System

Abstract

To provide a system according to an embodiment, capable of reducing a chance that a patient contacts a doctor in order to suppress a medical cost.SOLUTION: A system according to an embodiment, comprises a reception part, an analysis part, a presentation part, a purchase part, and a dispatch part. the reception part receives a symptom of a patient. The analysis part analyzes the symptom received by the reception part, and performs a primary consultation. The presentation part presents an over-the-counter medicine on the basis of a result determined by the analysis part. The purchase part purchases a medicine presented by the presentation part. The transmission part transmits the medicine purchased by the purchase part to a home of the patient.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has not done enough to reduce the number of times patients visit a doctor in order to contain medical costs, and there is room for improvement.

[0005] The system according to the embodiment aims to reduce the number of times a patient visits a doctor in order to reduce medical costs. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a presentation unit, a purchase unit, and a shipping unit. The reception unit receives symptoms of a patient. The analysis unit analyzes the symptoms received by the reception unit and performs a first examination. The presentation unit presents over-the-counter drugs based on the results determined by the analysis unit. The purchase unit purchases the drugs presented by the presentation unit online. The shipping unit ships the drugs purchased by the purchase unit to the patient's home. Effect of the Invention

[0007] The system according to the embodiment can reduce the number of times a patient visits a doctor in order to reduce medical costs. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The medical cost reduction system according to the embodiment of the present invention is a system that uses a generating AI to reduce medical costs and reduce the number of times the patient needs to see a doctor. In this medical cost reduction system, a patient consults a generating AI before seeing a doctor, and the generating AI performs a primary examination of the patient's symptoms and determines whether the patient can be treated with over-the-counter medicine or whether a visit to a hospital is necessary. If the patient can be treated with an over-the-counter medicine, the generating AI presents an appropriate medicine to the patient, and if the patient is satisfied, the patient can purchase the medicine online on the spot and have it shipped to the patient's home in one stop. For example, a patient inputs symptoms into the generating AI. For example, specific symptoms such as "sore throat" and "fever" are input. This information is input into the generating AI. The generating AI then analyzes the input symptoms and performs a primary examination. The generating AI analyzes the details of the symptoms and determines whether the patient can be treated with over-the-counter medicine or whether a visit to a hospital is necessary. For example, in the case of symptoms that can be treated with over-the-counter medicine, such as a mild cold or headache, the generating AI presents an appropriate medicine. If the patient is satisfied with the medicine presented by the generating AI, the patient can purchase the medicine online on the spot. The generative AI provides detailed information about the medicine to the patient and guides them through the purchasing process. For example, it provides information such as the efficacy, side effects, and usage of the medicine. Finally, when the patient purchases the medicine online, the medicine is shipped to the patient's home. This allows the patient to get the medicine they need without having to see a doctor. For example, common over-the-counter medicines such as cold medicines and painkillers are covered. This system reduces the number of times patients have to see a doctor and helps to control medical expenses. In addition, patients can easily get medicine at home, which increases convenience. For example, it is very convenient for people who do not have time to go to the hospital, such as busy businessmen and the elderly. This allows the medical expense control system to control medical expenses and reduce the number of times patients have to see a doctor.

[0029] The medical cost reduction system according to the embodiment includes a reception unit, an analysis unit, a presentation unit, a purchase unit, and a shipping unit. The reception unit receives the symptoms of a patient. The symptoms of the patient include, for example, a cold, a headache, and a stomachache, but are not limited to such examples. The reception unit can input specific symptoms such as, for example, a "sore throat" and a "fever". The analysis unit uses a generation AI to analyze the symptoms received by the reception unit and perform a first examination. The first examination is performed, for example, based on a symptom severity evaluation and a diagnostic algorithm, but is not limited to such examples. For example, the generation AI uses a text generation AI (for example, LLM) to analyze the symptoms of the patient and perform a first examination. The analysis unit can also use the generation AI to analyze the details of the symptoms and determine whether they can be treated with over-the-counter medicine or whether a hospital visit is necessary. The presentation unit uses the generation AI to present over-the-counter medicines based on the results determined by the analysis unit. The over-the-counter medicines include, for example, cold medicines, painkillers, and the like, but are not limited to such examples. The presentation unit, for example, presents an appropriate medicine to the patient using a generation AI and provides detailed information about the medicine. For example, information such as the efficacy, side effects, and usage of the medicine is provided. The purchase unit purchases the medicine presented by the presentation unit online. The online purchase is performed, for example, through a website or an app, but is not limited to such examples. For example, if the patient is satisfied with the presented medicine, the patient can purchase it online on the spot. The shipping unit ships the medicine purchased by the purchase unit to the patient's home. The shipping is performed, for example, through a delivery company, but is not limited to such examples. For example, the medicine is quickly shipped to the patient's home. As a result, the medical cost reduction system according to the embodiment can efficiently accept and analyze the patient's symptoms, present the appropriate medicine, and perform purchase and shipping in one stop.

[0030] The reception unit receives the symptoms of the patient. The symptoms of the patient include, but are not limited to, cold, headache, stomachache, etc. The reception unit allows the patient to input specific symptoms such as "sore throat" and "fever". Specifically, the reception unit provides an interface for the patient to use a smartphone or a personal computer to access a dedicated application or website and input symptoms. The patient can report the symptoms in detail through text input, selection from options, and even voice input. For example, by using a voice input function, the patient can easily complete the input by simply speaking their symptoms. In addition, the reception unit also simultaneously collects basic information of the patient (age, sex, medical history, etc.) and uses it as data to improve the accuracy of diagnosis in the analysis unit. Furthermore, the reception unit transmits the input information to the analysis unit in real time, enabling a quick response. In this way, the reception unit efficiently receives the symptoms of the patient and realizes a smooth data transfer to the analysis unit, which is the next step.

[0031] The analysis unit uses the generation AI to analyze the symptoms received by the reception unit and perform a first examination. The first examination is performed, for example, based on a symptom severity evaluation and a diagnostic algorithm, but is not limited to such examples. Specifically, the generation AI uses a text generation AI (for example, LLM) to analyze the patient's symptoms and perform a first examination. The generation AI uses natural language processing technology to analyze text data entered by the patient and evaluates the severity and urgency of the symptoms. For example, based on symptoms such as "sore throat" and "fever," it determines that there is a high possibility of a cold and evaluates the severity as low. In addition, the generation AI refers to past diagnostic data and medical literature and applies the optimal diagnostic algorithm for the symptoms. Furthermore, the analysis unit can use the generation AI to analyze the details of the symptoms and determine whether they can be treated with over-the-counter medicine or whether a visit to a hospital is necessary. For example, if the symptoms are those of a cold, it determines that they can be treated with over-the-counter cold medicine and determines that there is no need to visit a hospital. On the other hand, if the symptoms are severe or if multiple specific symptoms overlap, a visit to a hospital is recommended. This enables the analysis unit to quickly and accurately analyze the patient's symptoms and provide an appropriate diagnosis and treatment.

[0032] The presentation unit uses the generation AI to present over-the-counter drugs based on the results determined by the analysis unit. Over-the-counter drugs include, but are not limited to, cold medicines and painkillers. Specifically, the generation AI presents appropriate drugs to the patient and provides detailed information about the drugs. The generation AI selects the most suitable over-the-counter drug based on the patient's symptoms and the analysis results, and provides information on the drug's efficacy, side effects, and usage. For example, for cold symptoms, a specific cold medicine is recommended, and the ingredients, effects, and side effects of the drug are explained in detail. The presentation unit also provides specific instructions on how to use the drug and precautions, and supports the patient to use the drug correctly. Furthermore, the presentation unit can display reviews and ratings from other users as a reference for the patient when selecting a drug. This allows the patient to obtain information to help them choose the drug that is best for them. The presentation unit utilizes the advanced analysis capabilities of the generation AI to quickly and accurately present the most suitable drug to the patient, thereby reducing medical expenses and improving convenience for the patient.

[0033] The purchasing unit purchases the medicine presented by the presentation unit online. The online purchase is performed, for example, through a website or an app, but is not limited to such examples. Specifically, if the patient is satisfied with the presented medicine, the patient can purchase the medicine online on the spot. The purchasing unit provides an interface for the patient to select the presented medicine and perform the purchase procedure. The patient can select a payment method such as a credit card or electronic money and easily complete the purchase procedure. The purchasing unit also has a function of saving the purchase history and simplifying the purchase procedure from the next time onwards. For example, a list of medicines purchased in the past can be displayed to make it easy to purchase again. Furthermore, the purchasing unit strengthens security measures and safely manages the patient's personal information and payment information. This allows the patient to purchase medicine online with peace of mind. The purchasing unit increases the convenience of the patient and also contributes to reducing medical expenses.

[0034] The shipping unit ships the medicine purchased by the purchasing unit to the patient's home. The shipping is performed, for example, through a delivery company, but is not limited to such an example. Specifically, the medicine is promptly shipped to the patient's home. The shipping unit promptly packs the medicine for which the purchase procedure has been completed and hands it over to the delivery company. The delivery company selects the optimal delivery route based on the patient's address and delivers the medicine promptly and reliably. In addition, the shipping unit provides a function for tracking the delivery status in real time, so that the patient can check where his or her medicine is. For example, the delivery status is updated at any time through a shipping notification email or a notification function of the app, providing the patient with a sense of security. In addition, the shipping unit also takes measures to maintain the quality of the medicine, such as temperature control during delivery and shock prevention measures. This allows the patient to receive high-quality medicine. The shipping unit realizes prompt and reliable delivery, improving the convenience and satisfaction of the patient.

[0035] The medical cost reduction system includes a medical history section that performs analysis based on the patient's past medical history. The medical history section takes into account the patient's past medical history. The past medical history includes, for example, electronic medical records and the patient's self-reporting, but is not limited to such examples. For example, the medical history section can analyze the association with the current symptoms based on the patient's past medical history. The medical history section can also provide optimal diagnosis and treatment based on the patient's past medical history. This enables more appropriate diagnosis and treatment by taking into account the patient's past medical history. Some or all of the above-mentioned processing in the medical history section may be performed using, for example, AI, or may be performed without using AI. For example, the medical history section can input the patient's past medical history data to the generation AI and cause the generation AI to execute analysis of the medical history data.

[0036] The medical cost control system includes a supervision unit that is supervised by a medical expert. The supervision unit is supervised by a medical expert. The supervision by the medical expert includes, for example, confirmation of the diagnosis result and advice on the treatment policy, but is not limited to such examples. For example, the supervision unit can have a medical expert check the diagnosis result performed by the generation AI and make corrections or advice as necessary. The supervision unit can also improve the diagnostic algorithm of the generation AI based on the opinion of the medical expert. As a result, by receiving supervision from the medical expert, the reliability of the diagnosis and treatment is improved. Some or all of the above-mentioned processing in the supervision unit may be performed, for example, using AI or may be performed without using AI. For example, the supervision unit can present the diagnosis result performed by the generation AI to a medical expert and reflect the opinion of the medical expert in the generation AI.

[0037] The medical cost reduction system includes a guidance unit that provides guidance when a hospital visit is necessary. The guidance unit provides guidance when a hospital visit is necessary. When a hospital visit is necessary, for example, it is determined based on the severity and urgency of the symptoms, but is not limited to such an example. For example, when the generation AI determines that a hospital visit is necessary, the guidance unit can guide the patient to an appropriate hospital or medical department. In addition, the guidance unit can also arrange for an emergency contact number and an ambulance when a hospital visit is necessary. As a result, by providing appropriate guidance when a hospital visit is necessary, the patient can receive appropriate medical care. Part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, when the generation AI determines that a hospital visit is necessary, the guidance unit can input the information to the generation AI and cause the generation AI to execute appropriate guidance.

[0038] The medical cost control system includes a security unit that takes security measures to protect personal information and prevent unauthorized access. The security unit takes security measures to protect personal information and prevent unauthorized access. The security measures include, but are not limited to, data encryption and an unauthorized access prevention system. For example, the security unit can encrypt and safely store the patient's personal information. The security unit can also build a system that detects unauthorized access and responds quickly. This makes it possible to protect the privacy of patients by taking security measures to protect personal information and prevent unauthorized access. Some or all of the above-mentioned processing in the security unit may be performed using, for example, AI, or may be performed without using AI. For example, the security unit can detect and respond to unauthorized access using a generation AI.

[0039] The medical cost reduction system includes a guideline unit that provides a usage guideline. The guideline unit provides the usage guideline. The usage guideline includes, for example, terms of use and an operation manual, but is not limited to such examples. For example, the guideline unit can provide terms of use and an operation manual so that patients can use the system appropriately. The guideline unit can also provide guidelines that clearly explain how to use the system and points to note. As a result, by providing the usage guideline, patients can use the system appropriately. Some or all of the above-mentioned processing in the guideline unit may be performed, for example, using AI, or may be performed without using AI. For example, the guideline unit can create and provide the usage guideline using a generation AI.

[0040] The reception unit can analyze the patient's past symptom input history and provide an optimal input interface. For example, the reception unit can automatically display symptoms that the patient frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the patient has used in the past. Furthermore, the reception unit can predict and suggest symptoms to be used in a specific time period from the patient's past input history. In this way, by analyzing the past symptom input history, an optimal input interface can be provided to the patient. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the patient's past input history data to the generation AI and cause the generation AI to execute a suggestion of an optimal input interface.

[0041] The reception unit can guide the input of symptoms based on the patient's current living situation and environment. For example, when the patient is at work, the reception unit provides an interface that allows input in a short time. In addition, when the patient is at home, the reception unit can also provide an interface that prompts detailed input. Furthermore, when the patient is traveling, the reception unit can provide a simple input method to enable the patient to quickly input symptoms. This allows more appropriate input by guiding the input of symptoms according to the patient's living situation and environment. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the living situation data of the patient to the generation AI and cause the generation AI to execute the input guide.

[0042] The reception unit can provide information on symptoms and diseases specific to a region, taking into account the geographical location information of the patient. For example, when the patient is in a specific region, the reception unit provides information on diseases prevalent in that region. In addition, when the patient is traveling, the reception unit can also provide information on disease risks at the travel destination. Furthermore, the reception unit can provide information on related symptoms and diseases based on the climate and environment of the region in which the patient lives. In this way, information on symptoms and diseases specific to a region can be provided by taking into account the geographical location information of the patient. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location data of the patient to the generation AI and cause the generation AI to provide information specific to the region.

[0043] The reception unit can analyze the social media activity of the patient and acquire related symptoms and health information. For example, the reception unit guides the input of symptoms based on health information shared by the patient on social media. The reception unit can also refer to information on health-related accounts that the patient follows on social media. Furthermore, the reception unit can support the input of symptoms based on information on health communities that the patient participates in on social media. In this way, symptoms and health information related to the patient can be acquired by analyzing social media activity. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the social media data of the patient to the generation AI and cause the generation AI to acquire related information.

[0044] The analysis unit can improve the analysis accuracy by considering the patient's past medical history and family history during the analysis. The analysis unit, for example, analyzes the association with the current symptoms based on the patient's past medical history. The analysis unit can also reflect genetic factors in the analysis by considering the patient's family history. Furthermore, the analysis unit can compare the patient's past medical history with the current symptoms and provide optimal analysis results. As a result, the analysis accuracy is improved by considering the patient's past medical history and family history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the patient's medical history data to the generation AI and cause the generation AI to improve the analysis accuracy.

[0045] The analysis unit can perform the analysis while taking into consideration the patient's lifestyle and environmental factors. The analysis unit can analyze the cause of the symptoms, for example, while taking into consideration the patient's diet and exercise habits. The analysis unit can also analyze the cause of the symptoms while taking into consideration the patient's living environment and work environment. Furthermore, the analysis unit can also analyze the symptoms while taking into consideration the patient's stress level and sleep habits. This allows for more accurate analysis by taking into consideration the lifestyle and environmental factors. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can input the patient's lifestyle data into the generation AI and cause the generation AI to perform the analysis.

[0046] The analysis unit can perform an analysis of region-specific diseases and symptoms taking into account the geographical location information of the patient. For example, when the patient is in a specific region, the analysis unit performs an analysis of diseases prevalent in that region. In addition, when the patient is traveling, the analysis unit can also perform an analysis of disease risks at the travel destination. Furthermore, the analysis unit can also perform an analysis of related symptoms and diseases based on the climate and environment of the region in which the patient lives. This makes it possible to analyze region-specific diseases and symptoms by taking into account the geographical location information. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the geographical location data of the patient to the generation AI and cause the generation AI to execute an analysis of region-specific diseases and symptoms.

[0047] The analysis unit can analyze the social media activity of the patient and reflect related health information in the analysis. The analysis unit performs symptom analysis based on health information shared by the patient on social media, for example. The analysis unit can also refer to information on health-related accounts that the patient follows on social media. Furthermore, the analysis unit can also perform symptom analysis based on information on health communities that the patient participates in on social media. In this way, by analyzing social media activity, related health information can be reflected in the analysis. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the social media data of the patient to the generation AI and cause the generation AI to execute an analysis of the health information.

[0048] The presentation unit can select an appropriate drug by considering the patient's past drug allergies and side effect history at the time of presentation. The presentation unit, for example, selects a drug that does not cause allergies based on the patient's past drug allergies. The presentation unit can also select a drug with fewer side effects by considering the patient's past side effect history. Furthermore, the presentation unit can select an optimal drug based on the patient's past drug use history. This makes it possible to select an appropriate drug by considering the patient's past drug allergies and side effect history. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the patient's drug allergy data to the generation AI and cause the generation AI to select an appropriate drug.

[0049] The presentation unit can select a medicine taking into consideration the patient's lifestyle and dietary restrictions at the time of presentation. The presentation unit selects an appropriate medicine taking into consideration the patient's dietary restrictions, for example. The presentation unit can also select an optimal medicine taking into consideration the patient's lifestyle. Furthermore, the presentation unit can also select an appropriate medicine taking into consideration the patient's exercise habits. This makes it possible to select an optimal medicine for the patient by taking into consideration the lifestyle and dietary restrictions. A part or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the patient's lifestyle data into the generation AI and cause the generation AI to select a medicine.

[0050] The presentation unit can present region-specific medicines and treatments in consideration of the geographical location information of the patient. For example, when the patient is in a particular region, the presentation unit presents medicines commonly used in that region. In addition, when the patient is traveling, the presentation unit can also present medicines available at the travel destination. Furthermore, the presentation unit can also present appropriate medicines based on the climate and environment of the region in which the patient lives. In this way, region-specific medicines and treatments can be presented by taking into account the geographical location information. Part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI or may be performed without using AI. For example, the presentation unit can input the geographical location data of the patient to the generation AI and cause the generation AI to execute the presentation of region-specific medicines and treatments.

[0051] The presentation unit can analyze the patient's social media activity and present relevant medicines and treatments. The presentation unit presents appropriate medicines based on, for example, health information shared by the patient on social media. The presentation unit can also refer to information on health-related accounts that the patient follows on social media. Furthermore, the presentation unit can present appropriate medicines based on information on health communities in which the patient participates on social media. In this way, relevant medicines and treatments can be presented by analyzing social media activity. Some or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the patient's social media data into the generation AI and cause the generation AI to present medicines and treatments.

[0052] At the time of purchase, the purchasing unit can propose the optimal purchasing method by taking into account the patient's past purchase history. For example, the purchasing unit provides an option to purchase the same medicine again based on the medicine the patient has previously purchased. The purchasing unit can also propose the optimal payment method from the patient's past purchase history. Furthermore, the purchasing unit can analyze the patient's past purchase history and propose the most efficient purchasing method. In this way, the optimal purchasing method can be proposed to the patient by taking the past purchase history into account. A part or all of the above-mentioned processing in the purchasing unit may be performed, for example, using AI, or may be performed without using AI. For example, the purchasing unit can input the patient's purchase history data into the generating AI and cause the generating AI to execute a proposal of the optimal purchasing method.

[0053] The purchasing unit can provide a function of automatically inputting the patient's payment method and delivery address information at the time of purchase. For example, the purchasing unit automatically inputs the payment method used by the patient in the past. The purchasing unit can also automatically input the delivery address based on the patient's past delivery address information. Furthermore, the purchasing unit can automatically input the payment method and delivery address information based on the patient's account information. This makes it possible to streamline the purchase procedure by automatically inputting the payment method and delivery address information. Some or all of the above-mentioned processing in the purchasing unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing unit can input the patient's payment method and delivery address information into the generation AI and have the generation AI execute the automatic input.

[0054] The purchasing department can select the optimal delivery method and delivery company by taking into account the geographical location information of the patient. For example, if the patient is in a specific area, the purchasing department selects the fastest delivery method in that area. In addition, if the patient is traveling, the purchasing department can also select a delivery method that allows the patient to receive the product at the travel destination. Furthermore, the purchasing department can select the optimal delivery company by taking into account the reliability of delivery companies in the area where the patient lives. In this way, the optimal delivery method and delivery company can be selected by taking into account the geographical location information. A part or all of the above-mentioned processing in the purchasing department may be performed, for example, using AI, or may be performed without using AI. For example, the purchasing department can input the geographical location data of the patient into the generation AI and cause the generation AI to select the optimal delivery method and delivery company.

[0055] The purchasing unit can analyze the social media activity of the patient and provide related purchase information and reviews. The purchasing unit can provide related reviews based on, for example, purchase information shared by the patient on social media. The purchasing unit can also refer to reviews of health-related accounts that the patient follows on social media. Furthermore, the purchasing unit can provide purchase information based on reviews of health communities in which the patient participates on social media. In this way, by analyzing social media activity, related purchase information and reviews can be provided. Part or all of the above-mentioned processing in the purchasing unit can be performed, for example, using AI or without using AI. For example, the purchasing unit can input the social media data of the patient into the generation AI and cause the generation AI to provide purchase information and reviews.

[0056] At the time of delivery, the shipping unit can select the optimal delivery method by taking into account the patient's past delivery history. The shipping unit selects the optimal delivery method based on, for example, the patient's past delivery history. The shipping unit can also select a highly reliable delivery company from the patient's past delivery history. Furthermore, the shipping unit can analyze the patient's past delivery history and select the most efficient delivery method. In this way, the optimal delivery method can be selected by taking into account the past delivery history. Some or all of the above-mentioned processing in the shipping unit may be performed, for example, using AI, or may be performed without using AI. For example, the shipping unit can input the patient's delivery history data into the generation AI and cause the generation AI to select the optimal delivery method.

[0057] The dispatch unit can adjust the delivery schedule at the time of dispatch, taking into consideration the patient's living situation and the time period when the patient can receive the medicine. For example, if the patient is at work, the dispatch unit adjusts the delivery schedule to match the time when the patient finishes work. In addition, if the patient is at home, the dispatch unit can perform immediate delivery. Furthermore, if the patient is traveling, the dispatch unit can adjust the delivery schedule so that the patient can receive the medicine at the travel destination. In this way, the optimal delivery schedule can be provided to the patient by taking into consideration the living situation and the time period when the patient can receive the medicine. Some or all of the above-mentioned processing in the dispatch unit may be performed, for example, using AI, or may be performed without using AI. For example, the dispatch unit can input the living situation data of the patient to the generation AI and cause the generation AI to adjust the delivery schedule.

[0058] The dispatch unit can select an optimal delivery route by taking into account the geographical location information of the patient. For example, when the patient is in a specific area, the dispatch unit selects the quickest delivery route in that area. In addition, when the patient is traveling, the dispatch unit can also select a delivery route that allows the patient to receive the package at the travel destination. Furthermore, the dispatch unit can also select an optimal delivery route by taking into account the traffic conditions in the area where the patient lives. In this way, the optimal delivery route can be selected by taking into account the geographical location information. A part or all of the above-mentioned processing in the dispatch unit may be performed, for example, using AI, or may be performed without using AI. For example, the dispatch unit can input the geographical location data of the patient to the generation AI and cause the generation AI to select an optimal delivery route.

[0059] The dispatch unit can analyze the social media activity of the patient and provide related delivery information and reviews. For example, the dispatch unit can provide related reviews based on delivery information shared by the patient on social media. The dispatch unit can also refer to reviews of health-related accounts that the patient follows on social media. Furthermore, the dispatch unit can provide delivery information based on reviews of health communities in which the patient participates on social media. In this way, by analyzing social media activity, related delivery information and reviews can be provided. Some or all of the above-mentioned processing in the dispatch unit may be performed, for example, using AI, or may be performed without using AI. For example, the dispatch unit can input the social media data of the patient into the generation AI and cause the generation AI to provide delivery information and reviews.

[0060] The medical history section can provide a function of automatically inputting the patient's past medical history and family history when inputting the medical history. The medical history section automatically inputs related information based on the patient's past medical history, for example. The medical history section can also automatically input genetic factors in consideration of the patient's family history. Furthermore, the medical history section can provide an optimal input method based on the patient's past medical history and family history. This improves the efficiency of medical history input by automatically inputting the past medical history and family history. A part or all of the above-mentioned processing in the medical history section may be performed, for example, using AI, or may be performed without using AI. For example, the medical history section can input the patient's medical history data to the generation AI and cause the generation AI to execute the automatic input.

[0061] The medical history section can provide region-specific medical history information by taking into account the geographical location information of the patient. For example, when the patient is in a particular region, the medical history section can provide medical history information on diseases prevalent in that region. In addition, when the patient is traveling, the medical history section can also provide medical history information on disease risks at the travel destination. Furthermore, the medical history section can provide relevant medical history information based on the climate and environment of the region in which the patient lives. In this way, region-specific medical history information can be provided by taking into account the geographical location information. Some or all of the above-mentioned processing in the medical history section can be performed, for example, using AI or can be performed without using AI. For example, the medical history section can input the geographical location data of the patient to the generation AI and cause the generation AI to provide region-specific medical history information.

[0062] During supervision, the supervision department can select the optimal supervision method by taking into account the medical expert's past supervision history. The supervision department selects the optimal supervision method based on, for example, the medical expert's past supervision history. The supervision department can also select a highly reliable supervision method from the medical expert's past supervision history. Furthermore, the supervision department can analyze the medical expert's past supervision history and select the most efficient supervision method. In this way, the optimal supervision method can be selected by taking into account the past supervision history. Some or all of the above-mentioned processing in the supervision department may be performed, for example, using AI, or may be performed without using AI. For example, the supervision department can input the medical expert's supervision history data into the generation AI and cause the generation AI to select the optimal supervision method.

[0063] The supervision unit can provide region-specific supervision information taking into account the geographic location information of the patient. For example, if the patient is in a particular region, the supervision unit can provide supervision information on diseases prevalent in that region. In addition, if the patient is traveling, the supervision unit can also provide supervision information on disease risks at the travel destination. Furthermore, the supervision unit can provide relevant supervision information based on the climate and environment of the region in which the patient lives. In this way, region-specific supervision information can be provided by taking into account the geographic location information. Some or all of the above-mentioned processing in the supervision unit may be performed, for example, using AI or may be performed without using AI. For example, the supervision unit can input the geographic location data of the patient to the generation AI and cause the generation AI to provide region-specific supervision information.

[0064] The guidance unit can select the optimal guidance method by taking into consideration the patient's past medical history when providing guidance. The guidance unit selects the optimal guidance method based on, for example, the patient's past medical history. The guidance unit can also select a highly reliable guidance method from the patient's past medical history. Furthermore, the guidance unit can analyze the patient's past medical history and select the most efficient guidance method. In this way, the optimal guidance method can be selected by taking into consideration the past medical history. A part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the patient's medical history data to the generation AI and cause the generation AI to select the optimal guidance method.

[0065] The guidance unit can provide region-specific guidance information by taking into account the geographical location information of the patient. For example, when the patient is in a particular region, the guidance unit can provide guidance information on diseases prevalent in that region. In addition, when the patient is traveling, the guidance unit can also provide guidance information on disease risks at the travel destination. Furthermore, the guidance unit can provide relevant guidance information based on the climate and environment of the region in which the patient lives. In this way, region-specific guidance information can be provided by taking into account the geographical location information. A part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the geographical location data of the patient to the generation AI and cause the generation AI to provide region-specific guidance information.

[0066] The security unit can select the optimal security measure in consideration of the patient's past security history when taking security measures. The security unit selects the optimal security measure based on the patient's past security history, for example. The security unit can also select a highly reliable security measure from the patient's past security history. Furthermore, the security unit can analyze the patient's past security history and select the most efficient security measure. In this way, the optimal security measure can be selected by taking the past security history into consideration. A part or all of the above-mentioned processing in the security unit may be performed, for example, using AI, or may be performed without using AI. For example, the security unit can input the patient's security history data to the generation AI and cause the generation AI to select the optimal security measure.

[0067] The security unit can provide region-specific security information by taking into account the geographical location information of the patient. For example, if the patient is in a particular region, the security unit can provide information on security risks occurring in that region. In addition, if the patient is traveling, the security unit can also provide information on security risks at the travel destination. Furthermore, the security unit can provide related information by taking into account the security situation in the region where the patient lives. In this way, region-specific security information can be provided by taking into account the geographical location information. Some or all of the above-mentioned processing in the security unit may be performed, for example, using AI or may be performed without using AI. For example, the security unit can input the geographical location data of the patient to the generation AI and cause the generation AI to provide region-specific security information.

[0068] When providing a guideline, the guideline unit can provide an optimal guideline by taking into account the patient's past usage history. The guideline unit provides an optimal guideline based on, for example, the patient's past usage history. The guideline unit can also provide a highly reliable guideline from the patient's past usage history. Furthermore, the guideline unit can analyze the patient's past usage history and provide the most efficient guideline. This makes it possible to provide an optimal guideline by taking into account the past usage history. Some or all of the above-mentioned processing in the guideline unit may be performed using, for example, AI, or may be performed without using AI. For example, the guideline unit can input the patient's usage history data to the generation AI and cause the generation AI to provide the optimal guideline.

[0069] The guideline unit can provide region-specific guideline information taking into account the geographical location information of the patient. For example, when the patient is in a particular region, the guideline unit can provide guidelines regarding health risks occurring in that region. In addition, when the patient is traveling, the guideline unit can also provide guidelines regarding health risks at the travel destination. Furthermore, the guideline unit can provide relevant guidelines taking into account the health situation of the region in which the patient lives. In this way, region-specific guideline information can be provided by taking into account the geographical location information. Some or all of the above-mentioned processing in the guideline unit can be performed, for example, using AI or can be performed without using AI. For example, the guideline unit can input the geographical location data of the patient to the generation AI and cause the generation AI to provide region-specific guideline information.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The medical cost reduction system may further include a lifestyle habit unit that monitors the patient's lifestyle. The lifestyle habit unit collects daily activity data of the patient and provides it to the analysis unit. For example, the patient's amount of exercise, diet, sleep patterns, etc. may be monitored, and the health condition may be evaluated based on this data. The lifestyle habit unit may also provide the patient with advice to encourage healthy lifestyle habits. This may improve the patient's lifestyle and reduce medical costs from the perspective of preventive medicine.

[0072] The medical cost reduction system can further include a genetic information unit that analyzes the genetic information of the patient. The genetic information unit collects the genetic information of the patient and provides it to the analysis unit. For example, the genetic risk factors of the patient can be evaluated and a comprehensive health condition can be determined based on this data. The genetic information unit can also provide preventive measures to the patient based on the genetic risk. This makes it possible to realize personalized medicine that takes into account the genetic risk of the patient and reduce medical costs.

[0073] The medical cost reduction system can further include an environmental evaluation unit that evaluates the patient's environmental factors. The environmental evaluation unit evaluates the patient's living environment and work environment and provides the results to the analysis unit. For example, the air quality and noise level of the patient's living environment can be evaluated, and the health condition can be comprehensively judged based on this data. The environmental evaluation unit can also provide the patient with advice on improving their environment. This makes it possible to realize health management that takes into account the patient's environmental factors, and reduce medical costs.

[0074] The medical cost reduction system can further include a diet monitoring unit that monitors the patient's dietary content. The diet monitoring unit collects the patient's dietary content and provides it to the analysis unit. For example, it can evaluate the nutritional balance of the patient's diet and make a comprehensive judgment of the patient's health condition based on this data. The diet monitoring unit can also suggest nutritionally balanced meals to the patient. This can improve the patient's dietary content and reduce medical costs from the perspective of preventive medicine.

[0075] The medical cost reduction system can further include an exercise monitoring unit that monitors the patient's exercise habits. The exercise monitoring unit collects the patient's amount of exercise and provides it to the analysis unit. For example, it can evaluate the patient's lack of exercise and comprehensively judge the patient's health condition based on this data. The exercise monitoring unit can also propose an appropriate exercise plan to the patient. This can improve the patient's exercise habits and reduce medical costs from the perspective of preventive medicine.

[0076] The medical cost reduction system may further include a sleep monitoring unit that monitors the patient's sleep pattern. The sleep monitoring unit collects the patient's sleep data and provides it to the analysis unit. For example, the sleep quality of the patient may be evaluated, and the health condition may be comprehensively determined based on the data. The sleep monitoring unit may also provide the patient with advice to promote good quality sleep. This may improve the patient's sleep pattern and improve the overall health condition.

[0077] The process flow of the first embodiment will be briefly described below.

[0078] Step 1: The reception unit receives the patient's symptoms. The patient can input specific symptoms such as "sore throat" or "fever." This allows symptoms such as cold, headache, stomachache, etc. to be received. Step 2: The analysis unit uses the generation AI to analyze the symptoms received by the reception unit and conducts a first examination. The first examination is based on an evaluation of the severity of the symptoms and a diagnostic algorithm. The analysis unit also analyzes the details of the symptoms and determines whether they can be treated with over-the-counter medication or whether a visit to a hospital is necessary. Step 3: The presentation unit uses the generation AI to present over-the-counter drugs based on the results determined by the analysis unit. The presentation unit presents appropriate drugs to the patient and provides detailed information such as the drug's efficacy, side effects, and usage instructions. Step 4: The purchasing department purchases the medicines presented by the presenting department online. The online purchase is done through a website or an app. Step 5: The shipping department ships the medicine purchased by the purchasing department to the patient's home. The shipping is done through a delivery company, and the medicine is delivered to the patient's home promptly.

[0079] (Example 2) The medical cost reduction system according to the embodiment of the present invention is a system that uses a generating AI to reduce medical costs and reduce the number of times the patient needs to see a doctor. In this medical cost reduction system, a patient consults a generating AI before seeing a doctor, and the generating AI performs a primary examination of the patient's symptoms and determines whether the patient can be treated with over-the-counter medicine or whether a visit to a hospital is necessary. If the patient can be treated with an over-the-counter medicine, the generating AI presents an appropriate medicine to the patient, and if the patient is satisfied, the patient can purchase the medicine online on the spot and have it shipped to the patient's home in one stop. For example, a patient inputs symptoms into the generating AI. For example, specific symptoms such as "sore throat" and "fever" are input. This information is input into the generating AI. The generating AI then analyzes the input symptoms and performs a primary examination. The generating AI analyzes the details of the symptoms and determines whether the patient can be treated with over-the-counter medicine or whether a visit to a hospital is necessary. For example, in the case of symptoms that can be treated with over-the-counter medicine, such as a mild cold or headache, the generating AI presents an appropriate medicine. If the patient is satisfied with the medicine presented by the generating AI, the patient can purchase the medicine online on the spot. The generative AI provides detailed information about the medicine to the patient and guides them through the purchasing process. For example, it provides information such as the efficacy, side effects, and usage of the medicine. Finally, when the patient purchases the medicine online, the medicine is shipped to the patient's home. This allows the patient to get the medicine they need without having to see a doctor. For example, common over-the-counter medicines such as cold medicines and painkillers are covered. This system reduces the number of times patients have to see a doctor and helps to control medical expenses. In addition, patients can easily get medicine at home, which increases convenience. For example, it is very convenient for people who do not have time to go to the hospital, such as busy businessmen and the elderly. This allows the medical expense control system to control medical expenses and reduce the number of times patients have to see a doctor.

[0080] The medical cost reduction system according to the embodiment includes a reception unit, an analysis unit, a presentation unit, a purchase unit, and a shipping unit. The reception unit receives the symptoms of a patient. The symptoms of the patient include, for example, a cold, a headache, and a stomachache, but are not limited to such examples. The reception unit can input specific symptoms such as, for example, a "sore throat" and a "fever". The analysis unit uses a generation AI to analyze the symptoms received by the reception unit and perform a first examination. The first examination is performed, for example, based on a symptom severity evaluation and a diagnostic algorithm, but is not limited to such examples. For example, the generation AI uses a text generation AI (for example, LLM) to analyze the symptoms of the patient and perform a first examination. The analysis unit can also use the generation AI to analyze the details of the symptoms and determine whether they can be treated with over-the-counter medicine or whether a hospital visit is necessary. The presentation unit uses the generation AI to present over-the-counter medicines based on the results determined by the analysis unit. The over-the-counter medicines include, for example, cold medicines, painkillers, and the like, but are not limited to such examples. The presentation unit, for example, presents an appropriate medicine to the patient using a generation AI and provides detailed information about the medicine. For example, information such as the efficacy, side effects, and usage of the medicine is provided. The purchase unit purchases the medicine presented by the presentation unit online. The online purchase is performed, for example, through a website or an app, but is not limited to such examples. For example, if the patient is satisfied with the presented medicine, the patient can purchase it online on the spot. The shipping unit ships the medicine purchased by the purchase unit to the patient's home. The shipping is performed, for example, through a delivery company, but is not limited to such examples. For example, the medicine is quickly shipped to the patient's home. As a result, the medical cost reduction system according to the embodiment can efficiently accept and analyze the patient's symptoms, present the appropriate medicine, and perform purchase and shipping in one stop.

[0081] The reception unit receives the symptoms of the patient. The symptoms of the patient include, but are not limited to, cold, headache, stomachache, etc. The reception unit allows the patient to input specific symptoms such as "sore throat" and "fever". Specifically, the reception unit provides an interface for the patient to use a smartphone or a personal computer to access a dedicated application or website and input symptoms. The patient can report the symptoms in detail through text input, selection from options, and even voice input. For example, by using a voice input function, the patient can easily complete the input by simply speaking their symptoms. In addition, the reception unit also simultaneously collects basic information of the patient (age, sex, medical history, etc.) and uses it as data to improve the accuracy of diagnosis in the analysis unit. Furthermore, the reception unit transmits the input information to the analysis unit in real time, enabling a quick response. In this way, the reception unit efficiently receives the symptoms of the patient and realizes a smooth data transfer to the analysis unit, which is the next step.

[0082] The analysis unit uses the generation AI to analyze the symptoms received by the reception unit and perform a first examination. The first examination is performed, for example, based on a symptom severity evaluation and a diagnostic algorithm, but is not limited to such examples. Specifically, the generation AI uses a text generation AI (for example, LLM) to analyze the patient's symptoms and perform a first examination. The generation AI uses natural language processing technology to analyze text data entered by the patient and evaluates the severity and urgency of the symptoms. For example, based on symptoms such as "sore throat" and "fever," it determines that there is a high possibility of a cold and evaluates the severity as low. In addition, the generation AI refers to past diagnostic data and medical literature and applies the optimal diagnostic algorithm for the symptoms. Furthermore, the analysis unit can use the generation AI to analyze the details of the symptoms and determine whether they can be treated with over-the-counter medicine or whether a visit to a hospital is necessary. For example, if the symptoms are those of a cold, it determines that they can be treated with over-the-counter cold medicine and determines that there is no need to visit a hospital. On the other hand, if the symptoms are severe or if multiple specific symptoms overlap, a visit to a hospital is recommended. This enables the analysis unit to quickly and accurately analyze the patient's symptoms and provide an appropriate diagnosis and treatment.

[0083] The presentation unit uses the generation AI to present over-the-counter drugs based on the results determined by the analysis unit. Over-the-counter drugs include, but are not limited to, cold medicines and painkillers. Specifically, the generation AI presents appropriate drugs to the patient and provides detailed information about the drugs. The generation AI selects the most suitable over-the-counter drug based on the patient's symptoms and the analysis results, and provides information on the drug's efficacy, side effects, and usage. For example, for cold symptoms, a specific cold medicine is recommended, and the ingredients, effects, and side effects of the drug are explained in detail. The presentation unit also provides specific instructions on how to use the drug and precautions, and supports the patient to use the drug correctly. Furthermore, the presentation unit can display reviews and ratings from other users as a reference for the patient when selecting a drug. This allows the patient to obtain information to help them choose the drug that is best for them. The presentation unit utilizes the advanced analysis capabilities of the generation AI to quickly and accurately present the most suitable drug to the patient, thereby reducing medical expenses and improving convenience for the patient.

[0084] The purchasing unit purchases the medicine presented by the presentation unit online. The online purchase is performed, for example, through a website or an app, but is not limited to such examples. Specifically, if the patient is satisfied with the presented medicine, the patient can purchase the medicine online on the spot. The purchasing unit provides an interface for the patient to select the presented medicine and perform the purchase procedure. The patient can select a payment method such as a credit card or electronic money and easily complete the purchase procedure. The purchasing unit also has a function of saving the purchase history and simplifying the purchase procedure from the next time onwards. For example, a list of medicines purchased in the past can be displayed to make it easy to purchase again. Furthermore, the purchasing unit strengthens security measures and safely manages the patient's personal information and payment information. This allows the patient to purchase medicine online with peace of mind. The purchasing unit increases the convenience of the patient and also contributes to reducing medical expenses.

[0085] The shipping unit ships the medicine purchased by the purchasing unit to the patient's home. The shipping is performed, for example, through a delivery company, but is not limited to such an example. Specifically, the medicine is promptly shipped to the patient's home. The shipping unit promptly packs the medicine for which the purchase procedure has been completed and hands it over to the delivery company. The delivery company selects the optimal delivery route based on the patient's address and delivers the medicine promptly and reliably. In addition, the shipping unit provides a function for tracking the delivery status in real time, so that the patient can check where his or her medicine is. For example, the delivery status is updated at any time through a shipping notification email or a notification function of the app, providing the patient with a sense of security. In addition, the shipping unit also takes measures to maintain the quality of the medicine, such as temperature control during delivery and shock prevention measures. This allows the patient to receive high-quality medicine. The shipping unit realizes prompt and reliable delivery, improving the convenience and satisfaction of the patient.

[0086] The medical cost reduction system includes a medical history section that performs analysis based on the patient's past medical history. The medical history section takes into account the patient's past medical history. The past medical history includes, for example, electronic medical records and the patient's self-reporting, but is not limited to such examples. For example, the medical history section can analyze the association with the current symptoms based on the patient's past medical history. The medical history section can also provide optimal diagnosis and treatment based on the patient's past medical history. This enables more appropriate diagnosis and treatment by taking into account the patient's past medical history. Some or all of the above-mentioned processing in the medical history section may be performed using, for example, AI, or may be performed without using AI. For example, the medical history section can input the patient's past medical history data to the generation AI and cause the generation AI to execute analysis of the medical history data.

[0087] The medical cost control system includes a supervision unit that is supervised by a medical expert. The supervision unit is supervised by a medical expert. The supervision by the medical expert includes, for example, confirmation of the diagnosis result and advice on the treatment policy, but is not limited to such examples. For example, the supervision unit can have a medical expert check the diagnosis result performed by the generation AI and make corrections or advice as necessary. The supervision unit can also improve the diagnostic algorithm of the generation AI based on the opinion of the medical expert. As a result, by receiving supervision from the medical expert, the reliability of the diagnosis and treatment is improved. Some or all of the above-mentioned processing in the supervision unit may be performed, for example, using AI or may be performed without using AI. For example, the supervision unit can present the diagnosis result performed by the generation AI to a medical expert and reflect the opinion of the medical expert in the generation AI.

[0088] The medical cost reduction system includes a guidance unit that provides guidance when a hospital visit is necessary. The guidance unit provides guidance when a hospital visit is necessary. When a hospital visit is necessary, for example, it is determined based on the severity and urgency of the symptoms, but is not limited to such an example. For example, when the generation AI determines that a hospital visit is necessary, the guidance unit can guide the patient to an appropriate hospital or medical department. In addition, the guidance unit can also arrange for an emergency contact number and an ambulance when a hospital visit is necessary. As a result, by providing appropriate guidance when a hospital visit is necessary, the patient can receive appropriate medical care. Part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, when the generation AI determines that a hospital visit is necessary, the guidance unit can input the information to the generation AI and cause the generation AI to execute appropriate guidance.

[0089] The medical cost control system includes a security unit that takes security measures to protect personal information and prevent unauthorized access. The security unit takes security measures to protect personal information and prevent unauthorized access. The security measures include, but are not limited to, data encryption and an unauthorized access prevention system. For example, the security unit can encrypt and safely store the patient's personal information. The security unit can also build a system that detects unauthorized access and responds quickly. This makes it possible to protect the privacy of patients by taking security measures to protect personal information and prevent unauthorized access. Some or all of the above-mentioned processing in the security unit may be performed using, for example, AI, or may be performed without using AI. For example, the security unit can detect and respond to unauthorized access using a generation AI.

[0090] The medical cost reduction system includes a guideline unit that provides a usage guideline. The guideline unit provides the usage guideline. The usage guideline includes, for example, terms of use and an operation manual, but is not limited to such examples. For example, the guideline unit can provide terms of use and an operation manual so that patients can use the system appropriately. The guideline unit can also provide guidelines that clearly explain how to use the system and points to note. As a result, by providing the usage guideline, patients can use the system appropriately. Some or all of the above-mentioned processing in the guideline unit may be performed, for example, using AI, or may be performed without using AI. For example, the guideline unit can create and provide the usage guideline using a generation AI.

[0091] The reception unit can estimate the patient's emotion and adjust the symptom input method based on the estimated patient's emotion. For example, when the patient feels anxious, the reception unit provides a simple and intuitive interface and minimizes the input procedure. In addition, when the patient is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the patient is in a hurry, the reception unit can prioritize voice input and enable the patient to input symptoms quickly. This allows the patient to input symptoms more comfortably by adjusting the symptom input method according to the patient's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input facial expression data of the patient to the generation AI and cause the generation AI to execute emotion estimation.

[0092] The reception unit can analyze the patient's past symptom input history and provide an optimal input interface. For example, the reception unit can automatically display symptoms that the patient frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the patient has used in the past. Furthermore, the reception unit can predict and suggest symptoms to be used in a specific time period from the patient's past input history. In this way, by analyzing the past symptom input history, an optimal input interface can be provided to the patient. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the patient's past input history data to the generation AI and cause the generation AI to execute a suggestion of an optimal input interface.

[0093] The reception unit can guide the input of symptoms based on the patient's current living situation and environment. For example, when the patient is at work, the reception unit provides an interface that allows input in a short time. In addition, when the patient is at home, the reception unit can also provide an interface that prompts detailed input. Furthermore, when the patient is traveling, the reception unit can provide a simple input method to enable the patient to quickly input symptoms. This allows more appropriate input by guiding the input of symptoms according to the patient's living situation and environment. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the living situation data of the patient to the generation AI and cause the generation AI to execute the input guide.

[0094] The reception unit can estimate the patient's emotion and determine the priority of the input symptoms based on the estimated emotion of the patient. For example, when the patient feels strong pain, the reception unit prioritizes the analysis of the symptom. Also, when the patient feels mild discomfort, the reception unit can postpone the analysis of the symptom. Furthermore, when the patient inputs multiple symptoms, the reception unit can prioritize symptoms with high importance based on the emotion. In this way, by determining the priority of symptoms based on the emotion of the patient, it is possible to analyze important symptoms with priority. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the emotion data of the patient to the generation AI and cause the generation AI to execute the determination of the priority of symptoms.

[0095] The reception unit can provide information on symptoms and diseases specific to a region, taking into account the geographical location information of the patient. For example, when the patient is in a specific region, the reception unit provides information on diseases prevalent in that region. In addition, when the patient is traveling, the reception unit can also provide information on disease risks at the travel destination. Furthermore, the reception unit can provide information on related symptoms and diseases based on the climate and environment of the region in which the patient lives. In this way, information on symptoms and diseases specific to a region can be provided by taking into account the geographical location information of the patient. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location data of the patient to the generation AI and cause the generation AI to provide information specific to the region.

[0096] The reception unit can analyze the social media activity of the patient and acquire related symptoms and health information. For example, the reception unit guides the input of symptoms based on health information shared by the patient on social media. The reception unit can also refer to information on health-related accounts that the patient follows on social media. Furthermore, the reception unit can support the input of symptoms based on information on health communities that the patient participates in on social media. In this way, symptoms and health information related to the patient can be acquired by analyzing social media activity. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the social media data of the patient to the generation AI and cause the generation AI to acquire related information.

[0097] The analysis unit can estimate the patient's emotion and adjust the way of expressing the analysis result based on the estimated emotion of the patient. For example, when the patient feels anxious, the analysis unit provides a simple and easy-to-understand analysis result. In addition, when the patient is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, when the patient is in a hurry, the analysis unit can also provide a concise analysis result with the main points. In this way, by adjusting the way of expressing the analysis result according to the emotion of the patient, it is possible to provide an analysis result that is easy for the patient to understand. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the emotion data of the patient to the generation AI and cause the generation AI to adjust the way of expressing the analysis result.

[0098] The analysis unit can improve the analysis accuracy by considering the patient's past medical history and family history during the analysis. The analysis unit, for example, analyzes the association with the current symptoms based on the patient's past medical history. The analysis unit can also reflect genetic factors in the analysis by considering the patient's family history. Furthermore, the analysis unit can compare the patient's past medical history with the current symptoms and provide optimal analysis results. As a result, the analysis accuracy is improved by considering the patient's past medical history and family history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the patient's medical history data to the generation AI and cause the generation AI to improve the analysis accuracy.

[0099] The analysis unit can perform the analysis while taking into consideration the patient's lifestyle and environmental factors. The analysis unit can analyze the cause of the symptoms, for example, while taking into consideration the patient's diet and exercise habits. The analysis unit can also analyze the cause of the symptoms while taking into consideration the patient's living environment and work environment. Furthermore, the analysis unit can also analyze the symptoms while taking into consideration the patient's stress level and sleep habits. This allows for more accurate analysis by taking into consideration the lifestyle and environmental factors. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can input the patient's lifestyle data into the generation AI and cause the generation AI to perform the analysis.

[0100] The analysis unit can estimate the patient's emotion and determine the priority of the analysis results based on the estimated emotion of the patient. For example, when the patient is in strong pain, the analysis unit preferentially displays the analysis results of the symptom. Also, when the patient is in mild discomfort, the analysis unit can postpone the analysis results of the symptom. Furthermore, when the patient inputs multiple symptoms, the analysis unit can prioritize the analysis results of symptoms with high importance based on the emotion. In this way, the analysis results of important symptoms can be provided preferentially by determining the priority of the analysis results based on the emotion of the patient. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the emotion data of the patient to the generation AI and cause the generation AI to execute the determination of the priority of the analysis results.

[0101] The analysis unit can perform an analysis of region-specific diseases and symptoms taking into account the geographical location information of the patient. For example, when the patient is in a specific region, the analysis unit performs an analysis of diseases prevalent in that region. In addition, when the patient is traveling, the analysis unit can also perform an analysis of disease risks at the travel destination. Furthermore, the analysis unit can also perform an analysis of related symptoms and diseases based on the climate and environment of the region in which the patient lives. This makes it possible to analyze region-specific diseases and symptoms by taking into account the geographical location information. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the geographical location data of the patient to the generation AI and cause the generation AI to execute an analysis of region-specific diseases and symptoms.

[0102] The analysis unit can analyze the social media activity of the patient and reflect related health information in the analysis. The analysis unit performs symptom analysis based on health information shared by the patient on social media, for example. The analysis unit can also refer to information on health-related accounts that the patient follows on social media. Furthermore, the analysis unit can also perform symptom analysis based on information on health communities that the patient participates in on social media. In this way, by analyzing social media activity, related health information can be reflected in the analysis. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the social media data of the patient to the generation AI and cause the generation AI to execute an analysis of the health information.

[0103] The presentation unit can estimate the patient's emotions and adjust the medicine presentation method based on the estimated patient's emotions. For example, when the patient feels anxious, the presentation unit provides a simple and easy-to-understand medicine presentation method. Furthermore, when the patient is relaxed, the presentation unit can also provide detailed medicine information. Furthermore, when the patient is in a hurry, the presentation unit can also provide a concise medicine presentation method that focuses on the main points. This allows the medicine to be presented in a form that is easy for the patient to understand by adjusting the medicine presentation method according to the patient's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the presentation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the presentation unit can input the patient's emotion data to the generation AI and cause the generation AI to adjust the medicine presentation method.

[0104] The presentation unit can select an appropriate drug by considering the patient's past drug allergies and side effect history at the time of presentation. The presentation unit, for example, selects a drug that does not cause allergies based on the patient's past drug allergies. The presentation unit can also select a drug with fewer side effects by considering the patient's past side effect history. Furthermore, the presentation unit can select an optimal drug based on the patient's past drug use history. This makes it possible to select an appropriate drug by considering the patient's past drug allergies and side effect history. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the patient's drug allergy data to the generation AI and cause the generation AI to select an appropriate drug.

[0105] The presentation unit can select a medicine taking into consideration the patient's lifestyle and dietary restrictions at the time of presentation. The presentation unit selects an appropriate medicine taking into consideration the patient's dietary restrictions, for example. The presentation unit can also select an optimal medicine taking into consideration the patient's lifestyle. Furthermore, the presentation unit can also select an appropriate medicine taking into consideration the patient's exercise habits. This makes it possible to select an optimal medicine for the patient by taking into consideration the lifestyle and dietary restrictions. A part or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the patient's lifestyle data into the generation AI and cause the generation AI to select a medicine.

[0106] The presentation unit can estimate the patient's emotion and determine the priority order of medicines based on the estimated emotion of the patient. For example, when the patient is in strong pain, the presentation unit preferentially presents medicines for that symptom. In addition, when the patient is in mild discomfort, the presentation unit can also postpone the medicine for that symptom. Furthermore, when the patient inputs multiple symptoms, the presentation unit can also prioritize medicines for symptoms with high importance based on the emotion. In this way, by determining the priority order of medicines based on the emotion of the patient, it is possible to preferentially present medicines for important symptoms. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the presentation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the presentation unit can input the emotion data of the patient to the generation AI and cause the generation AI to execute the determination of the priority order of medicines.

[0107] The presentation unit can present region-specific medicines and treatments in consideration of the geographical location information of the patient. For example, when the patient is in a particular region, the presentation unit presents medicines commonly used in that region. In addition, when the patient is traveling, the presentation unit can also present medicines available at the travel destination. Furthermore, the presentation unit can also present appropriate medicines based on the climate and environment of the region in which the patient lives. In this way, region-specific medicines and treatments can be presented by taking into account the geographical location information. Part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI or may be performed without using AI. For example, the presentation unit can input the geographical location data of the patient to the generation AI and cause the generation AI to execute the presentation of region-specific medicines and treatments.

[0108] The presentation unit can analyze the patient's social media activity and present relevant medicines and treatments. The presentation unit presents appropriate medicines based on, for example, health information shared by the patient on social media. The presentation unit can also refer to information on health-related accounts that the patient follows on social media. Furthermore, the presentation unit can present appropriate medicines based on information on health communities in which the patient participates on social media. In this way, relevant medicines and treatments can be presented by analyzing social media activity. Some or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the patient's social media data into the generation AI and cause the generation AI to present medicines and treatments.

[0109] The purchasing unit can estimate the patient's emotions and adjust the method of guidance for the purchase procedure based on the estimated patient's emotions. For example, if the patient feels anxious, the purchasing unit provides simple and easy-to-understand guidance for the purchase procedure. In addition, if the patient is relaxed, the purchasing unit can also provide detailed guidance for the purchase procedure. Furthermore, if the patient is in a hurry, the purchasing unit can provide guidance so that the patient can complete the purchase procedure quickly. In this way, by adjusting the guidance method for the purchase procedure according to the patient's emotions, the purchase procedure can be guided in a form that is easy for the patient to understand. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the purchasing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the purchasing unit can input the patient's emotion data into the generation AI and cause the generation AI to adjust the guidance method for the purchase procedure.

[0110] At the time of purchase, the purchasing unit can propose the optimal purchasing method by taking into account the patient's past purchase history. For example, the purchasing unit provides an option to purchase the same medicine again based on the medicine the patient has previously purchased. The purchasing unit can also propose the optimal payment method from the patient's past purchase history. Furthermore, the purchasing unit can analyze the patient's past purchase history and propose the most efficient purchasing method. In this way, the optimal purchasing method can be proposed to the patient by taking the past purchase history into account. A part or all of the above-mentioned processing in the purchasing unit may be performed, for example, using AI, or may be performed without using AI. For example, the purchasing unit can input the patient's purchase history data into the generating AI and cause the generating AI to execute a proposal of the optimal purchasing method.

[0111] The purchasing unit can provide a function of automatically inputting the patient's payment method and delivery address information at the time of purchase. For example, the purchasing unit automatically inputs the payment method used by the patient in the past. The purchasing unit can also automatically input the delivery address based on the patient's past delivery address information. Furthermore, the purchasing unit can automatically input the payment method and delivery address information based on the patient's account information. This makes it possible to streamline the purchase procedure by automatically inputting the payment method and delivery address information. Some or all of the above-mentioned processing in the purchasing unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing unit can input the patient's payment method and delivery address information into the generation AI and have the generation AI execute the automatic input.

[0112] The purchasing unit can estimate the patient's emotion and determine the priority of the purchasing procedure based on the estimated emotion of the patient. For example, if the patient is in strong pain, the purchasing unit prioritizes the purchasing procedure. Also, if the patient is in mild discomfort, the purchasing unit can postpone the purchasing procedure. Furthermore, if the patient purchases multiple medicines, the purchasing unit can prioritize the purchasing procedure of medicines with high importance based on the emotion. In this way, by determining the priority of the purchasing procedure based on the emotion of the patient, the purchasing procedure of important medicines can be prioritized. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the purchasing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the purchasing unit can input the emotion data of the patient to the generation AI and cause the generation AI to execute the determination of the priority of the purchasing procedure.

[0113] The purchasing department can select the optimal delivery method and delivery company by taking into account the geographical location information of the patient. For example, if the patient is in a specific area, the purchasing department selects the fastest delivery method in that area. In addition, if the patient is traveling, the purchasing department can also select a delivery method that allows the patient to receive the product at the travel destination. Furthermore, the purchasing department can select the optimal delivery company by taking into account the reliability of delivery companies in the area where the patient lives. In this way, the optimal delivery method and delivery company can be selected by taking into account the geographical location information. A part or all of the above-mentioned processing in the purchasing department may be performed, for example, using AI, or may be performed without using AI. For example, the purchasing department can input the geographical location data of the patient into the generation AI and cause the generation AI to select the optimal delivery method and delivery company.

[0114] The purchasing unit can analyze the social media activity of the patient and provide related purchase information and reviews. The purchasing unit can provide related reviews based on, for example, purchase information shared by the patient on social media. The purchasing unit can also refer to reviews of health-related accounts that the patient follows on social media. Furthermore, the purchasing unit can provide purchase information based on reviews of health communities in which the patient participates on social media. In this way, by analyzing social media activity, related purchase information and reviews can be provided. Part or all of the above-mentioned processing in the purchasing unit can be performed, for example, using AI or without using AI. For example, the purchasing unit can input the social media data of the patient into the generation AI and cause the generation AI to provide purchase information and reviews.

[0115] The dispatch unit can estimate the patient's emotion and adjust the method of the dispatch notification based on the estimated emotion of the patient. For example, when the patient feels anxious, the dispatch unit provides a simple and easy-to-understand dispatch notification. In addition, when the patient is relaxed, the dispatch unit can also provide a detailed dispatch notification. Furthermore, when the patient is in a hurry, the dispatch unit can quickly provide the dispatch notification. In this way, by adjusting the method of the dispatch notification according to the emotion of the patient, the dispatch notification can be provided in a form that is easy for the patient to understand. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the dispatch unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dispatch unit can input the emotion data of the patient to the generation AI and cause the generation AI to adjust the method of the dispatch notification.

[0116] At the time of delivery, the shipping unit can select the optimal delivery method by taking into account the patient's past delivery history. The shipping unit selects the optimal delivery method based on, for example, the patient's past delivery history. The shipping unit can also select a highly reliable delivery company from the patient's past delivery history. Furthermore, the shipping unit can analyze the patient's past delivery history and select the most efficient delivery method. In this way, the optimal delivery method can be selected by taking into account the past delivery history. Some or all of the above-mentioned processing in the shipping unit may be performed, for example, using AI, or may be performed without using AI. For example, the shipping unit can input the patient's delivery history data into the generation AI and cause the generation AI to select the optimal delivery method.

[0117] The dispatch unit can adjust the delivery schedule at the time of dispatch, taking into consideration the patient's living situation and the time period when the patient can receive the medicine. For example, if the patient is at work, the dispatch unit adjusts the delivery schedule to match the time when the patient finishes work. In addition, if the patient is at home, the dispatch unit can perform immediate delivery. Furthermore, if the patient is traveling, the dispatch unit can adjust the delivery schedule so that the patient can receive the medicine at the travel destination. In this way, the optimal delivery schedule can be provided to the patient by taking into consideration the living situation and the time period when the patient can receive the medicine. Some or all of the above-mentioned processing in the dispatch unit may be performed, for example, using AI, or may be performed without using AI. For example, the dispatch unit can input the living situation data of the patient to the generation AI and cause the generation AI to adjust the delivery schedule.

[0118] The dispatch unit can estimate the patient's emotion and determine the priority of the shipping notification based on the estimated emotion of the patient. For example, when the patient is in strong pain, the dispatch unit can prioritize the shipping notification. Also, when the patient is in mild discomfort, the dispatch unit can postpone the shipping notification. Furthermore, when the patient purchases multiple medicines, the dispatch unit can prioritize the shipping notification of the medicine with high importance based on the emotion. In this way, the shipping notification of the important medicine can be prioritized by determining the priority of the shipping notification based on the emotion of the patient. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the dispatch unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dispatch unit can input the emotion data of the patient to the generation AI and cause the generation AI to execute the determination of the priority of the shipping notification.

[0119] The dispatch unit can select an optimal delivery route by taking into account the geographical location information of the patient. For example, when the patient is in a specific area, the dispatch unit selects the quickest delivery route in that area. In addition, when the patient is traveling, the dispatch unit can also select a delivery route that allows the patient to receive the package at the travel destination. Furthermore, the dispatch unit can also select an optimal delivery route by taking into account the traffic conditions in the area where the patient lives. In this way, the optimal delivery route can be selected by taking into account the geographical location information. A part or all of the above-mentioned processing in the dispatch unit may be performed, for example, using AI, or may be performed without using AI. For example, the dispatch unit can input the geographical location data of the patient to the generation AI and cause the generation AI to select an optimal delivery route.

[0120] The dispatch unit can analyze the social media activity of the patient and provide related delivery information and reviews. For example, the dispatch unit can provide related reviews based on delivery information shared by the patient on social media. The dispatch unit can also refer to reviews of health-related accounts that the patient follows on social media. Furthermore, the dispatch unit can provide delivery information based on reviews of health communities in which the patient participates on social media. In this way, by analyzing social media activity, related delivery information and reviews can be provided. Some or all of the above-mentioned processing in the dispatch unit may be performed, for example, using AI, or may be performed without using AI. For example, the dispatch unit can input the social media data of the patient into the generation AI and cause the generation AI to provide delivery information and reviews.

[0121] The medical history unit can estimate the patient's emotion and adjust the input method of the medical history based on the estimated emotion of the patient. For example, when the patient feels anxious, the medical history unit provides a simple and intuitive interface and minimizes the input procedure. In addition, when the patient is relaxed, the medical history unit can provide detailed input options and suggest a customizable input method. Furthermore, when the patient is in a hurry, the medical history unit can prioritize voice input and enable the patient to input the medical history quickly. This allows the patient to input the medical history comfortably by adjusting the input method of the medical history according to the patient's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the medical history unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the medical history unit can input the patient's emotion data to the generation AI and cause the generation AI to adjust the input method of the medical history.

[0122] The medical history section can provide a function of automatically inputting the patient's past medical history and family history when inputting the medical history. The medical history section automatically inputs related information based on the patient's past medical history, for example. The medical history section can also automatically input genetic factors in consideration of the patient's family history. Furthermore, the medical history section can provide an optimal input method based on the patient's past medical history and family history. This improves the efficiency of medical history input by automatically inputting the past medical history and family history. A part or all of the above-mentioned processing in the medical history section may be performed, for example, using AI, or may be performed without using AI. For example, the medical history section can input the patient's medical history data to the generation AI and cause the generation AI to execute the automatic input.

[0123] The medical history section can estimate the patient's emotion and determine the priority of the medical history based on the estimated emotion of the patient. For example, when the patient is in strong pain, the medical history section prioritizes input of the medical history. In addition, when the patient is in mild discomfort, the medical history section can postpone input of the medical history. Furthermore, when the patient inputs multiple medical histories, the medical history section can prioritize input of medical history with high importance based on emotion. In this way, by determining the priority of the medical history based on the emotion of the patient, it is possible to input important medical history with priority. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the medical history section may be performed using, for example, an AI, or may be performed without using an AI. For example, the medical history section can input the emotion data of the patient to the generation AI and cause the generation AI to execute the determination of the priority of the medical history.

[0124] The medical history section can provide region-specific medical history information by taking into account the geographical location information of the patient. For example, when the patient is in a particular region, the medical history section can provide medical history information on diseases prevalent in that region. In addition, when the patient is traveling, the medical history section can also provide medical history information on disease risks at the travel destination. Furthermore, the medical history section can provide relevant medical history information based on the climate and environment of the region in which the patient lives. In this way, region-specific medical history information can be provided by taking into account the geographical location information. Some or all of the above-mentioned processing in the medical history section can be performed, for example, using AI or can be performed without using AI. For example, the medical history section can input the geographical location data of the patient to the generation AI and cause the generation AI to provide region-specific medical history information.

[0125] The supervision unit can estimate the patient's emotions and adjust the display method of the supervision content based on the estimated patient's emotions. For example, when the patient feels anxious, the supervision unit provides simple and easy-to-understand supervision content. In addition, when the patient is relaxed, the supervision unit can also provide detailed supervision content. Furthermore, when the patient is in a hurry, the supervision unit can also provide concise supervision content that focuses on the main points. In this way, the supervision content can be provided in a form that is easy for the patient to understand by adjusting the display method of the supervision content according to the patient's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the supervision unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the supervision unit can input the patient's emotion data into the generation AI and cause the generation AI to adjust the display method of the supervision content.

[0126] During supervision, the supervision department can select the optimal supervision method by taking into account the medical expert's past supervision history. The supervision department selects the optimal supervision method based on, for example, the medical expert's past supervision history. The supervision department can also select a highly reliable supervision method from the medical expert's past supervision history. Furthermore, the supervision department can analyze the medical expert's past supervision history and select the most efficient supervision method. In this way, the optimal supervision method can be selected by taking into account the past supervision history. Some or all of the above-mentioned processing in the supervision department may be performed, for example, using AI, or may be performed without using AI. For example, the supervision department can input the medical expert's supervision history data into the generation AI and cause the generation AI to select the optimal supervision method.

[0127] The supervision unit can estimate the patient's emotion and determine the priority of the supervision contents based on the estimated emotion of the patient. For example, when the patient is in strong pain, the supervision unit preferentially displays the supervision contents. Also, when the patient is in mild discomfort, the supervision unit can postpone the supervision contents. Furthermore, when the patient inputs multiple symptoms, the supervision unit can prioritize the supervision contents of symptoms with high importance based on the emotion. In this way, by determining the priority of the supervision contents based on the emotion of the patient, it is possible to provide important supervision contents preferentially. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the supervision unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the supervision unit can input the emotion data of the patient to the generation AI and cause the generation AI to execute the determination of the priority of the supervision contents.

[0128] The supervision unit can provide region-specific supervision information taking into account the geographic location information of the patient. For example, if the patient is in a particular region, the supervision unit can provide supervision information on diseases prevalent in that region. In addition, if the patient is traveling, the supervision unit can also provide supervision information on disease risks at the travel destination. Furthermore, the supervision unit can provide relevant supervision information based on the climate and environment of the region in which the patient lives. In this way, region-specific supervision information can be provided by taking into account the geographic location information. Some or all of the above-mentioned processing in the supervision unit may be performed, for example, using AI or may be performed without using AI. For example, the supervision unit can input the geographic location data of the patient to the generation AI and cause the generation AI to provide region-specific supervision information.

[0129] The guidance unit can estimate the patient's emotion and adjust the guidance method based on the estimated patient's emotion. For example, when the patient feels anxious, the guidance unit provides a simple and easy-to-understand guidance method. Furthermore, when the patient is relaxed, the guidance unit can also provide a detailed guidance method. Furthermore, when the patient is in a hurry, the guidance unit can also enable the patient to complete the guidance quickly. This allows the guidance to be provided in a form that is easy for the patient to understand by adjusting the guidance method according to the patient's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the guidance unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the guidance unit can input the patient's emotion data to the generation AI and cause the generation AI to adjust the guidance method.

[0130] The guidance unit can select the optimal guidance method by taking into consideration the patient's past medical history when providing guidance. The guidance unit selects the optimal guidance method based on, for example, the patient's past medical history. The guidance unit can also select a highly reliable guidance method from the patient's past medical history. Furthermore, the guidance unit can analyze the patient's past medical history and select the most efficient guidance method. In this way, the optimal guidance method can be selected by taking into consideration the past medical history. A part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the patient's medical history data to the generation AI and cause the generation AI to select the optimal guidance method.

[0131] The guidance unit can estimate the patient's emotion and determine the priority of the guidance contents based on the estimated emotion of the patient. For example, when the patient is in strong pain, the guidance unit preferentially displays the guidance contents. Also, when the patient is in mild discomfort, the guidance unit can postpone the guidance contents. Furthermore, when the patient inputs multiple symptoms, the guidance unit can prioritize the guidance contents of the symptoms with high importance based on the emotion. In this way, by determining the priority of the guidance contents based on the emotion of the patient, important guidance contents can be provided preferentially. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the guidance unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the guidance unit can input the emotion data of the patient to the generation AI and cause the generation AI to execute the determination of the priority of the guidance contents.

[0132] The guidance unit can provide region-specific guidance information by taking into account the geographical location information of the patient. For example, when the patient is in a particular region, the guidance unit can provide guidance information on diseases prevalent in that region. In addition, when the patient is traveling, the guidance unit can also provide guidance information on disease risks at the travel destination. Furthermore, the guidance unit can provide relevant guidance information based on the climate and environment of the region in which the patient lives. In this way, region-specific guidance information can be provided by taking into account the geographical location information. A part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the geographical location data of the patient to the generation AI and cause the generation AI to provide region-specific guidance information.

[0133] The security unit can estimate the patient's emotions and adjust the method of security notification based on the estimated patient's emotions. For example, if the patient feels anxious, the security unit provides a simple and easy-to-understand security notification. In addition, the security unit can also provide a detailed security notification if the patient is relaxed. Furthermore, the security unit can quickly provide a security notification if the patient is in a hurry. This allows the security notification to be provided in a form that is easy for the patient to understand by adjusting the method of security notification according to the patient's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the security unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the security unit can input the patient's emotion data to the generation AI and cause the generation AI to adjust the method of security notification.

[0134] The security unit can select the optimal security measure in consideration of the patient's past security history when taking security measures. The security unit selects the optimal security measure based on the patient's past security history, for example. The security unit can also select a highly reliable security measure from the patient's past security history. Furthermore, the security unit can analyze the patient's past security history and select the most efficient security measure. In this way, the optimal security measure can be selected by taking the past security history into consideration. A part or all of the above-mentioned processing in the security unit may be performed, for example, using AI, or may be performed without using AI. For example, the security unit can input the patient's security history data to the generation AI and cause the generation AI to select the optimal security measure.

[0135] The security unit can estimate the patient's emotion and determine the priority of security notifications based on the estimated emotion of the patient. For example, if the patient feels strong anxiety, the security unit can prioritize the security notification. Also, if the patient feels mild anxiety, the security unit can postpone the security notification. Furthermore, if the patient receives multiple security notifications, the security unit can prioritize notifications with high importance based on the emotion. In this way, by determining the priority of security notifications based on the emotion of the patient, important security notifications can be provided preferentially. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the security unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the security unit can input the emotion data of the patient to the generation AI and cause the generation AI to execute the determination of the priority of security notifications.

[0136] The security unit can provide region-specific security information by taking into account the geographical location information of the patient. For example, if the patient is in a particular region, the security unit can provide information on security risks occurring in that region. In addition, if the patient is traveling, the security unit can also provide information on security risks at the travel destination. Furthermore, the security unit can provide related information by taking into account the security situation in the region where the patient lives. In this way, region-specific security information can be provided by taking into account the geographical location information. Some or all of the above-mentioned processing in the security unit may be performed, for example, using AI or may be performed without using AI. For example, the security unit can input the geographical location data of the patient to the generation AI and cause the generation AI to provide region-specific security information.

[0137] The guideline unit can estimate the patient's emotion and adjust the display method of the guideline based on the estimated emotion of the patient. For example, when the patient feels anxious, the guideline unit provides a simple and easy-to-understand guideline. Furthermore, the guideline unit can also provide a detailed guideline when the patient is relaxed. Furthermore, when the patient is in a hurry, the guideline unit can enable the patient to quickly check the guideline. In this way, the guideline can be provided in a form that is easy for the patient to understand by adjusting the display method of the guideline according to the emotion of the patient. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the guideline unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the guideline unit can input the emotion data of the patient to the generation AI and cause the generation AI to adjust the display method of the guideline.

[0138] When providing a guideline, the guideline unit can provide an optimal guideline by taking into account the patient's past usage history. The guideline unit provides an optimal guideline based on, for example, the patient's past usage history. The guideline unit can also provide a highly reliable guideline from the patient's past usage history. Furthermore, the guideline unit can analyze the patient's past usage history and provide the most efficient guideline. This makes it possible to provide an optimal guideline by taking into account the past usage history. Some or all of the above-mentioned processing in the guideline unit may be performed using, for example, AI, or may be performed without using AI. For example, the guideline unit can input the patient's usage history data to the generation AI and cause the generation AI to provide the optimal guideline.

[0139] The guideline unit can estimate the patient's emotion and determine the priority of the guideline based on the estimated emotion of the patient. For example, when the patient feels strong anxiety, the guideline unit preferentially displays the guideline. Also, when the patient feels mild anxiety, the guideline unit can postpone the guideline. Furthermore, when the patient checks multiple guidelines, the guideline unit can prioritize a guideline with a high importance based on emotion. In this way, important guidelines can be provided preferentially by determining the priority of the guidelines based on the emotion of the patient. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the guideline unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the guideline unit can input the emotion data of the patient to the generation AI and cause the generation AI to execute the determination of the priority of the guidelines.

[0140] The guideline unit can provide region-specific guideline information taking into account the geographical location information of the patient. For example, when the patient is in a particular region, the guideline unit can provide guidelines regarding health risks occurring in that region. In addition, when the patient is traveling, the guideline unit can also provide guidelines regarding health risks at the travel destination. Furthermore, the guideline unit can provide relevant guidelines taking into account the health situation of the region in which the patient lives. In this way, region-specific guideline information can be provided by taking into account the geographical location information. Some or all of the above-mentioned processing in the guideline unit can be performed, for example, using AI or can be performed without using AI. For example, the guideline unit can input the geographical location data of the patient to the generation AI and cause the generation AI to provide region-specific guideline information.

[0141] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0142] The medical cost reduction system may further include a lifestyle habit unit that monitors the patient's lifestyle. The lifestyle habit unit collects daily activity data of the patient and provides it to the analysis unit. For example, the patient's amount of exercise, diet, sleep patterns, etc. may be monitored, and the health condition may be evaluated based on this data. The lifestyle habit unit may also provide the patient with advice to encourage healthy lifestyle habits. This may improve the patient's lifestyle and reduce medical costs from the perspective of preventive medicine.

[0143] The medical cost containment system may further include a psychological evaluation unit that evaluates the psychological state of the patient. The psychological evaluation unit evaluates the psychological state of the patient and provides it to the analysis unit. For example, the patient's stress level and anxiety may be evaluated, and a comprehensive health condition may be determined based on this data. The psychological evaluation unit may also provide the patient with advice on relaxation methods and stress management. This may support the patient's psychological health and improve the patient's overall health.

[0144] The medical cost containment system can further include a social support unit that evaluates the patient's social support network. The social support unit evaluates the patient's relationships with family and friends and provides the results to the analysis unit. For example, it can evaluate the degree of social support the patient receives and comprehensively assess the patient's health condition based on this data. The social support unit can also provide the patient with advice on strengthening social support. This can support the patient's social health and improve the patient's overall health condition.

[0145] The medical cost reduction system can further include a genetic information unit that analyzes the genetic information of the patient. The genetic information unit collects the genetic information of the patient and provides it to the analysis unit. For example, the genetic risk factors of the patient can be evaluated and a comprehensive health condition can be determined based on this data. The genetic information unit can also provide preventive measures to the patient based on the genetic risk. This makes it possible to realize personalized medicine that takes into account the genetic risk of the patient and reduce medical costs.

[0146] The medical cost reduction system can further include an environmental evaluation unit that evaluates the patient's environmental factors. The environmental evaluation unit evaluates the patient's living environment and work environment and provides the results to the analysis unit. For example, the air quality and noise level of the patient's living environment can be evaluated, and the health condition can be comprehensively judged based on this data. The environmental evaluation unit can also provide the patient with advice on improving their environment. This makes it possible to realize health management that takes into account the patient's environmental factors, and reduce medical costs.

[0147] The medical cost reduction system may further include an emotion advice unit that estimates the emotion of the patient and provides health advice based on the estimated emotion. The emotion advice unit analyzes the emotion of the patient and provides appropriate health advice. For example, if the patient is feeling stressed, the emotion advice unit may suggest relaxation methods. Also, if the patient is feeling anxious, the emotion advice unit may provide information that provides a sense of security. This makes it possible to provide health advice according to the emotion of the patient and improve the overall health condition.

[0148] The medical cost reduction system can further include a diet monitoring unit that monitors the patient's dietary content. The diet monitoring unit collects the patient's dietary content and provides it to the analysis unit. For example, it can evaluate the nutritional balance of the patient's diet and make a comprehensive judgment of the patient's health condition based on this data. The diet monitoring unit can also suggest nutritionally balanced meals to the patient. This can improve the patient's dietary content and reduce medical costs from the perspective of preventive medicine.

[0149] The medical cost reduction system can further include an exercise monitoring unit that monitors the patient's exercise habits. The exercise monitoring unit collects the patient's amount of exercise and provides it to the analysis unit. For example, it can evaluate the patient's lack of exercise and comprehensively judge the patient's health condition based on this data. The exercise monitoring unit can also propose an appropriate exercise plan to the patient. This can improve the patient's exercise habits and reduce medical costs from the perspective of preventive medicine.

[0150] The medical cost reduction system may further include a sleep monitoring unit that monitors the patient's sleep pattern. The sleep monitoring unit collects the patient's sleep data and provides it to the analysis unit. For example, the sleep quality of the patient may be evaluated, and the health condition may be comprehensively determined based on the data. The sleep monitoring unit may also provide the patient with advice to promote good quality sleep. This may improve the patient's sleep pattern and improve the overall health condition.

[0151] The medical cost reduction system can further include an emotion payment unit that estimates the patient's emotions and proposes a medical cost payment method based on the estimated emotions. The emotion payment unit analyzes the patient's emotions and proposes an appropriate payment method. For example, if the patient feels anxious, it can propose installment payments. Also, if the patient feels relaxed, it can propose a lump sum payment. In this way, it is possible to propose a payment method according to the patient's emotions and reduce the burden of medical expenses.

[0152] The process flow of the second embodiment will be briefly described below.

[0153] Step 1: The reception unit receives the patient's symptoms. The patient can input specific symptoms such as "sore throat" or "fever." This allows symptoms such as cold, headache, stomachache, etc. to be received. Step 2: The analysis unit uses the generation AI to analyze the symptoms received by the reception unit and conducts a first examination. The first examination is based on an evaluation of the severity of the symptoms and a diagnostic algorithm. The analysis unit also analyzes the details of the symptoms and determines whether they can be treated with over-the-counter medication or whether a visit to a hospital is necessary. Step 3: The presentation unit uses the generation AI to present over-the-counter drugs based on the results determined by the analysis unit. The presentation unit presents appropriate drugs to the patient and provides detailed information such as the drug's efficacy, side effects, and usage instructions. Step 4: The purchasing department purchases the medicines presented by the presenting department online. The online purchase is done through a website or an app. Step 5: The shipping department ships the medicine purchased by the purchasing department to the patient's home. The shipping is done through a delivery company, and the medicine is delivered to the patient's home promptly.

[0154] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0155] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0156] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements including the reception unit, analysis unit, presentation unit, purchase unit, shipping unit, medical history unit, supervision unit, guidance unit, security unit, and guideline unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives the symptoms of the patient. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the symptoms using a generation AI and performs a primary examination. The presentation unit is realized by the specific processing unit 290 of the data processing device 12 and presents appropriate over-the-counter drugs to the patient. The purchase unit is realized by the control unit 46A of the smart device 14 and purchases drugs online. The shipping unit is realized by the specific processing unit 290 of the data processing device 12 and ships the purchased drugs to the patient's home. The medical history unit is realized by the specific processing unit 290 of the data processing device 12 and takes into account the patient's past medical history. The supervision unit is realized by the specific processing unit 290 of the data processing device 12 and receives supervision from a medical professional. The guidance unit is realized by the specific processing unit 290 of the data processing device 12, and provides guidance when a visit to a hospital is necessary. The security unit is realized by the specific processing unit 290 of the data processing device 12, and implements security measures to protect personal information and prevent unauthorized access. The guideline unit is realized by the specific processing unit 290 of the data processing device 12, and provides usage guidelines. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various modifications are possible.

[0158] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0159] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0160] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0161] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0162] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0164] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0165] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0166] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0167] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0168] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0169] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0170] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0171] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0172] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements including the above-mentioned reception unit, analysis unit, presentation unit, purchase unit, shipping unit, medical history unit, supervision unit, guidance unit, security unit, and guideline unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives the patient's symptoms. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the symptoms using a generation AI and performs a primary examination. The presentation unit is realized by the specific processing unit 290 of the data processing device 12 and presents appropriate over-the-counter drugs to the patient. The purchase unit is realized by the control unit 46A of the smart glasses 214 and purchases drugs online. The shipping unit is realized by the specific processing unit 290 of the data processing device 12 and ships the purchased drugs to the patient's home. The medical history unit is realized by the specific processing unit 290 of the data processing device 12 and takes into account the patient's past medical history. The supervision unit is realized by the specific processing unit 290 of the data processing device 12, and is supervised by a medical professional. The guidance unit is realized by the specific processing unit 290 of the data processing device 12, and provides guidance when a visit to a hospital is necessary. The security unit is realized by the specific processing unit 290 of the data processing device 12, and takes security measures to protect personal information and prevent unauthorized access. The guideline unit is realized by the specific processing unit 290 of the data processing device 12, and provides usage guidelines. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various modifications are possible.

[0174] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0175] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0176] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0177] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0178] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0179] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0180] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0181] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0182] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0183] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0184] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0185] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0186] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0187] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0188] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0189] Each of the multiple elements including the above-mentioned reception unit, analysis unit, presentation unit, purchase unit, shipping unit, medical history unit, supervision unit, guidance unit, security unit, and guideline unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 and receives the symptoms of the patient. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the symptoms using a generation AI and performs a primary examination. The presentation unit is realized by the specific processing unit 290 of the data processing device 12 and presents appropriate over-the-counter drugs to the patient. The purchase unit is realized by the control unit 46A of the headset type terminal 314 and purchases drugs online. The shipping unit is realized by the specific processing unit 290 of the data processing device 12 and ships the purchased drugs to the patient's home. The medical history unit is realized by the specific processing unit 290 of the data processing device 12 and takes into account the patient's past medical history. The supervision unit is realized by the specific processing unit 290 of the data processing device 12, and is supervised by a medical professional. The guidance unit is realized by the specific processing unit 290 of the data processing device 12, and provides guidance when a visit to a hospital is necessary. The security unit is realized by the specific processing unit 290 of the data processing device 12, and takes security measures to protect personal information and prevent unauthorized access. The guideline unit is realized by the specific processing unit 290 of the data processing device 12, and provides usage guidelines. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various modifications are possible.

[0190] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0191] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0192] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0193] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0194] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0195] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0196] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0197] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0198] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0199] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0200] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0201] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0202] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0203] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0204] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0205] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0206] Each of the multiple elements including the reception unit, analysis unit, presentation unit, purchase unit, shipping unit, medical history unit, supervision unit, guidance unit, security unit, and guideline unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives the symptoms of the patient. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the symptoms using a generation AI and performs a primary examination. The presentation unit is realized by the specific processing unit 290 of the data processing device 12 and presents appropriate over-the-counter drugs to the patient. The purchase unit is realized by the control unit 46A of the robot 414 and purchases drugs online. The shipping unit is realized by the specific processing unit 290 of the data processing device 12 and ships the purchased drugs to the patient's home. The medical history unit is realized by the specific processing unit 290 of the data processing device 12 and takes into account the patient's past medical history. The supervision unit is realized by the specific processing unit 290 of the data processing device 12 and receives supervision from a medical professional. The guidance unit is realized by the specific processing unit 290 of the data processing device 12, and provides guidance when a visit to a hospital is necessary. The security unit is realized by the specific processing unit 290 of the data processing device 12, and implements security measures to protect personal information and prevent unauthorized access. The guideline unit is realized by the specific processing unit 290 of the data processing device 12, and provides usage guidelines. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various modifications are possible.

[0207] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0208] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0209] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0210] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0211] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0212] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0213] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0214] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0215] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0216] In addition, 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 upon request from the data processing device 12.

[0217] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0218] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0219] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0220] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0221] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0222] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0223] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0224] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0225] (Appendix 1) a reception section for receiving a patient's symptoms; an analysis unit that analyzes the symptoms received by the reception unit and performs a first medical examination; a presentation unit that presents over-the-counter drugs based on the results determined by the analysis unit; a purchasing unit that purchases the medicine presented by the presenting unit online; A shipping unit that ships the medicine purchased by the purchasing unit to the patient's home. A system characterized by: (Appendix 2) Equipped with a medical history section that performs analysis based on the patient's past medical history 2. The system of claim 1, (Appendix 3) Equipping a supervision department that is supervised by medical experts 2. The system of claim 1, (Appendix 4) Equipped with an information desk that provides guidance when a visit to a hospital is necessary 2. The system of claim 1, (Appendix 5) We have a security department that takes measures to protect personal information and prevent unauthorized access. 2. The system of claim 1, (Appendix 6) Equipped with a guidelines section that provides usage guidelines 2. The system of claim 1, (Appendix 7) The reception unit is Infer patient emotions and adjust symptom entry methods based on the inferred emotions 2. The system of claim 1, (Appendix 8) The reception unit is Analyzes the patient's past symptom input history and provides the optimal input interface 2. The system of claim 1, (Appendix 9) Guides symptom entry based on the patient's current life situation and environment 2. The system of claim 1, (Appendix 10) The reception unit is Estimate the patient's emotions and prioritize the input symptoms based on the estimated patient emotions 2. The system of claim 1, (Appendix 11) The reception unit is Provide information about local symptoms and diseases based on a patient's geographic location 2. The system of claim 1, (Appendix 12) The reception unit is Analyze patients' social media activity to obtain relevant symptoms and health information 2. The system of claim 1, (Appendix 13) The analysis unit is Infer the patient's emotions and adjust the presentation of analysis results based on the inferred emotions. 2. The system of claim 1, (Appendix 14) During analysis, improve accuracy based on the patient's past medical and family history 2. The system of claim 1, (Appendix 15) At the time of analysis, the analysis will be based on the patient's lifestyle and environmental factors. 2. The system of claim 1, (Appendix 16) The analysis unit is Inferring the patient's emotions and prioritizing the analysis results based on the estimated patient's emotions 2. The system of claim 1, (Appendix 17) Perform analysis of local diseases and symptoms based on the patient's geographic location 2. The system of claim 1, (Appendix 18) The analysis unit is Analyze patients' social media activity and incorporate relevant health information into the analysis 2. The system of claim 1, (Appendix 19) The presentation unit is Infer patient emotions and adjust the way medicine is presented based on the inferred emotions 2. The system of claim 1, (Appendix 20) The presentation unit is When presenting, select the appropriate medication, taking into consideration the patient's past drug allergies and side effects history. 2. The system of claim 1, (Appendix 21) Drug selection based on the patient's lifestyle and dietary restrictions 2. The system of claim 1, (Appendix 22) The presentation unit is Estimate the patient's emotions and prioritize medications based on the estimated patient emotions 2. The system of claim 1, (Appendix 23) The presentation unit is Considering a patient's geographic location, suggesting localized medicines and treatments 2. The system of claim 1, (Appendix 24) The presentation unit is Analyzing patients' social media activity and suggesting relevant medications and treatments 2. The system of claim 1, (Appendix 25) The purchasing department: Infer patient emotions and tailor purchase guidance based on the inferred emotions 2. The system of claim 1, (Appendix 26) The purchasing department: When purchasing, suggest the best way to purchase by taking into account the patient's past purchase history 2. The system of claim 1, (Appendix 27) The purchasing department: Offer the ability to auto-fill patient payment and shipping information at the time of purchase 2. The system of claim 1, (Appendix 28) The purchasing department: Infer patient sentiment and prioritize checkout based on the inferred sentiment 2. The system of claim 1, (Appendix 29) Select the best delivery method and carrier based on the patient's geographic location 2. The system of claim 1, (Appendix 30) The purchasing department: Analyze patients' social media activity to provide relevant purchase information and reviews 2. The system of claim 1, (Appendix 31) The dispatch unit includes: Infer patient sentiment and adjust shipping notification methods based on the inferred patient sentiment 2. The system of claim 1, (Appendix 32) The dispatch unit includes: At the time of shipment, the optimal delivery method is selected taking into account the patient's past delivery history. 2. The system of claim 1, (Appendix 33) Adjust delivery schedules based on patients' living situations and availability 2. The system of claim 1, (Appendix 34) The dispatch unit includes: Estimating patient sentiment and prioritizing dispatch notifications based on the estimated patient sentiment 2. The system of claim 1, (Appendix 35) Optimize delivery routes based on the patient's geographic location 2. The system of claim 1, (Appendix 36) The dispatch unit includes: Analyze patients' social media activity to provide relevant delivery information and reviews 2. The system of claim 1, (Appendix 37) The medical history section includes: Inferring patient emotions and adjusting the way medical history is entered based on the inferred emotions 3. The system of claim 2, (Appendix 38) The medical history section includes: Provide a function to automatically input a patient's past medical history and family history when entering a medical history. 3. The system of claim 2, (Appendix 39) The medical history section includes: Estimate the patient's emotions and prioritize medical histories based on the estimated patient emotions 3. The system of claim 2, (Appendix 40) The medical history section includes: Provide location-specific medical history information based on the patient's geographic location 3. The system of claim 2, (Appendix 41) The supervisory department: Inferring patient emotions and adjusting the presentation of revision content based on the estimated patient emotions 4. The system of claim 3. (Appendix 42) The supervisory department: During supervision, the most appropriate supervision method is selected taking into account the medical expert's past supervision history. 4. The system of claim 3. (Appendix 43) The supervisory department: Estimate the patient's emotions and prioritize the supervision content based on the estimated patient's emotions 4. The system of claim 3. (Appendix 44) The supervisory department: Provide location-specific supervision information based on the patient's geographic location 4. The system of claim 3. (Appendix 45) The guide portion is Estimate the patient's emotions and adjust the guidance method based on the estimated patient's emotions 5. The system of claim 4. (Appendix 46) The guide portion is When providing information, the optimal guidance method is selected taking into account the patient's past medical history. 5. The system of claim 4. (Appendix 47) The guide portion is Estimate the patient's emotions and prioritize guidance content based on the estimated patient emotions 5. The system of claim 4. (Appendix 48) The guide portion is Provide location-specific guidance based on the patient's geographic location 5. The system of claim 4. (Appendix 49) The security unit includes: Inferring patient emotions and adjusting security notification methods based on the inferred patient emotions 6. The system of claim 5, (Appendix 50) The security unit includes: When implementing security measures, the most appropriate measures are selected taking into account the patient's past security history. 6. The system of claim 5, (Appendix 51) The security unit includes: Inferring patient emotions and prioritizing security notifications based on the inferred patient emotions 6. The system of claim 5, (Appendix 52) The security unit includes: Provide location-specific security information based on the patient's geographic location 6. The system of claim 5, (Appendix 53) The guideline portion includes: Estimate the patient's emotions and adjust the presentation of guidelines based on the estimated patient emotions 7. The system of claim 6, (Appendix 54) The guideline portion includes: When providing guidelines, provide optimal guidelines by taking into account the patient's past usage history 7. The system of claim 6, (Appendix 55) The guideline portion includes: Estimate patients' emotions and prioritize guidelines based on the estimated emotions 7. The system of claim 6, (Appendix 56) The guideline portion includes: Provide location-specific guideline information based on the patient's geographic location 7. The system of claim 6, [Explanation of symbols]

[0226] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception section for receiving a patient's symptoms; an analysis unit that analyzes the symptoms received by the reception unit and performs a first medical examination; a presentation unit that presents over-the-counter drugs based on the results determined by the analysis unit; a purchasing unit that purchases the medicine presented by the presenting unit online; A shipping unit that ships the medicine purchased by the purchasing unit to the patient's home. A system characterized by:

2. Equipped with a medical history section that performs analysis based on the patient's past medical history 2. The system of claim 1 .

3. Equipping a supervision department that is supervised by medical experts 2. The system of claim 1 .

4. Equipped with an information desk that provides guidance when a visit to a hospital is necessary 2. The system of claim 1 .

5. We have a security department that takes measures to protect personal information and prevent unauthorized access.

2. The system of claim 1 .

6. Equipped with a guidelines section that provides usage guidelines 2. The system of claim 1 .

7. The reception unit is Infer patient emotions and adjust symptom entry methods based on the inferred emotions 2. The system of claim 1 .

8. The reception unit is Analyzes the patient's past symptom input history and provides the optimal input interface 2. The system of claim 1 .

9. Guides symptom entry based on the patient's current life situation and environment 2. The system of claim 1 .

Images