System

Abstract

An object of a system according to an embodiment is to provide side effect information of a drug described in a prescription quickly and in detail.SOLUTION: A system includes a prescription information inputting unit, a generation AI, an information acquiring unit, and an information providing unit. The prescription information input unit inputs prescription information. The generating AI analyzes the prescription information inputted by the prescription information inputting unit. The information acquisition unit acquires information from a plurality of medicine databases. The information providing unit provides detailed side effect information based on the information acquired by the information acquisition unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to provide prompt and detailed information about side effects of drugs listed on prescriptions.

[0005] The system according to the embodiment aims to provide prompt and detailed information on side effects of prescription drugs. [Means for solving the problem]

[0006] The system according to the embodiment includes a prescription information input unit, a generation AI, an information acquisition unit, and an information provision unit. The prescription information input unit inputs prescription information. The generation AI analyzes the prescription information input by the prescription information input unit. The information acquisition unit acquires information from multiple pharmaceutical databases. The information provision unit provides detailed side effect information based on the information acquired by the information acquisition unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and in detail provide side effect information of the medicines prescribed. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) In the medical service system according to an embodiment of the present invention, when the drug information written on the prescription is input, the AI ​​generation immediately provides detailed side effect information, which allows the medical service system to deepen the patient's understanding of the drug's side effects and provide a sense of security.

[0029] A medical service system according to an embodiment includes a prescription information input unit, a generation AI, an information acquisition unit, and an information provision unit. The prescription information input unit inputs prescription information. For example, a patient or a medical professional inputs information about the medication listed on the prescription. The generation AI analyzes the prescription information input by the prescription information input unit. For example, the generation AI analyzes the name and dosage of the medication and extracts related information. The information acquisition unit acquires information from multiple pharmaceutical databases. For example, it collects information about side effects of medications from domestic and international pharmaceutical databases and academic paper databases. The information provision unit provides detailed side effect information based on the information acquired by the information acquisition unit. For example, it provides information that gastrointestinal disorders, bleeding tendency, and allergic reactions have been reported as side effects of aspirin 500 mg. This enables the medical service system according to an embodiment to deepen patients' understanding of medication side effects and provide a sense of security.

[0030] The prescription information input unit can analyze drug interactions and warn of potential risks. For example, when prescription information is input into the prescription information input unit, the generation AI automatically analyzes drug interactions and warns of potential risks. For example, it warns of the risk of bleeding from the combined use of aspirin and warfarin. The prescription information input unit also warns of dangerous combinations of certain drugs. For example, it warns of the risk of side effects from the combined use of statin drugs and grapefruit juice. The prescription information input unit also warns of the risk of bleeding from the combined use of antibiotics and anticoagulants. This makes it possible to improve patient safety by analyzing drug interactions and warning of potential risks.

[0031] The prescription information input unit can refer to the patient's past medical history and automatically assess the risk of allergies or hypersensitivity to specific drugs. For example, when prescription information is input into the prescription information input unit, the generation AI refers to the patient's past medical history and automatically assesses the risk of allergies or hypersensitivity to specific drugs. For example, it assesses the risk of penicillin allergy. The prescription information input unit also assesses the risk of hypersensitivity to NSAIDs. For example, it assesses the risk of allergy to sulfa drugs. This makes it possible to improve patient safety by referring to the patient's past medical history and assessing the risk of allergies and hypersensitivity.

[0032] The prescription information input unit can input prescription information using voice recognition technology. The prescription information input unit, for example, inputs prescription information using voice recognition technology, thereby reducing the effort required. For example, a patient can input the name and dosage of a medication by voice, and the generation AI automatically analyzes the information. The prescription information input unit also allows a healthcare professional to input prescription information by voice, and the generation AI automatically analyzes the information. For example, a patient can input medication information using the voice input function of a smartphone, and the generation AI automatically analyzes the information. This reduces the effort required by inputting prescription information using voice recognition technology.

[0033] The prescription information input unit can scan prescription information with a smartphone camera and automatically digitize it. The prescription information input unit, for example, scans prescription information with a smartphone camera and automatically digitizes it. For example, by simply taking a photo of the prescription, the generation AI automatically analyzes the information. The prescription information input unit also reads a QR code, which allows the generation AI to automatically analyze the information. For example, by reading the barcode on the prescription, the generation AI automatically analyzes the information. This allows prescription information to be scanned with a smartphone camera and automatically digitized, saving time and effort.

[0034] The information acquisition unit updates the information in the pharmaceutical database in real time, and can always provide the latest side effect information. The information acquisition unit, for example, updates the information in the pharmaceutical database in real time, and always provides the latest side effect information. For example, newly reported side effect information is immediately reflected. The information acquisition unit also periodically crawls the database to collect the latest information. For example, it automatically imports the latest information from pharmaceutical manufacturers. This allows the information in the pharmaceutical database to be updated in real time, and the latest side effect information to be always provided, thereby providing patients with the latest information.

[0035] The information acquisition unit can check the consistency of information between databases and, if there is a discrepancy, analyze the reason for it. The information acquisition unit, for example, checks the consistency of information between databases and, if there is a discrepancy, analyzes the reason for it. For example, it analyzes differences in side effect information reported in different databases. The information acquisition unit also identifies the cause of different side effect reports for the same drug. For example, it analyzes differences in database update frequency and information sources. In this way, by checking the consistency of information between databases and, if there is a discrepancy, analyzing the reason for it, highly reliable information can be provided.

[0036] The information acquisition unit can collect patient reviews or feedback in addition to the pharmaceutical database to reflect actual usage experiences. The information acquisition unit, for example, collects patient reviews and feedback in addition to the pharmaceutical database to reflect actual usage experiences. For example, patient testimonials may be integrated into the database. The information acquisition unit also collects feedback from online forums and social media. For example, feedback may be collected by conducting a questionnaire survey. In this way, by collecting patient reviews and feedback and reflecting actual usage experiences, more practical information can be provided.

[0037] The information acquisition unit can visually display the database information, allowing the user to intuitively understand it. The information acquisition unit, for example, visually displays the database information, allowing the user to intuitively understand it. For example, side effect information is displayed in a graph or chart. The information acquisition unit also indicates the effects and side effects of a drug with icons. For example, risk levels are displayed in different colors. In this way, the database information can be visually displayed, allowing the user to intuitively understand it, thereby improving the degree of comprehension of the information.

[0038] The information providing unit can provide the probability of side effects occurring based on statistical data, allowing the user to specifically understand the risks. The information providing unit, for example, provides the probability of side effects occurring based on statistical data, allowing the user to specifically understand the risks. For example, it shows how many people out of 1,000 will experience a side effect. The information providing unit also displays the occurrence probability by age and gender. For example, it shows the occurrence probability based on past clinical trial data. In this way, by providing the probability of side effects occurring based on statistical data, the user can specifically understand the risks.

[0039] The information providing unit can provide side effect information in the form of video or animation to make it easier to understand visually. For example, the information providing unit provides side effect information in the form of video or animation to make it easier to understand visually. For example, the mechanism of the side effect is explained using animation. The information providing unit also shows the symptoms of the side effect using video. For example, preventive measures for the side effect are explained using animation. In this way, by providing side effect information in the form of video or animation, it is easier for the user to understand visually.

[0040] The information providing unit provides side effect information in multiple languages, making it possible to accommodate international users. The information providing unit, for example, provides side effect information in multiple languages, making it possible to accommodate international users. For example, the information providing unit provides information in multiple languages, such as English, Spanish, and Chinese. The information providing unit also automatically translates information according to the user's language settings. For example, it accurately translates technical terms in each language. This allows side effect information to be provided in multiple languages, making it possible to accommodate international users.

[0041] The information providing unit can provide coping methods or preventive measures in addition to side effect information, thereby increasing the patient's sense of security. The information providing unit can, for example, provide coping methods and preventive measures in addition to side effect information, thereby increasing the patient's sense of security. For example, the information providing unit can explain specific coping methods in the event of a side effect. The information providing unit can also provide advice on lifestyle habits to prevent side effects. For example, the information providing unit can explain how to contact a medical institution in the event of a side effect. In this way, by providing coping methods and preventive measures in addition to side effect information, the patient's sense of security can be increased.

[0042] The information providing unit can check the patient's level of understanding in the form of a quiz, and provide additional information if the understanding is insufficient. The information providing unit, for example, checks the patient's level of understanding in the form of a quiz, and provides additional information if the understanding is insufficient. For example, it may ask a quiz about side effects and provide additional information depending on the rate of correct answers. The information providing unit may also ask a quiz about how to use a drug to check the level of understanding. For example, it may ask a quiz about the risk of side effects to check the level of understanding. In this way, the patient's level of understanding can be checked in the form of a quiz, and by providing additional information if the understanding is insufficient, the patient's level of understanding can be improved.

[0043] The information providing unit can provide an interactive simulation to deepen the patient's understanding. The information providing unit provides, for example, an interactive simulation to deepen the patient's understanding. For example, the mechanism by which a side effect occurs is shown by simulation. The information providing unit also shows the effects of a drug by simulation. For example, preventive measures for side effects are shown by simulation. In this way, by providing an interactive simulation, the patient's understanding can be deepened.

[0044] The information providing unit can collect patient feedback and reflect it in improving services. The information providing unit, for example, collects patient feedback and reflects it in improving services. For example, the information providing unit collects feedback by conducting an online survey. The information providing unit also sets up a feedback form to collect opinions. For example, the information providing unit periodically conducts patient interviews to collect opinions. In this way, patient feedback can be collected and reflected in improving services, thereby improving the quality of services.

[0045] The information providing unit provides specific usage examples in the form of a scenario, making it easier for the user to imagine actual usage situations. For example, the information providing unit provides specific usage examples in the form of a scenario, making it easier for the user to imagine actual usage situations. For example, the information providing unit shows a scenario in which a patient takes medicine. The information providing unit also shows a scenario for dealing with side effects when they occur. For example, the information providing unit shows a scenario until the effects of the medicine appear. In this way, by providing specific usage examples in the form of a scenario, it becomes easier for the user to imagine actual usage situations.

[0046] The information provision unit allows the generation AI to provide individualized advice based on usage examples. For example, the information provision unit may evaluate the risk of side effects for a specific medication and suggest specific ways to deal with them. The information provision unit may also suggest a medication schedule. For example, it may provide advice on how to store medication. In this way, the generation AI can provide individualized advice based on usage examples, allowing for more specific support to be provided to users.

[0047] The information providing unit can provide usage examples in the form of videos or animations to make them easier to understand visually. For example, the information providing unit can provide usage examples in the form of videos or animations to make them easier to understand visually. For example, the information providing unit can use videos to show how to take medicine. The information providing unit can also use animations to explain how to deal with side effects. For example, the information providing unit can use videos to show the process until the effects of the medicine appear. In this way, providing usage examples in the form of videos or animations makes it easier for the user to understand visually.

[0048] The information providing unit provides use cases in multiple languages, making it possible to accommodate international users. The information providing unit, for example, provides use cases in multiple languages, making it possible to accommodate international users. For example, use cases are provided in multiple languages, such as English, Spanish, and Chinese. The information providing unit also automatically translates use cases according to the user's language settings. For example, technical terms in each language are accurately translated. This allows use cases to be provided in multiple languages, making it possible to accommodate international users.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The medical service system may further include a lifestyle analysis unit that analyzes a patient's lifestyle. The lifestyle analysis unit collects data on the patient's diet, exercise, sleep patterns, etc., and identifies factors that affect the effectiveness and side effects of medication. For example, if a certain diet may reduce the effectiveness of a medication, the unit provides that information. The lifestyle analysis unit can also warn of the risk that lack of exercise increases the side effects of a certain medication. Furthermore, the system can analyze the impact of sleep patterns on medication effectiveness and suggest appropriate dosing times. This allows for customized advice based on the patient's lifestyle to maximize medication effectiveness and minimize the risk of side effects.

[0051] The medical service system can further include a genetic information analysis unit that analyzes a patient's genetic information. The genetic information analysis unit, for example, analyzes the patient's genetic data and evaluates responsiveness to a specific drug. For example, if a specific genetic mutation may reduce the effectiveness of a drug, the genetic information analysis unit can provide such information. The genetic information analysis unit can also display a warning to patients who are genetically at high risk of side effects. Furthermore, the genetic information analysis unit can also assist in selecting the most appropriate drug as part of personalized medicine. This makes it possible to provide customized treatment based on the patient's genetic information, thereby maximizing therapeutic effectiveness and minimizing the risk of side effects.

[0052] The medical service system can further include a social background analysis unit that analyzes the patient's social background. The social background analysis unit collects data on the patient's home environment, work environment, social support network, etc., and identifies factors that affect the effectiveness and side effects of medication. For example, if the patient's home environment is stressful, the social background analysis unit can provide stress management advice based on that information. Also, if the patient's work environment affects their medication schedule, the system can suggest appropriate medication times. Furthermore, if the patient lacks a social support network, the system can provide resources for receiving support. This makes it possible to maximize the effectiveness of treatment and minimize the risk of side effects by providing customized support based on the patient's social background.

[0053] The medical service system may further include an exercise data analysis unit that analyzes the patient's exercise data. The exercise data analysis unit collects data such as the patient's number of steps, heart rate, and exercise intensity, and identifies factors that affect the effectiveness and side effects of medication. For example, it may warn of the risk that lack of exercise increases the side effects of a particular medication. It may also provide information if exercise may enhance the effectiveness of a medication. Furthermore, the exercise data analysis unit may suggest an appropriate exercise plan. This allows for the provision of customized advice based on the patient's exercise data, maximizing the effectiveness of medication and minimizing the risk of side effects.

[0054] The medical service system may further include a nutritional data analysis unit that analyzes a patient's nutritional data. The nutritional data analysis unit collects data such as the patient's dietary content and nutrient intake, and identifies factors that affect the effectiveness and side effects of a drug. For example, if a specific nutrient may reduce the effectiveness of a drug, the unit provides that information. The nutritional data analysis unit may also warn of the risk that nutritional deficiencies may increase the side effects of a specific drug. Furthermore, the nutritional data analysis unit may also suggest an appropriate meal plan. This allows for customized advice based on the patient's nutritional data to maximize the effectiveness of the drug and minimize the risk of side effects.

[0055] The medical service system may further include a sleep data analysis unit that analyzes the patient's sleep data. The sleep data analysis unit collects data such as the patient's sleep duration, sleep quality, and sleep patterns, and identifies factors that affect the effectiveness and side effects of medication. For example, the sleep data analysis unit warns of the risk that lack of sleep increases the side effects of a particular medication. It can also provide information if good quality sleep may enhance the effectiveness of a medication. Furthermore, the sleep data analysis unit can suggest an appropriate sleeping environment. This allows for customized advice based on the patient's sleep data to maximize the effectiveness of medication and minimize the risk of side effects.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The prescription information input unit inputs prescription information. For example, a patient or a medical professional inputs information about the medicines listed on the prescription. Step 2: The generation AI analyzes the prescription information entered by the prescription information input unit. For example, the generation AI analyzes the name and dosage of the medicine and extracts related information. Step 3: The information acquisition unit acquires information from multiple drug databases. For example, it collects information on drug side effects from domestic and international drug databases and academic paper databases. Step 4: The information providing unit provides detailed side effect information based on the information acquired by the information acquiring unit. For example, it provides information that gastrointestinal disorders, bleeding tendency, and allergic reactions have been reported as side effects of aspirin 500 mg.

[0058] (Example 2) In the medical service system according to an embodiment of the present invention, when the drug information written on the prescription is input, the AI ​​generation immediately provides detailed side effect information, which allows the medical service system to deepen the patient's understanding of the drug's side effects and provide a sense of security.

[0059] A medical service system according to an embodiment includes a prescription information input unit, a generation AI, an information acquisition unit, and an information provision unit. The prescription information input unit inputs prescription information. For example, a patient or a medical professional inputs information about the medication listed on the prescription. The generation AI analyzes the prescription information input by the prescription information input unit. For example, the generation AI analyzes the name and dosage of the medication and extracts related information. The information acquisition unit acquires information from multiple pharmaceutical databases. For example, it collects information about side effects of medications from domestic and international pharmaceutical databases and academic paper databases. The information provision unit provides detailed side effect information based on the information acquired by the information acquisition unit. For example, it provides information that gastrointestinal disorders, bleeding tendency, and allergic reactions have been reported as side effects of aspirin 500 mg. This enables the medical service system according to an embodiment to deepen patients' understanding of medication side effects and provide a sense of security.

[0060] The prescription information input unit can analyze drug interactions and warn of potential risks. For example, when prescription information is input into the prescription information input unit, the generation AI automatically analyzes drug interactions and warns of potential risks. For example, it warns of the risk of bleeding from the combined use of aspirin and warfarin. The prescription information input unit also warns of dangerous combinations of certain drugs. For example, it warns of the risk of side effects from the combined use of statin drugs and grapefruit juice. The prescription information input unit also warns of the risk of bleeding from the combined use of antibiotics and anticoagulants. This makes it possible to improve patient safety by analyzing drug interactions and warning of potential risks.

[0061] The prescription information input unit can refer to the patient's past medical history and automatically assess the risk of allergies or hypersensitivity to specific drugs. For example, when prescription information is input into the prescription information input unit, the generation AI refers to the patient's past medical history and automatically assesses the risk of allergies or hypersensitivity to specific drugs. For example, it assesses the risk of penicillin allergy. The prescription information input unit also assesses the risk of hypersensitivity to NSAIDs. For example, it assesses the risk of allergy to sulfa drugs. This makes it possible to improve patient safety by referring to the patient's past medical history and assessing the risk of allergies and hypersensitivity.

[0062] The prescription information input unit can use the emotion estimation function to analyze the patient's anxieties or concerns about the medication and provide customized information based on that. For example, when the prescription information input unit inputs prescription information, the generation AI uses the emotion estimation function to analyze the patient's anxieties or concerns about the medication and provides customized information based on that. For example, it can provide specific advice to alleviate anxiety. The prescription information input unit also provides detailed explanations of the medication's side effects. For example, it can provide specific advice on how to use the medication and precautions. This allows the patient's anxieties and concerns to be analyzed and customized information to be provided, thereby increasing their sense of security.

[0063] The prescription information input unit can input prescription information using voice recognition technology. The prescription information input unit, for example, inputs prescription information using voice recognition technology, thereby reducing the effort required. For example, a patient can input the name and dosage of a medication by voice, and the generation AI automatically analyzes the information. The prescription information input unit also allows a healthcare professional to input prescription information by voice, and the generation AI automatically analyzes the information. For example, a patient can input medication information using the voice input function of a smartphone, and the generation AI automatically analyzes the information. This reduces the effort required by inputting prescription information using voice recognition technology.

[0064] The prescription information input unit can scan prescription information with a smartphone camera and automatically digitize it. The prescription information input unit, for example, scans prescription information with a smartphone camera and automatically digitizes it. For example, by simply taking a photo of the prescription, the generation AI automatically analyzes the information. The prescription information input unit also reads a QR code, which allows the generation AI to automatically analyze the information. For example, by reading the barcode on the prescription, the generation AI automatically analyzes the information. This allows prescription information to be scanned with a smartphone camera and automatically digitized, saving time and effort.

[0065] The prescription information input unit can use an emotion estimation function to analyze the patient's emotions in real time when inputting information and provide positive feedback. For example, when inputting prescription information, the prescription information input unit uses the emotion estimation function to analyze the patient's emotions in real time and provide positive feedback. For example, if the patient is feeling anxious, the prescription information input unit can display an encouraging message. The prescription information input unit can also provide advice to help the patient relax if they are feeling nervous. For example, it can provide a specific answer if the patient has a question. In this way, the patient's emotions in real time when inputting information can be analyzed and positive feedback can be provided, increasing the patient's sense of security.

[0066] The information acquisition unit updates the information in the pharmaceutical database in real time, and can always provide the latest side effect information. The information acquisition unit, for example, updates the information in the pharmaceutical database in real time, and always provides the latest side effect information. For example, newly reported side effect information is immediately reflected. The information acquisition unit also periodically crawls the database to collect the latest information. For example, it automatically imports the latest information from pharmaceutical manufacturers. This allows the information in the pharmaceutical database to be updated in real time, and the latest side effect information to be always provided, thereby providing patients with the latest information.

[0067] The information acquisition unit can check the consistency of information between databases and, if there is a discrepancy, analyze the reason for it. The information acquisition unit, for example, checks the consistency of information between databases and, if there is a discrepancy, analyzes the reason for it. For example, it analyzes differences in side effect information reported in different databases. The information acquisition unit also identifies the cause of different side effect reports for the same drug. For example, it analyzes differences in database update frequency and information sources. In this way, by checking the consistency of information between databases and, if there is a discrepancy, analyzing the reason for it, highly reliable information can be provided.

[0068] The information acquisition unit can use the emotion estimation function to evaluate the reliability of information acquired from the database and provide a sense of security to the user. The information acquisition unit, for example, uses the emotion estimation function to evaluate the reliability of information acquired from the database and provide a sense of security to the user. For example, a high emotion score is assigned to highly reliable information. The information acquisition unit also displays a warning for information with low reliability. For example, the source and basis of the information are clearly indicated. In this way, the reliability of information acquired from the database can be evaluated and a sense of security can be provided to the user, making it possible to provide highly reliable information.

[0069] The information acquisition unit can collect patient reviews or feedback in addition to the pharmaceutical database to reflect actual usage experiences. The information acquisition unit, for example, collects patient reviews and feedback in addition to the pharmaceutical database to reflect actual usage experiences. For example, patient testimonials may be integrated into the database. The information acquisition unit also collects feedback from online forums and social media. For example, feedback may be collected by conducting a questionnaire survey. In this way, by collecting patient reviews and feedback and reflecting actual usage experiences, more practical information can be provided.

[0070] The information acquisition unit can visually display the database information, allowing the user to intuitively understand it. The information acquisition unit, for example, visually displays the database information, allowing the user to intuitively understand it. For example, side effect information is displayed in a graph or chart. The information acquisition unit also indicates the effects and side effects of a drug with icons. For example, risk levels are displayed in different colors. In this way, the database information can be visually displayed, allowing the user to intuitively understand it, thereby improving the degree of comprehension of the information.

[0071] The information acquisition unit can use the emotion estimation function to collect the user's emotional reactions to the database information and improve the method of providing information. The information acquisition unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the database information and improve the method of providing information. For example, supplementary explanations are added to information that makes the user feel anxious. The information acquisition unit also restructures the information in a format that is easy for the user to understand. For example, information that elicits positive emotions is emphasized. In this way, by collecting the user's emotional reactions to the database information and improving the method of providing information, it is possible to improve the user's understanding and satisfaction.

[0072] The information providing unit can provide the probability of side effects occurring based on statistical data, allowing the user to specifically understand the risks. The information providing unit, for example, provides the probability of side effects occurring based on statistical data, allowing the user to specifically understand the risks. For example, it shows how many people out of 1,000 will experience a side effect. The information providing unit also displays the occurrence probability by age and gender. For example, it shows the occurrence probability based on past clinical trial data. In this way, by providing the probability of side effects occurring based on statistical data, the user can specifically understand the risks.

[0073] The information providing unit can use the emotion estimation function to analyze the degree to which the user understands the side effect information and provide additional information as needed. The information providing unit can, for example, use the emotion estimation function to analyze the degree to which the user understands the side effect information and provide additional information as needed. For example, if the level of understanding is low, a detailed explanation is added. The information providing unit can also provide illustrations or videos if the level of understanding is low. For example, if the level of understanding is low, additional information is provided in the form of an FAQ. In this way, the user's level of understanding can be improved by analyzing the degree to which the user understands the side effect information and providing additional information as needed.

[0074] The information providing unit can provide side effect information in the form of video or animation to make it easier to understand visually. For example, the information providing unit provides side effect information in the form of video or animation to make it easier to understand visually. For example, the mechanism of the side effect is explained using animation. The information providing unit also shows the symptoms of the side effect using video. For example, preventive measures for the side effect are explained using animation. In this way, by providing side effect information in the form of video or animation, it is easier for the user to understand visually.

[0075] The information providing unit provides side effect information in multiple languages, making it possible to accommodate international users. The information providing unit, for example, provides side effect information in multiple languages, making it possible to accommodate international users. For example, the information providing unit provides information in multiple languages, such as English, Spanish, and Chinese. The information providing unit also automatically translates information according to the user's language settings. For example, it accurately translates technical terms in each language. This allows side effect information to be provided in multiple languages, making it possible to accommodate international users.

[0076] The information providing unit can use the emotion estimation function to collect the user's emotional reactions to side effect information and improve the method of providing information. The information providing unit, for example, uses the emotion estimation function to collect the user's emotional reactions to side effect information and improve the method of providing information. For example, supplementary explanations are added to information that makes the user feel anxious. The information providing unit also restructures the information in a format that is easy for the user to understand. For example, information that elicits positive emotions is emphasized. In this way, by collecting the user's emotional reactions to side effect information and improving the method of providing information, it is possible to improve the user's understanding and satisfaction.

[0077] The information providing unit can provide coping methods or preventive measures in addition to side effect information, thereby increasing the patient's sense of security. The information providing unit can, for example, provide coping methods and preventive measures in addition to side effect information, thereby increasing the patient's sense of security. For example, the information providing unit can explain specific coping methods in the event of a side effect. The information providing unit can also provide advice on lifestyle habits to prevent side effects. For example, the information providing unit can explain how to contact a medical institution in the event of a side effect. In this way, by providing coping methods and preventive measures in addition to side effect information, the patient's sense of security can be increased.

[0078] The information providing unit can check the patient's level of understanding in the form of a quiz, and provide additional information if the understanding is insufficient. The information providing unit, for example, checks the patient's level of understanding in the form of a quiz, and provides additional information if the understanding is insufficient. For example, it may ask a quiz about side effects and provide additional information depending on the rate of correct answers. The information providing unit may also ask a quiz about how to use a drug to check the level of understanding. For example, it may ask a quiz about the risk of side effects to check the level of understanding. In this way, the patient's level of understanding can be checked in the form of a quiz, and by providing additional information if the understanding is insufficient, the patient's level of understanding can be improved.

[0079] The information providing unit can use the emotion estimation function to analyze the patient's anxiety or concerns in real time and provide appropriate support. The information providing unit, for example, uses the emotion estimation function to analyze the patient's anxiety or concerns in real time and provide appropriate support. For example, it displays an encouraging message to a patient who is feeling anxious. The information providing unit also provides specific ways of dealing with concerns to patients. For example, it provides detailed explanations to patients who have questions. In this way, the patient's anxiety or concerns can be analyzed in real time and appropriate support can be provided, thereby increasing the patient's sense of security.

[0080] The information providing unit can provide an interactive simulation to deepen the patient's understanding. The information providing unit provides, for example, an interactive simulation to deepen the patient's understanding. For example, the mechanism by which a side effect occurs is shown by simulation. The information providing unit also shows the effects of a drug by simulation. For example, preventive measures for side effects are shown by simulation. In this way, by providing an interactive simulation, the patient's understanding can be deepened.

[0081] The information providing unit can collect patient feedback and reflect it in improving services. The information providing unit, for example, collects patient feedback and reflects it in improving services. For example, the information providing unit collects feedback by conducting an online survey. The information providing unit also sets up a feedback form to collect opinions. For example, the information providing unit periodically conducts patient interviews to collect opinions. In this way, patient feedback can be collected and reflected in improving services, thereby improving the quality of services.

[0082] The information providing unit can use the emotion estimation function to collect the patient's emotional responses and improve the information provision method for promoting understanding. The information providing unit, for example, uses the emotion estimation function to collect the patient's emotional responses and improve the information provision method for promoting understanding. For example, supplementary explanations are added to information that makes the patient feel anxious. The information providing unit also restructures the information in a format that is easy for the patient to understand. For example, information that elicits positive emotions is emphasized. In this way, by collecting the patient's emotional responses and improving the information provision method for promoting understanding, it is possible to improve the patient's understanding and satisfaction.

[0083] The information providing unit provides specific usage examples in the form of a scenario, making it easier for the user to imagine actual usage situations. For example, the information providing unit provides specific usage examples in the form of a scenario, making it easier for the user to imagine actual usage situations. For example, the information providing unit shows a scenario in which a patient takes medicine. The information providing unit also shows a scenario for dealing with side effects when they occur. For example, the information providing unit shows a scenario until the effects of the medicine appear. In this way, by providing specific usage examples in the form of a scenario, it becomes easier for the user to imagine actual usage situations.

[0084] The information provision unit allows the generation AI to provide individualized advice based on usage examples. For example, the information provision unit may evaluate the risk of side effects for a specific medication and suggest specific ways to deal with them. The information provision unit may also suggest a medication schedule. For example, it may provide advice on how to store medication. In this way, the generation AI can provide individualized advice based on usage examples, allowing for more specific support to be provided to users.

[0085] The information providing unit can use the emotion estimation function to analyze the user's emotional response to a use case and provide an optimal use case. The information providing unit, for example, uses the emotion estimation function to analyze the user's emotional response to a use case and provide an optimal use case. For example, the information providing unit preferentially provides use cases that elicit positive emotions. The information providing unit also adds supplementary explanations to use cases that make the user feel uneasy. For example, the information providing unit reconstructs the use case in a format that is easy for the user to understand. In this way, the user's emotional response to a use case can be analyzed and the optimal use case can be provided, thereby improving the user's understanding and satisfaction.

[0086] The information providing unit can provide usage examples in the form of videos or animations to make them easier to understand visually. For example, the information providing unit can provide usage examples in the form of videos or animations to make them easier to understand visually. For example, the information providing unit can use videos to show how to take medicine. The information providing unit can also use animations to explain how to deal with side effects. For example, the information providing unit can use videos to show the process until the effects of the medicine appear. In this way, providing usage examples in the form of videos or animations makes it easier for the user to understand visually.

[0087] The information providing unit provides use cases in multiple languages, making it possible to accommodate international users. The information providing unit, for example, provides use cases in multiple languages, making it possible to accommodate international users. For example, use cases are provided in multiple languages, such as English, Spanish, and Chinese. The information providing unit also automatically translates use cases according to the user's language settings. For example, technical terms in each language are accurately translated. This allows use cases to be provided in multiple languages, making it possible to accommodate international users.

[0088] The information providing unit can use the emotion estimation function to collect the user's emotional reactions to the use cases and improve the method of providing information. The information providing unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the use cases and improve the method of providing information. For example, supplementary explanations are added to use cases that make the user feel anxious. The information providing unit also restructures the use cases in a format that is easy for the user to understand. For example, information that elicits positive emotions is emphasized. In this way, by collecting the user's emotional reactions to the use cases and improving the method of providing information, it is possible to improve the user's understanding and satisfaction.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The medical service system may further include a lifestyle analysis unit that analyzes a patient's lifestyle. The lifestyle analysis unit collects data on the patient's diet, exercise, sleep patterns, etc., and identifies factors that affect the effectiveness and side effects of medication. For example, if a certain diet may reduce the effectiveness of a medication, the unit provides that information. The lifestyle analysis unit can also warn of the risk that lack of exercise increases the side effects of a certain medication. Furthermore, the system can analyze the impact of sleep patterns on medication effectiveness and suggest appropriate dosing times. This allows for customized advice based on the patient's lifestyle to maximize medication effectiveness and minimize the risk of side effects.

[0091] The medical service system can further include a genetic information analysis unit that analyzes a patient's genetic information. The genetic information analysis unit, for example, analyzes the patient's genetic data and evaluates responsiveness to a specific drug. For example, if a specific genetic mutation may reduce the effectiveness of a drug, the genetic information analysis unit can provide such information. The genetic information analysis unit can also display a warning to patients who are genetically at high risk of side effects. Furthermore, the genetic information analysis unit can also assist in selecting the most appropriate drug as part of personalized medicine. This makes it possible to provide customized treatment based on the patient's genetic information, thereby maximizing therapeutic effectiveness and minimizing the risk of side effects.

[0092] The medical service system can also estimate the patient's emotions and adjust the medication schedule based on the estimated emotions. For example, if the patient is feeling stressed, it can suggest taking a medication with a relaxing effect. If the patient is feeling anxious, it can provide information to reassure the patient. Furthermore, if the patient is feeling fatigued, it can provide advice on how to get rest. This maximizes the effectiveness of treatment and increases the patient's sense of security by providing customized support based on the patient's emotions.

[0093] The medical service system can further include a social background analysis unit that analyzes the patient's social background. The social background analysis unit collects data on the patient's home environment, work environment, social support network, etc., and identifies factors that affect the effectiveness and side effects of medication. For example, if the patient's home environment is stressful, the social background analysis unit can provide stress management advice based on that information. Also, if the patient's work environment affects their medication schedule, the system can suggest appropriate medication times. Furthermore, if the patient lacks a social support network, the system can provide resources for receiving support. This makes it possible to maximize the effectiveness of treatment and minimize the risk of side effects by providing customized support based on the patient's social background.

[0094] The medical service system can further estimate the patient's emotions and provide advice on how to deal with medication side effects based on the estimated emotions. For example, if the patient feels anxious, specific advice on how to reduce anxiety can be provided. If the patient feels fear, information to reassure the patient can be provided. Furthermore, if the patient feels sad, an encouraging message can be displayed. In this way, by providing customized support based on the patient's emotions, it is possible to effectively provide advice on how to deal with side effects and increase the patient's sense of security.

[0095] The medical service system may further include an exercise data analysis unit that analyzes the patient's exercise data. The exercise data analysis unit collects data such as the patient's number of steps, heart rate, and exercise intensity, and identifies factors that affect the effectiveness and side effects of medication. For example, it may warn of the risk that lack of exercise increases the side effects of a particular medication. It may also provide information if exercise may enhance the effectiveness of a medication. Furthermore, the exercise data analysis unit may suggest an appropriate exercise plan. This allows for the provision of customized advice based on the patient's exercise data, maximizing the effectiveness of medication and minimizing the risk of side effects.

[0096] The medical service system can also estimate the patient's emotions and provide educational content about drug side effects based on the estimated emotions. For example, if the patient is feeling anxious, an educational video to alleviate the anxiety can be provided. If the patient has questions, educational materials with detailed explanations can be provided. Furthermore, if the patient is interested, relevant academic papers and research results can be provided. This allows for the provision of customized educational content based on the patient's emotions, deepening their understanding of side effects and increasing their sense of security.

[0097] The medical service system may further include a nutritional data analysis unit that analyzes a patient's nutritional data. The nutritional data analysis unit collects data such as the patient's dietary content and nutrient intake, and identifies factors that affect the effectiveness and side effects of a drug. For example, if a specific nutrient may reduce the effectiveness of a drug, the unit provides that information. The nutritional data analysis unit may also warn of the risk that nutritional deficiencies may increase the side effects of a specific drug. Furthermore, the nutritional data analysis unit may also suggest an appropriate meal plan. This allows for customized advice based on the patient's nutritional data to maximize the effectiveness of the drug and minimize the risk of side effects.

[0098] The medical service system can also estimate the patient's emotions and provide medication reminders based on the estimated emotions. For example, if the patient is forgetful, the system can send frequent reminders. If the patient is stressed, the system can include advice on how to relax in the reminder. Furthermore, if the patient is busy, the system can send reminders at appropriate times. This allows the system to provide customized reminders based on the patient's emotions, ensuring that the patient takes their medication and maximizing the effectiveness of treatment.

[0099] The medical service system may further include a sleep data analysis unit that analyzes the patient's sleep data. The sleep data analysis unit collects data such as the patient's sleep duration, sleep quality, and sleep patterns, and identifies factors that affect the effectiveness and side effects of medication. For example, the sleep data analysis unit warns of the risk that lack of sleep increases the side effects of a particular medication. It can also provide information if good quality sleep may enhance the effectiveness of a medication. Furthermore, the sleep data analysis unit can suggest an appropriate sleeping environment. This allows for customized advice based on the patient's sleep data to maximize the effectiveness of medication and minimize the risk of side effects.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The prescription information input unit inputs prescription information. For example, a patient or a medical professional inputs information about the medicines listed on the prescription. Step 2: The generation AI analyzes the prescription information entered by the prescription information input unit. For example, the generation AI analyzes the name and dosage of the medicine and extracts related information. Step 3: The information acquisition unit acquires information from multiple drug databases. For example, it collects information on drug side effects from domestic and international drug databases and academic paper databases. Step 4: The information providing unit provides detailed side effect information based on the information acquired by the information acquiring unit. For example, it provides information that gastrointestinal disorders, bleeding tendency, and allergic reactions have been reported as side effects of aspirin 500 mg.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] 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.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 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.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] 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.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] 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.

[0128] 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.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. 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 specific processing unit 290 using these models.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] 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.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0143] 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.

[0144] 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.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0146] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The 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.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0153] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0154] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0155] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0158] 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.

[0159] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0160] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0161] 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.

[0162] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0163] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0164] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0166] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0167] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0168] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0169] 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 prescription information input unit for inputting prescription information; A generation AI that analyzes the prescription information input by the prescription information input unit; an information acquisition unit that acquires information from a plurality of pharmaceutical databases; an information providing unit that provides detailed side effect information based on the information acquired by the information acquiring unit. A system characterized by:

2. The prescription information input unit Analyzing drug interactions and warning of potential risks 2. The system of claim 1.

3. The prescription information input unit Automatically assessing the risk of allergies or hypersensitivity to certain medications based on a patient's past medical history 2. The system of claim 1.

4. The prescription information input unit Analyze patients' fears or concerns about medications and provide tailored information based on those fears 2. The system of claim 1.

5. The prescription information input unit Enter prescription information using voice recognition technology 2. The system of claim 1.

6. The prescription information input unit Scanning prescription information with a smartphone camera and automatically digitizing it 2. The system of claim 1.

7. The prescription information input unit Analyze patient emotions in real time and provide positive feedback 2. The system of claim 1.

8. The information acquisition unit Update the drug database in real time to always provide the latest side effect information.

2. The system of claim 1.

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