system

Abstract

The system according to this embodiment aims to reduce waiting times at hospitals and alleviate patient stress. [Solution] The system according to the embodiment comprises a reception unit, a determination unit, a generation unit, and a provision unit. The reception unit receives input of symptoms. The determination unit determines the necessary tests and treatments based on the information received by the reception unit. The generation unit creates a diagnosis and treatment plan based on the information determined by the determination unit. The provision unit provides the diagnosis and treatment plan created by the generation 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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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 conventional technology, there is a problem that the waiting time in a hospital is long and stressful for patients.

[0005] The system according to the embodiment aims to reduce the waiting time in a hospital and relieve the stress of patients.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a determination unit, a generation unit, and a provision unit. The reception unit receives an input of symptoms. The determination unit determines necessary examinations and treatments based on the information received by the reception unit. The generation unit creates a diagnosis and a treatment plan based on the information determined by the determination unit. The provision unit provides the diagnosis and the treatment plan created by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can reduce waiting times at hospitals and alleviate patient stress. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 system according to an embodiment of the present invention is a multimodal generating AI "timefree" designed to reduce hospital waiting times and alleviate patient stress. This system consists of the following steps: First, it provides a simple diagnostic AI service at home, where the patient simply inputs their symptoms at home to determine the necessary tests and treatments. If the symptoms are mild, it generates home remedies and prescriptions for medication; if the symptoms are severe, it recommends the most suitable hospital and department. Next, it provides a primary care AI service for outpatient visits, where the patient inputs their symptoms using a smartphone or tablet upon arrival at the hospital, and the generating AI "timefree" creates a diagnosis and treatment plan based on that information. Finally, it provides ongoing support after consultation, with the generating AI "timefree" continuing to provide voice reminders for medication and lifestyle advice even after the consultation. For example, it provides a simple diagnostic AI service at home. The patient simply inputs their symptoms at home, and the generating AI "timefree" determines the necessary tests and treatments. For example, if the patient has cold symptoms, the generating AI "timefree" suggests home remedies and prescriptions for over-the-counter medications. If the symptoms are severe, such as a high fever or severe pain, the generating AI "timefree" recommends the most suitable hospital and department. This will help alleviate hospital congestion and reduce waiting times. Next, we will provide an AI-powered primary care service for outpatient visits. When a patient arrives at the hospital, they input their symptoms using a smartphone or tablet, and the generating AI "timefree" creates a diagnosis and treatment plan based on that information. For example, if a patient inputs a headache, the generating AI "timefree" analyzes the information and suggests possible diagnoses and treatment plans. This information is sent to the doctor, who uses it as a reference during consultation. This will streamline the doctor's work and shorten consultation times. Furthermore, we will provide ongoing support after consultation. Even after consultation, the generating AI "timefree" will provide voice reminders for medication and lifestyle advice. For example, it will remind patients to take their prescribed medication and provide advice on healthy eating and exercise. This will help prevent recurrence and improve the patient's health. In this way, using the generating AI "timefree" can reduce hospital waiting times and alleviate patient stress.Patients can undergo a preliminary assessment at home, visit the hospital only if necessary, and utilize an AI-powered primary care service to streamline their treatment during their visits. They can also receive ongoing support after their consultations. This can alleviate hospital congestion and improve patient satisfaction. As a result, the system can reduce hospital waiting times and lessen patient stress.

[0029] The system according to this embodiment comprises a reception unit, a determination unit, a generation unit, and a provision unit. The reception unit receives input of symptoms. For example, the reception unit accepts symptom input from a patient at home. Symptoms can be entered via a smartphone app or website. The determination unit determines the necessary tests and treatments based on the information received by the reception unit. For example, if the symptoms are mild, the determination unit generates home remedies and prescriptions for medication. If the symptoms are severe, the determination unit recommends the most suitable hospital or department. The generation unit creates diagnoses and treatment plans based on the information determined by the determination unit. For example, the generation unit accepts symptom input from a patient upon arrival at the hospital and creates diagnoses and treatment plans based on that information. The generation unit creates diagnoses and treatment plans using generation AI. The provision unit provides the diagnoses and treatment plans created by the generation unit. For example, the provision unit provides voice reminders for medication and lifestyle advice after the consultation. As a result, the system can reduce hospital waiting times and alleviate patient stress.

[0030] The reception desk accepts symptom entries. For example, it accepts symptom entries from patients at home. Specifically, the reception desk allows symptom entries via a smartphone app or website. The smartphone app provides a user-friendly interface, making it easy for patients to enter their symptoms. For example, it includes not only text input but also voice input and image upload functions, allowing patients to communicate their symptoms in detail. The website similarly provides an intuitive interface and is accessible from PCs and tablets. Furthermore, the reception desk automatically categorizes the entered data and processes it appropriately according to the type and urgency of the symptoms. For example, if the entered symptoms require urgent attention, it immediately notifies the assessment department to encourage a quick response. The reception desk also collects the patient's past medical history and allergy information, using this information to support more accurate diagnosis and treatment plan development. In this way, the reception desk can provide an environment where patients can easily and quickly enter their symptoms from home, improving the overall efficiency of the system.

[0031] The diagnostic unit determines the necessary tests and treatments based on the information received by the reception unit. Specifically, the diagnostic unit uses AI to analyze the entered symptoms and determine the appropriate response. For example, if the symptoms are mild, it generates home care instructions and medication prescriptions. The AI ​​refers to past data and medical guidelines to suggest the optimal course of action. If the symptoms are severe, it refers the patient to the most suitable hospital or department. The AI ​​selects the most suitable medical institution considering the patient's current location, hospital congestion, and specialist schedules. The diagnostic unit can also assess the urgency of the symptoms and, if necessary, arrange for an ambulance or notify emergency contacts. Furthermore, the diagnostic unit considers the patient's past medical history and allergy information to suggest appropriate tests and treatments. For example, it suggests alternative medications for patients allergic to specific drugs. The diagnostic unit can also collect patient feedback to continuously improve the accuracy of the AI's diagnosis. As a result, the diagnostic unit can quickly and accurately determine the necessary tests and treatments and provide patients with the best possible medical services.

[0032] The generation unit creates diagnoses and treatment plans based on the information determined by the judgment unit. Specifically, the generation unit uses a generation AI to create diagnoses and treatment plans. The generation AI generates optimal diagnoses and treatment plans based on the input symptoms, the patient's medical history, and information from the judgment unit. For example, when a patient arrives at the hospital, they input their symptoms, and the generation unit creates a diagnosis and treatment plan based on that information. The generation AI refers to the latest medical guidelines and research data to propose the most suitable treatment method. The generation unit also provides the generated diagnoses and treatment plans to physicians, supporting them in making final decisions. Furthermore, the generation unit records the process of generating diagnoses and treatment plans so that they can be used later for verification and improvement. This allows the generation unit to create diagnoses and treatment plans quickly and accurately, providing patients with the best possible medical services.

[0033] The service provider delivers diagnoses and treatment plans created by the generation provider. Specifically, it provides voice reminders for medication and lifestyle advice after consultations. The service provider notifies patients of diagnosis results and treatment plans, for example, through smartphone apps and websites. Using a voice assistant, it reminds patients of medication timing and precautions, supporting them in continuing treatment appropriately. It also provides advice on improving lifestyle habits to support patients in maintaining their health. Furthermore, the service provider can collect feedback from patients and continuously improve the accuracy and effectiveness of diagnoses and treatment plans. For example, it records the progress and changes in symptoms after patients receive treatment and updates the generation AI algorithm based on this data. In addition, the service provider can reliably transmit information using multiple communication methods. For example, it uses not only smartphone notifications but also voice calls, SMS, and email to reliably deliver important information. This allows the service provider to provide patients with diagnoses and treatment plans quickly and reliably, maximizing the effectiveness of treatment.

[0034] The reception desk can accept patients entering their symptoms from home. For example, the reception desk can accept patients entering their symptoms from home using a smartphone app. The reception desk can also accept patients entering their symptoms from home via a website. The reception desk can even use AI to accept patients entering their symptoms from home. This allows patients to enter their symptoms from home.

[0035] The diagnostic unit can generate home remedies and medication prescriptions for mild symptoms. For example, if the fever is below 37.5 degrees Celsius, the unit will suggest hydration and rest. For mild coughs, the unit can also suggest over-the-counter medication prescriptions. The diagnostic unit can also use AI to generate remedies and medication prescriptions for mild symptoms. This ensures that home remedies and medication prescriptions are provided for mild symptoms.

[0036] The diagnostic unit can refer patients to the most suitable hospitals and departments if their symptoms are severe. For example, if a patient has a fever of 39 degrees Celsius or higher, the unit will refer them to a hospital with specialists. If a patient is experiencing severe pain, the unit can also refer them to a well-equipped hospital. The diagnostic unit can also use AI to recommend the most suitable hospitals and departments for severe symptoms. This ensures that patients with severe symptoms are referred to the most appropriate hospitals and departments.

[0037] The generation unit accepts symptom input from patients upon arrival at the hospital and can create diagnoses and treatment plans based on that information. For example, the generation unit can accept symptom input from patients upon arrival at the hospital using a reception terminal. The generation unit can also accept symptom input from patients upon arrival at the hospital using a mobile app. The generation unit can also use generation AI to accept symptom input from patients upon arrival at the hospital and create diagnoses and treatment plans based on that information. This allows patients to input their symptoms upon arrival at the hospital and generate diagnoses and treatment plans.

[0038] The service provider can provide medication reminders and lifestyle advice via voice after a consultation. For example, the service provider can use voice alerts to remind patients to take their medication. It can also use text messages to remind patients to take their medication. The service provider can also use AI to provide medication reminders and lifestyle advice via voice after a consultation. This ensures that medication reminders and lifestyle advice are provided after the consultation.

[0039] The reception desk can analyze a patient's past symptom input history and suggest the optimal input method. For example, the reception desk can automatically display symptoms that the patient has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the patient has used in the past. The reception desk can predict and suggest symptoms that will be used during specific time periods based on the patient's past input history. The reception desk can also use AI to analyze a patient's past symptom input history and suggest the optimal input method. This ensures that the optimal input method is suggested based on the patient's past symptom input history.

[0040] The reception system can customize input fields based on the patient's current lifestyle and health status when symptoms are entered. For example, when a patient enters their current lifestyle, the reception system prioritizes displaying relevant symptoms based on that information. The reception system can also automatically adjust the necessary input fields according to the patient's health status. The reception system suggests relevant symptoms as input fields based on the patient's lifestyle (diet, exercise, etc.). The reception system can also use AI to customize input fields based on the patient's current lifestyle and health status when symptoms are entered. This ensures that input fields are customized according to the patient's lifestyle and health status.

[0041] The reception system can prioritize the input of highly relevant symptoms by considering the patient's geographical location when symptoms are entered. For example, if the patient is in a specific region, the reception system will prioritize the input of symptoms of diseases prevalent in that region. The reception system can also input symptoms that take into account region-specific health risks based on the patient's location. If the patient is traveling, the reception system will input symptoms based on the health risks of the travel destination. The reception system can also use AI to prioritize the input of highly relevant symptoms by considering the patient's geographical location when symptoms are entered. As a result, symptoms that are highly relevant based on the patient's geographical location will be prioritized.

[0042] The reception desk can analyze the patient's social media activity when symptoms are entered and input related symptoms. For example, the reception desk can extract information about recent health problems from the patient's social media posts and input related symptoms. The reception desk can also extract information about stress and anxiety from the patient's social media activity and input related symptoms. The reception desk can extract information about lifestyle habits from the patient's social media activity and input related symptoms. The reception desk can also use AI to analyze the patient's social media activity when symptoms are entered and input related symptoms. This ensures that related symptoms are entered based on the patient's social media activity.

[0043] The assessment unit can adjust the level of detail in its assessment based on the severity of the symptoms. For example, it provides detailed assessment results for important symptoms. It can also provide concise assessment results for minor symptoms. The assessment unit adjusts the level of detail in its assessment results in stages according to the severity of the symptoms. The assessment unit can also use AI to adjust the level of detail in its assessment based on the severity of the symptoms. This ensures that the level of detail in the assessment is adjusted according to the severity of the symptoms.

[0044] The diagnostic unit can apply different diagnostic algorithms depending on the symptom category during the diagnostic process. For example, the diagnostic unit applies a dedicated diagnostic algorithm to respiratory system symptoms. The diagnostic unit can also apply a dedicated diagnostic algorithm to digestive system symptoms. The diagnostic unit applies a dedicated diagnostic algorithm to neurological system symptoms. The diagnostic unit can also use AI to apply different diagnostic algorithms depending on the symptom category during the diagnostic process. This ensures that different diagnostic algorithms are applied depending on the symptom category.

[0045] The judgment unit can determine the priority of judgments based on the timing of symptom onset. For example, the judgment unit may prioritize recently occurring symptoms. The judgment unit can also prioritize symptoms that have persisted for a long time. The judgment unit adjusts the priority of judgments in stages according to the timing of symptom onset. The judgment unit can also use AI to determine the priority of judgments based on the timing of symptom onset. This ensures that the priority of judgments is determined according to the timing of symptom onset.

[0046] The judgment unit can adjust the order of judgments based on the relevance of the symptoms during the judgment process. For example, the judgment unit may prioritize the judgment of highly relevant symptoms. The judgment unit may also postpone the judgment of less relevant symptoms. The judgment unit adjusts the order of judgments in stages according to the relevance of the symptoms. The judgment unit can also use AI to adjust the order of judgments based on the relevance of the symptoms during the judgment process. This adjusts the order of judgments according to the relevance of the symptoms.

[0047] The generation unit can adjust the level of detail generated based on the severity of the symptoms when generating diagnoses and treatment plans. For example, the generation unit provides detailed diagnoses and treatment plans for important symptoms. The generation unit can also provide concise diagnoses and treatment plans for minor symptoms. The generation unit adjusts the level of detail of diagnoses and treatment plans in stages according to the severity of the symptoms. The generation unit can also use AI to adjust the level of detail generated based on the severity of the symptoms when generating diagnoses and treatment plans. This ensures that the level of detail of diagnoses and treatment plans is adjusted according to the severity of the symptoms.

[0048] The generation unit can apply different generation algorithms depending on the symptom category when generating diagnoses and treatment plans. For example, the generation unit applies a dedicated generation algorithm for respiratory symptoms. The generation unit can also apply a dedicated generation algorithm for digestive symptoms. The generation unit applies a dedicated generation algorithm for neurological symptoms. The generation unit can also use AI to apply different generation algorithms depending on the symptom category when generating diagnoses and treatment plans. This ensures that different generation algorithms are applied depending on the symptom category.

[0049] The generation unit can determine the priority of diagnosis and treatment plan generation based on the timing of symptom onset. For example, the generation unit can prioritize reflecting recently occurring symptoms in diagnosis and treatment plan. The generation unit can also prioritize reflecting long-lasting symptoms in diagnosis and treatment plan. The generation unit adjusts the priority of diagnosis and treatment plan in stages according to the timing of symptom onset. The generation unit can also use AI to determine the priority of diagnosis and treatment plan generation based on the timing of symptom onset. This ensures that the priority of diagnosis and treatment plan is determined according to the timing of symptom onset.

[0050] The generation unit can adjust the order of generation based on the relevance of symptoms when generating diagnoses and treatment plans. For example, the generation unit prioritizes reflecting highly relevant symptoms in diagnoses and treatment plans. The generation unit can also postpone less relevant symptoms. The generation unit adjusts the order of diagnoses and treatment plans in stages according to the relevance of symptoms. The generation unit can also use AI to adjust the order of generation based on the relevance of symptoms when generating diagnoses and treatment plans. This adjusts the order of diagnoses and treatment plans according to the relevance of symptoms.

[0051] The information provider can provide optimal information by referring to the patient's past medical history at the time of delivery. For example, the information provider can prioritize providing relevant information from the patient's past medical history. The information provider can also provide information for preventing recurrence based on the patient's past medical history. The information provider can refer to the patient's past medical history and provide individually customized information. The information provider can also use AI to refer to the patient's past medical history at the time of delivery and provide optimal information. This ensures that optimal information is provided based on the patient's past medical history.

[0052] The information delivery system can customize information based on the patient's current living situation at the time of delivery. For example, if the patient's current living situation is entered, the system will provide relevant information based on that. The system can also provide individually customized information based on the patient's lifestyle habits (diet, exercise, etc.). The system automatically adjusts and provides the necessary information according to the patient's current health condition. The system can also use AI to customize information based on the patient's current living situation at the time of delivery. This ensures that the information is customized according to the patient's living situation.

[0053] The information delivery system can provide optimal information by considering the patient's geographical location at the time of delivery. For example, if the patient is in a specific area, the system will prioritize providing information about diseases prevalent in that area. The system can also provide information about region-specific health risks based on the patient's location. If the patient is traveling, the system will provide information about health risks in their travel destination. The system can also use AI to provide optimal information by considering the patient's geographical location at the time of delivery. This ensures that the most relevant information is provided based on the patient's geographical location.

[0054] The service provider can analyze the patient's social media activity and provide relevant information at the time of delivery. For example, the service provider can extract information about recent health problems from the patient's social media posts and provide relevant information. The service provider can also extract information about stress and anxiety from the patient's social media activity and provide relevant information. The service provider can extract information about lifestyle habits from the patient's social media activity and provide relevant information. The service provider can also use AI to analyze the patient's social media activity and provide relevant information at the time of delivery. This ensures that relevant information is provided based on the patient's social media activity.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception desk can analyze a patient's past symptom input history and suggest the optimal input method. For example, it can automatically display symptoms that the patient has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the patient has used in the past (voice, text, etc.). Based on the patient's past input history, it can predict and suggest symptoms that will be used during specific time periods. This ensures that the optimal input method is suggested based on the patient's past symptom input history.

[0057] The reception system can customize input fields based on the patient's current lifestyle and health condition when they enter their symptoms. For example, when a patient enters their current lifestyle, the system prioritizes displaying relevant symptoms based on that information. It can also automatically adjust the necessary input fields according to the patient's health condition. Based on the patient's lifestyle (diet, exercise, etc.), it suggests relevant symptoms as input fields. This customizes the input fields according to the patient's lifestyle and health condition.

[0058] The assessment unit can adjust the level of detail in its assessment based on the severity of the symptoms. For example, it can provide detailed assessment results for important symptoms and concise results for minor symptoms. The level of detail in the assessment results is adjusted in stages according to the severity of the symptoms. This ensures that the level of detail in the assessment is adjusted according to the severity of the symptoms.

[0059] The generation unit can apply different generation algorithms depending on the symptom category when generating diagnoses and treatment plans. For example, a dedicated generation algorithm can be applied to respiratory symptoms, digestive symptoms, and neurological symptoms. This ensures that different generation algorithms are applied depending on the symptom category.

[0060] The information provider can provide optimal information by considering the patient's geographical location at the time of delivery. For example, if the patient is in a specific area, information about diseases prevalent in that area will be prioritized. Information about region-specific health risks can also be provided based on the patient's location. If the patient is traveling, information about health risks in their travel destination will be provided. This ensures that optimal information is provided based on the patient's geographical location.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk accepts symptom entries. For example, patients can enter their symptoms at home via a smartphone app or website. Step 2: The assessment unit determines the necessary tests and treatments based on the information received by the reception unit. For example, if the symptoms are mild, it will generate home care instructions and prescriptions for medication; if the symptoms are severe, it will refer the patient to the most suitable hospital or medical department. Step 3: The generation unit creates diagnoses and treatment plans based on the information determined by the judgment unit. For example, when a patient arrives at the hospital, their symptoms are entered, and the generation AI is used to create diagnoses and treatment plans based on that information. Step 4: The delivery unit provides the diagnosis and treatment plan created by the generation unit. For example, it provides audio reminders about medication and lifestyle advice after the consultation.

[0063] (Example of form 2) The system according to an embodiment of the present invention is a multimodal generating AI "timefree" designed to reduce hospital waiting times and alleviate patient stress. This system consists of the following steps: First, it provides a simple diagnostic AI service at home, where the patient simply inputs their symptoms at home to determine the necessary tests and treatments. If the symptoms are mild, it generates home remedies and prescriptions for medication; if the symptoms are severe, it recommends the most suitable hospital and department. Next, it provides a primary care AI service for outpatient visits, where the patient inputs their symptoms using a smartphone or tablet upon arrival at the hospital, and the generating AI "timefree" creates a diagnosis and treatment plan based on that information. Finally, it provides ongoing support after consultation, with the generating AI "timefree" continuing to provide voice reminders for medication and lifestyle advice even after the consultation. For example, it provides a simple diagnostic AI service at home. The patient simply inputs their symptoms at home, and the generating AI "timefree" determines the necessary tests and treatments. For example, if the patient has cold symptoms, the generating AI "timefree" suggests home remedies and prescriptions for over-the-counter medications. If the symptoms are severe, such as a high fever or severe pain, the generating AI "timefree" recommends the most suitable hospital and department. This will help alleviate hospital congestion and reduce waiting times. Next, we will provide an AI-powered primary care service for outpatient visits. When a patient arrives at the hospital, they input their symptoms using a smartphone or tablet, and the generating AI "timefree" creates a diagnosis and treatment plan based on that information. For example, if a patient inputs a headache, the generating AI "timefree" analyzes the information and suggests possible diagnoses and treatment plans. This information is sent to the doctor, who uses it as a reference during consultation. This will streamline the doctor's work and shorten consultation times. Furthermore, we will provide ongoing support after consultation. Even after consultation, the generating AI "timefree" will provide voice reminders for medication and lifestyle advice. For example, it will remind patients to take their prescribed medication and provide advice on healthy eating and exercise. This will help prevent recurrence and improve the patient's health. In this way, using the generating AI "timefree" can reduce hospital waiting times and alleviate patient stress.Patients can undergo a preliminary assessment at home, visit the hospital only if necessary, and utilize an AI-powered primary care service to streamline their treatment during their visits. They can also receive ongoing support after their consultations. This can alleviate hospital congestion and improve patient satisfaction. As a result, the system can reduce hospital waiting times and lessen patient stress.

[0064] The system according to this embodiment comprises a reception unit, a determination unit, a generation unit, and a provision unit. The reception unit receives input of symptoms. For example, the reception unit accepts symptom input from a patient at home. Symptoms can be entered via a smartphone app or website. The determination unit determines the necessary tests and treatments based on the information received by the reception unit. For example, if the symptoms are mild, the determination unit generates home remedies and prescriptions for medication. If the symptoms are severe, the determination unit recommends the most suitable hospital or department. The generation unit creates diagnoses and treatment plans based on the information determined by the determination unit. For example, the generation unit accepts symptom input from a patient upon arrival at the hospital and creates diagnoses and treatment plans based on that information. The generation unit creates diagnoses and treatment plans using generation AI. The provision unit provides the diagnoses and treatment plans created by the generation unit. For example, the provision unit provides voice reminders for medication and lifestyle advice after the consultation. As a result, the system can reduce hospital waiting times and alleviate patient stress.

[0065] The reception desk accepts symptom entries. For example, it accepts symptom entries from patients at home. Specifically, the reception desk allows symptom entries via a smartphone app or website. The smartphone app provides a user-friendly interface, making it easy for patients to enter their symptoms. For example, it includes not only text input but also voice input and image upload functions, allowing patients to communicate their symptoms in detail. The website similarly provides an intuitive interface and is accessible from PCs and tablets. Furthermore, the reception desk automatically categorizes the entered data and processes it appropriately according to the type and urgency of the symptoms. For example, if the entered symptoms require urgent attention, it immediately notifies the assessment department to encourage a quick response. The reception desk also collects the patient's past medical history and allergy information, using this information to support more accurate diagnosis and treatment plan development. In this way, the reception desk can provide an environment where patients can easily and quickly enter their symptoms from home, improving the overall efficiency of the system.

[0066] The diagnostic unit determines the necessary tests and treatments based on the information received by the reception unit. Specifically, the diagnostic unit uses AI to analyze the entered symptoms and determine the appropriate response. For example, if the symptoms are mild, it generates home care instructions and medication prescriptions. The AI ​​refers to past data and medical guidelines to suggest the optimal course of action. If the symptoms are severe, it refers the patient to the most suitable hospital or department. The AI ​​selects the most suitable medical institution considering the patient's current location, hospital congestion, and specialist schedules. The diagnostic unit can also assess the urgency of the symptoms and, if necessary, arrange for an ambulance or notify emergency contacts. Furthermore, the diagnostic unit considers the patient's past medical history and allergy information to suggest appropriate tests and treatments. For example, it suggests alternative medications for patients allergic to specific drugs. The diagnostic unit can also collect patient feedback to continuously improve the accuracy of the AI's diagnosis. As a result, the diagnostic unit can quickly and accurately determine the necessary tests and treatments and provide patients with the best possible medical services.

[0067] The generation unit creates diagnoses and treatment plans based on the information determined by the judgment unit. Specifically, the generation unit uses a generation AI to create diagnoses and treatment plans. The generation AI generates optimal diagnoses and treatment plans based on the input symptoms, the patient's medical history, and information from the judgment unit. For example, when a patient arrives at the hospital, they input their symptoms, and the generation unit creates a diagnosis and treatment plan based on that information. The generation AI refers to the latest medical guidelines and research data to propose the most suitable treatment method. The generation unit also provides the generated diagnoses and treatment plans to physicians, supporting them in making final decisions. Furthermore, the generation unit records the process of generating diagnoses and treatment plans so that they can be used later for verification and improvement. This allows the generation unit to create diagnoses and treatment plans quickly and accurately, providing patients with the best possible medical services.

[0068] The service provider delivers diagnoses and treatment plans created by the generation provider. Specifically, it provides voice reminders for medication and lifestyle advice after consultations. The service provider notifies patients of diagnosis results and treatment plans, for example, through smartphone apps and websites. Using a voice assistant, it reminds patients of medication timing and precautions, supporting them in continuing treatment appropriately. It also provides advice on improving lifestyle habits to support patients in maintaining their health. Furthermore, the service provider can collect feedback from patients and continuously improve the accuracy and effectiveness of diagnoses and treatment plans. For example, it records the progress and changes in symptoms after patients receive treatment and updates the generation AI algorithm based on this data. In addition, the service provider can reliably transmit information using multiple communication methods. For example, it uses not only smartphone notifications but also voice calls, SMS, and email to reliably deliver important information. This allows the service provider to provide patients with diagnoses and treatment plans quickly and reliably, maximizing the effectiveness of treatment.

[0069] The reception desk can accept patients entering their symptoms from home. For example, the reception desk can accept patients entering their symptoms from home using a smartphone app. The reception desk can also accept patients entering their symptoms from home via a website. The reception desk can even use AI to accept patients entering their symptoms from home. This allows patients to enter their symptoms from home.

[0070] The diagnostic unit can generate home remedies and medication prescriptions for mild symptoms. For example, if the fever is below 37.5 degrees Celsius, the unit will suggest hydration and rest. For mild coughs, the unit can also suggest over-the-counter medication prescriptions. The diagnostic unit can also use AI to generate remedies and medication prescriptions for mild symptoms. This ensures that home remedies and medication prescriptions are provided for mild symptoms.

[0071] The diagnostic unit can refer patients to the most suitable hospitals and departments if their symptoms are severe. For example, if a patient has a fever of 39 degrees Celsius or higher, the unit will refer them to a hospital with specialists. If a patient is experiencing severe pain, the unit can also refer them to a well-equipped hospital. The diagnostic unit can also use AI to recommend the most suitable hospitals and departments for severe symptoms. This ensures that patients with severe symptoms are referred to the most appropriate hospitals and departments.

[0072] The generation unit accepts symptom input from patients upon arrival at the hospital and can create diagnoses and treatment plans based on that information. For example, the generation unit can accept symptom input from patients upon arrival at the hospital using a reception terminal. The generation unit can also accept symptom input from patients upon arrival at the hospital using a mobile app. The generation unit can also use generation AI to accept symptom input from patients upon arrival at the hospital and create diagnoses and treatment plans based on that information. This allows patients to input their symptoms upon arrival at the hospital and generate diagnoses and treatment plans.

[0073] The service provider can provide medication reminders and lifestyle advice via voice after a consultation. For example, the service provider can use voice alerts to remind patients to take their medication. It can also use text messages to remind patients to take their medication. The service provider can also use AI to provide medication reminders and lifestyle advice via voice after a consultation. This ensures that medication reminders and lifestyle advice are provided after the consultation.

[0074] The reception desk can estimate the patient's emotions and adjust the symptom input interface based on the estimated emotions. For example, if the patient is feeling anxious, the reception desk will provide a simple and intuitive interface and minimize the input steps. If the patient is relaxed, the reception desk may also provide detailed input options and suggest customizable input methods. If the patient is in a hurry, the reception desk will prioritize voice input to allow for quick symptom input. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the symptom input interface to be adjusted according to the patient's emotions.

[0075] The reception desk can analyze a patient's past symptom input history and suggest the optimal input method. For example, the reception desk can automatically display symptoms that the patient has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the patient has used in the past. The reception desk can predict and suggest symptoms that will be used during specific time periods based on the patient's past input history. The reception desk can also use AI to analyze a patient's past symptom input history and suggest the optimal input method. This ensures that the optimal input method is suggested based on the patient's past symptom input history.

[0076] The reception system can customize input fields based on the patient's current lifestyle and health status when symptoms are entered. For example, when a patient enters their current lifestyle, the reception system prioritizes displaying relevant symptoms based on that information. The reception system can also automatically adjust the necessary input fields according to the patient's health status. The reception system suggests relevant symptoms as input fields based on the patient's lifestyle (diet, exercise, etc.). The reception system can also use AI to customize input fields based on the patient's current lifestyle and health status when symptoms are entered. This ensures that input fields are customized according to the patient's lifestyle and health status.

[0077] The reception desk can estimate the patient's emotions and, based on the estimated emotions, determine the priority of symptoms to be entered. For example, if the patient is feeling anxious, the reception desk may prompt them to enter the most important symptoms first. If the patient is relaxed, the reception desk may also prompt them to enter detailed symptoms. If the patient is in a hurry, the reception desk may prompt them to enter only the main symptoms. 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. This determines the priority of symptoms to be entered according to the patient's emotions.

[0078] The reception system can prioritize the input of highly relevant symptoms by considering the patient's geographical location when symptoms are entered. For example, if the patient is in a specific region, the reception system will prioritize the input of symptoms of diseases prevalent in that region. The reception system can also input symptoms that take into account region-specific health risks based on the patient's location. If the patient is traveling, the reception system will input symptoms based on the health risks of the travel destination. The reception system can also use AI to prioritize the input of highly relevant symptoms by considering the patient's geographical location when symptoms are entered. As a result, symptoms that are highly relevant based on the patient's geographical location will be prioritized.

[0079] The reception desk can analyze the patient's social media activity when symptoms are entered and input related symptoms. For example, the reception desk can extract information about recent health problems from the patient's social media posts and input related symptoms. The reception desk can also extract information about stress and anxiety from the patient's social media activity and input related symptoms. The reception desk can extract information about lifestyle habits from the patient's social media activity and input related symptoms. The reception desk can also use AI to analyze the patient's social media activity when symptoms are entered and input related symptoms. This ensures that related symptoms are entered based on the patient's social media activity.

[0080] The judgment unit can estimate the patient's emotions and adjust the way the judgment result is expressed based on the estimated emotions. For example, if the patient is feeling anxious, the judgment unit provides a simple and reassuring expression. If the patient is relaxed, the judgment unit can also provide an expression that includes detailed information. If the patient is in a hurry, the judgment unit provides a concise expression that gets straight to the point. 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. This allows the expression of the judgment result to be adjusted according to the patient's emotions.

[0081] The assessment unit can adjust the level of detail in its assessment based on the severity of the symptoms. For example, it provides detailed assessment results for important symptoms. It can also provide concise assessment results for minor symptoms. The assessment unit adjusts the level of detail in its assessment results in stages according to the severity of the symptoms. The assessment unit can also use AI to adjust the level of detail in its assessment based on the severity of the symptoms. This ensures that the level of detail in the assessment is adjusted according to the severity of the symptoms.

[0082] The diagnostic unit can apply different diagnostic algorithms depending on the symptom category during the diagnostic process. For example, the diagnostic unit applies a dedicated diagnostic algorithm to respiratory system symptoms. The diagnostic unit can also apply a dedicated diagnostic algorithm to digestive system symptoms. The diagnostic unit applies a dedicated diagnostic algorithm to neurological system symptoms. The diagnostic unit can also use AI to apply different diagnostic algorithms depending on the symptom category during the diagnostic process. This ensures that different diagnostic algorithms are applied depending on the symptom category.

[0083] The assessment unit can estimate the patient's emotions and prioritize the assessment results based on the estimated emotions. For example, if the patient is feeling anxious, the assessment unit will prioritize displaying the most important assessment results. If the patient is relaxed, the assessment unit can also sequentially display detailed assessment results. If the patient is in a hurry, the assessment unit will prioritize displaying only the main assessment results. 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. This determines the priority of the assessment results according to the patient's emotions.

[0084] The judgment unit can determine the priority of judgments based on the timing of symptom onset. For example, the judgment unit may prioritize recently occurring symptoms. The judgment unit can also prioritize symptoms that have persisted for a long time. The judgment unit adjusts the priority of judgments in stages according to the timing of symptom onset. The judgment unit can also use AI to determine the priority of judgments based on the timing of symptom onset. This ensures that the priority of judgments is determined according to the timing of symptom onset.

[0085] The judgment unit can adjust the order of judgments based on the relevance of the symptoms during the judgment process. For example, the judgment unit may prioritize the judgment of highly relevant symptoms. The judgment unit may also postpone the judgment of less relevant symptoms. The judgment unit adjusts the order of judgments in stages according to the relevance of the symptoms. The judgment unit can also use AI to adjust the order of judgments based on the relevance of the symptoms during the judgment process. This adjusts the order of judgments according to the relevance of the symptoms.

[0086] The generation unit can estimate the patient's emotions and adjust the way diagnoses and treatment plans are presented based on the estimated emotions. For example, if the patient is feeling anxious, the generation unit provides a simple and reassuring presentation. If the patient is relaxed, the generation unit can also provide a presentation that includes detailed information. If the patient is in a hurry, the generation unit provides a concise presentation that gets straight to the point. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the presentation of diagnoses and treatment plans to be adjusted according to the patient's emotions.

[0087] The generation unit can adjust the level of detail generated based on the severity of the symptoms when generating diagnoses and treatment plans. For example, the generation unit provides detailed diagnoses and treatment plans for important symptoms. The generation unit can also provide concise diagnoses and treatment plans for minor symptoms. The generation unit adjusts the level of detail of diagnoses and treatment plans in stages according to the severity of the symptoms. The generation unit can also use AI to adjust the level of detail generated based on the severity of the symptoms when generating diagnoses and treatment plans. This ensures that the level of detail of diagnoses and treatment plans is adjusted according to the severity of the symptoms.

[0088] The generation unit can apply different generation algorithms depending on the symptom category when generating diagnoses and treatment plans. For example, the generation unit applies a dedicated generation algorithm for respiratory symptoms. The generation unit can also apply a dedicated generation algorithm for digestive symptoms. The generation unit applies a dedicated generation algorithm for neurological symptoms. The generation unit can also use AI to apply different generation algorithms depending on the symptom category when generating diagnoses and treatment plans. This ensures that different generation algorithms are applied depending on the symptom category.

[0089] The generation unit can estimate the patient's emotions and prioritize diagnoses and treatment plans based on those estimated emotions. For example, if the patient is feeling anxious, the generation unit will prioritize displaying the most important diagnoses and treatment plans. If the patient is relaxed, the generation unit can also sequentially display detailed diagnoses and treatment plans. If the patient is in a hurry, the generation unit will prioritize displaying only the main diagnoses and treatment plans. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the prioritization of diagnoses and treatment plans according to the patient's emotions.

[0090] The generation unit can determine the priority of diagnosis and treatment plan generation based on the timing of symptom onset. For example, the generation unit can prioritize reflecting recently occurring symptoms in diagnosis and treatment plan. The generation unit can also prioritize reflecting long-lasting symptoms in diagnosis and treatment plan. The generation unit adjusts the priority of diagnosis and treatment plan in stages according to the timing of symptom onset. The generation unit can also use AI to determine the priority of diagnosis and treatment plan generation based on the timing of symptom onset. This ensures that the priority of diagnosis and treatment plan is determined according to the timing of symptom onset.

[0091] The generation unit can adjust the order of generation based on the relevance of symptoms when generating diagnoses and treatment plans. For example, the generation unit prioritizes reflecting highly relevant symptoms in diagnoses and treatment plans. The generation unit can also postpone less relevant symptoms. The generation unit adjusts the order of diagnoses and treatment plans in stages according to the relevance of symptoms. The generation unit can also use AI to adjust the order of generation based on the relevance of symptoms when generating diagnoses and treatment plans. This adjusts the order of diagnoses and treatment plans according to the relevance of symptoms.

[0092] The service provider can estimate the patient's emotions and adjust the way the information is presented based on the estimated emotions. For example, if the patient is feeling anxious, the service provider can provide a simple and reassuring presentation. If the patient is relaxed, the service provider can also provide a presentation that includes detailed information. If the patient is in a hurry, the service provider can provide a concise presentation that gets straight to the point. 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. This allows the presentation of the information to be adjusted according to the patient's emotions.

[0093] The information provider can provide optimal information by referring to the patient's past medical history at the time of delivery. For example, the information provider can prioritize providing relevant information from the patient's past medical history. The information provider can also provide information for preventing recurrence based on the patient's past medical history. The information provider can refer to the patient's past medical history and provide individually customized information. The information provider can also use AI to refer to the patient's past medical history at the time of delivery and provide optimal information. This ensures that optimal information is provided based on the patient's past medical history.

[0094] The information delivery system can customize information based on the patient's current living situation at the time of delivery. For example, if the patient's current living situation is entered, the system will provide relevant information based on that. The system can also provide individually customized information based on the patient's lifestyle habits (diet, exercise, etc.). The system automatically adjusts and provides the necessary information according to the patient's current health condition. The system can also use AI to customize information based on the patient's current living situation at the time of delivery. This ensures that the information is customized according to the patient's living situation.

[0095] The information provider can estimate the patient's emotions and prioritize the information to be provided based on those emotions. For example, if the patient is feeling anxious, the provider will prioritize providing the most important information. If the patient is relaxed, the provider may also provide detailed information sequentially. If the patient is in a hurry, the provider will prioritize providing only the main information. 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. This determines the priority of the information to be provided according to the patient's emotions.

[0096] The information delivery system can provide optimal information by considering the patient's geographical location at the time of delivery. For example, if the patient is in a specific area, the system will prioritize providing information about diseases prevalent in that area. The system can also provide information about region-specific health risks based on the patient's location. If the patient is traveling, the system will provide information about health risks in their travel destination. The system can also use AI to provide optimal information by considering the patient's geographical location at the time of delivery. This ensures that the most relevant information is provided based on the patient's geographical location.

[0097] The service provider can analyze the patient's social media activity and provide relevant information at the time of delivery. For example, the service provider can extract information about recent health problems from the patient's social media posts and provide relevant information. The service provider can also extract information about stress and anxiety from the patient's social media activity and provide relevant information. The service provider can extract information about lifestyle habits from the patient's social media activity and provide relevant information. The service provider can also use AI to analyze the patient's social media activity and provide relevant information at the time of delivery. This ensures that relevant information is provided based on the patient's social media activity.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The reception desk can estimate the patient's emotions and adjust the symptom input interface based on the estimated emotions. For example, if the patient is feeling anxious, it can provide a simple and intuitive interface and minimize the input steps. If the patient is relaxed, it can provide detailed input options and suggest customizable input methods. If the patient is in a hurry, it can prioritize voice input to allow for quick symptom input. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. This allows the symptom input interface to be adjusted according to the patient's emotions.

[0100] The judgment unit can estimate the patient's emotions and adjust the way the judgment result is expressed based on the estimated emotions. For example, if the patient is feeling anxious, it can provide a simple and reassuring expression. If the patient is relaxed, it can provide an expression that includes detailed information. If the patient is in a hurry, it can provide a concise expression that gets straight to the point. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. This allows the expression of the judgment result to be adjusted according to the patient's emotions.

[0101] The generation unit can estimate the patient's emotions and adjust the way diagnoses and treatment plans are presented based on those estimated emotions. For example, if the patient is feeling anxious, it can provide simple and reassuring expressions. If the patient is relaxed, it can provide expressions that include detailed information. If the patient is in a hurry, it can provide concise and to-the-point expressions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. This allows the way diagnoses and treatment plans are presented to be adjusted according to the patient's emotions.

[0102] The information provider can estimate the patient's emotions and adjust the way the information is presented based on those estimated emotions. For example, if the patient is feeling anxious, it can provide a simple and reassuring presentation. If the patient is relaxed, it can provide a presentation that includes detailed information. If the patient is in a hurry, it can provide a concise presentation that gets straight to the point. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. This allows the presentation of the information to be adjusted according to the patient's emotions.

[0103] The information delivery unit can estimate the patient's emotions and prioritize the information to be delivered based on those emotions. For example, if the patient is feeling anxious, the most important information will be provided first. If the patient is relaxed, detailed information can be provided sequentially. If the patient is in a hurry, only the main information will be provided first. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. This determines the priority of the information to be delivered according to the patient's emotions.

[0104] The reception desk can analyze a patient's past symptom input history and suggest the optimal input method. For example, it can automatically display symptoms that the patient has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the patient has used in the past (voice, text, etc.). Based on the patient's past input history, it can predict and suggest symptoms that will be used during specific time periods. This ensures that the optimal input method is suggested based on the patient's past symptom input history.

[0105] The reception system can customize input fields based on the patient's current lifestyle and health condition when they enter their symptoms. For example, when a patient enters their current lifestyle, the system prioritizes displaying relevant symptoms based on that information. It can also automatically adjust the necessary input fields according to the patient's health condition. Based on the patient's lifestyle (diet, exercise, etc.), it suggests relevant symptoms as input fields. This customizes the input fields according to the patient's lifestyle and health condition.

[0106] The assessment unit can adjust the level of detail in its assessment based on the severity of the symptoms. For example, it can provide detailed assessment results for important symptoms and concise results for minor symptoms. The level of detail in the assessment results is adjusted in stages according to the severity of the symptoms. This ensures that the level of detail in the assessment is adjusted according to the severity of the symptoms.

[0107] The generation unit can apply different generation algorithms depending on the symptom category when generating diagnoses and treatment plans. For example, a dedicated generation algorithm can be applied to respiratory symptoms, digestive symptoms, and neurological symptoms. This ensures that different generation algorithms are applied depending on the symptom category.

[0108] The information provider can provide optimal information by considering the patient's geographical location at the time of delivery. For example, if the patient is in a specific area, information about diseases prevalent in that area will be prioritized. Information about region-specific health risks can also be provided based on the patient's location. If the patient is traveling, information about health risks in their travel destination will be provided. This ensures that optimal information is provided based on the patient's geographical location.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The reception desk accepts symptom entries. For example, patients can enter their symptoms at home via a smartphone app or website. Step 2: The assessment unit determines the necessary tests and treatments based on the information received by the reception unit. For example, if the symptoms are mild, it will generate home care instructions and prescriptions for medication; if the symptoms are severe, it will refer the patient to the most suitable hospital or medical department. Step 3: The generation unit creates diagnoses and treatment plans based on the information determined by the judgment unit. For example, when a patient arrives at the hospital, their symptoms are entered, and the generation AI is used to create diagnoses and treatment plans based on that information. Step 4: The delivery unit provides the diagnosis and treatment plan created by the generation unit. For example, it provides audio reminders about medication and lifestyle advice after the consultation.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the reception unit, determination unit, generation unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives the patient's input of symptoms at home. The determination unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and determines the necessary tests and treatments based on the information received by the reception unit. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and creates diagnoses and treatment plans based on the information determined by the determination unit. The provision unit is implemented, for example, by the output device 40 of the smart device 14 and provides voice reminders for medication and lifestyle advice after the consultation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the reception unit, determination unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives the patient's input of symptoms at home. The determination unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and determines the necessary tests and treatments based on the information received by the reception unit. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and creates diagnoses and treatment plans based on the information determined by the determination unit. The provision unit is implemented, for example, by the speaker 240 of the smart glasses 214 and provides voice reminders for medication and lifestyle advice after the consultation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the reception unit, judgment unit, generation unit, and provision 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 microphone 238 of the headset terminal 314 and receives the patient's input of symptoms at home. The judgment unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and determines the necessary tests and treatments based on the information received by the reception unit. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and creates diagnoses and treatment plans based on the information determined by the judgment unit. The provision unit is implemented by, for example, the speaker 240 of the headset terminal 314 and provides voice reminders for medication and lifestyle advice after the consultation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the reception unit, determination unit, generation unit, and provision 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 microphone 238 of the robot 414 and receives the patient's input of symptoms at home. The determination unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and determines the necessary tests and treatments based on the information received by the reception unit. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and creates diagnoses and treatment plans based on the information determined by the determination unit. The provision unit is implemented by, for example, the speaker 240 of the robot 414 and provides voice reminders for medication and lifestyle advice after the consultation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A reception area where symptoms are entered, A determination unit that determines the necessary tests and treatments based on the information received by the reception unit, A generation unit that creates a diagnosis and treatment plan based on the information determined by the determination unit, The system comprises a providing unit that provides diagnoses and treatment plans created by the generating unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is We accept patients to enter their symptoms at home. The system described in Appendix 1, characterized by the features described herein. (Note 3) The determination unit, If the symptoms are mild, we will generate information on how to manage them at home and prescribe medication. The system described in Appendix 1, characterized by the features described herein. (Note 4) The determination unit, If your symptoms are severe, we will refer you to the most suitable hospital or medical department. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is When a patient arrives at the hospital, they are asked to enter their symptoms, and this information is used to create a diagnosis and treatment plan. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, After your appointment, we will provide audio reminders about medication and lifestyle advice. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the patient's emotions and adjusts the symptom input interface based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze the patient's past symptom input history and suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering symptoms, the input fields are customized based on the patient's current living situation and health status. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the patient's emotions and determines the priority of symptoms to input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering symptoms, the system prioritizes entering symptoms that are highly relevant to the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering symptoms, the system analyzes the patient's social media activity and enters relevant symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 13) The determination unit, The system estimates the patient's emotions and adjusts the way the assessment results are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The determination unit, During the assessment, the level of detail is adjusted based on the severity of the symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 15) The determination unit, When making a diagnosis, different diagnostic algorithms are applied depending on the category of symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 16) The determination unit, The system estimates the patient's emotions and prioritizes the assessment results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The determination unit, When making a diagnosis, the priority of the diagnosis is determined based on the timing of symptom onset. The system described in Appendix 1, characterized by the features described herein. (Note 18) The determination unit, During the assessment, the order of assessment will be adjusted based on the relevance of the symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is We estimate the patient's emotions and adjust the way we express diagnoses and treatment plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating diagnoses and treatment plans, adjust the level of detail based on the severity of the symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating diagnoses and treatment plans, different generation algorithms are applied depending on the category of symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is The system estimates the patient's emotions and prioritizes diagnoses and treatment plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating diagnoses and treatment plans, the priority of generation is determined based on the timing of symptom onset. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating diagnoses and treatment plans, the order of generation is adjusted based on the relevance of symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, We estimate the patient's emotions and adjust the way we present information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing information, the patient's past medical history is referenced to provide the most appropriate information. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing information, customize it based on the patient's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates the patient's emotions and prioritizes the information to be provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing information, we will consider the patient's geographical location to provide the most appropriate information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing information, we analyze the patient's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 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. A reception area where symptoms are entered, A determination unit that determines the necessary tests and treatments based on the information received by the reception unit, A generation unit that creates a diagnosis and treatment plan based on the information determined by the determination unit, The system comprises a providing unit that provides diagnoses and treatment plans created by the generating unit. A system characterized by the following features.

2. The aforementioned reception unit is We accept patients to enter their symptoms at home. The system according to feature 1.

3. The determination unit, If the symptoms are mild, we will generate information on how to manage them at home and prescribe medication. The system according to feature 1.

4. The determination unit, If your symptoms are severe, we will refer you to the most suitable hospital or medical department. The system according to feature 1.

5. The generating unit is When a patient arrives at the hospital, they are asked to enter their symptoms, and this information is used to create a diagnosis and treatment plan. The system according to feature 1.

6. The aforementioned supply unit is, After your appointment, we will provide audio reminders about medication and lifestyle advice. The system according to feature 1.

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

8. The aforementioned reception unit is We analyze the patient's past symptom input history and suggest the optimal input method. The system according to feature 1.

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