently entered in the past as candidates. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest symptoms that will be used during specific time periods based on the user's past input history. This allows the reception desk to suggest the optimal input method based on past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can suggest an input method using an AI model that takes the user's past symptom input history data as input and outputs the optimal input method.

[0035] The reception system can complete the input content based on the user's current health status and past medical history when symptoms are entered. For example, the reception system can refer to the user's past medical history and automatically complete relevant symptoms. The reception system can also monitor the user's current health status and complete the input content in real time. Furthermore, the reception system can suggest relevant symptoms based on the medical treatment the user has received in the past. This allows for the provision of more accurate information by completing the input content. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI. For example, the reception system can complete the input content using an AI model that takes the user's health status and medical history data as input and outputs the optimal input content.

[0036] The reception system can prioritize the input of highly relevant symptoms by considering the user's geographical location when symptoms are entered. For example, if the user is in a specific region, the reception system can prioritize the input of symptoms of diseases prevalent in that region. Furthermore, if the user is traveling, the reception system can also prompt the user to input symptoms based on the health risks at their travel destination. Additionally, if the user is in a specific environment, the reception system can prioritize the input of symptoms related to that environment. This allows for the input of highly relevant symptoms based on geographical location information. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI. For example, the reception system can use an AI model that takes the user's geographical location data as input and outputs the most appropriate symptoms to prompt the user to input symptoms.

[0037] The reception desk can analyze the user's social media activity when symptoms are entered and suggest relevant symptoms. For example, the reception desk can suggest relevant symptoms based on health information shared by the user on social media. It can also analyze the content of the user's social media posts and suggest symptoms related to their current health condition. Furthermore, the reception desk can suggest relevant symptoms based on health information shared by the user's social media friends. This allows for the suggestion of relevant symptoms based on social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can suggest symptoms using an AI model that takes the user's social media data as input and outputs the most appropriate symptoms.

[0038] The search unit can suggest the most suitable hospital by referring to past search history when searching based on symptoms. For example, the search unit can suggest the most suitable hospital based on hospitals the user has searched for in the past. The search unit can also suggest hospitals that avoid congestion based on the user's past search history. Furthermore, the search unit can analyze the user's past search history and suggest the most efficient hospital. This allows the search unit to suggest the most suitable hospital based on past search history. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can suggest hospitals using an AI model that takes the user's past search history data as input and outputs the most suitable hospital.

[0039] The search unit can optimize search results by considering the hospital's specialization and facilities when searching based on symptoms. For example, the search unit can prioritize displaying hospitals with specialized departments that address the symptoms. It can also suggest the most suitable hospital based on the hospital's facilities. Furthermore, the search unit can suggest the most suitable hospital by considering hospital ratings and user reviews. This allows for the suggestion of the most suitable hospital by considering specialization and facilities. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can optimize search results using an AI model that takes hospital specialization and facilities data as input and outputs the most suitable hospital.

[0040] The search unit can suggest the most suitable hospital based on symptoms, taking into account the user's geographical location. For example, the search unit may prioritize displaying the hospital closest to the user's current location. It can also suggest the optimal route based on the user's geographical location. Furthermore, it can suggest hospitals that avoid congestion, taking into account the user's geographical location. This allows for the suggestion of the most suitable hospital based on geographical location. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can suggest hospitals using an AI model that takes the user's geographical location data as input and outputs the most suitable hospital.

[0041] The search unit can analyze the user's social media activity and suggest relevant hospitals when searching based on symptoms. For example, the search unit can suggest the best hospital based on hospital information shared on the user's social media. It can also analyze the content of the user's social media posts and suggest hospitals relevant to the user's current health condition. Furthermore, the search unit can suggest the best hospital based on hospital information shared by the user's social media friends. This allows the search unit to suggest relevant hospitals based on social media activity. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can suggest hospitals using an AI model that takes the user's social media data as input and outputs the best hospital.

[0042] The location identification unit can select the optimal identification method by referring to the user's past location information history when determining the current location. For example, the location identification unit can determine the current location based on places the user has visited in the past. The location identification unit can also select the optimal identification method from the user's past location information history. Furthermore, the location identification unit can analyze the user's past location information to determine the current location. This allows for the selection of the optimal identification method based on past location information history. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without AI. For example, the location identification unit can select an identification method using an AI model that takes the user's past location information data as input and outputs the optimal identification method.

[0043] The location identification unit can improve the accuracy of location information by considering the user's device information when determining the current location. For example, the location identification unit can determine the current location using the GPS function of the user's smartphone. The location identification unit can also improve the accuracy of location information based on the user's device information. Furthermore, the location identification unit can determine the current location using the sensor information of the user's device. This improves the accuracy of location information based on device information. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without using AI. For example, the location identification unit can improve accuracy by using an AI model that takes the user's device information data as input and outputs the optimal current location.

[0044] The identification unit can select the optimal identification method when determining the current location, taking into account the user's geographical location information. For example, the identification unit can select the optimal identification method based on the user's current location. The identification unit can also select the optimal identification method by taking into account the user's geographical location information. Furthermore, the identification unit can determine the current location based on the user's geographical location information. This allows for the selection of the optimal identification method based on geographical location information. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can select an identification method using an AI model that takes the user's geographical location information data as input and outputs the optimal identification method.

[0045] The location identification unit can analyze the user's social media activity and obtain relevant location information when determining the current location. For example, the location identification unit can determine the current location based on location information shared on the user's social media. The location identification unit can also determine the current location by analyzing the content of the user's social media posts. Furthermore, the location identification unit can determine the current location based on location information shared by the user's social media friends. This allows for the acquisition of relevant location information based on social media activity. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without AI. For example, the location identification unit can acquire location information using an AI model that takes the user's social media data as input and outputs the optimal current location.

[0046] The service provider can provide optimal information by referring to the user's past hospital usage history when providing hospital information. For example, the service provider can suggest the most suitable hospital based on information about hospitals the user has used in the past. The service provider can also suggest hospitals that avoid congestion based on the user's past hospital usage history. Furthermore, the service provider can analyze the user's past hospital usage history and suggest the most efficient hospital. This allows the service provider to provide optimal information based on past hospital usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide information using an AI model that takes the user's past hospital usage history data as input and outputs the optimal hospital.

[0047] The service provider can optimize information when providing hospital information by considering hospital ratings and user reviews. For example, the service provider can suggest the most suitable hospital based on hospital ratings. It can also analyze user reviews and suggest the most suitable hospital. Furthermore, it can comprehensively consider hospital ratings and user reviews to suggest the most suitable hospital. This allows for the suggestion of the most suitable hospital based on ratings and user reviews. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can optimize information using an AI model that takes hospital rating and user review data as input and outputs the most suitable hospital.

[0048] The service provider can provide optimal information by considering the user's geographical location when providing hospital information. For example, the service provider can prioritize displaying the hospital closest to the user's current location. Furthermore, the service provider can suggest the optimal route based on the user's geographical location. In addition, the service provider can suggest hospitals that avoid congestion by considering the user's geographical location. This allows for the provision of optimal hospital information based on geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide information using an AI model that takes the user's geographical location data as input and outputs optimal hospital information.

[0049] The service provider can analyze the user's social media activity and provide relevant information when providing hospital information. For example, the service provider can suggest the most suitable hospital based on hospital information shared on the user's social media. It can also analyze the content of the user's social media posts and suggest hospitals relevant to the user's current health condition. Furthermore, the service provider can suggest the most suitable hospital based on hospital information shared by the user's social media friends. This allows the service provider to provide relevant hospital information based on social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide information using an AI model that takes the user's social media data as input and outputs the most suitable hospital information.

[0050] The congestion status acquisition unit can predict the current congestion status by referring to past congestion data when acquiring congestion status. For example, the congestion status acquisition unit predicts the current congestion status based on past congestion data. The congestion status acquisition unit can also analyze past congestion data and suggest the least congested time slots. Furthermore, the congestion status acquisition unit can suggest routes that avoid congestion based on past congestion data. This allows for the prediction of the current congestion status based on past congestion data. Some or all of the above processing in the congestion status acquisition unit may be performed using AI, for example, or without AI. For example, the congestion status acquisition unit can predict the current congestion status using an AI model that takes past congestion data as input and outputs the optimal congestion status.

[0051] The congestion status acquisition unit can acquire the optimal congestion status by considering the user's geographical location information when acquiring congestion status. For example, the congestion status acquisition unit can prioritize displaying the congestion status of the hospital closest to the user's current location. The congestion status acquisition unit can also acquire the optimal congestion status based on the user's geographical location information. Furthermore, the congestion status acquisition unit can acquire the congestion status of hospitals to avoid congestion by considering the user's geographical location information. This allows for the acquisition of the optimal congestion status based on geographical location information. Some or all of the above processing in the congestion status acquisition unit may be performed using AI, for example, or without using AI. For example, the congestion status acquisition unit can acquire congestion status using an AI model that takes the user's geographical location information data as input and outputs the optimal congestion status.

[0052] The consultation time acquisition unit can predict the current consultation time by referring to past consultation time data when acquiring consultation time. For example, the consultation time acquisition unit predicts the current consultation time based on past consultation time data. The consultation time acquisition unit can also analyze past consultation time data and suggest the time slot with the shortest consultation time. Furthermore, the consultation time acquisition unit can predict consultation time based on past consultation time data. This allows for the prediction of the current consultation time based on past consultation time data. Some or all of the above processing in the consultation time acquisition unit may be performed using AI, for example, or without AI. For example, the consultation time acquisition unit can predict the current consultation time using an AI model that takes past consultation time data as input and outputs the optimal consultation time.

[0053] The consultation time acquisition unit can acquire the optimal consultation time by considering the user's geographical location information when acquiring consultation times. For example, the consultation time acquisition unit can prioritize displaying the consultation time of the hospital closest to the user's current location. The consultation time acquisition unit can also acquire the optimal consultation time based on the user's geographical location information. Furthermore, the consultation time acquisition unit can acquire consultation times by considering the user's geographical location information. This allows for the acquisition of the optimal consultation time based on geographical location information. Some or all of the above processing in the consultation time acquisition unit may be performed using AI, for example, or without using AI. For example, the consultation time acquisition unit can acquire consultation times using an AI model that takes the user's geographical location information data as input and outputs the optimal consultation time.

[0054] The detailed information input unit can suggest the optimal input method by referring to the user's past symptom input history when detailed information is entered. For example, the detailed information input unit can automatically display details that the user has frequently entered in the past as candidates. The detailed information input unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the detailed information input unit can predict and suggest details to be used at a specific time period based on the user's past input history. This allows the system to suggest the optimal input method based on the user's past symptom input history. Some or all of the above processing in the detailed information input unit may be performed using AI, for example, or without AI. For example, the detailed information input unit can suggest an input method using an AI model that takes the user's past symptom input history data as input and outputs the optimal input method.

[0055] The details input unit can prioritize inputting highly relevant details by considering the user's geographical location information when details are entered. For example, if the user is in a specific region, the details input unit can prioritize inputting details about diseases prevalent in that region. Furthermore, if the user is traveling, the details input unit can also prioritize inputting details based on the health risks at their travel destination. Additionally, if the user is in a specific environment, the details input unit can prioritize inputting details related to that environment. This allows for the input of highly relevant details based on geographical location information. Some or all of the above processing in the details input unit may be performed using AI, for example, or without AI. For example, the details input unit can use an AI model that takes the user's geographical location data as input and outputs the most relevant details to input details.

[0056] The Location Information Technology Department can select the optimal location information technology by referring to past location information data when selecting a location information technology. For example, the Location Information Technology Department can select the optimal location information technology based on past location information data. The Location Information Technology Department can also analyze past location information data and select the technology with the highest accuracy. Furthermore, the Location Information Technology Department can select the most efficient technology based on past location information data. This allows for the selection of the optimal technology based on past location information data. Some or all of the above processing in the Location Information Technology Department may be performed using AI, for example, or without AI. For example, the Location Information Technology Department can select a technology using an AI model that takes past location information data as input and outputs the optimal location information technology.

[0057] The Location Information Technology Department can select the optimal location information technology by considering the user's device information when selecting location information technology. For example, the Location Information Technology Department can select the optimal location information technology based on the GPS function of the user's smartphone. The Location Information Technology Department can also select the technology with the highest accuracy based on the user's device information. Furthermore, the Location Information Technology Department can select the most efficient technology based on the sensor information of the user's device. This allows for the selection of the optimal technology based on device information. Some or all of the above processing in the Location Information Technology Department may be performed using AI, for example, or without AI. For example, the Location Information Technology Department can select technology using an AI model that takes user device information data as input and outputs the optimal location information technology.

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

[0059] A hospital search system can analyze a user's past hospital visit history and suggest the most suitable hospital. For example, if a user has previously used a specific hospital, the system can suggest the most suitable hospital for similar symptoms based on the hospital's rating and treatment details. It can also suggest the most suitable hospital by considering the congestion and operating hours of hospitals the user has previously visited. Furthermore, it can analyze a user's past hospital visit history and suggest the most suitable hospital for a specific time of day. In this way, the system can suggest the most suitable hospital based on past visit history.

[0060] A hospital search system can monitor a user's current health status and suggest the most suitable hospital. For example, it can acquire vital data such as the user's heart rate and blood pressure in real time and suggest the best hospital based on that data. It can also quickly suggest the best hospital if the user's health status suddenly changes. Furthermore, it can monitor the user's health status over the long term and suggest hospitals that provide regular health checkups. This allows for the suggestion of the most suitable hospital based on the user's current health status.

[0061] A hospital search system can suggest the optimal route to a hospital, taking into account the user's geographical location. For example, it can suggest the shortest route from the user's current location to the nearest hospital. It can also suggest the most efficient route, taking traffic conditions into consideration. Furthermore, if the user is using a specific mode of transportation, it can suggest the most suitable route for that mode. In this way, it can suggest the optimal route to a hospital based on geographical location information.

[0062] A hospital search system can analyze a user's past search history and suggest the most suitable hospital. For example, it can suggest the best hospital for similar symptoms based on the ratings and treatment details of hospitals the user has previously searched for. It can also suggest hospitals that are less crowded based on the user's past search history. Furthermore, it can analyze the user's past search history and suggest the best hospital for a specific time of day. In this way, it can suggest the most suitable hospital based on past search history.

[0063] A hospital search system can analyze a user's social media activity and suggest relevant hospitals. For example, it can suggest the most suitable hospital based on health information shared by the user on social media. It can also analyze the content of the user's social media posts and suggest hospitals relevant to their current health condition. Furthermore, it can suggest the most suitable hospital based on hospital information shared by the user's social media friends. In this way, it can suggest relevant hospitals based on social media activity.

[0064] A hospital search system can suggest the most suitable hospital's consultation hours, taking into account the user's geographical location. For example, it can prioritize displaying the consultation hours of the hospital closest to the user's current location. It can also suggest the most suitable consultation hours based on the user's geographical location. Furthermore, if the user is in a specific region, it can suggest the most suitable hospital considering the consultation hours of that region. In this way, it can suggest the most suitable consultation hours based on geographical location information.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk accepts input of symptoms of a sudden illness. Symptoms of a sudden illness include, but are not limited to, heart attacks, strokes, and allergic reactions. The reception desk accepts, for example, the user entering symptoms such as "I have a chest ache" or "I have a headache." The reception desk can also provide multiple input methods, such as voice input and text input. For example, the reception desk can use voice recognition technology to convert the symptoms dictated by the user into text data. Step 2: The search unit searches for hospitals based on the symptoms received by the reception unit. For example, the search unit identifies hospitals with specialized departments based on the symptoms. For example, for the symptom "chest pain," the search unit searches for hospitals specializing in cardiology. The search unit can also optimize search results by considering the hospital's specialization and facilities. For example, the search unit suggests the most suitable hospital based on the hospital's medical departments and types of medical equipment. Step 3: The identification unit identifies the user's current location from among the hospitals found by the search unit. The identification unit obtains the current location, for example, by using the GPS function of the user's smartphone. This provides the identification unit with the user's location information. The identification unit can also improve the accuracy of the location information by using Wi-Fi location information and cell tower information in combination. For example, the identification unit combines GPS data and Wi-Fi location information to determine the current location more accurately. Step 4: The provisioning unit provides information on the nearest hospital from the current location identified by the identification unit. The provisioning unit provides information such as the address, phone number, and medical department of the nearest hospital from the user's current location. The provisioning unit can also suggest the most suitable hospital, taking into account the hospital's congestion level and operating hours. For example, the provisioning unit suggests a hospital to avoid congestion based on real-time congestion data. This allows the hospital search system according to the embodiment to quickly find an appropriate medical institution in case of an emergency. Some or all of the above processing in the provisioning unit may be performed using AI, for example, or without AI. For example, the provisioning unit can provide hospital information using an AI model that takes hospital information as input and outputs the most suitable hospital.

[0067] (Example of form 2) The hospital search system according to an embodiment of the present invention is a system that uses AI to search for hospitals in the event of a sudden illness and tells the user which hospital is closest to their current location. In this hospital search system, the user inputs the symptoms of a sudden illness, the AI ​​searches for an appropriate hospital based on those symptoms, identifies the user's current location, and provides information on the nearest hospital. For example, if the user inputs symptoms such as "chest pain" or "headache," this information is input into the AI. Next, the AI ​​analyzes the input symptoms and searches for an appropriate hospital. The AI ​​identifies hospitals with specialized medical departments based on the symptoms. For example, for the symptom "chest pain," it searches for hospitals specializing in cardiology. Furthermore, the AI ​​identifies the user's current location. The current location is obtained using the GPS function of the user's smartphone. This provides the AI ​​with the user's location information. Finally, the AI ​​identifies the hospital closest to the user's current location and provides that information. For example, if the user inputs "chest pain" and their current location is identified, information on the nearest hospital specializing in cardiology will be provided. This allows the user to quickly access an appropriate medical institution. This mechanism ensures the safety of the user by allowing them to quickly find an appropriate medical institution in the event of a sudden illness. For example, even outside of normal clinic hours, such as at night or on holidays, AI can search for the nearest hospital and provide it to the user, enabling a quick response. This allows hospital search systems to quickly find appropriate medical facilities in times of emergency.

[0068] The hospital search system according to this embodiment comprises a reception unit, a search unit, a selection unit, and a provision unit. The reception unit accepts input of symptoms of a sudden illness. Symptoms of a sudden illness include, but are not limited to, heart attacks, strokes, and allergic reactions. The reception unit accepts input of symptoms such as "I have a chest ache" or "I have a headache." The reception unit can also provide multiple input methods, such as voice input and text input. For example, the reception unit can use voice recognition technology to convert the symptoms dictated by the user into text data. The search unit searches for hospitals based on the symptoms accepted by the reception unit. The search unit identifies hospitals with specialized departments based on the symptoms. For example, the search unit searches for hospitals specializing in cardiology for the symptom "I have a chest ache." The search unit can also optimize search results by considering the hospital's specialization and equipment information. For example, the search unit suggests the most suitable hospital based on the hospital's medical departments and types of medical equipment. The selection unit identifies the user's current location from among the hospitals found by the search unit. The identification unit obtains the user's current location, for example, by using the GPS function of the user's smartphone. This provides the identification unit with the user's location information. The identification unit can also improve the accuracy of the location information by using Wi-Fi location information and cellular base station information in combination. For example, the identification unit combines GPS data and Wi-Fi location information to determine a more accurate current location. The provision unit provides information on the nearest hospital from the current location determined by the identification unit. For example, the provision unit provides information such as the address, phone number, and medical department of the nearest hospital from the user's current location. The provision unit can also suggest the most suitable hospital, taking into account the hospital's congestion level and operating hours. For example, the provision unit suggests a hospital to avoid congestion based on real-time congestion data. As a result, the hospital search system according to this embodiment can quickly find an appropriate medical institution in case of an emergency. Some or all of the above processing in the provision unit may be performed using AI, for example, or without AI. For example, the provision unit can provide hospital information using an AI model that takes hospital information as input and outputs the most suitable hospital.

[0069] The hospital search system includes a congestion status acquisition unit that acquires the congestion status of hospitals. The congestion status acquisition unit acquires the congestion status of hospitals. The congestion status includes, but is not limited to, real-time data, historical data, and predictive data. For example, the congestion status acquisition unit acquires real-time congestion status data of hospitals. The congestion status acquisition unit can also predict the current congestion status based on historical congestion data. For example, the congestion status acquisition unit analyzes historical congestion data and suggests the least congested time slots. The congestion status acquisition unit can also suggest routes that avoid congestion. For example, based on historical congestion data, the congestion status acquisition unit suggests hospitals that avoid congestion. This allows users to select a hospital while considering the congestion status. Some or all of the above processing in the congestion status acquisition unit may be performed using, for example, AI, or not using AI. For example, the congestion status acquisition unit can acquire congestion status using an AI model that takes hospital congestion status data as input and outputs the optimal hospital.

[0070] The hospital search system includes a consultation time acquisition unit that obtains consultation hours. The consultation time acquisition unit obtains the consultation hours of hospitals. Consultation hours include, but are not limited to, the start time, end time, and days when the hospital is closed. For example, the consultation time acquisition unit obtains the start and end times of hospital consultations. The consultation time acquisition unit can also obtain days when the hospital is closed. For example, based on the hospital's consultation time data, the consultation time acquisition unit suggests the time slot with the shortest consultation time. The consultation time acquisition unit can also predict the current consultation time based on past consultation time data. For example, the consultation time acquisition unit analyzes past consultation time data and suggests the most efficient consultation time. This allows users to select a hospital considering consultation time. Some or all of the above processing in the consultation time acquisition unit may be performed using, for example, AI, or not using AI. For example, the consultation time acquisition unit can obtain consultation hours using an AI model that takes hospital consultation time data as input and outputs the optimal consultation time.

[0071] The hospital search system includes a detailed information input section that accepts detailed symptom information. This detailed information includes, but is not limited to, the type of symptom, the time of onset, and the severity of the symptom. For example, the detailed information input section accepts the user entering details such as "I have a chest ache" or "I have a headache." The detailed information input section can also provide multiple input methods, such as voice input and text input. For example, the detailed information input section can use voice recognition technology to convert the user's verbalized symptom details into text data, enabling detailed symptom input. Some or all of the above processing in the detailed information input section may be performed using, for example, AI, or not. For example, the detailed information input section can accept detailed symptom information using an AI model that takes the user's detailed symptom data as input and outputs the most suitable hospital.

[0072] The hospital search system includes a location information technology unit that uses multiple location information technologies in combination. The location information technology unit uses multiple location information technologies, including, but not limited to, GPS, Wi-Fi location information, and cellular base station information. For example, the location information technology unit combines GPS data and Wi-Fi location information to determine a more accurate current location. The location information technology unit can also improve the accuracy of location information by using cellular base station information in combination. For example, the location information technology unit combines GPS data and cellular base station information to determine the current location, thereby improving the accuracy of location information. Some or all of the above-described processing in the location information technology unit may be performed using, for example, AI, or without AI. For example, the location information technology unit can use a location information technology in combination with an AI model that takes multiple location information data as input and outputs the optimal current location.

[0073] The reception desk can estimate the user's emotions and adjust the symptom input interface based on the estimated emotions. For example, if the user is feeling anxious, the reception desk can provide a simple and intuitive interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick symptom input. This allows for an interface tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can adjust the interface using an AI model that takes user emotion data as input and outputs an optimal interface.

[0074] The reception desk can analyze the user's past symptom input history and suggest the optimal input method. For example, the reception desk can automatically display symptoms that the user has frequently entered in the past as candidates. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest symptoms that will be used during specific time periods based on the user's past input history. This allows the reception desk to suggest the optimal input method based on past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can suggest an input method using an AI model that takes the user's past symptom input history data as input and outputs the optimal input method.

[0075] The reception system can complete the input content based on the user's current health status and past medical history when symptoms are entered. For example, the reception system can refer to the user's past medical history and automatically complete relevant symptoms. The reception system can also monitor the user's current health status and complete the input content in real time. Furthermore, the reception system can suggest relevant symptoms based on the medical treatment the user has received in the past. This allows for the provision of more accurate information by completing the input content. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI. For example, the reception system can complete the input content using an AI model that takes the user's health status and medical history data as input and outputs the optimal input content.

[0076] The reception unit can estimate the user's emotions and determine the priority of inputs based on the estimated emotions. For example, if the user is nervous, the reception unit may prompt for input starting with the most important symptoms. If the user is relaxed, the reception unit may also prioritize detailed symptom input. Furthermore, if the user is in a hurry, the reception unit may only ask for input of the main symptoms. This allows for the determination of input priorities according to emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can determine priorities using an AI model that takes user emotion data as input and outputs the optimal priority of inputs.

[0077] The reception system can prioritize the input of highly relevant symptoms by considering the user's geographical location when symptoms are entered. For example, if the user is in a specific region, the reception system can prioritize the input of symptoms of diseases prevalent in that region. Furthermore, if the user is traveling, the reception system can also prompt the user to input symptoms based on the health risks at their travel destination. Additionally, if the user is in a specific environment, the reception system can prioritize the input of symptoms related to that environment. This allows for the input of highly relevant symptoms based on geographical location information. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI. For example, the reception system can use an AI model that takes the user's geographical location data as input and outputs the most appropriate symptoms to prompt the user to input symptoms.

[0078] The reception desk can analyze the user's social media activity when symptoms are entered and suggest relevant symptoms. For example, the reception desk can suggest relevant symptoms based on health information shared by the user on social media. It can also analyze the content of the user's social media posts and suggest symptoms related to their current health condition. Furthermore, the reception desk can suggest relevant symptoms based on health information shared by the user's social media friends. This allows for the suggestion of relevant symptoms based on social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can suggest symptoms using an AI model that takes the user's social media data as input and outputs the most appropriate symptoms.

[0079] The search unit can estimate the user's emotions and adjust how search results are displayed based on the estimated emotions. For example, if the user is feeling anxious, the search unit will display simple and highly visible search results. If the user is relaxed, the search unit can also display search results containing more detailed information. Furthermore, if the user is in a hurry, the search unit can display only the most important information. This provides a search result display method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, or not. For example, the search unit can adjust the display method using an AI model that takes user emotion data as input and outputs the optimal search results.

[0080] The search unit can suggest the most suitable hospital by referring to past search history when searching based on symptoms. For example, the search unit can suggest the most suitable hospital based on hospitals the user has searched for in the past. The search unit can also suggest hospitals that avoid congestion based on the user's past search history. Furthermore, the search unit can analyze the user's past search history and suggest the most efficient hospital. This allows the search unit to suggest the most suitable hospital based on past search history. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can suggest hospitals using an AI model that takes the user's past search history data as input and outputs the most suitable hospital.

[0081] The search unit can optimize search results by considering the hospital's specialization and facilities when searching based on symptoms. For example, the search unit can prioritize displaying hospitals with specialized departments that address the symptoms. It can also suggest the most suitable hospital based on the hospital's facilities. Furthermore, the search unit can suggest the most suitable hospital by considering hospital ratings and user reviews. This allows for the suggestion of the most suitable hospital by considering specialization and facilities. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can optimize search results using an AI model that takes hospital specialization and facilities data as input and outputs the most suitable hospital.

[0082] The search unit can estimate the user's emotions and determine the priority of search results based on the estimated emotions. For example, if the user is feeling anxious, the search unit will prioritize displaying the most reliable hospitals. If the user is relaxed, the search unit may also prioritize displaying hospitals with detailed information. Furthermore, if the user is in a hurry, the search unit may prioritize displaying the nearest hospitals. This allows for the prioritization of search results according to emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can determine priorities using an AI model that takes user emotion data as input and outputs the optimal priority of search results.

[0083] The search unit can suggest the most suitable hospital based on symptoms, taking into account the user's geographical location. For example, the search unit may prioritize displaying the hospital closest to the user's current location. It can also suggest the optimal route based on the user's geographical location. Furthermore, it can suggest hospitals that avoid congestion, taking into account the user's geographical location. This allows for the suggestion of the most suitable hospital based on geographical location. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can suggest hospitals using an AI model that takes the user's geographical location data as input and outputs the most suitable hospital.

[0084] The search unit can analyze the user's social media activity and suggest relevant hospitals when searching based on symptoms. For example, the search unit can suggest the best hospital based on hospital information shared on the user's social media. It can also analyze the content of the user's social media posts and suggest hospitals relevant to the user's current health condition. Furthermore, the search unit can suggest the best hospital based on hospital information shared by the user's social media friends. This allows the search unit to suggest relevant hospitals based on social media activity. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can suggest hospitals using an AI model that takes the user's social media data as input and outputs the best hospital.

[0085] The location tracking unit can estimate the user's emotions and adjust the accuracy of location tracking based on the estimated emotions. For example, if the user is feeling anxious, the location tracking unit will perform location tracking with high accuracy. If the user is relaxed, the location tracking unit can also track the location with normal accuracy. Furthermore, if the user is in a hurry, the location tracking unit can track the location quickly. This allows for location tracking accuracy tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the location tracking unit may be performed using AI, or not. For example, the location tracking unit can adjust accuracy using an AI model that takes user emotion data as input and outputs the optimal location.

[0086] The location identification unit can select the optimal identification method by referring to the user's past location information history when determining the current location. For example, the location identification unit can determine the current location based on places the user has visited in the past. The location identification unit can also select the optimal identification method from the user's past location information history. Furthermore, the location identification unit can analyze the user's past location information to determine the current location. This allows for the selection of the optimal identification method based on past location information history. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without AI. For example, the location identification unit can select an identification method using an AI model that takes the user's past location information data as input and outputs the optimal identification method.

[0087] The location identification unit can improve the accuracy of location information by considering the user's device information when determining the current location. For example, the location identification unit can determine the current location using the GPS function of the user's smartphone. The location identification unit can also improve the accuracy of location information based on the user's device information. Furthermore, the location identification unit can determine the current location using the sensor information of the user's device. This improves the accuracy of location information based on device information. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without using AI. For example, the location identification unit can improve accuracy by using an AI model that takes the user's device information data as input and outputs the optimal current location.

[0088] The identification unit can estimate the user's emotions and determine the priority for determining the current location based on the estimated emotions. For example, if the user is feeling anxious, the identification unit will determine the current location in the most accurate way. The identification unit can also determine the current location in the usual way if the user is relaxed. Furthermore, if the user is in a hurry, the identification unit can determine the current location quickly. This allows for the determination of a priority for determining the current location according to emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can determine priorities using an AI model that takes user emotion data as input and outputs the optimal current location.

[0089] The identification unit can select the optimal identification method when determining the current location, taking into account the user's geographical location information. For example, the identification unit can select the optimal identification method based on the user's current location. The identification unit can also select the optimal identification method by taking into account the user's geographical location information. Furthermore, the identification unit can determine the current location based on the user's geographical location information. This allows for the selection of the optimal identification method based on geographical location information. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can select an identification method using an AI model that takes the user's geographical location information data as input and outputs the optimal identification method.

[0090] The location identification unit can analyze the user's social media activity and obtain relevant location information when determining the current location. For example, the location identification unit can determine the current location based on location information shared on the user's social media. The location identification unit can also determine the current location by analyzing the content of the user's social media posts. Furthermore, the location identification unit can determine the current location based on location information shared by the user's social media friends. This allows for the acquisition of relevant location information based on social media activity. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without AI. For example, the location identification unit can acquire location information using an AI model that takes the user's social media data as input and outputs the optimal current location.

[0091] The information provider can estimate the user's emotions and adjust the way hospital information is provided based on the estimated emotions. For example, if the user is feeling anxious, the provider can provide simple and easily understandable information. If the user is relaxed, the provider can also provide detailed information. Furthermore, if the user is in a hurry, the provider can provide only the most important information. This allows for the provision of hospital information in a way that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, or not. For example, the information provider can adjust the method of provision using an AI model that takes user emotion data as input and outputs optimal hospital information.

[0092] The service provider can provide optimal information by referring to the user's past hospital usage history when providing hospital information. For example, the service provider can suggest the most suitable hospital based on information about hospitals the user has used in the past. The service provider can also suggest hospitals that avoid congestion based on the user's past hospital usage history. Furthermore, the service provider can analyze the user's past hospital usage history and suggest the most efficient hospital. This allows the service provider to provide optimal information based on past hospital usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide information using an AI model that takes the user's past hospital usage history data as input and outputs the optimal hospital.

[0093] The service provider can optimize information when providing hospital information by considering hospital ratings and user reviews. For example, the service provider can suggest the most suitable hospital based on hospital ratings. It can also analyze user reviews and suggest the most suitable hospital. Furthermore, it can comprehensively consider hospital ratings and user reviews to suggest the most suitable hospital. This allows for the suggestion of the most suitable hospital based on ratings and user reviews. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can optimize information using an AI model that takes hospital rating and user review data as input and outputs the most suitable hospital.

[0094] The service provider can estimate the user's emotions and prioritize hospital information based on those emotions. For example, if the user is feeling anxious, the service provider will prioritize displaying the most reliable hospitals. If the user is relaxed, the service provider can also prioritize displaying hospitals with detailed information. Furthermore, if the user is in a hurry, the service provider can prioritize displaying the nearest hospitals. This allows for the prioritization of hospital information according to emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can determine priorities using an AI model that takes user emotion data as input and outputs the optimal priority of hospital information.

[0095] The service provider can provide optimal information by considering the user's geographical location when providing hospital information. For example, the service provider can prioritize displaying the hospital closest to the user's current location. Furthermore, the service provider can suggest the optimal route based on the user's geographical location. In addition, the service provider can suggest hospitals that avoid congestion by considering the user's geographical location. This allows for the provision of optimal hospital information based on geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide information using an AI model that takes the user's geographical location data as input and outputs optimal hospital information.

[0096] The service provider can analyze the user's social media activity and provide relevant information when providing hospital information. For example, the service provider can suggest the most suitable hospital based on hospital information shared on the user's social media. It can also analyze the content of the user's social media posts and suggest hospitals relevant to the user's current health condition. Furthermore, the service provider can suggest the most suitable hospital based on hospital information shared by the user's social media friends. This allows the service provider to provide relevant hospital information based on social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide information using an AI model that takes the user's social media data as input and outputs the most suitable hospital information.

[0097] The congestion status acquisition unit can estimate the user's emotions and adjust the method of acquiring congestion status based on the estimated user emotions. For example, if the user is feeling anxious, the congestion status acquisition unit will acquire congestion status with high accuracy. Also, if the user is relaxed, the congestion status acquisition unit can acquire congestion status with normal accuracy. Furthermore, if the user is in a hurry, the congestion status acquisition unit can acquire congestion status quickly. This makes it possible to provide a method of acquiring congestion status according to emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the congestion status acquisition unit may be performed using AI, for example, or without AI. For example, the congestion status acquisition unit can adjust the acquisition method using an AI model that takes user emotion data as input and outputs the optimal congestion status.

[0098] The congestion status acquisition unit can predict the current congestion status by referring to past congestion data when acquiring congestion status. For example, the congestion status acquisition unit predicts the current congestion status based on past congestion data. The congestion status acquisition unit can also analyze past congestion data and suggest the least congested time slots. Furthermore, the congestion status acquisition unit can suggest routes that avoid congestion based on past congestion data. This allows for the prediction of the current congestion status based on past congestion data. Some or all of the above processing in the congestion status acquisition unit may be performed using AI, for example, or without AI. For example, the congestion status acquisition unit can predict the current congestion status using an AI model that takes past congestion data as input and outputs the optimal congestion status.

[0099] The congestion status acquisition unit can estimate the user's emotions and determine the priority of congestion statuses based on the estimated user emotions. For example, if the user is feeling anxious, the congestion status acquisition unit will prioritize displaying the most reliable congestion status. Furthermore, if the user is relaxed, the congestion status acquisition unit can prioritize displaying congestion statuses that include detailed information. Additionally, if the user is in a hurry, the congestion status acquisition unit can prioritize displaying the most important congestion status. This allows for the determination of congestion status priorities according to emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the congestion status acquisition unit may be performed using AI, or not. For example, the congestion status acquisition unit can determine priorities using an AI model that takes user emotion data as input and outputs the optimal congestion status priority.

[0100] The congestion status acquisition unit can acquire the optimal congestion status by considering the user's geographical location information when acquiring congestion status. For example, the congestion status acquisition unit can prioritize displaying the congestion status of the hospital closest to the user's current location. The congestion status acquisition unit can also acquire the optimal congestion status based on the user's geographical location information. Furthermore, the congestion status acquisition unit can acquire the congestion status of hospitals to avoid congestion by considering the user's geographical location information. This allows for the acquisition of the optimal congestion status based on geographical location information. Some or all of the above processing in the congestion status acquisition unit may be performed using AI, for example, or without using AI. For example, the congestion status acquisition unit can acquire congestion status using an AI model that takes the user's geographical location information data as input and outputs the optimal congestion status.

[0101] The consultation time acquisition unit can estimate the user's emotions and adjust the method of acquiring consultation time based on the estimated user emotions. For example, if the user is feeling anxious, the consultation time acquisition unit will acquire consultation time with high accuracy. Also, if the user is relaxed, the consultation time acquisition unit can acquire consultation time with normal accuracy. Furthermore, if the user is in a hurry, the consultation time acquisition unit can acquire consultation time quickly. This provides a method of acquiring consultation time that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the consultation time acquisition unit may be performed using AI, for example, or without AI. For example, the consultation time acquisition unit can adjust the acquisition method using an AI model that takes user emotion data as input and outputs the optimal consultation time.

[0102] The consultation time acquisition unit can predict the current consultation time by referring to past consultation time data when acquiring consultation time. For example, the consultation time acquisition unit predicts the current consultation time based on past consultation time data. The consultation time acquisition unit can also analyze past consultation time data and suggest the time slot with the shortest consultation time. Furthermore, the consultation time acquisition unit can predict consultation time based on past consultation time data. This allows for the prediction of the current consultation time based on past consultation time data. Some or all of the above processing in the consultation time acquisition unit may be performed using AI, for example, or without AI. For example, the consultation time acquisition unit can predict the current consultation time using an AI model that takes past consultation time data as input and outputs the optimal consultation time.

[0103] The consultation time acquisition unit can estimate the user's emotions and determine the priority of consultation times based on the estimated emotions. For example, if the user is feeling anxious, the consultation time acquisition unit will prioritize displaying the most reliable consultation times. Furthermore, if the user is relaxed, the consultation time acquisition unit can prioritize displaying consultation times containing detailed information. Additionally, if the user is in a hurry, the consultation time acquisition unit can prioritize displaying the most important consultation times. This allows for the determination of consultation time priorities according to emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the consultation time acquisition unit may be performed using AI, or not. For example, the consultation time acquisition unit can determine priorities using an AI model that takes user emotion data as input and outputs the optimal priority of consultation times.

[0104] The consultation time acquisition unit can acquire the optimal consultation time by considering the user's geographical location information when acquiring consultation times. For example, the consultation time acquisition unit can prioritize displaying the consultation time of the hospital closest to the user's current location. The consultation time acquisition unit can also acquire the optimal consultation time based on the user's geographical location information. Furthermore, the consultation time acquisition unit can acquire consultation times by considering the user's geographical location information. This allows for the acquisition of the optimal consultation time based on geographical location information. Some or all of the above processing in the consultation time acquisition unit may be performed using AI, for example, or without using AI. For example, the consultation time acquisition unit can acquire consultation times using an AI model that takes the user's geographical location information data as input and outputs the optimal consultation time.

[0105] The detailed input unit can estimate the user's emotions and adjust the detailed input interface based on the estimated emotions. For example, if the user is feeling anxious, the detailed input unit can provide a simple and intuitive interface and minimize the input steps. If the user is relaxed, the detailed input unit can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the detailed input unit can prioritize voice input to allow for quick detail input. This enables the provision of an emotion-responsive detailed input interface. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the detailed input unit may be performed using AI or not. For example, the detailed input unit can adjust the interface using an AI model that takes user emotion data as input and outputs an optimal interface.

[0106] The detailed information input unit can suggest the optimal input method by referring to the user's past symptom input history when detailed information is entered. For example, the detailed information input unit can automatically display details that the user has frequently entered in the past as candidates. The detailed information input unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the detailed information input unit can predict and suggest details to be used at a specific time period based on the user's past input history. This allows the system to suggest the optimal input method based on the user's past symptom input history. Some or all of the above processing in the detailed information input unit may be performed using AI, for example, or without AI. For example, the detailed information input unit can suggest an input method using an AI model that takes the user's past symptom input history data as input and outputs the optimal input method.

[0107] The detail input unit can estimate the user's emotions and determine the priority of detail inputs based on the estimated emotions. For example, if the user is feeling anxious, the detail input unit will prompt for input of the most important details first. It can also prioritize detail inputs if the user is relaxed. Furthermore, if the user is in a hurry, the detail input unit can request only the main details. This allows for the determination of detail input priorities according to emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the detail input unit may be performed using AI or not. For example, the detail input unit can determine priorities using an AI model that takes user emotion data as input and outputs the optimal priority of detail inputs.

[0108] The details input unit can prioritize inputting highly relevant details by considering the user's geographical location information when details are entered. For example, if the user is in a specific region, the details input unit can prioritize inputting details about diseases prevalent in that region. Furthermore, if the user is traveling, the details input unit can also prioritize inputting details based on the health risks at their travel destination. Additionally, if the user is in a specific environment, the details input unit can prioritize inputting details related to that environment. This allows for the input of highly relevant details based on geographical location information. Some or all of the above processing in the details input unit may be performed using AI, for example, or without AI. For example, the details input unit can use an AI model that takes the user's geographical location data as input and outputs the most relevant details to input details.

[0109] The Location Information Technology Department can estimate the user's emotions and select location information technology based on the estimated emotions. For example, if the user is feeling anxious, the Location Information Technology Department can select highly accurate location information technology. If the user is relaxed, the Location Information Technology Department can also select location information technology with normal accuracy. Furthermore, if the user is in a hurry, the Location Information Technology Department can select technology that can quickly acquire location information. This allows for the selection of location information technology according to emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Location Information Technology Department may be performed using AI, or not using AI. For example, the Location Information Technology Department can select technology using an AI model that takes user emotion data as input and outputs the optimal location information technology.

[0110] The Location Information Technology Department can select the optimal location information technology by referring to past location information data when selecting a location information technology. For example, the Location Information Technology Department can select the optimal location information technology based on past location information data. The Location Information Technology Department can also analyze past location information data and select the technology with the highest accuracy. Furthermore, the Location Information Technology Department can select the most efficient technology based on past location information data. This allows for the selection of the optimal technology based on past location information data. Some or all of the above processing in the Location Information Technology Department may be performed using AI, for example, or without AI. For example, the Location Information Technology Department can select a technology using an AI model that takes past location information data as input and outputs the optimal location information technology.

[0111] The location information technology unit can estimate the user's emotions and determine the priority of location information technologies based on the estimated emotions. For example, if the user is feeling anxious, the location information technology unit will prioritize using the most accurate location information technology. If the user is relaxed, the location information technology unit may also use location information technology of normal accuracy. Furthermore, if the user is in a hurry, the location information technology unit may prioritize using technology that can quickly acquire location information. This allows for the determination of location information technology priorities according to emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the location information technology unit may be performed using AI, or not. For example, the location information technology unit can determine priorities using an AI model that takes user emotion data as input and outputs the optimal priority of location information technologies.

[0112] The Location Information Technology Department can select the optimal location information technology by considering the user's device information when selecting location information technology. For example, the Location Information Technology Department can select the optimal location information technology based on the GPS function of the user's smartphone. The Location Information Technology Department can also select the technology with the highest accuracy based on the user's device information. Furthermore, the Location Information Technology Department can select the most efficient technology based on the sensor information of the user's device. This allows for the selection of the optimal technology based on device information. Some or all of the above processing in the Location Information Technology Department may be performed using AI, for example, or without AI. For example, the Location Information Technology Department can select technology using an AI model that takes user device information data as input and outputs the optimal location information technology.

[0113] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0114] A hospital search system can analyze a user's past hospital visit history and suggest the most suitable hospital. For example, if a user has previously used a specific hospital, the system can suggest the most suitable hospital for similar symptoms based on the hospital's rating and treatment details. It can also suggest the most suitable hospital by considering the congestion and operating hours of hospitals the user has previously visited. Furthermore, it can analyze a user's past hospital visit history and suggest the most suitable hospital for a specific time of day. In this way, the system can suggest the most suitable hospital based on past visit history.

[0115] A hospital search system can monitor a user's current health status and suggest the most suitable hospital. For example, it can acquire vital data such as the user's heart rate and blood pressure in real time and suggest the best hospital based on that data. It can also quickly suggest the best hospital if the user's health status suddenly changes. Furthermore, it can monitor the user's health status over the long term and suggest hospitals that provide regular health checkups. This allows for the suggestion of the most suitable hospital based on the user's current health status.

[0116] A hospital search system can estimate a user's emotions and provide hospital ratings based on those emotions. For example, if a user is feeling anxious, it can prioritize displaying ratings for highly reliable hospitals. If the user is relaxed, it can provide detailed rating information. Furthermore, if the user is in a hurry, it can provide concise rating information. This allows for the provision of hospital ratings tailored to the user's emotions.

[0117] A hospital search system can suggest the optimal route to a hospital, taking into account the user's geographical location. For example, it can suggest the shortest route from the user's current location to the nearest hospital. It can also suggest the most efficient route, taking traffic conditions into consideration. Furthermore, if the user is using a specific mode of transportation, it can suggest the most suitable route for that mode. In this way, it can suggest the optimal route to a hospital based on geographical location information.

[0118] A hospital search system can estimate a user's emotions and provide hospital congestion information based on those emotions. For example, if a user is feeling anxious, it can prioritize displaying less crowded hospitals. If a user is relaxed, it can provide detailed congestion information. Furthermore, if a user is in a hurry, it can display the congestion information concisely. This allows for the provision of congestion information tailored to the user's emotions.

[0119] A hospital search system can analyze a user's past search history and suggest the most suitable hospital. For example, it can suggest the best hospital for similar symptoms based on the ratings and treatment details of hospitals the user has previously searched for. It can also suggest hospitals that are less crowded based on the user's past search history. Furthermore, it can analyze the user's past search history and suggest the best hospital for a specific time of day. In this way, it can suggest the most suitable hospital based on past search history.

[0120] A hospital search system can estimate a user's emotions and provide information on consultation hours based on those emotions. For example, if a user is feeling anxious, it can prioritize displaying hospitals with longer consultation hours. If a user is relaxed, it can provide detailed consultation hour information. Furthermore, if a user is in a hurry, it can display consultation hours concisely. This allows for the provision of consultation hour information tailored to the user's emotions.

[0121] A hospital search system can analyze a user's social media activity and suggest relevant hospitals. For example, it can suggest the most suitable hospital based on health information shared by the user on social media. It can also analyze the content of the user's social media posts and suggest hospitals relevant to their current health condition. Furthermore, it can suggest the most suitable hospital based on hospital information shared by the user's social media friends. In this way, it can suggest relevant hospitals based on social media activity.

[0122] The hospital search system can estimate the user's emotions and suggest hospital specialties based on those emotions. For example, if the user is feeling anxious, it can prioritize displaying highly reliable specialties. If the user is relaxed, it can provide detailed information about specialties. Furthermore, if the user is in a hurry, it can display only the main specialties. This allows the system to provide medical specialty information tailored to the user's emotions.

[0123] A hospital search system can suggest the most suitable hospital's consultation hours, taking into account the user's geographical location. For example, it can prioritize displaying the consultation hours of the hospital closest to the user's current location. It can also suggest the most suitable consultation hours based on the user's geographical location. Furthermore, if the user is in a specific region, it can suggest the most suitable hospital considering the consultation hours of that region. In this way, it can suggest the most suitable consultation hours based on geographical location information.

[0124] The following briefly describes the processing flow for example form 2.

[0125] Step 1: The reception desk accepts input of symptoms of a sudden illness. Symptoms of a sudden illness include, but are not limited to, heart attacks, strokes, and allergic reactions. The reception desk accepts, for example, the user entering symptoms such as "I have a chest ache" or "I have a headache." The reception desk can also provide multiple input methods, such as voice input and text input. For example, the reception desk can use voice recognition technology to convert the symptoms dictated by the user into text data. Step 2: The search unit searches for hospitals based on the symptoms received by the reception unit. For example, the search unit identifies hospitals with specialized departments based on the symptoms. For example, for the symptom "chest pain," the search unit searches for hospitals specializing in cardiology. The search unit can also optimize search results by considering the hospital's specialization and facilities. For example, the search unit suggests the most suitable hospital based on the hospital's medical departments and types of medical equipment. Step 3: The identification unit identifies the user's current location from among the hospitals found by the search unit. The identification unit obtains the current location, for example, by using the GPS function of the user's smartphone. This provides the identification unit with the user's location information. The identification unit can also improve the accuracy of the location information by using Wi-Fi location information and cell tower information in combination. For example, the identification unit combines GPS data and Wi-Fi location information to determine the current location more accurately. Step 4: The provisioning unit provides information on the nearest hospital from the current location identified by the identification unit. The provisioning unit provides information such as the address, phone number, and medical department of the nearest hospital from the user's current location. The provisioning unit can also suggest the most suitable hospital, taking into account the hospital's congestion level and operating hours. For example, the provisioning unit suggests a hospital to avoid congestion based on real-time congestion data. This allows the hospital search system according to the embodiment to quickly find an appropriate medical institution in case of an emergency. Some or all of the above processing in the provisioning unit may be performed using AI, for example, or without AI. For example, the provisioning unit can provide hospital information using an AI model that takes hospital information as input and outputs the most suitable hospital.

[0126] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0127] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0128] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0129] Each of the multiple elements described above, including the reception unit, search unit, identification unit, provision unit, congestion status acquisition unit, consultation time acquisition unit, detailed reception unit, and location information technology unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives the user's symptom input. The search unit is implemented by the identification processing unit 290 of the data processing unit 12 and searches for a hospital based on the entered symptoms. The identification unit uses the GPS function of the smart device 14 to determine the current location. The provision unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides information on the nearest hospital. The congestion status acquisition unit is implemented by the identification processing unit 290 of the data processing unit 12 and acquires the congestion status of the hospital. The consultation time acquisition unit is implemented by the identification processing unit 290 of the data processing unit 12 and acquires the consultation time of the hospital. The detailed reception unit is implemented by the control unit 46A of the smart device 14 and receives detailed symptom input. The location information technology unit determines the current location by using the GPS function and Wi-Fi location information of the smart device 14 in combination. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0130] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0131] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the reception unit, search unit, identification unit, provision unit, congestion status acquisition unit, consultation time acquisition unit, detailed reception unit, and location information technology unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives the user's symptom input. The search unit is implemented by the identification processing unit 290 of the data processing device 12 and searches for a hospital based on the entered symptoms. The identification unit uses the GPS function of the smart glasses 214 to determine the current location. The provision unit is implemented by the identification processing unit 290 of the data processing device 12 and provides information on the nearest hospital. The congestion status acquisition unit is implemented by the identification processing unit 290 of the data processing device 12 and acquires the congestion status of the hospital. The consultation time acquisition unit is implemented by the identification processing unit 290 of the data processing device 12 and acquires the consultation time of the hospital. The detailed reception unit is implemented by the control unit 46A of the smart glasses 214 and receives detailed symptom input. The location information technology unit determines the current location by using the GPS function and Wi-Fi location information of the smart glasses 214 in combination. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0146] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0147] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the reception unit, search unit, identification unit, provision unit, congestion status acquisition unit, consultation time acquisition unit, detailed reception unit, and location information technology unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives the user's symptom input. The search unit is implemented by the identification processing unit 290 of the data processing unit 12 and searches for a hospital based on the entered symptoms. The identification unit uses the GPS function of the headset terminal 314 to determine the current location. The provision unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides information on the nearest hospital. The congestion status acquisition unit is implemented by the identification processing unit 290 of the data processing unit 12 and acquires the congestion status of the hospital. The consultation time acquisition unit is implemented by the identification processing unit 290 of the data processing unit 12 and acquires the consultation time of the hospital. The detailed reception unit is implemented by the control unit 46A of the headset terminal 314 and receives detailed symptom input. The location information technology unit determines the current location by using both the GPS function and Wi-Fi location information of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0163] As shown in Figure 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.

[0164] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0165] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0166] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0167] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0168] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0169] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0170] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0171] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0172] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0173] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0174] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0175] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0176] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0177] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0178] Each of the multiple elements described above, including the reception unit, search unit, identification unit, provision unit, congestion status acquisition unit, consultation time acquisition unit, detailed reception unit, and location information technology unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives the user's symptom input. The search unit is implemented by the identification processing unit 290 of the data processing unit 12 and searches for a hospital based on the input symptoms. The identification unit uses the GPS function of the robot 414 to determine the current location. The provision unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides information on the nearest hospital. The congestion status acquisition unit is implemented by the identification processing unit 290 of the data processing unit 12 and acquires the congestion status of the hospital. The consultation time acquisition unit is implemented by the identification processing unit 290 of the data processing unit 12 and acquires the consultation time of the hospital. The detailed reception unit is implemented by the control unit 46A of the robot 414 and receives detailed symptom input. The location information technology unit determines the robot 414's current location using both its GPS function and Wi-Fi location information. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0179] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0180] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0181] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0182] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0183] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0184] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0185] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0186] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0187] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0188] 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.

[0189] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0190] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0191] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0192] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0193] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0194] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0195] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0196] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0197] (Note 1) The reception area accepts entries of sudden illness symptoms, A search unit that searches for hospitals based on the symptoms received by the reception unit, A search unit identifies the user's current location from among the hospitals found by the search unit, The system includes a provisioning unit that provides information on the nearest hospital from the current location identified by the aforementioned specific unit. A system characterized by the following features. (Note 2) It includes a congestion status acquisition unit that acquires the congestion status of the hospital. The system described in Appendix 1, characterized by the features described herein. (Note 3) It is equipped with a unit for obtaining consultation time. The system described in Appendix 1, characterized by the features described herein. (Note 4) It is equipped with a detailed information reception section that accepts detailed input of symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 5) It includes a location information technology unit that uses several location information technologies in combination. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the symptom input interface based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It analyzes the user's past symptom input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering symptoms, the system completes the input based on the user's current health status and past medical history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of inputs based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering symptoms, the system prioritizes the input of symptoms that are most relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When a user enters their symptoms, the system analyzes their social media activity and suggests relevant symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned search unit, It estimates the user's sentiment and adjusts how search results are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned search unit, When searching based on symptoms, we refer to your past search history to suggest the most suitable hospital. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned search unit, When searching based on symptoms, search results are optimized based on the hospital's specialization and facilities. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned search unit, It estimates the user's emotions and determines the priority of search results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned search unit, When searching based on symptoms, the system suggests the most suitable hospital considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned search unit, When users search based on symptoms, the system analyzes their social media activity and suggests relevant hospitals. The system described in Appendix 1, characterized by the features described herein. (Note 18) The specified part is, It estimates the user's emotions and adjusts the accuracy of location identification based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The specified part is, When determining the current location, the system selects the optimal location method by referring to the user's past location history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The specified part is, When determining the current location, the accuracy of location information is improved by taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The specified part is, It estimates the user's emotions and determines the priority for location identification based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The specified part is, When determining the current location, the system selects the optimal location method based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The specified part is, When the user's current location is determined, their social media activity is analyzed and relevant location information is obtained. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, The system estimates the user's emotions and adjusts how hospital information is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing hospital information, we refer to the user's past hospital usage history to provide the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing hospital information, we optimize the information by taking into account hospital ratings and user reviews. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, The system estimates user sentiment and prioritizes hospital information based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing hospital information, we provide the most relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing hospital information, we analyze the user's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned congestion status acquisition unit, The system estimates the user's emotions and adjusts how congestion information is obtained based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned congestion status acquisition unit, When acquiring congestion status, past congestion data is referenced to predict the current congestion status. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned congestion status acquisition unit, The system estimates user sentiment and prioritizes congestion levels based on the estimated sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned congestion status acquisition unit, When obtaining congestion status, the system obtains the optimal congestion status based on the user's geographical location information. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned medical consultation time acquisition unit, The system estimates the user's emotions and adjusts how consultation times are obtained based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned medical consultation time acquisition unit, When obtaining consultation time data, past consultation time data is used to predict the current consultation time. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned medical consultation time acquisition unit, The system estimates the user's emotions and prioritizes consultation times based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned medical consultation time acquisition unit, When obtaining appointment times, the system will acquire the optimal appointment time based on the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned detailed reception unit is, It estimates the user's emotions and adjusts the detailed input interface based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned detailed reception unit is, When users enter detailed information, the system will refer to their past symptom entry history to suggest the most suitable input method. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned detailed reception unit is, The system estimates the user's emotions and prioritizes detailed input based on those emotions. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned detailed reception unit is, When users enter detailed information, the system prioritizes entering relevant details based on their geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned location information technology unit, The system estimates the user's emotions and selects location-based technologies based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 43) The aforementioned location information technology unit, When selecting location information technology, the optimal technology is chosen by referring to past location information data. The system described in Appendix 5, characterized by the features described herein. (Note 44) The aforementioned location information technology unit, It estimates the user's emotions and prioritizes location-based technologies based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 45) The aforementioned location information technology unit, When selecting location-based technologies, the optimal technology is selected based on the user's device information. The system described in Appendix 5, characterized by the features described herein. [Explanation of symbols]

[0198] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception area accepts entries of sudden illness symptoms, A search unit that searches for hospitals based on the symptoms received by the reception unit, A search unit identifies the user's current location from among the hospitals found by the search unit, The system includes a provisioning unit that provides information on the nearest hospital from the current location identified by the aforementioned specific unit. A system characterized by the following features.

2. It includes a congestion status acquisition unit that acquires the congestion status of the hospital. The system according to feature 1.

3. It is equipped with a unit for obtaining consultation time. The system according to feature 1.

4. It is equipped with a detailed information reception section that accepts detailed input of symptoms. The system according to feature 1.

5. It includes a location information technology unit that uses several location information technologies in combination. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the symptom input interface based on the estimated user emotions. The system according to feature 1.

7. The aforementioned reception unit is It analyzes the user's past symptom input history and suggests the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is When entering symptoms, the system completes the input based on the user's current health status and past medical history. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and determines the priority of inputs based on the estimated user emotions. The system according to feature 1.

10. The aforementioned reception unit is When entering symptoms, the system prioritizes the input of symptoms that are most relevant based on the user's geographical location. The system according to feature 1.

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