System

Abstract

To provide a system for saving a labor cost for a pharmacist and allowing purchase of medicine in a less populated area and at late-night hours.SOLUTION: A system includes a collection part, an analysis part, and a guidance part. The collection part collects a user's electronic medical record or medication notebook history. The analysis part analyzes information collected by the collection part. The guidance part provides medication guidance in accordance with the analysis result obtained by the analysis part.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] Conventional technology had issues such as high labor costs for pharmacists and the difficulty of purchasing medicines in sparsely populated areas or late at night.

[0005] The system according to the embodiment aims to reduce the labor costs of pharmacists and to enable the purchase of medicines even in sparsely populated areas or late at night. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and an instruction unit. The collection unit collects a user's electronic medical record or medicine notebook history. The analysis unit analyzes the information collected by the collection unit. The instruction unit provides medication instruction based on the analysis results obtained by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can reduce the labor costs of pharmacists and enable the purchase of medicines even in sparsely populated areas or late at night. [Brief description of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The medication instruction system according to the embodiment of the present invention is a system in which AI provides medication instruction for "Class 1 drugs" (such as painkillers and corona antigen test kits) at drugstores instead of the user. Furthermore, by analyzing the user's electronic medical record and medicine notebook history, more accurate medication instruction can be provided. This system is expected to save on the labor costs of pharmacists, who are high-quality human resources, and to make it possible to purchase medicines in sparsely populated areas and late at night. In the future, if stability and recognition are confirmed, it may be possible to consider prescribing medicines through prescriptions. For example, when a user purchases a "Class 1 drug" at a drugstore, AI provides medication instruction. For example, when a user purchases a painkiller, AI explains how to use it and precautions. At this time, AI analyzes the user's electronic medical record and medicine notebook history to provide optimal medication instruction for each individual user. For example, if a user has had an allergic reaction to a specific drug in the past, appropriate instruction is provided based on that information. Next, AI analyzes the user's electronic medical record and medicine notebook history. This allows the user's health condition and past drug use history to be understood, and more accurate medication instruction can be provided. For example, if a user has had a particular disease in the past, the system will provide appropriate guidance based on that information. Furthermore, this system can save on labor costs for pharmacists, who are highly skilled personnel. By having AI provide medication instructions, the burden on pharmacists can be reduced, enabling efficient business operations. In addition, it will be possible to purchase medicines in sparsely populated areas and late at night. For example, even if a user suddenly needs medicine late at night, the AI ​​will provide medication instructions, allowing the user to purchase the medicine with peace of mind. In the future, if the stability and recognition of the system are confirmed, it will be possible to consider prescribing medicines through prescriptions. This will allow even more users to conveniently obtain medicines. This will allow the medication instruction system to provide more accurate medication instructions based on the user's electronic medical record and medicine notebook history.

[0029] The medication instruction system according to the embodiment includes a collection unit, an analysis unit, and an instruction unit. The collection unit collects the history of the user's electronic medical record and medicine notebook. The history of the user's electronic medical record and medicine notebook includes, for example, medical records, prescription history, allergy information, etc., but is not limited to such examples. The collection unit can collect information by, for example, extraction from a database, user input, data collection from a sensor, etc. The analysis unit analyzes the information collected by the collection unit. The analysis is performed by, for example, statistical analysis, machine learning algorithm, data mining, etc., but is not limited to such examples. For example, the analysis unit uses a machine learning algorithm to analyze the user's health condition and past drug use history. In addition, the analysis unit can also grasp the trend of the user's health condition using statistical analysis. Furthermore, the analysis unit can extract patterns related to the user's health condition using data mining technology. The instruction unit provides medication instruction based on the analysis results obtained by the analysis unit. The medication instruction includes, for example, the contents of how to use the medicine, precautions, explanations of side effects, etc., but is not limited to such examples. For example, the guidance unit provides an appropriate method of using the medicine based on the user's health condition. The guidance unit can also provide appropriate precautions based on the user's allergy information. Furthermore, the guidance unit can also explain side effects of the medicine. As a result, the medication instruction system according to the embodiment can provide more accurate medication instruction based on the history of the user's electronic medical record and medicine notebook. Some or all of the above-mentioned processing in the guidance unit may be performed using AI, for example, or may be performed without using AI. For example, the guidance unit can provide medication instruction using an AI model that inputs the analysis results obtained by the analysis unit and outputs medication instruction. As a result, the medication instruction system can provide more accurate medication instruction based on the history of the user's electronic medical record and medicine notebook.

[0030] The collection unit collects the user's electronic medical record and medicine notebook history. The user's electronic medical record and medicine notebook history include, for example, medical records, prescription history, allergy information, etc., but are not limited to such examples. The collection unit can collect information by, for example, extraction from a database, user input, data collection from a sensor, etc. Specifically, medical records and prescription history are automatically extracted from the electronic medical record, and past drug use history and allergy information are collected from the medicine notebook. These data are collected using a secure communication protocol and stored in a central database. In the case of user input, the user himself can input information through a dedicated application or web portal. For example, the user inputs his or her health condition and medication status into an app, and the data is transmitted to the collection unit. In addition, in the case of data collection from a sensor, data such as the user's vital signs and activity level can be collected in real time using sensors of a wearable device or smartphone. This allows the collection unit to collect a wide range of data from various information sources and comprehensively grasp the user's health condition. Furthermore, the collection unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data will be stored on a cloud server and made accessible to the analysis and leadership departments. In addition, the frequency and accuracy of data collection can be adjusted to respond flexibly to specific situations and conditions. This allows the collection department to collect data efficiently and effectively, improving the performance of the entire system.

[0031] The analysis unit analyzes the information collected by the collection unit. The analysis is performed by, for example, statistical analysis, machine learning algorithms, data mining, and other methods, but is not limited to these examples. Specifically, the machine learning algorithm is used to analyze the user's health condition and past drug use history. For example, based on past prescription history, the effect of a specific drug on the user is predicted, and the optimal drug selection is supported. In addition, statistical analysis can be used to grasp the trend of the user's health condition. For example, fluctuations in blood pressure and blood sugar levels are analyzed to detect changes in the health condition early. Furthermore, data mining technology can be used to extract patterns related to the user's health condition. For example, a precursor to the appearance of a specific symptom is detected, and preventive measures are proposed. This allows the analysis unit to analyze the collected data from multiple angles and grasp the user's health condition in detail. Furthermore, the analysis unit can utilize past data and statistical information to perform long-term health management and risk assessment. For example, based on past health data, the risk of a specific disease is predicted, and early measures are proposed. In addition, anomaly detection algorithms can be used to detect patterns that are different from the norm or abnormal data, and issue early warnings. This enables the analysis unit to not only grasp the situation in real time, but also handle long-term health management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The guidance unit provides medication guidance based on the analysis results obtained by the analysis unit. The medication guidance includes, for example, the contents of how to use the medicine, precautions, and explanations of side effects, but is not limited to such examples. Specifically, an appropriate method of using the medicine is provided based on the health condition of the user. For example, the timing and amount of taking a specific medicine are instructed in detail so that the user can use the medicine accurately. In addition, appropriate precautions can be provided based on the user's allergy information. For example, if the user has an allergy to a specific ingredient, the user is instructed to avoid using medicines containing that ingredient. Furthermore, an explanation of the side effects of the medicine can be provided. For example, the side effects that may be caused by a specific medicine are explained in advance so that the user can deal with them appropriately. This allows the guidance unit to provide appropriate medication guidance according to the user's health condition and individual needs. Part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can provide medication guidance using an AI model that inputs the analysis results obtained by the analysis unit and outputs medication guidance. The AI ​​model learns based on past data and specialized knowledge and provides optimal guidance content to the user. This allows the medication instruction system to provide more accurate medication instructions based on the history of the user's electronic medical record and medicine notebook. Furthermore, the instruction department can collect user feedback and continuously improve the accuracy and effectiveness of the instruction content. For example, the AI ​​model is retrained based on the feedback provided by the user to optimize the instruction content. In addition, the instruction department can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered by using not only smartphone notifications but also voice calls, SMS, emails, etc. in combination. This allows the instruction department to quickly and reliably provide action instructions to the user and minimize the risk of damage.

[0033] The guidance unit may include an identification unit that identifies the allergy information of the user. The identification unit identifies the allergy information of the user. The allergy information includes, for example, the type of allergen, past allergic reactions, and a doctor's diagnosis, but is not limited to such examples. The identification unit identifies the allergy information of the user, for example, using an allergen test. The identification unit can also identify the allergy information based on the doctor's diagnosis. Furthermore, the identification unit can also identify the allergy information based on the user's self-report. As a result, the identification unit can identify the allergy information of the user, thereby enabling safer medication guidance. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input the allergy information of the user and identify the allergy information using an AI model that identifies the allergy information.

[0034] The guidance unit may include a providing unit that provides a method for using the medicine. The providing unit provides the method for using the medicine. The method for using the medicine includes, for example, a dosage, a time for taking the medicine, and a method for taking the medicine (oral, injection, etc.), but is not limited to such examples. The providing unit provides, for example, a dosage of the medicine. The providing unit can also provide a time for taking the medicine. Furthermore, the providing unit can also provide a method for taking the medicine (oral, injection, etc.). In this way, the providing unit can provide the method for using the medicine, thereby enabling the user to use the medicine accurately. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide the method for using the medicine using an AI model that inputs the method for using the medicine and provides the method for using the medicine.

[0035] The collection unit can analyze the user's past medical history and select an appropriate collection method. For example, the collection unit preferentially collects data from medical institutions that the user frequently visited in the past. The collection unit can also preferentially collect data related to a specific disease from the user's past medical history. Furthermore, the collection unit can analyze the user's past medical history and collect the most relevant data. This allows the collection unit to select the optimal collection method based on the user's past medical history, thereby enabling efficient information collection. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past medical history to the generation AI and cause the generation AI to select the optimal collection method.

[0036] The collection unit can perform filtering based on the user's current health condition and lifestyle habits when collecting the history of the electronic medical record and the medicine notebook. The collection unit, for example, collects only highly relevant data based on the user's current health condition. The collection unit can also filter necessary data based on the user's lifestyle habits (for example, smoking and drinking habits). Furthermore, the collection unit can collect optimal data by comprehensively considering the user's current health condition and lifestyle habits. This allows the collection unit to collect highly relevant information by filtering based on the user's current health condition and lifestyle habits. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's health condition data to the generation AI and cause the generation AI to perform filtering.

[0037] The collection unit can preferentially collect highly relevant information based on the geographical location information of the user when collecting the history of the electronic medical record or the medicine notebook. For example, the collection unit preferentially collects data of medical institutions in the area where the user is currently located. The collection unit can also collect highly relevant medical data based on the geographical location information of the user. Furthermore, the collection unit can also collect optimal data by taking into account the user's current location information. This allows the collection unit to efficiently collect highly relevant information by taking into account the user's geographical location information. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant information.

[0038] The collection unit can analyze the user's social media activities and collect related information when collecting the history of the electronic medical record and the medicine notebook. For example, the collection unit analyzes health-related posts from the user's social media activities and collects related data. The collection unit can also collect necessary data based on health information shared by the user on social media. Furthermore, the collection unit can comprehensively analyze the user's social media activities and collect optimal data. This allows the collection unit to efficiently collect related information by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's social media data to the generation AI and cause the generation AI to collect related information.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the user's health condition during the analysis. For example, when the user's health condition is serious, the analysis unit performs a detailed analysis. In addition, when the user's health condition is stable, the analysis unit can also perform a concise analysis. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the importance of the user's health condition. As a result, the analysis unit can provide appropriate analysis results by adjusting the level of detail of the analysis according to the importance of the user's health condition. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's health condition data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the medical history of the user during analysis. For example, if the user has had a specific disease in the past, the analysis unit applies an analysis algorithm related to the disease. The analysis unit can also select an optimal analysis algorithm based on the medical history of the user. Furthermore, the analysis unit can apply different analysis algorithms taking into account the medical history of the user. As a result, the analysis unit can provide highly accurate analysis results by applying an optimal analysis algorithm based on the medical history of the user. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the medical history data of the user to the generation AI and cause the generation AI to apply the optimal analysis algorithm.

[0041] The analysis unit can determine the priority of the analysis based on the time of submission of the user's medical history at the time of analysis. For example, the analysis unit preferentially analyzes the medical history recently submitted by the user. The analysis unit can also determine the priority of the analysis based on the time of submission of the user's medical history. Furthermore, the analysis unit can determine the optimal analysis order taking into account the time of submission of the user's medical history. As a result, the analysis unit determines the priority of the analysis based on the time of submission of the user's medical history, thereby enabling efficient analysis. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's medical history data to the generation AI and cause the generation AI to execute the determination of the priority of the analysis.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the user during analysis. For example, the analysis unit preferentially analyzes data related to the user's current health condition. The analysis unit can also preferentially analyze highly relevant data based on the user's past medical history. Furthermore, the analysis unit can determine an optimal analysis order based on the relevance of the user. This allows the analysis unit to adjust the order of analysis based on the relevance of the user, thereby enabling efficient analysis. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance data of the user to the generation AI and cause the generation AI to adjust the order of analysis.

[0043] The guidance unit can adjust the level of detail of the guidance based on the importance of the user's health condition when providing medication guidance. For example, when the user's health condition is serious, the guidance unit provides detailed medication guidance. In addition, when the user's health condition is stable, the guidance unit can also provide concise medication guidance. Furthermore, the guidance unit can adjust the level of detail of the guidance according to the importance of the user's health condition. As a result, the guidance unit can provide appropriate guidance by adjusting the level of detail of the guidance according to the importance of the user's health condition. Part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI or may be performed without using AI. For example, the guidance unit can input the user's health condition data into the generation AI and cause the generation AI to adjust the level of detail of the guidance.

[0044] The guidance unit can apply different guidance algorithms according to the medical history of the user when providing medication guidance. For example, if the user has had a specific disease in the past, the guidance unit applies a guidance algorithm related to the disease. The guidance unit can also select an optimal guidance algorithm based on the medical history of the user. Furthermore, the guidance unit can apply different guidance algorithms in consideration of the medical history of the user. As a result, the guidance unit can apply an optimal guidance algorithm based on the medical history of the user, thereby enabling highly accurate guidance. Part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI or may be performed without using AI. For example, the guidance unit can input the medical history data of the user to the generation AI and cause the generation AI to apply the optimal guidance algorithm.

[0045] The guidance unit can determine the priority of the guidance based on the submission time of the user's medical history when providing medication guidance. For example, the guidance unit gives priority to the medical history recently submitted by the user. The guidance unit can also determine the priority of the guidance based on the submission time of the user's medical history. Furthermore, the guidance unit can determine the optimal guidance order taking into account the submission time of the user's medical history. This enables the guidance unit to provide efficient guidance by determining the priority of the guidance based on the submission time of the user's medical history. Part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI or may be performed without using AI. For example, the guidance unit can input the user's medical history data into the generation AI and cause the generation AI to determine the priority of the guidance.

[0046] The instruction unit can adjust the order of instructions based on the relevance of the user when providing medication instructions. For example, the instruction unit gives priority to data related to the user's current health condition. The instruction unit can also give priority to highly relevant data based on the user's past medical history. Furthermore, the instruction unit can determine an optimal instruction order based on the relevance of the user. This allows the instruction unit to adjust the order of instructions based on the relevance of the user, thereby enabling efficient instruction. A part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction unit can input the user's relevance data to the generation AI and cause the generation AI to adjust the order of instructions.

[0047] When identifying allergy information, the identification unit can select an appropriate identification method by analyzing the user's past allergic reaction history. For example, if the user has had an allergic reaction to a specific drug in the past, the identification unit selects the identification method based on that information. The identification unit can also analyze the user's past allergic reaction history and select the most relevant identification method. Furthermore, the identification unit can select the optimal identification method by comprehensively considering the user's allergic reaction history. As a result, the identification unit can select the optimal identification method based on the user's past allergic reaction history, thereby enabling highly accurate identification of allergy information. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI or may be performed without using AI. For example, the identification unit can input the user's allergic reaction history data to the generation AI and cause the generation AI to select the optimal identification method.

[0048] The identification unit can select an appropriate identification method based on the geographical location information of the user when identifying the allergy information. The identification unit, for example, identifies the allergy information based on data of a medical institution in the area where the user is currently located. The identification unit can also identify highly relevant allergy information based on the geographical location information of the user. Furthermore, the identification unit can select an optimal identification method for allergy information by taking into account the user's current location information. In this way, the identification unit can efficiently identify highly relevant allergy information by taking into account the user's geographical location information. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI or may be performed without using AI. For example, the identification unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal identification method.

[0049] When providing the drug usage method, the providing unit can select an appropriate provision method based on the user's past drug use history. The providing unit provides an optimal usage method based on, for example, the user's past drug use history. The providing unit can also provide a highly relevant usage method by referring to the user's past drug use history. Furthermore, the providing unit can provide an optimal usage method by comprehensively considering the user's drug use history. As a result, the providing unit can provide a highly accurate usage method by selecting an optimal provision method based on the user's past drug use history. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's drug use history data to the generating AI and cause the generating AI to select the optimal provision method.

[0050] The providing unit can select an appropriate providing method based on the user's device information when providing the drug usage method. For example, when the user is using a smartphone, the providing unit provides the drug usage method that matches the screen size. In addition, when the user is using a tablet, the providing unit can also provide the drug usage method that is optimized for a large screen. Furthermore, when the user is using a smartwatch, the providing unit can also provide a concise and highly visible drug usage method. Thereby, the providing unit can efficiently provide a highly relevant usage method by taking into account the user's device information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's device information to the generating AI and cause the generating AI to select the optimal providing method.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The medication instruction system may further include a lifestyle collection unit that collects lifestyle data of the user. The lifestyle collection unit collects, for example, data such as the user's diet, exercise, and sleep patterns. This allows the collection unit to grasp the health condition of the user more comprehensively based on the lifestyle data of the user. For example, if the user is taking medicine for high blood pressure, the lifestyle collection unit can collect the user's dietary content and grasp the salt intake. In addition, by collecting exercise data, it is possible to suggest that lack of exercise may be the cause. Furthermore, by analyzing sleep patterns, it is possible to evaluate the impact of lack of sleep on health. As a result, the lifestyle collection unit can provide more accurate medication instruction by collecting the lifestyle data of the user.

[0053] The guidance unit may include an interaction evaluation unit that evaluates drug interactions based on the user's medication history. For example, when the user is taking multiple drugs, the interaction evaluation unit evaluates the drug interactions and identifies potential risks. This allows the guidance unit to allow the user to use the drugs safely. For example, when the user is taking an antibiotic and an anticoagulant at the same time, the interaction evaluation unit can evaluate the drug interactions and warn of bleeding risks. In addition, when the user adds a new drug, the interaction evaluation unit can evaluate interactions with existing drugs and provide appropriate guidance. Furthermore, the interaction evaluation unit can analyze patterns of interactions that have occurred in the past based on the user's medication history and predict future risks. This allows the interaction evaluation unit to provide safer medication guidance by evaluating drug interactions based on the user's medication history.

[0054] The instruction unit may include a visual aid unit that provides visual aid when providing medication instruction to the user. The visual aid unit provides, for example, videos and illustrations showing how to use the medicine. This makes it easier for the user to understand visually. For example, when the user uses an injection drug, the visual aid unit can provide a video showing the injection procedure. Also, by providing an illustration showing how to take the tablet, the user can understand the correct way to take it. Furthermore, the visual aid unit can visually show the side effects and precautions of the drug so that the user does not miss important information. As a result, the visual aid unit can provide more effective instruction by visually assisting the user in medication instruction.

[0055] The collection unit can analyze the user's past medical history and determine the priority of data to be collected. For example, if the user has suffered from heart disease in the past, the collection unit can prioritize collection of data related to the heart (e.g., heart rate and blood pressure). Also, if the user has suffered from diabetes in the past, data related to blood sugar levels can be prioritized. Furthermore, the collection unit can comprehensively analyze the user's past medical history and prioritize collection of the most relevant data. This allows the collection unit to efficiently collect information by determining the priority of data to be collected based on the user's past medical history.

[0056] The collection unit can filter the electronic medical record and medicine notebook history based on the user's current health condition and lifestyle habits when collecting the history. For example, if the user is currently receiving treatment for high blood pressure, the collection unit can preferentially collect data related to blood pressure. Also, if the user is a smoker, the collection unit can filter and collect data related to smoking. Furthermore, the collection unit can collect optimal data by comprehensively considering the user's current health condition and lifestyle habits. This allows the collection unit to collect highly relevant information by filtering based on the user's current health condition and lifestyle habits.

[0057] When collecting electronic medical records or medicine notebook history, the collection unit can preferentially collect highly relevant information based on the user's geographical location information. For example, data on medical institutions in the area where the user is currently located is preferentially collected. The collection unit can also collect highly relevant medical data based on the user's geographical location information. Furthermore, the collection unit can collect optimal data by taking into account the user's current location information. This allows the collection unit to efficiently collect highly relevant information by taking into account the user's geographical location information.

[0058] The collection unit can analyze the user's social media activity and collect related information when collecting electronic medical records and medicine notebook history. For example, the collection unit can analyze health-related posts from the user's social media activity and collect related data. The collection unit can also collect necessary data based on health information shared by the user on social media. Furthermore, the collection unit can comprehensively analyze the user's social media activity and collect optimal data. This allows the collection unit to efficiently collect related information by analyzing the user's social media activity.

[0059] The process flow of the first embodiment will be briefly described below.

[0060] Step 1: The collection unit collects the user's electronic medical record and medicine notebook history. This includes medical records, prescription history, allergy information, etc. Collection methods include extraction from a database, user input, and data collection from sensors. Step 2: The analysis unit analyzes the information collected by the collection unit. Analysis methods include statistical analysis, machine learning algorithms, data mining, etc. For example, the analysis unit analyzes the user's health condition and past drug use history to grasp trends in the health condition and extract patterns related to the health condition. Step 3: The guidance unit provides medication guidance based on the analysis results obtained by the analysis unit. Medication guidance includes instructions on how to use the medicine, precautions, and explanations of side effects. For example, it provides appropriate instructions on how to use the medicine based on the user's health condition, provides appropriate precautions based on allergy information, and explains side effects of the medicine.

[0061] (Example 2) The medication instruction system according to the embodiment of the present invention is a system in which AI provides medication instruction for "Class 1 drugs" (such as painkillers and corona antigen test kits) at drugstores instead of the user. Furthermore, by analyzing the user's electronic medical record and medicine notebook history, more accurate medication instruction can be provided. This system is expected to save on the labor costs of pharmacists, who are high-quality human resources, and to make it possible to purchase medicines in sparsely populated areas and late at night. In the future, if stability and recognition are confirmed, it may be possible to consider prescribing medicines through prescriptions. For example, when a user purchases a "Class 1 drug" at a drugstore, AI provides medication instruction. For example, when a user purchases a painkiller, AI explains how to use it and precautions. At this time, AI analyzes the user's electronic medical record and medicine notebook history to provide optimal medication instruction for each individual user. For example, if a user has had an allergic reaction to a specific drug in the past, appropriate instruction is provided based on that information. Next, AI analyzes the user's electronic medical record and medicine notebook history. This allows the user's health condition and past drug use history to be understood, and more accurate medication instruction can be provided. For example, if a user has had a particular disease in the past, the system will provide appropriate guidance based on that information. Furthermore, this system can save on labor costs for pharmacists, who are highly skilled personnel. By having AI provide medication instructions, the burden on pharmacists can be reduced, enabling efficient business operations. In addition, it will be possible to purchase medicines in sparsely populated areas and late at night. For example, even if a user suddenly needs medicine late at night, the AI ​​will provide medication instructions, allowing the user to purchase the medicine with peace of mind. In the future, if the stability and recognition of the system are confirmed, it will be possible to consider prescribing medicines through prescriptions. This will allow even more users to conveniently obtain medicines. This will allow the medication instruction system to provide more accurate medication instructions based on the user's electronic medical record and medicine notebook history.

[0062] The medication instruction system according to the embodiment includes a collection unit, an analysis unit, and an instruction unit. The collection unit collects the history of the user's electronic medical record and medicine notebook. The history of the user's electronic medical record and medicine notebook includes, for example, medical records, prescription history, allergy information, etc., but is not limited to such examples. The collection unit can collect information by, for example, extraction from a database, user input, data collection from a sensor, etc. The analysis unit analyzes the information collected by the collection unit. The analysis is performed by, for example, statistical analysis, machine learning algorithm, data mining, etc., but is not limited to such examples. For example, the analysis unit uses a machine learning algorithm to analyze the user's health condition and past drug use history. In addition, the analysis unit can also grasp the trend of the user's health condition using statistical analysis. Furthermore, the analysis unit can extract patterns related to the user's health condition using data mining technology. The instruction unit provides medication instruction based on the analysis results obtained by the analysis unit. The medication instruction includes, for example, the contents of how to use the medicine, precautions, explanations of side effects, etc., but is not limited to such examples. For example, the guidance unit provides an appropriate method of using the medicine based on the user's health condition. The guidance unit can also provide appropriate precautions based on the user's allergy information. Furthermore, the guidance unit can also explain side effects of the medicine. As a result, the medication instruction system according to the embodiment can provide more accurate medication instruction based on the history of the user's electronic medical record and medicine notebook. Some or all of the above-mentioned processing in the guidance unit may be performed using AI, for example, or may be performed without using AI. For example, the guidance unit can provide medication instruction using an AI model that inputs the analysis results obtained by the analysis unit and outputs medication instruction. As a result, the medication instruction system can provide more accurate medication instruction based on the history of the user's electronic medical record and medicine notebook.

[0063] The collection unit collects the user's electronic medical record and medicine notebook history. The user's electronic medical record and medicine notebook history include, for example, medical records, prescription history, allergy information, etc., but are not limited to such examples. The collection unit can collect information by, for example, extraction from a database, user input, data collection from a sensor, etc. Specifically, medical records and prescription history are automatically extracted from the electronic medical record, and past drug use history and allergy information are collected from the medicine notebook. These data are collected using a secure communication protocol and stored in a central database. In the case of user input, the user himself can input information through a dedicated application or web portal. For example, the user inputs his or her health condition and medication status into an app, and the data is transmitted to the collection unit. In addition, in the case of data collection from a sensor, data such as the user's vital signs and activity level can be collected in real time using sensors of a wearable device or smartphone. This allows the collection unit to collect a wide range of data from various information sources and comprehensively grasp the user's health condition. Furthermore, the collection unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data will be stored on a cloud server and made accessible to the analysis and leadership departments. In addition, the frequency and accuracy of data collection can be adjusted to respond flexibly to specific situations and conditions. This allows the collection department to collect data efficiently and effectively, improving the performance of the entire system.

[0064] The analysis unit analyzes the information collected by the collection unit. The analysis is performed by, for example, statistical analysis, machine learning algorithms, data mining, and other methods, but is not limited to these examples. Specifically, the machine learning algorithm is used to analyze the user's health condition and past drug use history. For example, based on past prescription history, the effect of a specific drug on the user is predicted, and the optimal drug selection is supported. In addition, statistical analysis can be used to grasp the trend of the user's health condition. For example, fluctuations in blood pressure and blood sugar levels are analyzed to detect changes in the health condition early. Furthermore, data mining technology can be used to extract patterns related to the user's health condition. For example, a precursor to the appearance of a specific symptom is detected, and preventive measures are proposed. This allows the analysis unit to analyze the collected data from multiple angles and grasp the user's health condition in detail. Furthermore, the analysis unit can utilize past data and statistical information to perform long-term health management and risk assessment. For example, based on past health data, the risk of a specific disease is predicted, and early measures are proposed. In addition, anomaly detection algorithms can be used to detect patterns that are different from the norm or abnormal data, and issue early warnings. This enables the analysis unit to not only grasp the situation in real time, but also handle long-term health management and anomaly detection, improving the reliability and safety of the entire system.

[0065] The guidance unit provides medication guidance based on the analysis results obtained by the analysis unit. The medication guidance includes, for example, the contents of how to use the medicine, precautions, and explanations of side effects, but is not limited to such examples. Specifically, an appropriate method of using the medicine is provided based on the health condition of the user. For example, the timing and amount of taking a specific medicine are instructed in detail so that the user can use the medicine accurately. In addition, appropriate precautions can be provided based on the user's allergy information. For example, if the user has an allergy to a specific ingredient, the user is instructed to avoid using medicines containing that ingredient. Furthermore, an explanation of the side effects of the medicine can be provided. For example, the side effects that may be caused by a specific medicine are explained in advance so that the user can deal with them appropriately. This allows the guidance unit to provide appropriate medication guidance according to the user's health condition and individual needs. Part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can provide medication guidance using an AI model that inputs the analysis results obtained by the analysis unit and outputs medication guidance. The AI ​​model learns based on past data and specialized knowledge and provides optimal guidance content to the user. This allows the medication instruction system to provide more accurate medication instructions based on the history of the user's electronic medical record and medicine notebook. Furthermore, the instruction department can collect user feedback and continuously improve the accuracy and effectiveness of the instruction content. For example, the AI ​​model is retrained based on the feedback provided by the user to optimize the instruction content. In addition, the instruction department can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered by using not only smartphone notifications but also voice calls, SMS, emails, etc. in combination. This allows the instruction department to quickly and reliably provide action instructions to the user and minimize the risk of damage.

[0066] The guidance unit may include an identification unit that identifies the allergy information of the user. The identification unit identifies the allergy information of the user. The allergy information includes, for example, the type of allergen, past allergic reactions, and a doctor's diagnosis, but is not limited to such examples. The identification unit identifies the allergy information of the user, for example, using an allergen test. The identification unit can also identify the allergy information based on the doctor's diagnosis. Furthermore, the identification unit can also identify the allergy information based on the user's self-report. As a result, the identification unit can identify the allergy information of the user, thereby enabling safer medication guidance. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input the allergy information of the user and identify the allergy information using an AI model that identifies the allergy information.

[0067] The guidance unit may include a providing unit that provides a method for using the medicine. The providing unit provides the method for using the medicine. The method for using the medicine includes, for example, a dosage, a time for taking the medicine, and a method for taking the medicine (oral, injection, etc.), but is not limited to such examples. The providing unit provides, for example, a dosage of the medicine. The providing unit can also provide a time for taking the medicine. Furthermore, the providing unit can also provide a method for taking the medicine (oral, injection, etc.). In this way, the providing unit can provide the method for using the medicine, thereby enabling the user to use the medicine accurately. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide the method for using the medicine using an AI model that inputs the method for using the medicine and provides the method for using the medicine.

[0068] The collection unit can estimate the user's emotion and adjust the collection timing of the electronic medical record and the history of the medicine notebook based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit delays the collection timing so that the user can provide information in a relaxed state. In addition, when the user is relaxed, the collection unit can immediately collect the history of the electronic medical record and the medicine notebook and quickly start the analysis. Furthermore, when the user is in a hurry, the collection unit can advance the collection timing to quickly collect necessary information. This allows the collection unit to adjust the collection timing according to the user's emotion and collect more appropriate information. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0069] The collection unit can analyze the user's past medical history and select an appropriate collection method. For example, the collection unit preferentially collects data from medical institutions that the user frequently visited in the past. The collection unit can also preferentially collect data related to a specific disease from the user's past medical history. Furthermore, the collection unit can analyze the user's past medical history and collect the most relevant data. This allows the collection unit to select the optimal collection method based on the user's past medical history, thereby enabling efficient information collection. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past medical history to the generation AI and cause the generation AI to select the optimal collection method.

[0070] The collection unit can perform filtering based on the user's current health condition and lifestyle habits when collecting the history of the electronic medical record and the medicine notebook. The collection unit, for example, collects only highly relevant data based on the user's current health condition. The collection unit can also filter necessary data based on the user's lifestyle habits (for example, smoking and drinking habits). Furthermore, the collection unit can collect optimal data by comprehensively considering the user's current health condition and lifestyle habits. This allows the collection unit to collect highly relevant information by filtering based on the user's current health condition and lifestyle habits. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's health condition data to the generation AI and cause the generation AI to perform filtering.

[0071] The collection unit can estimate the user's emotion and determine the priority of information to be collected based on the estimated user's emotion. For example, when the user is nervous, the collection unit preferentially collects the most important information. In addition, when the user is relaxed, the collection unit can also collect detailed information. Furthermore, when the user is in a hurry, the collection unit can preferentially collect information that can be collected quickly. In this way, the collection unit can preferentially collect important information by determining the priority of information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0072] The collection unit can preferentially collect highly relevant information based on the geographical location information of the user when collecting the history of the electronic medical record or the medicine notebook. For example, the collection unit preferentially collects data of medical institutions in the area where the user is currently located. The collection unit can also collect highly relevant medical data based on the geographical location information of the user. Furthermore, the collection unit can also collect optimal data by taking into account the user's current location information. This allows the collection unit to efficiently collect highly relevant information by taking into account the user's geographical location information. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant information.

[0073] The collection unit can analyze the user's social media activities and collect related information when collecting the history of the electronic medical record and the medicine notebook. For example, the collection unit analyzes health-related posts from the user's social media activities and collects related data. The collection unit can also collect necessary data based on health information shared by the user on social media. Furthermore, the collection unit can comprehensively analyze the user's social media activities and collect optimal data. This allows the collection unit to efficiently collect related information by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's social media data to the generation AI and cause the generation AI to collect related information.

[0074] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, when the user is nervous, the analysis unit provides a simple and highly visible analysis result. In addition, the analysis unit can also provide a detailed analysis result when the user is relaxed. Furthermore, when the user is in a hurry, the analysis unit can provide an analysis result that covers the main points. In this way, the analysis unit can provide a more appropriate analysis result by adjusting the expression method of the analysis according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0075] The analysis unit can adjust the level of detail of the analysis based on the importance of the user's health condition during the analysis. For example, when the user's health condition is serious, the analysis unit performs a detailed analysis. In addition, when the user's health condition is stable, the analysis unit can also perform a concise analysis. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the importance of the user's health condition. As a result, the analysis unit can provide appropriate analysis results by adjusting the level of detail of the analysis according to the importance of the user's health condition. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's health condition data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0076] The analysis unit can apply different analysis algorithms depending on the medical history of the user during analysis. For example, if the user has had a specific disease in the past, the analysis unit applies an analysis algorithm related to the disease. The analysis unit can also select an optimal analysis algorithm based on the medical history of the user. Furthermore, the analysis unit can apply different analysis algorithms taking into account the medical history of the user. As a result, the analysis unit can provide highly accurate analysis results by applying an optimal analysis algorithm based on the medical history of the user. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the medical history data of the user to the generation AI and cause the generation AI to apply the optimal analysis algorithm.

[0077] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, when the user is in a hurry, the analysis unit provides a short and to-the-point analysis. The analysis unit can also provide a detailed analysis when the user is relaxed. Furthermore, when the user is excited, the analysis unit can provide an analysis with a visually stimulating effect. In this way, the analysis unit can provide an appropriate analysis result by adjusting the length of the analysis according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0078] The analysis unit can determine the priority of the analysis based on the time of submission of the user's medical history at the time of analysis. For example, the analysis unit preferentially analyzes the medical history recently submitted by the user. The analysis unit can also determine the priority of the analysis based on the time of submission of the user's medical history. Furthermore, the analysis unit can determine the optimal analysis order taking into account the time of submission of the user's medical history. As a result, the analysis unit determines the priority of the analysis based on the time of submission of the user's medical history, thereby enabling efficient analysis. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's medical history data to the generation AI and cause the generation AI to execute the determination of the priority of the analysis.

[0079] The analysis unit can adjust the order of analysis based on the relevance of the user during analysis. For example, the analysis unit preferentially analyzes data related to the user's current health condition. The analysis unit can also preferentially analyze highly relevant data based on the user's past medical history. Furthermore, the analysis unit can determine an optimal analysis order based on the relevance of the user. This allows the analysis unit to adjust the order of analysis based on the relevance of the user, thereby enabling efficient analysis. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance data of the user to the generation AI and cause the generation AI to adjust the order of analysis.

[0080] The guidance unit can estimate the user's emotion and adjust the way of expressing the medication instruction based on the estimated user's emotion. For example, when the user is nervous, the guidance unit provides simple and highly visible medication instruction. In addition, when the user is relaxed, the guidance unit can also provide detailed medication instruction. Furthermore, when the user is in a hurry, the guidance unit can also provide medication instruction with the main points. This allows the guidance unit to adjust the way of expressing the medication instruction according to the user's emotion, thereby enabling appropriate guidance. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the guidance unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the guidance unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0081] The guidance unit can adjust the level of detail of the guidance based on the importance of the user's health condition when providing medication guidance. For example, when the user's health condition is serious, the guidance unit provides detailed medication guidance. In addition, when the user's health condition is stable, the guidance unit can also provide concise medication guidance. Furthermore, the guidance unit can adjust the level of detail of the guidance according to the importance of the user's health condition. As a result, the guidance unit can provide appropriate guidance by adjusting the level of detail of the guidance according to the importance of the user's health condition. Part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI or may be performed without using AI. For example, the guidance unit can input the user's health condition data into the generation AI and cause the generation AI to adjust the level of detail of the guidance.

[0082] The guidance unit can apply different guidance algorithms according to the medical history of the user when providing medication guidance. For example, if the user has had a specific disease in the past, the guidance unit applies a guidance algorithm related to the disease. The guidance unit can also select an optimal guidance algorithm based on the medical history of the user. Furthermore, the guidance unit can apply different guidance algorithms in consideration of the medical history of the user. As a result, the guidance unit can apply an optimal guidance algorithm based on the medical history of the user, thereby enabling highly accurate guidance. Part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI or may be performed without using AI. For example, the guidance unit can input the medical history data of the user to the generation AI and cause the generation AI to apply the optimal guidance algorithm.

[0083] The guidance unit can estimate the user's emotion and adjust the length of the medication instruction based on the estimated user's emotion. For example, when the user is in a hurry, the guidance unit provides short and to the point medication instruction. In addition, when the user is relaxed, the guidance unit can provide detailed medication instruction. Furthermore, when the user is excited, the guidance unit can provide medication instruction with a visually stimulating effect. This allows the guidance unit to adjust the length of the medication instruction according to the user's emotion, thereby enabling appropriate guidance. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the guidance unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the guidance unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0084] The guidance unit can determine the priority of the guidance based on the submission time of the user's medical history when providing medication guidance. For example, the guidance unit gives priority to the medical history recently submitted by the user. The guidance unit can also determine the priority of the guidance based on the submission time of the user's medical history. Furthermore, the guidance unit can determine the optimal guidance order taking into account the submission time of the user's medical history. This enables the guidance unit to provide efficient guidance by determining the priority of the guidance based on the submission time of the user's medical history. Part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI or may be performed without using AI. For example, the guidance unit can input the user's medical history data into the generation AI and cause the generation AI to determine the priority of the guidance.

[0085] The instruction unit can adjust the order of instructions based on the relevance of the user when providing medication instructions. For example, the instruction unit gives priority to data related to the user's current health condition. The instruction unit can also give priority to highly relevant data based on the user's past medical history. Furthermore, the instruction unit can determine an optimal instruction order based on the relevance of the user. This allows the instruction unit to adjust the order of instructions based on the relevance of the user, thereby enabling efficient instruction. A part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction unit can input the user's relevance data to the generation AI and cause the generation AI to adjust the order of instructions.

[0086] The identification unit can estimate the user's emotion and adjust the method of identifying allergy information based on the estimated user's emotion. For example, when the user is nervous, the identification unit provides a simple and highly visible method of identifying allergy information. In addition, when the user is relaxed, the identification unit can also provide a method of identifying allergy information that focuses on the main points when the user is in a hurry. In this way, the identification unit can provide appropriate allergy information by adjusting the method of identifying allergy information according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the identification unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0087] When identifying allergy information, the identification unit can select an appropriate identification method by analyzing the user's past allergic reaction history. For example, if the user has had an allergic reaction to a specific drug in the past, the identification unit selects the identification method based on that information. The identification unit can also analyze the user's past allergic reaction history and select the most relevant identification method. Furthermore, the identification unit can select the optimal identification method by comprehensively considering the user's allergic reaction history. As a result, the identification unit can select the optimal identification method based on the user's past allergic reaction history, thereby enabling highly accurate identification of allergy information. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI or may be performed without using AI. For example, the identification unit can input the user's allergic reaction history data to the generation AI and cause the generation AI to select the optimal identification method.

[0088] The identification unit can estimate the user's emotion and determine the priority of allergy information based on the estimated user's emotion. For example, when the user is nervous, the identification unit preferentially identifies the most important allergy information. In addition, when the user is relaxed, the identification unit can also identify detailed allergy information. Furthermore, when the user is in a hurry, the identification unit can preferentially identify allergy information that can be quickly identified. In this way, the identification unit can preferentially identify important allergy information by determining the priority of allergy information according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the identification unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0089] The identification unit can select an appropriate identification method based on the geographical location information of the user when identifying the allergy information. The identification unit, for example, identifies the allergy information based on data of a medical institution in the area where the user is currently located. The identification unit can also identify highly relevant allergy information based on the geographical location information of the user. Furthermore, the identification unit can select an optimal identification method for allergy information by taking into account the user's current location information. In this way, the identification unit can efficiently identify highly relevant allergy information by taking into account the user's geographical location information. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI or may be performed without using AI. For example, the identification unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal identification method.

[0090] The providing unit can estimate the user's emotion and adjust the method of providing the drug usage method based on the estimated user's emotion. For example, when the user is nervous, the providing unit provides a simple and highly visible drug usage method. In addition, when the user is relaxed, the providing unit can also provide a detailed drug usage method. Furthermore, when the user is in a hurry, the providing unit can also provide a drug usage method that covers the main points. In this way, the providing unit can provide an appropriate usage method by adjusting the method of providing the drug usage method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0091] When providing the drug usage method, the providing unit can select an appropriate provision method based on the user's past drug use history. The providing unit provides an optimal usage method based on, for example, the user's past drug use history. The providing unit can also provide a highly relevant usage method by referring to the user's past drug use history. Furthermore, the providing unit can provide an optimal usage method by comprehensively considering the user's drug use history. As a result, the providing unit can provide a highly accurate usage method by selecting an optimal provision method based on the user's past drug use history. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's drug use history data to the generating AI and cause the generating AI to select the optimal provision method.

[0092] The providing unit can estimate the user's emotion and determine the priority of the drug usage method based on the estimated user's emotion. For example, when the user is nervous, the providing unit provides the most important drug usage method with priority. In addition, when the user is relaxed, the providing unit can also provide a detailed drug usage method. Furthermore, when the user is in a hurry, the providing unit can also provide a drug usage method that can be provided quickly with priority. In this way, the providing unit can determine the priority of the drug usage method according to the user's emotion, thereby providing an important usage method with priority. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0093] The providing unit can select an appropriate providing method based on the user's device information when providing the drug usage method. For example, when the user is using a smartphone, the providing unit provides the drug usage method that matches the screen size. In addition, when the user is using a tablet, the providing unit can also provide the drug usage method that is optimized for a large screen. Furthermore, when the user is using a smartwatch, the providing unit can also provide a concise and highly visible drug usage method. Thereby, the providing unit can efficiently provide a highly relevant usage method by taking into account the user's device information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's device information to the generating AI and cause the generating AI to select the optimal providing method.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The medication instruction system may further include a lifestyle collection unit that collects lifestyle data of the user. The lifestyle collection unit collects, for example, data such as the user's diet, exercise, and sleep patterns. This allows the collection unit to grasp the health condition of the user more comprehensively based on the lifestyle data of the user. For example, if the user is taking medicine for high blood pressure, the lifestyle collection unit can collect the user's dietary content and grasp the salt intake. In addition, by collecting exercise data, it is possible to suggest that lack of exercise may be the cause. Furthermore, by analyzing sleep patterns, it is possible to evaluate the impact of lack of sleep on health. As a result, the lifestyle collection unit can provide more accurate medication instruction by collecting the lifestyle data of the user.

[0096] The guidance unit may include an interaction evaluation unit that evaluates drug interactions based on the user's medication history. For example, when the user is taking multiple drugs, the interaction evaluation unit evaluates the drug interactions and identifies potential risks. This allows the guidance unit to allow the user to use the drugs safely. For example, when the user is taking an antibiotic and an anticoagulant at the same time, the interaction evaluation unit can evaluate the drug interactions and warn of bleeding risks. In addition, when the user adds a new drug, the interaction evaluation unit can evaluate interactions with existing drugs and provide appropriate guidance. Furthermore, the interaction evaluation unit can analyze patterns of interactions that have occurred in the past based on the user's medication history and predict future risks. This allows the interaction evaluation unit to provide safer medication guidance by evaluating drug interactions based on the user's medication history.

[0097] The instruction unit may include a visual aid unit that provides visual aid when providing medication instruction to the user. The visual aid unit provides, for example, videos and illustrations showing how to use the medicine. This makes it easier for the user to understand visually. For example, when the user uses an injection drug, the visual aid unit can provide a video showing the injection procedure. Also, by providing an illustration showing how to take the tablet, the user can understand the correct way to take it. Furthermore, the visual aid unit can visually show the side effects and precautions of the drug so that the user does not miss important information. As a result, the visual aid unit can provide more effective instruction by visually assisting the user in medication instruction.

[0098] The collection unit can estimate the user's emotions and adjust the type of data to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting data related to stress (e.g., heart rate and sleep patterns). Also, if the user is relaxed, the collection unit can collect general health data (e.g., diet and exercise). Furthermore, if the user is in a hurry, the collection unit can prioritize collecting data that can be collected quickly (e.g., a simple questionnaire). This allows the collection unit to adjust the type of data to be collected according to the user's emotions, thereby enabling more appropriate information collection.

[0099] The collection unit can analyze the user's past medical history and determine the priority of data to be collected. For example, if the user has suffered from heart disease in the past, the collection unit can prioritize collection of data related to the heart (e.g., heart rate and blood pressure). Also, if the user has suffered from diabetes in the past, data related to blood sugar levels can be prioritized. Furthermore, the collection unit can comprehensively analyze the user's past medical history and prioritize collection of the most relevant data. This allows the collection unit to efficiently collect information by determining the priority of data to be collected based on the user's past medical history.

[0100] The collection unit can filter the electronic medical record and medicine notebook history based on the user's current health condition and lifestyle habits when collecting the history. For example, if the user is currently receiving treatment for high blood pressure, the collection unit can preferentially collect data related to blood pressure. Also, if the user is a smoker, the collection unit can filter and collect data related to smoking. Furthermore, the collection unit can collect optimal data by comprehensively considering the user's current health condition and lifestyle habits. This allows the collection unit to collect highly relevant information by filtering based on the user's current health condition and lifestyle habits.

[0101] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. For example, when the user is nervous, the most important information is collected with priority. When the user is relaxed, detailed information can be collected. Furthermore, when the user is in a hurry, information that can be collected quickly can be collected with priority. In this way, the collection unit can collect important information with priority by determining the priority of information according to the user's emotions.

[0102] When collecting electronic medical records or medicine notebook history, the collection unit can preferentially collect highly relevant information based on the user's geographical location information. For example, data on medical institutions in the area where the user is currently located is preferentially collected. The collection unit can also collect highly relevant medical data based on the user's geographical location information. Furthermore, the collection unit can collect optimal data by taking into account the user's current location information. This allows the collection unit to efficiently collect highly relevant information by taking into account the user's geographical location information.

[0103] The collection unit can analyze the user's social media activity and collect related information when collecting electronic medical records and medicine notebook history. For example, the collection unit can analyze health-related posts from the user's social media activity and collect related data. The collection unit can also collect necessary data based on health information shared by the user on social media. Furthermore, the collection unit can comprehensively analyze the user's social media activity and collect optimal data. This allows the collection unit to efficiently collect related information by analyzing the user's social media activity.

[0104] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided. Furthermore, if the user is in a hurry, an analysis result that focuses on the main points can be provided. In this way, the analysis unit can provide more appropriate analysis results by adjusting the way the analysis is presented according to the user's emotions.

[0105] The process flow of the second embodiment will be briefly described below.

[0106] Step 1: The collection unit collects the user's electronic medical record and medicine notebook history. This includes medical records, prescription history, allergy information, etc. Collection methods include extraction from a database, user input, and data collection from sensors. Step 2: The analysis unit analyzes the information collected by the collection unit. Analysis methods include statistical analysis, machine learning algorithms, data mining, etc. For example, the analysis unit analyzes the user's health condition and past drug use history to grasp trends in the health condition and extract patterns related to the health condition. Step 3: The guidance unit provides medication guidance based on the analysis results obtained by the analysis unit. Medication guidance includes instructions on how to use the medicine, precautions, and explanations of side effects. For example, it provides appropriate instructions on how to use the medicine based on the user's health condition, provides appropriate precautions based on allergy information, and explains side effects of the medicine.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0108] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0109] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0110] Each of the multiple elements including the above-mentioned collection unit, analysis unit, and instruction unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14, and collects the user's electronic medical record and medicine notebook history. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The instruction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides medication instruction based on the analysis result. The instruction unit may be realized, for example, by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 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.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] 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.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0118] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0121] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0125] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements including the collection unit, analysis unit, and instruction unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214, and collects the user's electronic medical record and medicine notebook history. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The instruction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides medication instruction based on the analysis result. The instruction unit may be realized, for example, by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 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.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] 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.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0137] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements including the collection unit, analysis unit, and instruction unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314, and collects the user's electronic medical record and medicine notebook history. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The instruction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides medication instruction based on the analysis results. The instruction unit may be realized, for example, by the control unit 46A of the headset type terminal 314. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0150] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0151] 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.

[0152] 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.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0154] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0159] Each of the multiple elements including the above-mentioned collection unit, analysis unit, and instruction unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414, and collects the user's electronic medical record and medicine notebook history. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The instruction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides medication instruction based on the analysis results. The instruction unit may be realized, for example, by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0160] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0161] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0162] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0163] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0164] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0165] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0167] 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.

[0168] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0169] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0170] 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.

[0171] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0172] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0173] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration in which a processor is used that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0174] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0175] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0176] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0177] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0178] (Appendix 1) A collection unit that collects a user's electronic medical record or medicine notebook history; an analysis unit that analyzes the information collected by the collection unit; an instruction unit that provides medication instruction based on the analysis results obtained by the analysis unit; Equipped A system characterized by: (Appendix 2) The leadership team: The device includes an identification unit for identifying the allergy information of the user. 2. The system of claim 1, (Appendix 3) The leadership team: A provision unit is provided that provides a method for using the drug. 2. The system of claim 1, (Appendix 4) The collecting unit includes: To estimate a user's emotion and adjust a collection timing of an electronic medical record or a medicine notebook history based on the estimated user's emotion. 2. The system of claim 1, (Appendix 5) The collecting unit includes: Analyze the user's past medical history and select the appropriate collection method 2. The system of claim 1, (Appendix 6) The collecting unit includes: When collecting electronic medical records or medicine notebook histories, filtering is performed based on the user's current health condition or lifestyle habits. 2. The system of claim 1, (Appendix 7) The collecting unit includes: Estimate user emotions and prioritize information to be collected based on the estimated user emotions. 2. The system of claim 1, (Appendix 8) The collecting unit includes: When collecting electronic medical records or medical record histories, the system prioritizes collection of relevant information based on the user's geographic location information. 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting electronic medical records or medical record histories, the user's social media activity is analyzed to collect relevant information. 2. The system of claim 1, (Appendix 10) The analysis unit includes: Infer user sentiment and adjust analytics presentation based on the inferred user sentiment 2. The system of claim 1, (Appendix 11) The analysis unit includes: During analysis, adjust the level of detail based on the importance of the user's health condition 2. The system of claim 1, (Appendix 12) The analysis unit includes: During analysis, different analysis algorithms are applied depending on the user's medical history. 2. The system of claim 1, (Appendix 13) The analysis unit includes: Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 14) The analysis unit includes: During analysis, prioritize analysis based on when the user provided their medical history 2. The system of claim 1, (Appendix 15) The analysis unit includes: During analysis, adjust the order of analysis based on user relevance 2. The system of claim 1, (Appendix 16) The leadership team: Estimating the user's emotions and adjusting the way medication instructions are presented based on the estimated user's emotions 2. The system of claim 1, (Appendix 17) The leadership team: When providing medication advice, adjust the level of detail based on the importance of the user's health condition 2. The system of claim 1, (Appendix 18) The leadership team: When providing medication advice, different advice algorithms are applied depending on the user's medical history. 2. The system of claim 1, (Appendix 19) The leadership team: To estimate a user's emotion and adjust the length of a medication instruction based on the estimated user's emotion. 2. The system of claim 1, (Appendix 20) The leadership team: When providing medication advice, prioritize advice based on when the user submitted their medical history 2. The system of claim 1, (Appendix 21) The leadership team: When providing medication instructions, adjust the order of instructions based on the user's relevance 2. The system of claim 1, (Appendix 22) The identification unit is To estimate a user's emotion and adjust a method for identifying allergy information based on the estimated user's emotion. 3. The system of claim 2, (Appendix 23) The identification unit is When identifying allergy information, an appropriate identification method is selected by analyzing the user's past allergic reaction history. 3. The system of claim 2, (Appendix 24) The identification unit is Estimate user's emotions and prioritize allergy information based on the estimated user's emotions 3. The system of claim 2, (Appendix 25) The identification unit is When identifying allergy information, an appropriate identification method is selected based on the user's geographic location information. 3. The system of claim 2, (Appendix 26) The providing unit is To estimate a user's emotion and adjust a method of providing a drug usage instruction based on the estimated user's emotion. 4. The system of claim 3. (Appendix 27) The providing unit is When providing drug usage instructions, the appropriate method is selected based on the user's past drug usage history. 4. The system of claim 3. (Appendix 28) The providing unit is Estimate the user's emotions and prioritize drug use methods based on the estimated user emotions. 4. The system of claim 3. (Appendix 29) The providing unit is When providing drug usage instructions, the appropriate method is selected based on the user's device information 4. The system of claim 3. [Explanation of symbols]

[0179] 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 collection unit that collects a user's electronic medical record or medicine notebook history; an analysis unit that analyzes the information collected by the collection unit; an instruction unit that provides medication instruction based on the analysis results obtained by the analysis unit; Equipped A system characterized by:

2. The leadership team: The device includes an identification unit for identifying the allergy information of the user.

2. The system of claim 1 .

3. The leadership team: A provision unit is provided that provides a method for using the drug.

2. The system of claim 1 .

4. The collecting unit includes: To estimate a user's emotion and adjust a collection timing of an electronic medical record or a medicine notebook history based on the estimated user's emotion.

2. The system of claim 1 .

5. The collecting unit includes: Analyze the user's past medical history and select the appropriate collection method 2. The system of claim 1 .

6. The collecting unit includes: When collecting electronic medical records or medicine notebook histories, filtering is performed based on the user's current health condition or lifestyle habits.

2. The system of claim 1 .

7. The collecting unit includes: Estimate user emotions and prioritize information to be collected based on the estimated user emotions.

2. The system of claim 1 .

8. The collecting unit includes: When collecting electronic medical records or medical record histories, the system prioritizes collection of relevant information based on the user's geographic location information.

2. The system of claim 1 .

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