System

Abstract

An object of a system according to an embodiment is to provide a user with an effect of a medicine and a correct way of taking the medicine in an easy-to-understand manner.SOLUTION: A system includes a medicine name input part, an analysis part, an effect explanation part, and a drinking method instruction part. The medicine name input section inputs a medicine name. The analysis section analyzes the name of the medicine input by the medicine name input section. The effect explanation unit explains the effect of the drug analyzed by the analysis unit. The way of taking instruction section instructs a correct way of taking the medicine analyzed by the analysis section.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 made it difficult for patients to understand the effects of medicine and how to take it correctly, and there are risks associated with taking it incorrectly.

[0005] The system according to the embodiment aims to provide users with information about the effects of medicine and the correct way to take it in an easy-to-understand manner. [Means for solving the problem]

[0006] The system according to the embodiment includes a medicine name input unit, an analysis unit, an effect explanation unit, and a dosage instruction unit. The medicine name input unit inputs the name of the medicine. The analysis unit analyzes the name of the medicine input by the medicine name input unit. The effect explanation unit explains the effect of the medicine analyzed by the analysis unit. The dosage instruction unit instructs the correct way to take the medicine analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide the user with information about the effects of medicine and the correct way to take it in an easy-to-understand manner. [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) The drug information provision system according to an embodiment of the present invention is a system that provides users with accurate information about the drug's effects, dosage, and drug interaction precautions by providing the name of the drug prescribed at the pharmacy to a generation AI, while also referencing past prescription data.

[0029] A drug information provision system according to an embodiment includes a drug name input unit, an analysis unit, an effect explanation unit, and a dosage instruction unit. The drug name input unit allows a user to input a drug name. For example, the user inputs the drug name using a smartphone keyboard. The drug name input unit can also recognize drug names using voice input. For example, when a user speaks a drug name into a microphone, it is converted into text using voice recognition technology. The analysis unit analyzes the drug name input by the drug name input unit. For example, a generation AI compares the drug name with a database to obtain information about the drug. The generation AI can analyze the drug name using a text generation AI (e.g., LLM) or a multimodal generation AI. The effect explanation unit explains the drug effect analyzed by the analysis unit. For example, the generation AI may respond in the form of, "Aspirin is effective in reducing pain and inflammation." The effect explanation unit can also explain the drug's side effects and precautions for use. For example, the generation AI may respond in the form of, "Aspirin may put a strain on the stomach and intestines, so please take it after a meal." The dosage instruction unit instructs the user on the correct way to take the medicine analyzed by the analysis unit. For example, the generation AI may respond in the form of "Take one aspirin tablet with water after each meal." The dosage instruction unit may also instruct the user on the timing and dosage of the medicine. For example, the response may be "Take aspirin three times a day after each meal." This allows the drug information provision system according to the embodiment to allow the user to input the name of the medicine and understand the effects and dosage of the medicine. For example, by understanding the effects of the medicine, the user can take the medicine at the appropriate time. Furthermore, receiving instructions on how to take the medicine can maximize the effectiveness of the medicine.

[0030] The drug name input unit recognizes drug names using voice input, and the generation AI can analyze the voice data. For example, a user can use a smartphone microphone to input the name of a drug by voice, and the generation AI converts the voice data into text and analyzes it. For example, when the user pronounces "aspirin," the generation AI recognizes the speech and obtains drug information. Furthermore, the drug name input unit can present multiple candidates, taking into account differences in the user's pronunciation during voice input. For example, it could display both "aspirin" and "aspirina" as candidates, allowing the user to select the correct drug name. Furthermore, the drug name input unit can use technology that removes background noise during voice input to accurately recognize drug names. For example, it can accurately recognize "aspirin" even in a noisy environment and provide drug information. This allows users to easily input drug names using voice input.

[0031] The drug name input section can provide an input completion function by referencing the user's past search history and prescription history. For example, when a user begins to enter a drug name, the generation AI automatically completes candidates based on past search history and prescription history. For example, if the user enters "as," candidates such as "aspirin" and "aspartic acid" are displayed. The drug name input section can also refer to the user's past prescription history and prioritize the display of names of drugs previously prescribed to the user. For example, if the user has been prescribed "aspirin" in the past, entering "as" will display "aspirin" first. The drug name input section can also learn the user's input history and prioritize the completion of frequently used drug names. For example, if a user frequently searches for "aspirin," simply entering "a" will display "aspirin." This makes it easier for users to enter drug names by referencing past history.

[0032] The drug name input unit can input and analyze drug names through a smartphone app or wearable device, allowing users to easily access the system anywhere. For example, a smartphone app can be developed to allow users to input and analyze drug names. For example, when a user opens the app and inputs "aspirin," the generation AI provides drug information. The drug name input unit can also input drug names via voice using a wearable device (e.g., a smartwatch), and the generation AI can analyze the data. For example, when a user speaks "aspirin" into a smartwatch, the generation AI displays drug information. The drug name input unit can also input and analyze drug names through a smartphone app or wearable device, allowing users to easily obtain drug information while on the go. For example, the user can check the information immediately after receiving the drug at the pharmacy. This allows users to easily input and analyze drug names anywhere.

[0033] The medicine name input unit can obtain information directly from the medicine package using a barcode scanner. For example, the medicine name input unit uses a smartphone camera to scan the barcode printed on the medicine package, and the generation AI analyzes the information. For example, scanning an aspirin package displays information about the medicine. The medicine name input unit can also add a QR code to medicine packages provided at pharmacies, allowing users to obtain information about the medicine by scanning it. For example, when the QR code is scanned, the generation AI displays the medicine's effects and how to take it. The medicine name input unit can also obtain information directly from the medicine package using a barcode scanner, eliminating the need for users to manually enter information. For example, instead of entering the name of the medicine, the information can be displayed simply by scanning the barcode. This makes it easy to obtain medicine information using a barcode scanner.

[0034] The effect explanation unit can provide individually customized information taking into account the user's health condition and past medical history. For example, the generation AI can refer to the user's health condition and past medical history to individually customize and explain the effects of a drug. For example, for a user who has previously suffered from a stomach ulcer, the effect explanation unit can provide a detailed explanation of the effects of aspirin on the gastrointestinal tract. The effect explanation unit can also provide individually customized explanations of the effects of a drug based on the user's health data. For example, for a user with diabetes, the effect of aspirin on blood sugar levels can be explained. The effect explanation unit can also provide individually customized explanations of the effects of a drug based on the user's past medical history. For example, for a user with a history of heart disease, the effect of aspirin on the cardiovascular system can be explained in detail. This makes it possible to provide individually customized information based on the user's health condition and past medical history.

[0035] The effect explanation unit can use visual aids to make the effects of a drug easier to understand visually when explaining the effects of the drug. For example, when the generation AI explains the effects of a drug, the effect explanation unit uses diagrams and graphs to make it easier to understand visually. For example, a graph showing the effects of aspirin can be displayed to visually explain the pain relief effect. The effect explanation unit can also use videos to explain the effects of the drug to make it easier for the user to visually understand. For example, an animated video showing how aspirin works in the body can be displayed. The effect explanation unit can also use visual aids to provide an interface that visually explains the effects of the drug. For example, illustrations and icons showing the effects of aspirin can be displayed to allow the user to intuitively understand. In this way, the use of visual aids makes it easier to visually understand the effects of the drug.

[0036] The effect explanation unit can provide explanations in different languages ​​when explaining the effects of a drug, enabling multilingual support. For example, when the generation AI explains the effects of a drug, the effect explanation unit provides an explanation in a language selected by the user. For example, the effects of aspirin are explained in multiple languages, such as English, Spanish, and Chinese. The effect explanation unit can also provide a multilingual interface, allowing the user to understand the effects of the drug in their preferred language. For example, a language can be selected within the app, and the effects of aspirin can be displayed in that language. The effect explanation unit can also use the generation AI's automatic translation function to translate the effects of the drug into different languages ​​in real time and provide them to the user. For example, the effects of aspirin can be translated from Japanese to English and displayed. This allows multilingual support, allowing the effects of the drug to be explained in different languages.

[0037] The effect explanation unit can provide customized information according to the user's age and gender. For example, the generation AI can customize and explain the effects of a drug by taking into account the user's age and gender. For example, it can provide detailed explanations about the side effects of aspirin to elderly people. The effect explanation unit can also develop an interface that provides customized information according to the user's age and gender. For example, it can explain the use of aspirin during pregnancy to women. The effect explanation unit can also customize and provide individually customized information about the effects of a drug based on the generation AI's age and gender. For example, it can explain the use of aspirin during sports to younger people. This makes it possible to provide information according to the user's age and gender.

[0038] The dosage instruction unit can suggest the optimal dosage by taking into consideration the user's lifestyle and eating patterns. For example, the generation AI in the dosage instruction unit refers to the user's lifestyle and eating patterns and individually customizes and instructs how to take medicine. For example, a user who does not eat breakfast may be suggested to take aspirin after lunch. The dosage instruction unit can also provide customized dosage instructions by the generation AI based on the user's eating patterns. For example, a vegetarian user may be suggested to not take aspirin with certain ingredients. The dosage instruction unit can also provide customized dosage instructions by taking into consideration the user's lifestyle. For example, a user who works the night shift may be suggested to take aspirin at night. This allows the generation AI to suggest the optimal dosage based on the user's lifestyle and eating patterns.

[0039] The dosage instruction unit can provide a reminder function to help the user remember to take their medicine. For example, the generation AI can provide the reminder function to notify the user when it's time to take their medicine. For example, a notification can be displayed on a smartphone when it's time to take their aspirin. The dosage instruction unit can also use the reminder function to help the user not forget to take their medicine. For example, a voice assistant can remind the user when it's time to take their aspirin. The generation AI can also provide the reminder function to automatically add the time for the user to take their medicine to a calendar. For example, the time to take their aspirin is displayed on a calendar and a notification is set. This allows the reminder function to help the user not forget to take their medicine.

[0040] The medication instruction unit can work in conjunction with the user's smart home devices and provide reminders through a voice assistant. For example, the generation AI in the medication instruction unit works in conjunction with smart home devices and reminds the user how to take their medicine through a voice assistant. For example, Alexa or Google Home can notify the user when it's time to take their aspirin. The medication instruction unit can also work in conjunction with the user's smart home devices to create a system in which a voice assistant reminds the user how to take their medicine. For example, a smart speaker can notify the user when it's time to take their aspirin. The medication instruction unit can also work in conjunction with smart home devices and provide a function to remind the user how to take their medicine through a voice assistant. For example, a smart display can display the time to take their aspirin. This allows the system to work in conjunction with smart home devices and provide reminders through a voice assistant.

[0041] The dosage instruction unit can work in conjunction with the user's fitness tracker to suggest dosage instructions based on the user's exercise and activity level. For example, the generation AI in the dosage instruction unit can work in conjunction with the fitness tracker to suggest dosage instructions based on the user's exercise and activity level. For example, the generation AI can instruct the user to take aspirin after exercise. The dosage instruction unit can also work in conjunction with the user's fitness tracker to customize and provide dosage instructions based on the user's exercise and activity level. For example, the generation AI can adjust the timing of aspirin intake on days when the user is very active. The dosage instruction unit can also work in conjunction with the fitness tracker to suggest dosage instructions based on the user's exercise data. For example, the generation AI can instruct the user to take aspirin before exercise. This allows the unit to work in conjunction with the fitness tracker to suggest dosage instructions based on the user's exercise and activity level.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The drug name input section can provide an input completion function that takes into account the user's health condition and past medical history. For example, when a user begins to enter a drug name, the generation AI automatically completes candidates based on past medical history. For example, if the user enters "as," candidates such as "aspirin" and "aspartic acid" that have been prescribed in the past are displayed. The drug name input section can also refer to the user's health condition and prioritize the display of specific drug names. For example, for a user with a history of heart disease, names of drugs related to the cardiovascular system are prioritized. The drug name input section can also learn the user's medical history and prioritize the completion of frequently used drug names. For example, if a user frequently searches for "aspirin," simply entering "a" will display "aspirin." This makes it easier for users to enter drug names by referring to their past medical history and health condition.

[0044] The drug name input section can provide an input completion function that takes into account the user's lifestyle and eating patterns. For example, when a user begins to enter a drug name, the generation AI automatically completes candidates based on the user's lifestyle and eating patterns. For example, if a user enters "as," candidates such as "aspirin" and "aspartic acid" will be displayed for a user who does not eat breakfast. The drug name input section can also refer to the user's eating patterns and prioritize the display of specific drug names. For example, for a vegetarian user, names of plant-derived drugs will be prioritized. The drug name input section can also learn the user's lifestyle and prioritize the completion of frequently used drug names. For example, if a user frequently searches for "aspirin," simply entering "a" will display "aspirin." This makes it easier for users to enter drug names by referring to their lifestyle and eating patterns.

[0045] The effect explanation unit can provide individually customized information taking into account the user's health condition and past medical history. For example, the generation AI can refer to the user's health condition and past medical history to provide an individually customized explanation of the effects of a medicine. For example, for a user who has previously suffered from a stomach ulcer, the generation AI can provide a detailed explanation of the effects of aspirin on the gastrointestinal tract. The effect explanation unit can also provide individually customized explanations of the effects of a medicine based on the user's health data. For example, for a user with diabetes, the generation AI can provide an explanation of the effects of aspirin on blood sugar levels. The effect explanation unit can also provide individually customized explanations of the effects of a medicine taking into account the user's past medical history. For example, for a user with a history of heart disease, the generation AI can provide a detailed explanation of the effects of aspirin on the cardiovascular system. This makes it possible to provide individually customized information based on the user's health condition and past medical history.

[0046] The effect explanation unit can use visual aids to make the effects of a drug easier to understand visually when explaining the effects of the drug. For example, when the generation AI explains the effects of a drug, it uses diagrams and graphs to make it easier to understand visually. For example, it can display a graph showing the effects of aspirin to visually explain its pain-relieving effect. The effect explanation unit can also use videos to explain the effects of the drug to make it easier for the user to understand visually. For example, it can display an animated video showing how aspirin works in the body. The effect explanation unit can also use visual aids to provide an interface that visually explains the effects of the drug. For example, it can display illustrations and icons showing the effects of aspirin to allow the user to intuitively understand. In this way, the use of visual aids makes it easier to visually understand the effects of the drug.

[0047] The dosage instruction unit can suggest the optimal dosage by taking into account the user's lifestyle and eating patterns. For example, the generation AI can refer to the user's lifestyle and eating patterns and provide individually customized instructions on how to take medicine. For example, a user who does not eat breakfast can be suggested to take aspirin after lunch. The dosage instruction unit can also provide customized instructions on how to take medicine by the generation AI based on the user's eating patterns. For example, a vegetarian user can be told not to take aspirin with certain ingredients. The dosage instruction unit can also provide individually customized instructions on how to take medicine by taking into account the user's lifestyle. For example, a user who works the night shift can be suggested to take aspirin at night. This allows the system to suggest the optimal dosage based on the user's lifestyle and eating patterns.

[0048] The dosage instruction unit can provide a reminder function to help the user remember to take their medicine. For example, the generation AI can provide the reminder function and notify the user when it's time to take their medicine. For example, a notification can be displayed on a smartphone when it's time to take their aspirin. The dosage instruction unit can also use the reminder function to help the user not forget to take their medicine. For example, a voice assistant can remind the user when it's time to take their aspirin. The generation AI can also provide the reminder function to help the user automatically add the time it's time to take their medicine to a calendar. For example, the time to take their aspirin is displayed on a calendar and a notification is set. This allows the reminder function to help the user not forget to take their medicine.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The medicine name input unit allows the user to input the name of the medicine. For example, the user inputs the name of the medicine using the keyboard on the smartphone. The medicine name input unit can also recognize the name of the medicine using voice input. For example, when the user speaks the name of the medicine into the microphone, it is converted into text using voice recognition technology. Step 2: The analysis unit analyzes the drug name entered by the drug name input unit. For example, the generation AI compares the drug name with a database and obtains information about the drug. The generation AI can analyze the drug name using text generation AI (e.g., LLM) or multimodal generation AI. Step 3: The effect explanation section explains the effects of the drug analyzed by the analysis section. For example, the generation AI might respond, "Aspirin is effective in reducing pain and inflammation." The effect explanation section can also explain the drug's side effects and precautions for use. For example, it might respond, "Aspirin can put a strain on the stomach and intestines, so please take it after a meal." Step 4: The dosage instruction unit instructs the user on the correct way to take the medicine analyzed by the analysis unit. For example, the generation AI might respond, "Take one aspirin tablet with water after a meal." The dosage instruction unit can also instruct the user on the timing and dosage of the medicine. For example, it might respond, "Take aspirin three times a day after each meal."

[0051] (Example 2) The drug information provision system according to an embodiment of the present invention is a system that provides users with accurate information about the drug's effects, dosage, and drug interaction precautions by providing the name of the drug prescribed at the pharmacy to a generation AI, while also referencing past prescription data.

[0052] A drug information provision system according to an embodiment includes a drug name input unit, an analysis unit, an effect explanation unit, and a dosage instruction unit. The drug name input unit allows a user to input a drug name. For example, the user inputs the drug name using a smartphone keyboard. The drug name input unit can also recognize drug names using voice input. For example, when a user speaks a drug name into a microphone, it is converted into text using voice recognition technology. The analysis unit analyzes the drug name input by the drug name input unit. For example, a generation AI compares the drug name with a database to obtain information about the drug. The generation AI can analyze the drug name using a text generation AI (e.g., LLM) or a multimodal generation AI. The effect explanation unit explains the drug effect analyzed by the analysis unit. For example, the generation AI may respond in the form of, "Aspirin is effective in reducing pain and inflammation." The effect explanation unit can also explain the drug's side effects and precautions for use. For example, the generation AI may respond in the form of, "Aspirin may put a strain on the stomach and intestines, so please take it after a meal." The dosage instruction unit instructs the user on the correct way to take the medicine analyzed by the analysis unit. For example, the generation AI may respond in the form of "Take one aspirin tablet with water after each meal." The dosage instruction unit may also instruct the user on the timing and dosage of the medicine. For example, the response may be "Take aspirin three times a day after each meal." This allows the drug information provision system according to the embodiment to allow the user to input the name of the medicine and understand the effects and dosage of the medicine. For example, by understanding the effects of the medicine, the user can take the medicine at the appropriate time. Furthermore, receiving instructions on how to take the medicine can maximize the effectiveness of the medicine.

[0053] The drug name input unit recognizes drug names using voice input, and the generation AI can analyze the voice data. For example, a user can use a smartphone microphone to input the name of a drug by voice, and the generation AI converts the voice data into text and analyzes it. For example, when the user pronounces "aspirin," the generation AI recognizes the speech and obtains drug information. Furthermore, the drug name input unit can present multiple candidates, taking into account differences in the user's pronunciation during voice input. For example, it could display both "aspirin" and "aspirina" as candidates, allowing the user to select the correct drug name. Furthermore, the drug name input unit can use technology that removes background noise during voice input to accurately recognize drug names. For example, it can accurately recognize "aspirin" even in a noisy environment and provide drug information. This allows users to easily input drug names using voice input.

[0054] The drug name input section can provide an input completion function by referencing the user's past search history and prescription history. For example, when a user begins to enter a drug name, the generation AI automatically completes candidates based on past search history and prescription history. For example, if the user enters "as," candidates such as "aspirin" and "aspartic acid" are displayed. The drug name input section can also refer to the user's past prescription history and prioritize the display of names of drugs previously prescribed to the user. For example, if the user has been prescribed "aspirin" in the past, entering "as" will display "aspirin" first. The drug name input section can also learn the user's input history and prioritize the completion of frequently used drug names. For example, if a user frequently searches for "aspirin," simply entering "a" will display "aspirin." This makes it easier for users to enter drug names by referencing past history.

[0055] The medicine name input unit can use emotion estimation to analyze the user's emotions when entering a medicine name and provide advice to reduce stress and anxiety. For example, when a user enters a medicine name, the generation AI analyzes their facial expressions and voice tone to detect stress and anxiety. For example, if the user is nervous, it displays advice to relax. The medicine name input unit can also use emotion estimation to monitor the user's emotions in real time when entering a medicine name and provide positive feedback. For example, if anxiety is detected when entering "aspirin," it displays a message such as "Don't worry, that's the correct medicine name." The medicine name input unit can also customize the input interface according to the user's emotional state. For example, if the user is feeling stressed, it can change the color of the input screen to a calming color to enhance the relaxation effect. This allows the system to analyze the user's emotions and provide advice to reduce stress and anxiety.

[0056] The drug name input unit can input and analyze drug names through a smartphone app or wearable device, allowing users to easily access the system anywhere. For example, a smartphone app can be developed to allow users to input and analyze drug names. For example, when a user opens the app and inputs "aspirin," the generation AI provides drug information. The drug name input unit can also input drug names via voice using a wearable device (e.g., a smartwatch), and the generation AI can analyze the data. For example, when a user speaks "aspirin" into a smartwatch, the generation AI displays drug information. The drug name input unit can also input and analyze drug names through a smartphone app or wearable device, allowing users to easily obtain drug information while on the go. For example, the user can check the information immediately after receiving the drug at the pharmacy. This allows users to easily input and analyze drug names anywhere.

[0057] The medicine name input unit can obtain information directly from the medicine package using a barcode scanner. For example, the medicine name input unit uses a smartphone camera to scan the barcode printed on the medicine package, and the generation AI analyzes the information. For example, scanning an aspirin package displays information about the medicine. The medicine name input unit can also add a QR code to medicine packages provided at pharmacies, allowing users to obtain information about the medicine by scanning it. For example, when the QR code is scanned, the generation AI displays the medicine's effects and how to take it. The medicine name input unit can also obtain information directly from the medicine package using a barcode scanner, eliminating the need for users to manually enter information. For example, instead of entering the name of the medicine, the information can be displayed simply by scanning the barcode. This makes it easy to obtain medicine information using a barcode scanner.

[0058] The effect explanation unit can provide individually customized information taking into account the user's health condition and past medical history. For example, the generation AI can refer to the user's health condition and past medical history to individually customize and explain the effects of a drug. For example, for a user who has previously suffered from a stomach ulcer, the effect explanation unit can provide a detailed explanation of the effects of aspirin on the gastrointestinal tract. The effect explanation unit can also provide individually customized explanations of the effects of a drug based on the user's health data. For example, for a user with diabetes, the effect of aspirin on blood sugar levels can be explained. The effect explanation unit can also provide individually customized explanations of the effects of a drug based on the user's past medical history. For example, for a user with a history of heart disease, the effect of aspirin on the cardiovascular system can be explained in detail. This makes it possible to provide individually customized information based on the user's health condition and past medical history.

[0059] The effect explanation unit can use visual aids to make the effects of a drug easier to understand visually when explaining the effects of the drug. For example, when the generation AI explains the effects of a drug, the effect explanation unit uses diagrams and graphs to make it easier to understand visually. For example, a graph showing the effects of aspirin can be displayed to visually explain the pain relief effect. The effect explanation unit can also use videos to explain the effects of the drug to make it easier for the user to visually understand. For example, an animated video showing how aspirin works in the body can be displayed. The effect explanation unit can also use visual aids to provide an interface that visually explains the effects of the drug. For example, illustrations and icons showing the effects of aspirin can be displayed to allow the user to intuitively understand. In this way, the use of visual aids makes it easier to visually understand the effects of the drug.

[0060] The effect explanation unit can use the emotion estimation function to analyze the user's emotions when understanding the effects of the medicine and provide additional information to improve understanding. For example, the effect explanation unit uses a generation AI to analyze the user's emotions and understand the emotions when understanding the effects of the medicine. For example, if the user is feeling anxious, the effect explanation unit can provide additional explanations or information that provides reassurance. The effect explanation unit can also use the emotion estimation function to monitor the user's emotions in real time when understanding the effects of the medicine and provide additional information to improve understanding. For example, if the user has doubts, a detailed explanation can be added. The effect explanation unit can also provide additional information to help the user understand the effects of the medicine depending on the user's emotional state. For example, if the user is interested, related research results or statistical data can be provided. This makes it possible to analyze the user's emotions and provide additional information to improve understanding.

[0061] The effect explanation unit can provide explanations in different languages ​​when explaining the effects of a drug, enabling multilingual support. For example, when the generation AI explains the effects of a drug, the effect explanation unit provides an explanation in a language selected by the user. For example, the effects of aspirin are explained in multiple languages, such as English, Spanish, and Chinese. The effect explanation unit can also provide a multilingual interface, allowing the user to understand the effects of the drug in their preferred language. For example, a language can be selected within the app, and the effects of aspirin can be displayed in that language. The effect explanation unit can also use the generation AI's automatic translation function to translate the effects of the drug into different languages ​​in real time and provide them to the user. For example, the effects of aspirin can be translated from Japanese to English and displayed. This allows multilingual support, allowing the effects of the drug to be explained in different languages.

[0062] The effect explanation unit can provide customized information according to the user's age and gender. For example, the generation AI can customize and explain the effects of a drug by taking into account the user's age and gender. For example, it can provide detailed explanations about the side effects of aspirin to elderly people. The effect explanation unit can also develop an interface that provides customized information according to the user's age and gender. For example, it can explain the use of aspirin during pregnancy to women. The effect explanation unit can also customize and provide individually customized information about the effects of a drug based on the generation AI's age and gender. For example, it can explain the use of aspirin during sports to younger people. This makes it possible to provide information according to the user's age and gender.

[0063] The effect explanation unit can use the emotion estimation function to monitor the user's emotions in real time as they understand the effects of the drug and provide feedback to elicit positive emotions. For example, the effect explanation unit uses a generation AI to monitor the user's emotions in real time and understand the emotions they feel as they understand the effects of the drug. For example, if the user is feeling anxious, it provides feedback that gives a sense of security. The effect explanation unit can also use the emotion estimation function to monitor the user's emotions in real time as they understand the effects of the drug and provide feedback to elicit positive emotions. For example, if the user has doubts, it adds a detailed explanation. The effect explanation unit can also provide additional information to help the user understand the effects of the drug depending on the user's emotional state. For example, if the user is interested, it provides related research results or statistical data. This makes it possible to monitor the user's emotions in real time and provide feedback to elicit positive emotions.

[0064] The dosage instruction unit can suggest the optimal dosage by taking into consideration the user's lifestyle and eating patterns. For example, the generation AI in the dosage instruction unit refers to the user's lifestyle and eating patterns and individually customizes and instructs how to take medicine. For example, a user who does not eat breakfast may be suggested to take aspirin after lunch. The dosage instruction unit can also provide customized dosage instructions by the generation AI based on the user's eating patterns. For example, a vegetarian user may be suggested to not take aspirin with certain ingredients. The dosage instruction unit can also provide customized dosage instructions by taking into consideration the user's lifestyle. For example, a user who works the night shift may be suggested to take aspirin at night. This allows the generation AI to suggest the optimal dosage based on the user's lifestyle and eating patterns.

[0065] The dosage instruction unit can provide a reminder function to help the user remember to take their medicine. For example, the generation AI can provide the reminder function to notify the user when it's time to take their medicine. For example, a notification can be displayed on a smartphone when it's time to take their aspirin. The dosage instruction unit can also use the reminder function to help the user not forget to take their medicine. For example, a voice assistant can remind the user when it's time to take their aspirin. The generation AI can also provide the reminder function to automatically add the time for the user to take their medicine to a calendar. For example, the time to take their aspirin is displayed on a calendar and a notification is set. This allows the reminder function to help the user not forget to take their medicine.

[0066] The dosage instruction unit can use the emotion estimation function to analyze the user's emotions when understanding how to take the medicine and provide additional information to improve understanding. For example, the dosage instruction unit uses a generation AI to analyze the user's emotions and understand the emotions when understanding how to take the medicine. For example, if the user feels anxious, the dosage instruction unit can provide additional explanations or information that provides reassurance. The dosage instruction unit can also use the emotion estimation function to monitor the user's emotions in real time when understanding how to take the medicine and provide additional information to improve understanding. For example, if the user has questions, detailed explanations can be added. The dosage instruction unit can also provide additional information to help the user understand how to take the medicine depending on the user's emotional state. For example, if the user is interested, related research results or statistical data can be provided. This makes it possible to analyze the user's emotions and provide additional information to improve understanding.

[0067] The medication instruction unit can work in conjunction with the user's smart home devices and provide reminders through a voice assistant. For example, the generation AI in the medication instruction unit works in conjunction with smart home devices and reminds the user how to take their medicine through a voice assistant. For example, Alexa or Google Home can notify the user when it's time to take their aspirin. The medication instruction unit can also work in conjunction with the user's smart home devices to create a system in which a voice assistant reminds the user how to take their medicine. For example, a smart speaker can notify the user when it's time to take their aspirin. The medication instruction unit can also work in conjunction with smart home devices and provide a function to remind the user how to take their medicine through a voice assistant. For example, a smart display can display the time to take their aspirin. This allows the system to work in conjunction with smart home devices and provide reminders through a voice assistant.

[0068] The dosage instruction unit can work in conjunction with the user's fitness tracker to suggest dosage instructions based on the user's exercise and activity level. For example, the generation AI in the dosage instruction unit can work in conjunction with the fitness tracker to suggest dosage instructions based on the user's exercise and activity level. For example, the generation AI can instruct the user to take aspirin after exercise. The dosage instruction unit can also work in conjunction with the user's fitness tracker to customize and provide dosage instructions based on the user's exercise and activity level. For example, the generation AI can adjust the timing of aspirin intake on days when the user is very active. The dosage instruction unit can also work in conjunction with the fitness tracker to suggest dosage instructions based on the user's exercise data. For example, the generation AI can instruct the user to take aspirin before exercise. This allows the unit to work in conjunction with the fitness tracker to suggest dosage instructions based on the user's exercise and activity level.

[0069] The dosage instruction unit can use the emotion estimation function to monitor the user's emotions in real time as they understand how to take their medicine and provide feedback to elicit positive emotions. For example, the dosage instruction unit uses a generation AI to monitor the user's emotions in real time and understand the emotions they feel as they understand how to take their medicine. For example, if the user feels anxious, it provides feedback that gives a sense of security. The dosage instruction unit can also use the emotion estimation function to monitor the user's emotions in real time as they understand how to take their medicine and provide feedback to elicit positive emotions. For example, if the user has questions, it adds detailed explanations. The dosage instruction unit can also provide additional information to help the user understand how to take their medicine depending on the user's emotional state. For example, if the user is interested, it provides related research results or statistical data. This makes it possible to monitor the user's emotions in real time and provide feedback to elicit positive emotions.

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

[0071] The drug name input section can provide an input completion function that takes into account the user's health condition and past medical history. For example, when a user begins to enter a drug name, the generation AI automatically completes candidates based on past medical history. For example, if the user enters "as," candidates such as "aspirin" and "aspartic acid" that have been prescribed in the past are displayed. The drug name input section can also refer to the user's health condition and prioritize the display of specific drug names. For example, for a user with a history of heart disease, names of drugs related to the cardiovascular system are prioritized. The drug name input section can also learn the user's medical history and prioritize the completion of frequently used drug names. For example, if a user frequently searches for "aspirin," simply entering "a" will display "aspirin." This makes it easier for users to enter drug names by referring to their past medical history and health condition.

[0072] The drug name input unit can analyze the user's emotions and provide advice to reduce stress and anxiety during input. For example, when a user inputs a drug name, the generation AI analyzes facial expressions and voice tone to detect stress or anxiety. For example, if the user is nervous, it displays advice to relax. The drug name input unit can also use emotion estimation to monitor the user's emotions in real time as they input the drug name and provide positive feedback. For example, if anxiety is detected when entering "aspirin," it displays a message such as "Don't worry, that's the correct drug name." The drug name input unit can also customize the input interface according to the user's emotional state. For example, if the user is feeling stressed, it can change the color of the input screen to a calming color to enhance the relaxation effect. This allows the system to analyze the user's emotions and provide advice to reduce stress and anxiety.

[0073] The drug name input section can provide an input completion function that takes into account the user's lifestyle and eating patterns. For example, when a user begins to enter a drug name, the generation AI automatically completes candidates based on the user's lifestyle and eating patterns. For example, if a user enters "as," candidates such as "aspirin" and "aspartic acid" will be displayed for a user who does not eat breakfast. The drug name input section can also refer to the user's eating patterns and prioritize the display of specific drug names. For example, for a vegetarian user, names of plant-derived drugs will be prioritized. The drug name input section can also learn the user's lifestyle and prioritize the completion of frequently used drug names. For example, if a user frequently searches for "aspirin," simply entering "a" will display "aspirin." This makes it easier for users to enter drug names by referring to their lifestyle and eating patterns.

[0074] The effect explanation unit can analyze the user's emotions and understand their feelings when understanding the effects of the drug. For example, the generation AI can monitor the user's emotions in real time and understand their feelings when understanding the effects of the drug. For example, if the user is feeling anxious, it can provide feedback that gives a sense of security. The effect explanation unit can also use the emotion estimation function to monitor the user's emotions in real time when understanding the effects of the drug and provide feedback to elicit positive emotions. For example, if the user has doubts, it can add detailed explanations. The effect explanation unit can also provide additional information to help the user understand the effects of the drug depending on the user's emotional state. For example, if the user is interested, it can provide related research results or statistical data. This makes it possible to monitor the user's emotions in real time and provide feedback that elicits positive emotions.

[0075] The effect explanation unit can provide individually customized information taking into account the user's health condition and past medical history. For example, the generation AI can refer to the user's health condition and past medical history to provide an individually customized explanation of the effects of a medicine. For example, for a user who has previously suffered from a stomach ulcer, the generation AI can provide a detailed explanation of the effects of aspirin on the gastrointestinal tract. The effect explanation unit can also provide individually customized explanations of the effects of a medicine based on the user's health data. For example, for a user with diabetes, the generation AI can provide an explanation of the effects of aspirin on blood sugar levels. The effect explanation unit can also provide individually customized explanations of the effects of a medicine taking into account the user's past medical history. For example, for a user with a history of heart disease, the generation AI can provide a detailed explanation of the effects of aspirin on the cardiovascular system. This makes it possible to provide individually customized information based on the user's health condition and past medical history.

[0076] The effect explanation unit can use visual aids to make the effects of a drug easier to understand visually when explaining the effects of the drug. For example, when the generation AI explains the effects of a drug, it uses diagrams and graphs to make it easier to understand visually. For example, it can display a graph showing the effects of aspirin to visually explain its pain-relieving effect. The effect explanation unit can also use videos to explain the effects of the drug to make it easier for the user to understand visually. For example, it can display an animated video showing how aspirin works in the body. The effect explanation unit can also use visual aids to provide an interface that visually explains the effects of the drug. For example, it can display illustrations and icons showing the effects of aspirin to allow the user to intuitively understand. In this way, the use of visual aids makes it easier to visually understand the effects of the drug.

[0077] The effect explanation unit can use the emotion estimation function to analyze the user's emotions when understanding the effects of the drug and provide additional information to improve understanding. For example, the generation AI analyzes the user's emotions and understands their feelings when understanding the effects of the drug. For example, if the user is feeling anxious, it provides additional explanations or information that provides reassurance. The effect explanation unit can also use the emotion estimation function to monitor the user's emotions in real time when understanding the effects of the drug and provide additional information to improve understanding. For example, if the user has doubts, it adds detailed explanations. The effect explanation unit can also provide additional information to help the user understand the effects of the drug depending on the user's emotional state. For example, if the user is interested, it provides related research results or statistical data. This makes it possible to analyze the user's emotions and provide additional information to improve understanding.

[0078] The dosage instruction unit can suggest the optimal dosage by taking into account the user's lifestyle and eating patterns. For example, the generation AI can refer to the user's lifestyle and eating patterns and provide individually customized instructions on how to take medicine. For example, a user who does not eat breakfast can be suggested to take aspirin after lunch. The dosage instruction unit can also provide customized instructions on how to take medicine by the generation AI based on the user's eating patterns. For example, a vegetarian user can be told not to take aspirin with certain ingredients. The dosage instruction unit can also provide individually customized instructions on how to take medicine by taking into account the user's lifestyle. For example, a user who works the night shift can be suggested to take aspirin at night. This allows the system to suggest the optimal dosage based on the user's lifestyle and eating patterns.

[0079] The dosage instruction unit can provide a reminder function to help the user remember to take their medicine. For example, the generation AI can provide the reminder function and notify the user when it's time to take their medicine. For example, a notification can be displayed on a smartphone when it's time to take their aspirin. The dosage instruction unit can also use the reminder function to help the user not forget to take their medicine. For example, a voice assistant can remind the user when it's time to take their aspirin. The generation AI can also provide the reminder function to help the user automatically add the time it's time to take their medicine to a calendar. For example, the time to take their aspirin is displayed on a calendar and a notification is set. This allows the reminder function to help the user not forget to take their medicine.

[0080] The dosage instruction unit can use the emotion estimation function to analyze the user's emotions when understanding how to take the medicine and provide additional information to improve understanding. For example, the generation AI analyzes the user's emotions and understands their emotions when understanding how to take the medicine. For example, if the user feels anxious, additional explanations or reassuring information is provided. The dosage instruction unit can also use the emotion estimation function to monitor the user's emotions in real time when understanding how to take the medicine and provide additional information to improve understanding. For example, if the user has questions, detailed explanations are added. The dosage instruction unit can also provide additional information to help the user understand how to take the medicine depending on the user's emotional state. For example, if the user is interested, related research results or statistical data are provided. This allows the user's emotions to be analyzed and additional information to improve understanding to be provided.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The medicine name input unit allows the user to input the name of the medicine. For example, the user inputs the name of the medicine using the keyboard on the smartphone. The medicine name input unit can also recognize the name of the medicine using voice input. For example, when the user speaks the name of the medicine into the microphone, it is converted into text using voice recognition technology. Step 2: The analysis unit analyzes the drug name entered by the drug name input unit. For example, the generation AI compares the drug name with a database and obtains information about the drug. The generation AI can analyze the drug name using text generation AI (e.g., LLM) or multimodal generation AI. Step 3: The effect explanation section explains the effects of the drug analyzed by the analysis section. For example, the generation AI might respond, "Aspirin is effective in reducing pain and inflammation." The effect explanation section can also explain the drug's side effects and precautions for use. For example, it might respond, "Aspirin can put a strain on the stomach and intestines, so please take it after a meal." Step 4: The dosage instruction unit instructs the user on the correct way to take the medicine analyzed by the analysis unit. For example, the generation AI might respond, "Take one aspirin tablet with water after a meal." The dosage instruction unit can also instruct the user on the timing and dosage of the medicine. For example, it might respond, "Take aspirin three times a day after each meal."

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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 medicine name input section for inputting the name of the medicine; an analysis unit that analyzes the name of the medicine input by the medicine name input unit; an effect explanation unit that explains the effects of the drug analyzed by the analysis unit; and a dosage instruction unit that instructs the user on the correct way to take the medicine analyzed by the analysis unit. A system characterized by:

2. The medicine name input unit is Recognizes drug names using voice input, and the generative AI analyzes the voice data.

2. The system of claim 1.

3. The medicine name input unit is Provides input completion functionality by referencing the user's past search history and prescription history 2. The system of claim 1.

4. The medicine name input unit is Analyzes emotions when users enter medication names and provides advice to reduce stress and anxiety 2. The system of claim 1.

5. The medicine name input unit is Drug names can be entered and analyzed via a smartphone app or wearable device, making it easy for users to use anywhere.

2. The system of claim 1.

6. The medicine name input unit is Use a barcode scanner to capture information directly from the medicine package 2. The system of claim 1.

Images