system

Abstract

The system according to this embodiment aims to efficiently acquire, analyze, and provide drug information. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires drug information. The analysis unit analyzes the information acquired by the acquisition unit. The provision unit provides the results analyzed by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, the information of medicines has not been sufficiently obtained, analyzed, and provided efficiently, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently obtain, analyze, and provide information on medicines.

Means for Solving the Problems

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires information on medicines. The analysis unit analyzes the information acquired by the acquisition unit. The provision unit provides the result analyzed by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can efficiently acquire, analyze, and provide drug information. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) The drug information provision system according to an embodiment of the present invention is a system that uses a generating AI to provide drug information, evaluates the patient's symptoms and physical condition, and allows pharmacists to provide advice based on the latest research results. This drug information provision system works by having the patient input the name of the drug prescribed at the pharmacy into the generating AI, which then provides a dialogue-based response regarding the drug's effects, how to take it, and precautions regarding drug interactions, referencing past prescription data. For example, if a patient asks, "How should I take this medicine?", the generating AI provides specific advice such as, "It is recommended to take this medicine after meals. Also, caution is needed when taking it with other medications." Next, if the patient wishes, they can input their symptoms, physical condition, and medication status into the AI ​​to evaluate the effectiveness of the prescribed drug. For example, if a patient inputs, "My headache went away after taking this medicine," the AI ​​evaluates it as, "This medicine appears to be effective for headaches." Furthermore, when a pharmacist processes a prescription, the AI ​​is used to provide advice to the patient based on new treatment methods and drug research results. For example, a pharmacist might provide advice such as, "According to the latest research, there may be a more effective treatment for this drug." This enables safe and appropriate prescriptions. The system allows patients to obtain accurate information about the effects and dosage of their medications, and enables pharmacists to provide appropriate advice based on the latest research findings. Thus, the drug information system provides quick and accurate answers to patients' questions about their medications, and allows pharmacists to provide appropriate advice to patients based on the latest research findings.

[0029] The drug information provision system according to this embodiment comprises an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires drug information. The acquisition unit can acquire information such as drug components, efficacy, and side effects from a database. The acquisition unit can also acquire drug information using sensors. For example, the acquisition unit can acquire drug component information from a database and collect information on efficacy and side effects. Furthermore, the acquisition unit can also refer to drug usage history and past prescription data of patients. The analysis unit analyzes the information acquired by the acquisition unit. The analysis unit can analyze the effects of drugs using data mining techniques, for example. Furthermore, the analysis unit can evaluate the risk of drug side effects using statistical analysis. For example, the analysis unit can extract patterns related to drug effects using data mining techniques and evaluate the risk of side effects using statistical analysis. Furthermore, the analysis unit can also analyze drug information using generative AI. The provision unit provides the results analyzed by the analysis unit. The provision unit can display the analysis results through a user interface, for example. Furthermore, the provision unit can provide the analysis results as a report using a report generation function. For example, the information provision unit displays information about the effects and side effects of drugs through a user interface and provides the analysis results as a report using a report generation function. This allows the drug information provision system according to this embodiment to efficiently acquire, analyze, and provide drug information.

[0030] The drug information provision system includes an input section for entering the patient's symptoms or physical condition. The input section allows the patient to input symptoms such as fever, headache, and blood pressure. The input section can also accept information about the patient's physical condition. For example, the patient can input their current physical condition, and the system can analyze that information. Furthermore, the input section can also use generative AI to input the patient's symptoms and physical condition. For example, the patient can input their symptoms verbally, and the generative AI can analyze the voice and convert it into text data. This allows for the provision of more detailed information by allowing patients to input their symptoms and physical condition.

[0031] The evaluation unit can evaluate the information entered by the input unit. The evaluation unit can evaluate the information entered by the input unit. For example, the evaluation unit can use scoring technology to evaluate the patient's symptoms and physical condition. The evaluation unit can also evaluate the patient's symptoms and physical condition using diagnostic criteria. For example, the evaluation unit can use scoring technology to evaluate the severity of the patient's symptoms and diagnostic criteria to evaluate the patient's physical condition. Furthermore, the evaluation unit can use generative AI to evaluate the patient's symptoms and physical condition. For example, the evaluation unit uses generative AI to analyze the patient's symptoms and physical condition and makes an evaluation based on the results. This allows for the provision of more appropriate advice by evaluating the entered information.

[0032] The reference section can be based on the latest research findings. The reference section can refer to, for example, medical papers and clinical trial results. Furthermore, the reference section can refer to research findings on the latest treatments and drugs. For example, the reference section can collect information on the effects and side effects of drugs based on the latest medical papers, and evaluate the safety and effectiveness of drugs based on clinical trial results. In addition, the reference section can refer to the latest research findings using generative AI. For example, the reference section can use generative AI to analyze the latest research findings and provide information based on those results. This allows for the provision of more accurate information by referencing the latest research findings.

[0033] The data acquisition unit can be based on past prescription data. The data acquisition unit can refer to data such as the type, dosage, and duration of previously prescribed medications. Furthermore, the data acquisition unit can refer to the patient's past medication history. For example, the data acquisition unit can provide information about the current prescription based on data of previously prescribed medications. In addition, the data acquisition unit can analyze past prescription data using generation AI. For example, the data acquisition unit can have generation AI analyze past prescription data and provide information based on the results. This allows for the provision of more appropriate information by referring to past prescription data.

[0034] The information provider can provide information in a conversational format. The information provider can provide information using, for example, a chatbot or a voice assistant. Furthermore, the information provider can provide information in a conversational format through a user interface. For example, the information provider can use a chatbot to provide real-time answers to patient questions and a voice assistant to provide information verbally. In addition, the information provider can use generative AI to provide information in a conversational format. For example, the information provider can use generative AI to provide conversational answers to patient questions. This conversational format makes it possible to provide information that is easy for patients to understand.

[0035] The drug information provision system includes an acquisition unit that analyzes the patient's past drug use history and selects the optimal method of information acquisition. The acquisition unit analyzes the patient's past drug use history and selects the optimal method of information acquisition. For example, the acquisition unit can select the optimal method of information acquisition based on information about drugs used in the past. Furthermore, the acquisition unit can prioritize the acquisition of information about specific drugs from the patient's past drug use history. For example, the acquisition unit selects the optimal method of information acquisition based on information about drugs used in the past and prioritizes the acquisition of information about specific drugs from the patient's past drug use history. In addition, the acquisition unit can analyze the patient's past drug use history using a generating AI. For example, the acquisition unit uses a generating AI to analyze the patient's past drug use history and selects the optimal method of information acquisition based on the results. This allows for the selection of the optimal method of information acquisition by analyzing past usage history. For example, the optimal method of information acquisition can be selected based on information about drugs used in the past. Furthermore, it can prioritize the acquisition of information about specific drugs from the patient's past drug use history. It can also analyze the patient's past drug use history and select the most efficient method of information acquisition.

[0036] The drug information provision system includes an acquisition unit that filters drug information based on the patient's current health status and lifestyle. The acquisition unit can filter drug information based on the patient's current health status. For example, it can filter relevant drug information considering the patient's current health status. It can also filter appropriate drug information based on the patient's lifestyle. Furthermore, the acquisition unit can analyze the patient's health status and lifestyle using a generation AI and filter information based on the results. This allows for more appropriate information provision by filtering information based on the patient's health status and lifestyle. For example, it can filter relevant drug information considering the patient's current health status. It can also filter appropriate drug information based on the patient's lifestyle. Additionally, it can provide drug information while excluding unnecessary information based on the patient's health status and lifestyle.

[0037] The drug information provision system includes an acquisition unit that prioritizes the acquisition of highly relevant information based on the patient's geographical location when acquiring drug information. The acquisition unit can, for example, acquire the patient's current location using GPS data and acquire highly relevant drug information based on that information. The acquisition unit can also acquire the patient's geographical location using location information services. For example, the acquisition unit can use GPS data to prioritize the acquisition of information on drugs available at nearby pharmacies based on the patient's current location. Furthermore, the acquisition unit can analyze the patient's geographical location using generating AI and acquire information based on the results. For example, the acquisition unit uses generating AI to analyze the patient's geographical location and acquires highly relevant information based on the results. This allows for the provision of more relevant information by considering geographical location. For example, it can prioritize the acquisition of information on drugs available at nearby pharmacies based on the patient's current location. It can also prioritize the acquisition of region-specific drug information by considering the patient's geographical location. Additionally, it can prioritize the acquisition of information on drugs prescribed at the nearest medical institution based on the patient's location information.

[0038] The drug information provision system includes an acquisition unit that analyzes the patient's social media activity and acquires relevant information when acquiring drug information. The acquisition unit can, for example, analyze the content of the patient's social media posts and acquire relevant drug information. Furthermore, the acquisition unit can acquire appropriate drug information based on health information shared by the patient on social media. For example, the acquisition unit analyzes the patient's social media activity, acquires relevant drug information, and acquires appropriate drug information based on health information shared by the patient on social media. In addition, the acquisition unit can analyze the patient's social media activity using generative AI and acquire information based on the results. For example, the acquisition unit uses generative AI to analyze the patient's social media activity and acquires relevant information based on the results. This allows for the provision of more relevant information by analyzing social media activity. For example, it analyzes the patient's social media activity and acquires relevant drug information. It can also acquire appropriate drug information based on health information shared by the patient on social media. Furthermore, it can prioritize the acquisition of information on drugs the patient is interested in based on their social media activity.

[0039] The drug information provision system includes an analysis unit that adjusts the level of detail of the analysis based on the importance of the drug during analysis. The analysis unit can, for example, evaluate the importance of a drug using risk assessment and adjust the level of detail based on the results. Alternatively, the analysis unit can evaluate the importance of a drug based on clinical significance and adjust the level of detail based on the results. For example, the analysis unit can perform a detailed analysis of important drugs using risk assessment and a basic analysis of common drugs based on clinical significance. Furthermore, the analysis unit can evaluate the importance of a drug using generative AI and adjust the level of detail based on the results. For example, the analysis unit can have generative AI analyze the importance of a drug and adjust the level of detail based on the results. This allows for more appropriate information provision by adjusting the level of detail of the analysis based on the importance of the drug. For example, a detailed analysis can be performed for important drugs. A basic analysis can also be performed for common drugs. Furthermore, for drugs used under specific conditions, analysis can be performed according to those conditions.

[0040] The drug information provision system includes an analysis unit that applies different analysis algorithms depending on the drug category during analysis. The analysis unit can, for example, classify drug categories based on therapeutic classification and apply different analysis algorithms according to that category. Alternatively, the analysis unit can classify drug categories based on component classification and apply different analysis algorithms according to that category. For example, the analysis unit can apply a specific analysis algorithm to antibiotics based on therapeutic classification and a different analysis algorithm to analgesics based on component classification. Furthermore, the analysis unit can classify drug categories using generation AI and apply different analysis algorithms based on the results. For example, the analysis unit can have generation AI analyze drug categories and apply different analysis algorithms based on the results. This allows for more appropriate information provision by applying different analysis algorithms according to the drug category. For example, a specific analysis algorithm can be applied to antibiotics. A different analysis algorithm can also be applied to analgesics. Furthermore, a completely different analysis algorithm can be applied to vitamins.

[0041] The drug information provision system includes an analysis unit that determines the priority of analysis based on the timing of drug prescriptions during analysis. The analysis unit can, for example, evaluate the timing of drug prescriptions based on the prescription date and determine the priority of analysis based on the results. Alternatively, the analysis unit can evaluate the timing of drug prescriptions based on the prescription period and determine the priority of analysis based on the results. For example, the analysis unit can prioritize analysis of recently prescribed drugs based on the prescription date, and analyze previously prescribed drugs as needed based on the prescription period. Furthermore, the analysis unit can use generating AI to evaluate the timing of drug prescriptions and determine the priority of analysis based on the results. For example, the analysis unit can use generating AI to analyze the timing of drug prescriptions and determine the priority of analysis based on the results. This allows for more appropriate information provision by determining the priority of analysis based on the timing of drug prescriptions. For example, recently prescribed drugs are prioritized for analysis. Previously prescribed drugs can also be analyzed as needed. Additionally, drugs prescribed within a specific period can be analyzed with a priority order corresponding to the period.

[0042] The drug information provision system includes an analysis unit that adjusts the order of analysis based on the relevance of drugs during analysis. The analysis unit can, for example, evaluate drug relevance using correlation analysis and adjust the order of analysis based on the results. Alternatively, the analysis unit can evaluate drug relevance based on co-occurrence relationships and adjust the order of analysis based on the results. For example, the analysis unit can use correlation analysis to prioritize the analysis of information related to medications the patient is currently taking, and then, based on co-occurrence relationships, analyze information related to medications the patient has taken in the past. Furthermore, the analysis unit can use generative AI to evaluate drug relevance and adjust the order of analysis based on the results. For example, the analysis unit can use generative AI to analyze drug relevance and adjust the order of analysis based on the results. This allows for more appropriate information provision by adjusting the order of analysis based on drug relevance. For example, it can prioritize the analysis of information related to medications the patient is currently taking. It can also analyze information related to medications the patient has taken in the past. Finally, it can analyze information related to medications the patient may take in the future.

[0043] The drug information provision system includes a provision unit that adjusts the level of detail provided based on the importance of the drug when providing information. The provision unit can, for example, evaluate the importance of a drug using risk assessment and adjust the level of detail based on the results. Alternatively, the provision unit can evaluate the importance of a drug based on clinical significance and adjust the level of detail based on the results. For example, the provision unit can use risk assessment to provide detailed information for important drugs and provide basic information for common drugs based on clinical significance. Furthermore, the provision unit can use generative AI to evaluate the importance of a drug and adjust the level of detail based on the results. For example, the provision unit can use generative AI to analyze the importance of a drug and adjust the level of detail based on the results. This allows for more appropriate information provision by adjusting the level of detail based on the importance of the drug. For example, it can provide detailed information for important drugs, basic information for common drugs, and information tailored to specific conditions for drugs used under particular circumstances.

[0044] The drug information provision system includes a provisioning unit that applies different provisioning algorithms depending on the drug category when providing information. The provisioning unit can, for example, classify drug categories based on therapeutic classification and apply different provisioning algorithms according to that category. Alternatively, the provisioning unit can classify drug categories based on ingredient classification and apply different provisioning algorithms according to that category. For example, the provisioning unit can apply a specific provisioning algorithm to antibiotics based on therapeutic classification, and a different provisioning algorithm to analgesics based on ingredient classification. Furthermore, the provisioning unit can classify drug categories using generative AI and apply different provisioning algorithms based on the results. For example, the provisioning unit can have generative AI analyze drug categories and apply different provisioning algorithms based on the results. This allows for more appropriate information provision by applying different provisioning algorithms according to drug categories. For example, a specific provisioning algorithm can be applied to antibiotics. A different provisioning algorithm can also be applied to analgesics. Furthermore, a completely different provisioning algorithm can be applied to vitamins.

[0045] The drug information provision system includes a provision unit that determines the priority of information provision based on the timing of drug prescriptions. The provision unit can, for example, evaluate the timing of drug prescriptions based on the prescription date and determine the priority of information provision based on the results. Alternatively, the provision unit can evaluate the timing of drug prescriptions based on the prescription period and determine the priority of information provision based on the results. For example, based on the prescription date, the provision unit can prioritize providing information for recently prescribed drugs, and based on the prescription period, it can provide information for previously prescribed drugs as needed. Furthermore, the provision unit can use generating AI to evaluate the timing of drug prescriptions and determine the priority of information provision based on the results. For example, the provision unit can use generating AI to analyze the timing of drug prescriptions and determine the priority of information provision based on the results. This allows for more appropriate information provision by determining the priority of information provision based on the timing of drug prescriptions. For example, information can be prioritized for recently prescribed drugs. Information can also be provided for previously prescribed drugs as needed. Additionally, information can be provided for drugs prescribed within a specific period with a priority according to that period.

[0046] The drug information provision system includes a provisioning unit that adjusts the order of information provision based on the relevance of the drugs. The provisioning unit can, for example, evaluate the relevance of drugs using correlation analysis and adjust the order of provision based on the results. Alternatively, the provisioning unit can evaluate the relevance of drugs based on co-occurrence relationships and adjust the order of provision based on the results. For example, the provisioning unit can use correlation analysis to prioritize information related to the medication the patient is currently taking, and then, based on co-occurrence relationships, provide information related to medications the patient has taken in the past. Furthermore, the provisioning unit can use generative AI to evaluate the relevance of drugs and adjust the order of provision based on the results. For example, the provisioning unit can use generative AI to analyze the relevance of drugs and adjust the order of provision based on the results. This allows for more appropriate information provision by adjusting the order of provision based on drug relevance. For example, it can prioritize information related to the medication the patient is currently taking. It can also provide information related to medications the patient has taken in the past. Finally, it can provide information related to medications the patient may take in the future.

[0047] The drug information provision system includes an input unit that analyzes the patient's past input history and selects the optimal input method. The input unit can select the optimal input method based on the patient's past input history (e.g., voice, text). It can also predict and suggest input methods to be used during specific time periods based on the patient's past input history. For example, the input unit can select the optimal input method based on past input methods and predict and suggest input methods to be used during specific time periods based on the patient's past input history. Furthermore, the input unit can analyze the patient's past input history using generative AI and select the optimal input method based on the results. This allows for the selection of the optimal input method by analyzing past input history. For example, it can select the optimal input method based on the patient's past input methods. It can also predict and suggest input methods to be used during specific time periods based on the patient's past input history. Additionally, it can analyze the patient's past input history and select the most efficient input method.

[0048] The drug information provision system includes an input unit that filters information based on the patient's current health status and lifestyle during input. The input unit can, for example, filter relevant input items considering the patient's current health status. It can also filter appropriate input items based on the patient's lifestyle. For instance, the input unit filters relevant input items considering the patient's current health status and filters appropriate input items based on the patient's lifestyle. Furthermore, the input unit can analyze the patient's health status and lifestyle using generative AI and filter information based on the results. This allows for more appropriate information input by filtering information based on the patient's health status and lifestyle. For example, it can filter relevant input items considering the patient's current health status. It can also filter appropriate input items based on the patient's lifestyle. Additionally, it can prompt input by excluding unnecessary input items based on the patient's health status and lifestyle.

[0049] The drug information provision system includes an input unit that prioritizes inputting highly relevant information while considering the patient's geographical location. The input unit can, for example, obtain the patient's current location using GPS data and input highly relevant information based on that information. The input unit can also obtain the patient's geographical location using location information services. For example, the input unit can use GPS data to prioritize inputting information about medications available at nearby pharmacies based on the patient's current location. Furthermore, the input unit can analyze the patient's geographical location using generation AI and input information based on the results. For example, the input unit uses generation AI to analyze the patient's geographical location and inputs highly relevant information based on the results. This allows for the input of more relevant information by considering geographical location. For example, it can prioritize inputting information about medications available at nearby pharmacies based on the patient's current location. It can also prioritize inputting information about region-specific medications while considering the patient's geographical location. Additionally, it can prioritize inputting information about medications prescribed at the nearest medical institution based on the patient's location information.

[0050] The drug information provision system includes an input unit that analyzes the patient's social media activity and inputs relevant information during data entry. The input unit can, for example, analyze the content of the patient's social media posts and input relevant drug information. It can also input appropriate drug information based on health information shared by the patient on social media. Furthermore, the input unit can analyze the patient's social media activity using generative AI and input information based on the results. For example, the input unit uses generative AI to analyze the patient's social media activity and inputs relevant information based on the results. This allows for the input of more relevant information by analyzing social media activity. For example, it analyzes the patient's social media activity and inputs relevant drug information. It can also input appropriate drug information based on health information shared by the patient on social media. Additionally, it can prioritize inputting information about drugs the patient is interested in based on their social media activity.

[0051] The drug information provision system includes an evaluation unit that, during evaluation, analyzes the patient's past medication history to select the optimal evaluation method. The evaluation unit can, for example, select the optimal evaluation method based on information about medications previously taken. Furthermore, the evaluation unit can prioritize the selection of evaluation methods for specific medications based on the patient's past medication history. For example, the evaluation unit can select the optimal evaluation method based on information about medications previously taken, and prioritize the selection of evaluation methods for specific medications based on the patient's past medication history. Additionally, the evaluation unit can analyze the patient's past medication history using generative AI and select the optimal evaluation method based on the results. This allows for the selection of the optimal evaluation method by analyzing past medication history. For example, the evaluation unit can select the optimal evaluation method based on the patient's past medication history. It can also prioritize the selection of evaluation methods for specific medications based on the patient's past medication history. Furthermore, it can analyze the patient's past medication history and select the most efficient evaluation method.

[0052] The drug information provision system includes an evaluation unit that customizes the evaluation methods based on the patient's current health status during evaluation. The evaluation unit customizes the evaluation methods based on the patient's current health status during evaluation. For example, the evaluation unit can customize relevant evaluation methods considering the patient's current health status. The evaluation unit can also customize appropriate evaluation methods based on the patient's health status. For example, the evaluation unit customizes relevant evaluation methods considering the patient's current health status and customizes appropriate evaluation methods based on the patient's health status. Furthermore, the evaluation unit can analyze the patient's health status using generative AI and customize the evaluation methods based on the results. For example, the evaluation unit uses generative AI to analyze the patient's health status and customizes the evaluation methods based on the results. This makes it possible to perform more appropriate evaluations by customizing the evaluation methods based on the patient's health status. For example, it can customize relevant evaluation methods considering the patient's current health status. It can also customize appropriate evaluation methods based on the patient's health status. It can also perform evaluations by excluding unnecessary evaluation methods based on the patient's health status.

[0053] The drug information provision system includes an evaluation unit that selects the optimal evaluation method during evaluation, taking into account the patient's geographical location information. The evaluation unit can, for example, obtain the patient's current location using GPS data and select the optimal evaluation method based on that information. The evaluation unit can also obtain the patient's geographical location information using location information services. For example, the evaluation unit can use GPS data to prioritize the selection of evaluation methods used at nearby medical institutions based on the patient's current location. Furthermore, the evaluation unit can analyze the patient's geographical location information using generative AI and select an evaluation method based on the results. For example, the evaluation unit can use generative AI to analyze the patient's geographical location information and select the optimal evaluation method based on the results. This allows for the selection of a more appropriate evaluation method by considering geographical location information. For example, it can prioritize the selection of evaluation methods used at nearby medical institutions based on the patient's current location. It can also prioritize the selection of region-specific evaluation methods by considering the patient's geographical location information. Additionally, it can prioritize the selection of evaluation methods used at the nearest medical institution based on the patient's location information.

[0054] The drug information provision system includes an evaluation unit that analyzes the patient's social media activity and proposes evaluation methods during the evaluation process. The evaluation unit can, for example, analyze the content of the patient's social media posts and propose relevant evaluation methods. It can also propose appropriate evaluation methods based on health information shared by the patient on social media. For example, the evaluation unit analyzes the patient's social media activity, proposes relevant evaluation methods, and proposes appropriate evaluation methods based on health information shared by the patient on social media. Furthermore, the evaluation unit can analyze the patient's social media activity using generative AI and propose evaluation methods based on the results. For example, the evaluation unit uses generative AI to analyze the patient's social media activity and proposes relevant evaluation methods based on the results. This allows for the proposal of more appropriate evaluation methods by analyzing social media activity. For example, it can analyze the patient's social media activity and propose relevant evaluation methods. It can also propose appropriate evaluation methods based on health information shared by the patient on social media. Additionally, it can prioritize suggesting evaluation methods that the patient is interested in based on their social media activity.

[0055] The drug information provision system includes a reference unit that applies the optimal reference algorithm by referring to past research results when information is referenced. The reference unit applies the optimal reference algorithm by referring to past research results when information is referenced. For example, the reference unit can apply the optimal reference algorithm based on past research results. Furthermore, the reference unit can prioritize the application of a reference algorithm related to a specific drug based on past research results. For example, the reference unit applies the optimal reference algorithm based on past research results and prioritizes the application of a reference algorithm related to a specific drug based on past research results. In addition, the reference unit can analyze past research results using generative AI and apply the optimal reference algorithm based on the results. This allows the application of the optimal reference algorithm by referring to past research results. For example, it applies the optimal reference algorithm based on past research results. It can also prioritize the application of a reference algorithm related to a specific drug based on past research results. Furthermore, it can analyze past research results and apply the most efficient reference algorithm.

[0056] The drug information provision system includes a reference unit that applies different reference methods to each drug category during retrieval. The reference unit can, for example, classify drug categories based on therapeutic classification and apply different reference methods according to that category. It can also classify drug categories based on ingredient classification and apply different reference methods according to that category. For example, the reference unit can apply a specific reference method to antibiotics based on therapeutic classification, and a different reference method to analgesics based on ingredient classification. Furthermore, the reference unit can classify drug categories using generating AI and apply different reference methods based on the results. For example, the reference unit can have generating AI analyze drug categories and apply different reference methods based on the results. This allows for more appropriate information provision by applying different reference methods to each drug category. For example, a specific reference method can be applied to antibiotics. A different reference method can also be applied to analgesics. And yet another different reference method can be applied to vitamins.

[0057] The drug information provision system includes a reference unit that weights references based on the timing of drug prescriptions. The reference unit can, for example, evaluate the timing of drug prescriptions based on the prescription date and weight the references based on the result. Alternatively, the reference unit can evaluate the timing of drug prescriptions based on the prescription period and weight the references based on the result. For example, the reference unit can prioritize referencing recently prescribed drugs based on the prescription date, and reference previously prescribed drugs as needed based on the prescription period. Furthermore, the reference unit can use generating AI to evaluate the timing of drug prescriptions and weight the references based on the result. For example, the reference unit can use generating AI to analyze the timing of drug prescriptions and weight the references based on the result. This allows for more appropriate information provision by weighting references based on the timing of drug prescriptions. For example, recently prescribed drugs are prioritized for reference. Previously prescribed drugs can also be referenced as needed. Additionally, drugs prescribed within a specific period can be referenced with weights corresponding to that period.

[0058] The drug information provision system includes a reference unit that improves the accuracy of references by referencing drug-related literature during the referencing process. The reference unit can, for example, reference drug-related literature using a literature database. It can also reference drug-related literature based on citation relationships. For example, the reference unit can reference drug-related literature using a literature database and also reference drug-related literature based on citation relationships. Furthermore, the reference unit can analyze drug-related literature using generative AI and improve the accuracy of references based on the results. For example, the reference unit uses generative AI to analyze drug-related literature and improves the accuracy of references based on the results. This allows for improved accuracy of references by referencing drug-related literature. For example, it can improve accuracy based on drug-related literature. It can also prioritize improving the accuracy of references for specific drugs from drug-related literature. Additionally, it can analyze drug-related literature and improve the most efficient reference accuracy.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The data acquisition unit can also consider the patient's allergy information when acquiring drug information. For example, the unit can acquire the patient's allergy information from a database and filter drug ingredients and side effects based on that information. Furthermore, the unit can exclude drugs that may cause allergic reactions based on the patient's allergy information. In addition, the unit can analyze the patient's allergy information using generating AI and acquire drug information based on the results. This allows for the provision of safer drug information by considering the patient's allergy information. For example, if a patient is allergic to a specific ingredient, drugs containing that ingredient can be excluded. The unit can also provide information on alternative medications based on the patient's allergy information. Furthermore, it can provide advice on preventing allergic reactions based on the patient's allergy information.

[0061] The input unit allows users to input information about the patient's lifestyle when entering their symptoms and physical condition. For example, it can input information about the patient's diet, exercise, sleep, and other lifestyle habits. Furthermore, the input unit can evaluate symptoms and physical condition based on this lifestyle information. It can also use a generation AI to analyze the patient's lifestyle information and evaluate symptoms and physical condition based on the results. This allows for the provision of more detailed information by considering the patient's lifestyle. For example, it can evaluate the effectiveness and side effects of medication based on the patient's diet. It can also provide advice to maximize the effectiveness of medication based on the patient's exercise habits. Furthermore, it can adjust the timing of medication administration based on the patient's sleep habits.

[0062] The reference section can also provide information tailored to the patient's age and gender when basing its information on the latest research findings. For example, the reference section can refer to relevant research findings while considering the patient's age and gender. Furthermore, the reference section can provide information on appropriate treatments and medications based on the patient's age and gender. In addition, the reference section can use generative AI to analyze the patient's age and gender and provide information based on the results. This enables the provision of information tailored to the patient's age and gender. For example, it can provide information on the effects and side effects of medications appropriate for their age. It can also provide information on treatments and medications appropriate for their gender. Furthermore, it can assess specific risks based on age and gender and provide appropriate advice.

[0063] The data acquisition unit can also consider the patient's genetic information when basing its analysis on past prescription data. For example, the unit can acquire the patient's genetic information from a database and use that information to evaluate the drug's effectiveness and side effects. Furthermore, the unit can predict the patient's response to a specific drug based on the patient's genetic information. In addition, the unit can analyze the patient's genetic information using generative AI and provide drug information based on the results. This allows for more personalized information provision by considering the patient's genetic information. For example, it can predict the effectiveness and side effects of a drug based on genetic information. It can also provide information on alternative drugs based on genetic information. Furthermore, it can assess specific risks based on genetic information and provide appropriate advice.

[0064] The drug information provision system includes an acquisition unit that analyzes a patient's past drug use history and selects the optimal information acquisition method. For example, the acquisition unit can select the optimal information acquisition method based on information about medications used in the past. Furthermore, the acquisition unit can prioritize the acquisition of information about specific medications from the patient's past drug use history. For instance, the acquisition unit selects the optimal information acquisition method based on information about medications used in the past and prioritizes the acquisition of information about specific medications from the patient's past drug use history. In addition, the acquisition unit can analyze the patient's past drug use history using a generating AI. For example, the acquisition unit uses a generating AI to analyze the patient's past drug use history and selects the optimal information acquisition method based on the results. This allows for the selection of the optimal information acquisition method by analyzing past usage history. For example, the optimal information acquisition method is selected based on information about medications the patient has used in the past. It can also prioritize the acquisition of information about specific medications from the patient's past drug use history. Furthermore, it can analyze the patient's past drug use history and select the most efficient information acquisition method.

[0065] The drug information provision system includes an acquisition unit that filters drug information based on the patient's current health status and lifestyle. For example, the acquisition unit can filter relevant drug information considering the patient's current health status. It can also filter appropriate drug information based on the patient's lifestyle. For instance, the acquisition unit filters relevant drug information considering the patient's current health status and filters appropriate drug information based on the patient's lifestyle. Furthermore, the acquisition unit can analyze the patient's health status and lifestyle using a generating AI and filter information based on the results. This allows for more appropriate information provision by filtering information based on the patient's health status and lifestyle. For example, it can filter relevant drug information considering the patient's current health status. It can also filter appropriate drug information based on the patient's lifestyle. Additionally, it can provide drug information while excluding unnecessary information based on the patient's health status and lifestyle.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The acquisition unit acquires drug information. The acquisition unit can acquire information such as drug ingredients, efficacy, and side effects from a database. The acquisition unit can also acquire drug information using sensors. Furthermore, the acquisition unit can refer to drug usage history and past prescription data of patients. Step 2: The analysis unit analyzes the information acquired by the acquisition unit. The analysis unit can, for example, analyze the effects of a drug using data mining techniques. The analysis unit can also evaluate the risk of drug side effects using statistical analysis. Furthermore, the analysis unit can analyze drug information using generative AI. Step 3: The service provider provides the results analyzed by the analysis unit. The service provider can, for example, display the analysis results through a user interface. Alternatively, the service provider can provide the analysis results as a report using a report generation function.

[0068] (Example of form 2) The drug information provision system according to an embodiment of the present invention is a system that uses a generating AI to provide drug information, evaluates the patient's symptoms and physical condition, and allows pharmacists to provide advice based on the latest research results. This drug information provision system works by having the patient input the name of the drug prescribed at the pharmacy into the generating AI, which then provides a dialogue-based response regarding the drug's effects, how to take it, and precautions regarding drug interactions, referencing past prescription data. For example, if a patient asks, "How should I take this medicine?", the generating AI provides specific advice such as, "It is recommended to take this medicine after meals. Also, caution is needed when taking it with other medications." Next, if the patient wishes, they can input their symptoms, physical condition, and medication status into the AI ​​to evaluate the effectiveness of the prescribed drug. For example, if a patient inputs, "My headache went away after taking this medicine," the AI ​​evaluates it as, "This medicine appears to be effective for headaches." Furthermore, when a pharmacist processes a prescription, the AI ​​is used to provide advice to the patient based on new treatment methods and drug research results. For example, a pharmacist might provide advice such as, "According to the latest research, there may be a more effective treatment for this drug." This enables safe and appropriate prescriptions. The system allows patients to obtain accurate information about the effects and dosage of their medications, and enables pharmacists to provide appropriate advice based on the latest research findings. Thus, the drug information system provides quick and accurate answers to patients' questions about their medications, and allows pharmacists to provide appropriate advice to patients based on the latest research findings.

[0069] The drug information provision system according to this embodiment comprises an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires drug information. The acquisition unit can acquire information such as drug components, efficacy, and side effects from a database. The acquisition unit can also acquire drug information using sensors. For example, the acquisition unit can acquire drug component information from a database and collect information on efficacy and side effects. Furthermore, the acquisition unit can also refer to drug usage history and past prescription data of patients. The analysis unit analyzes the information acquired by the acquisition unit. The analysis unit can analyze the effects of drugs using data mining techniques, for example. Furthermore, the analysis unit can evaluate the risk of drug side effects using statistical analysis. For example, the analysis unit can extract patterns related to drug effects using data mining techniques and evaluate the risk of side effects using statistical analysis. Furthermore, the analysis unit can also analyze drug information using generative AI. The provision unit provides the results analyzed by the analysis unit. The provision unit can display the analysis results through a user interface, for example. Furthermore, the provision unit can provide the analysis results as a report using a report generation function. For example, the information provision unit displays information about the effects and side effects of drugs through a user interface and provides the analysis results as a report using a report generation function. This allows the drug information provision system according to this embodiment to efficiently acquire, analyze, and provide drug information.

[0070] The drug information provision system includes an input section for entering the patient's symptoms or physical condition. The input section allows the patient to input symptoms such as fever, headache, and blood pressure. The input section can also accept information about the patient's physical condition. For example, the patient can input their current physical condition, and the system can analyze that information. Furthermore, the input section can also use generative AI to input the patient's symptoms and physical condition. For example, the patient can input their symptoms verbally, and the generative AI can analyze the voice and convert it into text data. This allows for the provision of more detailed information by allowing patients to input their symptoms and physical condition.

[0071] The evaluation unit can evaluate the information entered by the input unit. The evaluation unit can evaluate the information entered by the input unit. For example, the evaluation unit can use scoring technology to evaluate the patient's symptoms and physical condition. The evaluation unit can also evaluate the patient's symptoms and physical condition using diagnostic criteria. For example, the evaluation unit can use scoring technology to evaluate the severity of the patient's symptoms and diagnostic criteria to evaluate the patient's physical condition. Furthermore, the evaluation unit can use generative AI to evaluate the patient's symptoms and physical condition. For example, the evaluation unit uses generative AI to analyze the patient's symptoms and physical condition and makes an evaluation based on the results. This allows for the provision of more appropriate advice by evaluating the entered information.

[0072] The reference section can be based on the latest research findings. The reference section can refer to, for example, medical papers and clinical trial results. Furthermore, the reference section can refer to research findings on the latest treatments and drugs. For example, the reference section can collect information on the effects and side effects of drugs based on the latest medical papers, and evaluate the safety and effectiveness of drugs based on clinical trial results. In addition, the reference section can refer to the latest research findings using generative AI. For example, the reference section can use generative AI to analyze the latest research findings and provide information based on those results. This allows for the provision of more accurate information by referencing the latest research findings.

[0073] The data acquisition unit can be based on past prescription data. The data acquisition unit can refer to data such as the type, dosage, and duration of previously prescribed medications. Furthermore, the data acquisition unit can refer to the patient's past medication history. For example, the data acquisition unit can provide information about the current prescription based on data of previously prescribed medications. In addition, the data acquisition unit can analyze past prescription data using generation AI. For example, the data acquisition unit can have generation AI analyze past prescription data and provide information based on the results. This allows for the provision of more appropriate information by referring to past prescription data.

[0074] The information provider can provide information in a conversational format. The information provider can provide information using, for example, a chatbot or a voice assistant. Furthermore, the information provider can provide information in a conversational format through a user interface. For example, the information provider can use a chatbot to provide real-time answers to patient questions and a voice assistant to provide information verbally. In addition, the information provider can use generative AI to provide information in a conversational format. For example, the information provider can use generative AI to provide conversational answers to patient questions. This conversational format makes it possible to provide information that is easy for patients to understand.

[0075] The drug information provision system includes an acquisition unit that estimates the patient's emotions and adjusts the timing of drug information acquisition based on the estimated emotions. The acquisition unit estimates the patient's emotions and adjusts the timing of drug information acquisition based on the estimated emotions. The acquisition unit can estimate the patient's emotions using, for example, facial recognition technology. The acquisition unit can also estimate the patient's emotions using voice analysis technology. For example, the acquisition unit can estimate emotions from the patient's facial expressions using facial recognition technology, and estimate emotions from the tone and speed of the patient's voice using voice analysis technology. Furthermore, the acquisition unit can also estimate the patient's emotions using generative AI. For example, the acquisition unit has the generative AI analyze the patient's emotions and adjusts the information acquisition timing based on the results. This makes it possible to provide more appropriate information by adjusting the information acquisition timing according to the patient's emotions. For example, if the patient is feeling anxious, drug information can be acquired and provided immediately. If the patient is relaxed, drug information can be acquired and provided at an appropriate time. If the patient is in a hurry, drug information can be acquired and provided quickly.

[0076] The drug information provision system includes an acquisition unit that analyzes the patient's past drug use history and selects the optimal method of information acquisition. The acquisition unit analyzes the patient's past drug use history and selects the optimal method of information acquisition. For example, the acquisition unit can select the optimal method of information acquisition based on information about drugs used in the past. Furthermore, the acquisition unit can prioritize the acquisition of information about specific drugs from the patient's past drug use history. For example, the acquisition unit selects the optimal method of information acquisition based on information about drugs used in the past and prioritizes the acquisition of information about specific drugs from the patient's past drug use history. In addition, the acquisition unit can analyze the patient's past drug use history using a generating AI. For example, the acquisition unit uses a generating AI to analyze the patient's past drug use history and selects the optimal method of information acquisition based on the results. This allows for the selection of the optimal method of information acquisition by analyzing past usage history. For example, the optimal method of information acquisition can be selected based on information about drugs used in the past. Furthermore, it can prioritize the acquisition of information about specific drugs from the patient's past drug use history. It can also analyze the patient's past drug use history and select the most efficient method of information acquisition.

[0077] The drug information provision system includes an acquisition unit that filters drug information based on the patient's current health status and lifestyle. The acquisition unit can filter drug information based on the patient's current health status. For example, it can filter relevant drug information considering the patient's current health status. It can also filter appropriate drug information based on the patient's lifestyle. Furthermore, the acquisition unit can analyze the patient's health status and lifestyle using a generation AI and filter information based on the results. This allows for more appropriate information provision by filtering information based on the patient's health status and lifestyle. For example, it can filter relevant drug information considering the patient's current health status. It can also filter appropriate drug information based on the patient's lifestyle. Additionally, it can provide drug information while excluding unnecessary information based on the patient's health status and lifestyle.

[0078] The drug information provision system includes an acquisition unit that estimates the patient's emotions and determines the priority of drug information to be acquired based on the estimated emotions. The acquisition unit can estimate the patient's emotions using, for example, facial recognition technology. The acquisition unit can also estimate the patient's emotions using voice analysis technology. For example, the acquisition unit can estimate emotions from the patient's facial expressions using facial recognition technology, and estimate emotions from the tone and speed of the patient's voice using voice analysis technology. Furthermore, the acquisition unit can also estimate the patient's emotions using generative AI. For example, the acquisition unit uses generative AI to analyze the patient's emotions and determines the priority of information based on the results. This makes it possible to provide more appropriate information by determining the priority of information according to the patient's emotions. For example, if the patient is feeling anxious, important drug information will be prioritized. If the patient is relaxed, detailed drug information will be prioritized. If the patient is in a hurry, only the minimum necessary drug information will be prioritized.

[0079] The drug information provision system includes an acquisition unit that prioritizes the acquisition of highly relevant information based on the patient's geographical location when acquiring drug information. The acquisition unit can, for example, acquire the patient's current location using GPS data and acquire highly relevant drug information based on that information. The acquisition unit can also acquire the patient's geographical location using location information services. For example, the acquisition unit can use GPS data to prioritize the acquisition of information on drugs available at nearby pharmacies based on the patient's current location. Furthermore, the acquisition unit can analyze the patient's geographical location using generating AI and acquire information based on the results. For example, the acquisition unit uses generating AI to analyze the patient's geographical location and acquires highly relevant information based on the results. This allows for the provision of more relevant information by considering geographical location. For example, it can prioritize the acquisition of information on drugs available at nearby pharmacies based on the patient's current location. It can also prioritize the acquisition of region-specific drug information by considering the patient's geographical location. Additionally, it can prioritize the acquisition of information on drugs prescribed at the nearest medical institution based on the patient's location information.

[0080] The drug information provision system includes an acquisition unit that analyzes the patient's social media activity and acquires relevant information when acquiring drug information. The acquisition unit can, for example, analyze the content of the patient's social media posts and acquire relevant drug information. Furthermore, the acquisition unit can acquire appropriate drug information based on health information shared by the patient on social media. For example, the acquisition unit analyzes the patient's social media activity, acquires relevant drug information, and acquires appropriate drug information based on health information shared by the patient on social media. In addition, the acquisition unit can analyze the patient's social media activity using generative AI and acquire information based on the results. For example, the acquisition unit uses generative AI to analyze the patient's social media activity and acquires relevant information based on the results. This allows for the provision of more relevant information by analyzing social media activity. For example, it analyzes the patient's social media activity and acquires relevant drug information. It can also acquire appropriate drug information based on health information shared by the patient on social media. Furthermore, it can prioritize the acquisition of information on drugs the patient is interested in based on their social media activity.

[0081] The drug information provision system includes an analysis unit that estimates the patient's emotions and adjusts the presentation of the analysis based on the estimated emotions. The analysis unit can estimate the patient's emotions using, for example, facial recognition technology. The analysis unit can also estimate the patient's emotions using voice analysis technology. For example, the analysis unit can estimate emotions from the patient's facial expressions using facial recognition technology, and estimate emotions from the tone and speed of the patient's voice using voice analysis technology. Furthermore, the analysis unit can also estimate the patient's emotions using generative AI. For example, the analysis unit has the generative AI analyze the patient's emotions and adjusts the presentation of the analysis based on the results. This allows for the provision of more appropriate information by adjusting the presentation of the analysis according to the patient's emotions. For example, if the patient is feeling anxious, a simple and easy-to-understand presentation method is used. If the patient is relaxed, detailed analysis results can be provided. If the patient is in a hurry, concise analysis results that get straight to the point can be provided.

[0082] The drug information provision system includes an analysis unit that adjusts the level of detail of the analysis based on the importance of the drug during analysis. The analysis unit can, for example, evaluate the importance of a drug using risk assessment and adjust the level of detail based on the results. Alternatively, the analysis unit can evaluate the importance of a drug based on clinical significance and adjust the level of detail based on the results. For example, the analysis unit can perform a detailed analysis of important drugs using risk assessment and a basic analysis of common drugs based on clinical significance. Furthermore, the analysis unit can evaluate the importance of a drug using generative AI and adjust the level of detail based on the results. For example, the analysis unit can have generative AI analyze the importance of a drug and adjust the level of detail based on the results. This allows for more appropriate information provision by adjusting the level of detail of the analysis based on the importance of the drug. For example, a detailed analysis can be performed for important drugs. A basic analysis can also be performed for common drugs. Furthermore, for drugs used under specific conditions, analysis can be performed according to those conditions.

[0083] The drug information provision system includes an analysis unit that applies different analysis algorithms depending on the drug category during analysis. The analysis unit can, for example, classify drug categories based on therapeutic classification and apply different analysis algorithms according to that category. Alternatively, the analysis unit can classify drug categories based on component classification and apply different analysis algorithms according to that category. For example, the analysis unit can apply a specific analysis algorithm to antibiotics based on therapeutic classification and a different analysis algorithm to analgesics based on component classification. Furthermore, the analysis unit can classify drug categories using generation AI and apply different analysis algorithms based on the results. For example, the analysis unit can have generation AI analyze drug categories and apply different analysis algorithms based on the results. This allows for more appropriate information provision by applying different analysis algorithms according to the drug category. For example, a specific analysis algorithm can be applied to antibiotics. A different analysis algorithm can also be applied to analgesics. Furthermore, a completely different analysis algorithm can be applied to vitamins.

[0084] The drug information provision system includes an analysis unit that estimates the patient's emotions and adjusts the length of the analysis based on the estimated emotions. The analysis unit can estimate the patient's emotions using, for example, facial recognition technology. The analysis unit can also estimate the patient's emotions using voice analysis technology. For example, the analysis unit can estimate emotions from the patient's facial expressions using facial recognition technology, and estimate emotions from the tone and speed of the patient's voice using voice analysis technology. Furthermore, the analysis unit can also estimate the patient's emotions using generative AI. For example, the analysis unit has the generative AI analyze the patient's emotions and adjusts the length of the analysis based on the results. This allows for more appropriate information to be provided by adjusting the length of the analysis according to the patient's emotions. For example, if the patient is feeling anxious, a short and concise analysis result can be provided. If the patient is relaxed, a detailed analysis result can be provided. If the patient is in a hurry, a brief analysis result can be provided.

[0085] The drug information provision system includes an analysis unit that determines the priority of analysis based on the timing of drug prescriptions during analysis. The analysis unit can, for example, evaluate the timing of drug prescriptions based on the prescription date and determine the priority of analysis based on the results. Alternatively, the analysis unit can evaluate the timing of drug prescriptions based on the prescription period and determine the priority of analysis based on the results. For example, the analysis unit can prioritize analysis of recently prescribed drugs based on the prescription date, and analyze previously prescribed drugs as needed based on the prescription period. Furthermore, the analysis unit can use generating AI to evaluate the timing of drug prescriptions and determine the priority of analysis based on the results. For example, the analysis unit can use generating AI to analyze the timing of drug prescriptions and determine the priority of analysis based on the results. This allows for more appropriate information provision by determining the priority of analysis based on the timing of drug prescriptions. For example, recently prescribed drugs are prioritized for analysis. Previously prescribed drugs can also be analyzed as needed. Additionally, drugs prescribed within a specific period can be analyzed with a priority order corresponding to the period.

[0086] The drug information provision system includes an analysis unit that adjusts the order of analysis based on the relevance of drugs during analysis. The analysis unit can, for example, evaluate drug relevance using correlation analysis and adjust the order of analysis based on the results. Alternatively, the analysis unit can evaluate drug relevance based on co-occurrence relationships and adjust the order of analysis based on the results. For example, the analysis unit can use correlation analysis to prioritize the analysis of information related to medications the patient is currently taking, and then, based on co-occurrence relationships, analyze information related to medications the patient has taken in the past. Furthermore, the analysis unit can use generative AI to evaluate drug relevance and adjust the order of analysis based on the results. For example, the analysis unit can use generative AI to analyze drug relevance and adjust the order of analysis based on the results. This allows for more appropriate information provision by adjusting the order of analysis based on drug relevance. For example, it can prioritize the analysis of information related to medications the patient is currently taking. It can also analyze information related to medications the patient has taken in the past. Finally, it can analyze information related to medications the patient may take in the future.

[0087] The drug information provision system includes a provision unit that estimates the patient's emotions and adjusts the method of information provision based on the estimated emotions. The provision unit can estimate the patient's emotions using, for example, facial recognition technology. It can also estimate the patient's emotions using voice analysis technology. For example, the provision unit can estimate emotions from the patient's facial expressions using facial recognition technology, and estimate emotions from the tone and speed of the patient's voice using voice analysis technology. Furthermore, the provision unit can estimate the patient's emotions using generative AI. For example, the provision unit can have the generative AI analyze the patient's emotions and adjust the method of information provision based on the results. This makes it possible to provide more appropriate information by adjusting the method of information provision according to the patient's emotions. For example, if the patient is feeling anxious, information can be provided in a simple and easy-to-understand way. If the patient is relaxed, detailed information can be provided. If the patient is in a hurry, concise information that gets straight to the point can be provided.

[0088] The drug information provision system includes a provision unit that adjusts the level of detail provided based on the importance of the drug when providing information. The provision unit can, for example, evaluate the importance of a drug using risk assessment and adjust the level of detail based on the results. Alternatively, the provision unit can evaluate the importance of a drug based on clinical significance and adjust the level of detail based on the results. For example, the provision unit can use risk assessment to provide detailed information for important drugs and provide basic information for common drugs based on clinical significance. Furthermore, the provision unit can use generative AI to evaluate the importance of a drug and adjust the level of detail based on the results. For example, the provision unit can use generative AI to analyze the importance of a drug and adjust the level of detail based on the results. This allows for more appropriate information provision by adjusting the level of detail based on the importance of the drug. For example, it can provide detailed information for important drugs, basic information for common drugs, and information tailored to specific conditions for drugs used under particular circumstances.

[0089] The drug information provision system includes a provisioning unit that applies different provisioning algorithms depending on the drug category when providing information. The provisioning unit can, for example, classify drug categories based on therapeutic classification and apply different provisioning algorithms according to that category. Alternatively, the provisioning unit can classify drug categories based on ingredient classification and apply different provisioning algorithms according to that category. For example, the provisioning unit can apply a specific provisioning algorithm to antibiotics based on therapeutic classification, and a different provisioning algorithm to analgesics based on ingredient classification. Furthermore, the provisioning unit can classify drug categories using generative AI and apply different provisioning algorithms based on the results. For example, the provisioning unit can have generative AI analyze drug categories and apply different provisioning algorithms based on the results. This allows for more appropriate information provision by applying different provisioning algorithms according to drug categories. For example, a specific provisioning algorithm can be applied to antibiotics. A different provisioning algorithm can also be applied to analgesics. Furthermore, a completely different provisioning algorithm can be applied to vitamins.

[0090] The drug information delivery system includes a delivery unit that estimates the patient's emotions and adjusts the length of the information delivery based on the estimated emotions. The delivery unit can estimate the patient's emotions using, for example, facial recognition technology. It can also estimate the patient's emotions using voice analysis technology. For example, the delivery unit can estimate emotions from the patient's facial expressions using facial recognition technology, and estimate emotions from the tone and speed of the patient's voice using voice analysis technology. Furthermore, the delivery unit can estimate the patient's emotions using generative AI. For example, the delivery unit can have the generative AI analyze the patient's emotions and adjust the length of the information delivery based on the results. This allows for more appropriate information delivery by adjusting the length of the information delivery according to the patient's emotions. For example, if the patient is feeling anxious, short, concise information can be provided. If the patient is relaxed, detailed information can be provided. If the patient is in a hurry, brief information can be provided.

[0091] The drug information provision system includes a provision unit that determines the priority of information provision based on the timing of drug prescriptions. The provision unit can, for example, evaluate the timing of drug prescriptions based on the prescription date and determine the priority of information provision based on the results. Alternatively, the provision unit can evaluate the timing of drug prescriptions based on the prescription period and determine the priority of information provision based on the results. For example, based on the prescription date, the provision unit can prioritize providing information for recently prescribed drugs, and based on the prescription period, it can provide information for previously prescribed drugs as needed. Furthermore, the provision unit can use generating AI to evaluate the timing of drug prescriptions and determine the priority of information provision based on the results. For example, the provision unit can use generating AI to analyze the timing of drug prescriptions and determine the priority of information provision based on the results. This allows for more appropriate information provision by determining the priority of information provision based on the timing of drug prescriptions. For example, information can be prioritized for recently prescribed drugs. Information can also be provided for previously prescribed drugs as needed. Additionally, information can be provided for drugs prescribed within a specific period with a priority according to that period.

[0092] The drug information provision system includes a provisioning unit that adjusts the order of information provision based on the relevance of the drugs. The provisioning unit can, for example, evaluate the relevance of drugs using correlation analysis and adjust the order of provision based on the results. Alternatively, the provisioning unit can evaluate the relevance of drugs based on co-occurrence relationships and adjust the order of provision based on the results. For example, the provisioning unit can use correlation analysis to prioritize information related to the medication the patient is currently taking, and then, based on co-occurrence relationships, provide information related to medications the patient has taken in the past. Furthermore, the provisioning unit can use generative AI to evaluate the relevance of drugs and adjust the order of provision based on the results. For example, the provisioning unit can use generative AI to analyze the relevance of drugs and adjust the order of provision based on the results. This allows for more appropriate information provision by adjusting the order of provision based on drug relevance. For example, it can prioritize information related to the medication the patient is currently taking. It can also provide information related to medications the patient has taken in the past. Finally, it can provide information related to medications the patient may take in the future.

[0093] The drug information provision system includes an input unit that estimates the patient's emotions and adjusts the timing of input based on the estimated emotions. The input unit can estimate the patient's emotions using, for example, facial recognition technology. The input unit can also estimate the patient's emotions using voice analysis technology. For example, the input unit can estimate emotions from the patient's facial expressions using facial recognition technology, and estimate emotions from the tone and speed of the patient's voice using voice analysis technology. Furthermore, the input unit can also estimate the patient's emotions using generative AI. For example, the input unit can have the generative AI analyze the patient's emotions and adjust the timing of input based on the results. This allows for more appropriate information input by adjusting the timing of input according to the patient's emotions. For example, if the patient is feeling anxious, the system can prompt for input immediately. If the patient is relaxed, the system can prompt for input at an appropriate time. If the patient is in a hurry, the system can prompt for input quickly.

[0094] The drug information provision system includes an input unit that analyzes the patient's past input history and selects the optimal input method. The input unit can select the optimal input method based on the patient's past input history (e.g., voice, text). It can also predict and suggest input methods to be used during specific time periods based on the patient's past input history. For example, the input unit can select the optimal input method based on past input methods and predict and suggest input methods to be used during specific time periods based on the patient's past input history. Furthermore, the input unit can analyze the patient's past input history using generative AI and select the optimal input method based on the results. This allows for the selection of the optimal input method by analyzing past input history. For example, it can select the optimal input method based on the patient's past input methods. It can also predict and suggest input methods to be used during specific time periods based on the patient's past input history. Additionally, it can analyze the patient's past input history and select the most efficient input method.

[0095] The drug information provision system includes an input unit that filters information based on the patient's current health status and lifestyle during input. The input unit can, for example, filter relevant input items considering the patient's current health status. It can also filter appropriate input items based on the patient's lifestyle. For instance, the input unit filters relevant input items considering the patient's current health status and filters appropriate input items based on the patient's lifestyle. Furthermore, the input unit can analyze the patient's health status and lifestyle using generative AI and filter information based on the results. This allows for more appropriate information input by filtering information based on the patient's health status and lifestyle. For example, it can filter relevant input items considering the patient's current health status. It can also filter appropriate input items based on the patient's lifestyle. Additionally, it can prompt input by excluding unnecessary input items based on the patient's health status and lifestyle.

[0096] The drug information provision system includes an input unit that estimates the patient's emotions and determines the priority of inputs based on the estimated emotions. The input unit can estimate the patient's emotions using, for example, facial recognition technology. The input unit can also estimate the patient's emotions using voice analysis technology. For example, the input unit can estimate emotions from the patient's facial expressions using facial recognition technology, and estimate emotions from the tone and speed of the patient's voice using voice analysis technology. Furthermore, the input unit can also estimate the patient's emotions using generative AI. For example, the input unit can have the generative AI analyze the patient's emotions and determine the priority of inputs based on the results. This allows for more appropriate information input by determining the priority of inputs according to the patient's emotions. For example, if the patient is feeling anxious, important input items can be prioritized. If the patient is relaxed, detailed input items can be prioritized. If the patient is in a hurry, only the minimum necessary input items can be prioritized.

[0097] The drug information provision system includes an input unit that prioritizes inputting highly relevant information while considering the patient's geographical location. The input unit can, for example, obtain the patient's current location using GPS data and input highly relevant information based on that information. The input unit can also obtain the patient's geographical location using location information services. For example, the input unit can use GPS data to prioritize inputting information about medications available at nearby pharmacies based on the patient's current location. Furthermore, the input unit can analyze the patient's geographical location using generation AI and input information based on the results. For example, the input unit uses generation AI to analyze the patient's geographical location and inputs highly relevant information based on the results. This allows for the input of more relevant information by considering geographical location. For example, it can prioritize inputting information about medications available at nearby pharmacies based on the patient's current location. It can also prioritize inputting information about region-specific medications while considering the patient's geographical location. Additionally, it can prioritize inputting information about medications prescribed at the nearest medical institution based on the patient's location information.

[0098] The drug information provision system includes an input unit that analyzes the patient's social media activity and inputs relevant information during data entry. The input unit can, for example, analyze the content of the patient's social media posts and input relevant drug information. It can also input appropriate drug information based on health information shared by the patient on social media. Furthermore, the input unit can analyze the patient's social media activity using generative AI and input information based on the results. For example, the input unit uses generative AI to analyze the patient's social media activity and inputs relevant information based on the results. This allows for the input of more relevant information by analyzing social media activity. For example, it analyzes the patient's social media activity and inputs relevant drug information. It can also input appropriate drug information based on health information shared by the patient on social media. Additionally, it can prioritize inputting information about drugs the patient is interested in based on their social media activity.

[0099] The drug information provision system includes an evaluation unit that estimates the patient's emotions and adjusts the evaluation method based on the estimated emotions. The evaluation unit can estimate the patient's emotions using, for example, facial recognition technology. The evaluation unit can also estimate the patient's emotions using voice analysis technology. For example, the evaluation unit can estimate emotions from the patient's facial expressions using facial recognition technology, and estimate emotions from the tone and speed of the patient's voice using voice analysis technology. Furthermore, the evaluation unit can also estimate the patient's emotions using generative AI. For example, the evaluation unit has the generative AI analyze the patient's emotions and adjusts the evaluation method based on the results. This allows for a more appropriate evaluation by adjusting the evaluation method according to the patient's emotions. For example, if the patient is feeling anxious, a simple and easy-to-understand evaluation method can be used. If the patient is relaxed, a detailed evaluation method can be used. If the patient is in a hurry, a concise evaluation method that focuses on the essentials can be used.

[0100] The drug information provision system includes an evaluation unit that, during evaluation, analyzes the patient's past medication history to select the optimal evaluation method. The evaluation unit can, for example, select the optimal evaluation method based on information about medications previously taken. Furthermore, the evaluation unit can prioritize the selection of evaluation methods for specific medications based on the patient's past medication history. For example, the evaluation unit can select the optimal evaluation method based on information about medications previously taken, and prioritize the selection of evaluation methods for specific medications based on the patient's past medication history. Additionally, the evaluation unit can analyze the patient's past medication history using generative AI and select the optimal evaluation method based on the results. This allows for the selection of the optimal evaluation method by analyzing past medication history. For example, the evaluation unit can select the optimal evaluation method based on the patient's past medication history. It can also prioritize the selection of evaluation methods for specific medications based on the patient's past medication history. Furthermore, it can analyze the patient's past medication history and select the most efficient evaluation method.

[0101] The drug information provision system includes an evaluation unit that customizes the evaluation methods based on the patient's current health status during evaluation. The evaluation unit customizes the evaluation methods based on the patient's current health status during evaluation. For example, the evaluation unit can customize relevant evaluation methods considering the patient's current health status. The evaluation unit can also customize appropriate evaluation methods based on the patient's health status. For example, the evaluation unit customizes relevant evaluation methods considering the patient's current health status and customizes appropriate evaluation methods based on the patient's health status. Furthermore, the evaluation unit can analyze the patient's health status using generative AI and customize the evaluation methods based on the results. For example, the evaluation unit uses generative AI to analyze the patient's health status and customizes the evaluation methods based on the results. This makes it possible to perform more appropriate evaluations by customizing the evaluation methods based on the patient's health status. For example, it can customize relevant evaluation methods considering the patient's current health status. It can also customize appropriate evaluation methods based on the patient's health status. It can also perform evaluations by excluding unnecessary evaluation methods based on the patient's health status.

[0102] The drug information provision system includes an evaluation unit that estimates the patient's emotions and determines the priority of evaluations based on the estimated emotions. The evaluation unit can estimate the patient's emotions using, for example, facial recognition technology. The evaluation unit can also estimate the patient's emotions using voice analysis technology. For example, the evaluation unit can estimate emotions from the patient's facial expressions using facial recognition technology, and estimate emotions from the tone and speed of the patient's voice using voice analysis technology. Furthermore, the evaluation unit can also estimate the patient's emotions using generative AI. For example, the evaluation unit can have the generative AI analyze the patient's emotions and determine the priority of evaluations based on the results. This makes it possible to perform more appropriate evaluations by determining the priority of evaluations according to the patient's emotions. For example, if the patient is feeling anxious, important evaluation items will be evaluated first. If the patient is relaxed, detailed evaluation items may be evaluated first. If the patient is in a hurry, only the minimum necessary evaluation items may be evaluated first.

[0103] The drug information provision system includes an evaluation unit that selects the optimal evaluation method during evaluation, taking into account the patient's geographical location information. The evaluation unit can, for example, obtain the patient's current location using GPS data and select the optimal evaluation method based on that information. The evaluation unit can also obtain the patient's geographical location information using location information services. For example, the evaluation unit can use GPS data to prioritize the selection of evaluation methods used at nearby medical institutions based on the patient's current location. Furthermore, the evaluation unit can analyze the patient's geographical location information using generative AI and select an evaluation method based on the results. For example, the evaluation unit can use generative AI to analyze the patient's geographical location information and select the optimal evaluation method based on the results. This allows for the selection of a more appropriate evaluation method by considering geographical location information. For example, it can prioritize the selection of evaluation methods used at nearby medical institutions based on the patient's current location. It can also prioritize the selection of region-specific evaluation methods by considering the patient's geographical location information. Additionally, it can prioritize the selection of evaluation methods used at the nearest medical institution based on the patient's location information.

[0104] The drug information provision system includes an evaluation unit that analyzes the patient's social media activity and proposes evaluation methods during the evaluation process. The evaluation unit can, for example, analyze the content of the patient's social media posts and propose relevant evaluation methods. It can also propose appropriate evaluation methods based on health information shared by the patient on social media. For example, the evaluation unit analyzes the patient's social media activity, proposes relevant evaluation methods, and proposes appropriate evaluation methods based on health information shared by the patient on social media. Furthermore, the evaluation unit can analyze the patient's social media activity using generative AI and propose evaluation methods based on the results. For example, the evaluation unit uses generative AI to analyze the patient's social media activity and proposes relevant evaluation methods based on the results. This allows for the proposal of more appropriate evaluation methods by analyzing social media activity. For example, it can analyze the patient's social media activity and propose relevant evaluation methods. It can also propose appropriate evaluation methods based on health information shared by the patient on social media. Additionally, it can prioritize suggesting evaluation methods that the patient is interested in based on their social media activity.

[0105] The drug information provision system includes a reference unit that estimates the patient's emotions and selects research results to refer to based on the estimated emotions. The reference unit can estimate the patient's emotions using, for example, facial recognition technology. It can also estimate the patient's emotions using voice analysis technology. For example, the reference unit can estimate emotions from the patient's facial expressions using facial recognition technology, and from the tone and speed of the patient's voice using voice analysis technology. Furthermore, the reference unit can estimate the patient's emotions using generative AI. For example, the reference unit uses generative AI to analyze the patient's emotions and selects research results to refer to based on the results. This allows for more appropriate information provision by selecting research results according to the patient's emotions. For example, if the patient is feeling anxious, research results that provide a sense of reassurance will be prioritized. If the patient is relaxed, detailed research results can be referred to. If the patient is in a hurry, concise research results that get straight to the point can be referred to.

[0106] The drug information provision system includes a reference unit that applies the optimal reference algorithm by referring to past research results when information is referenced. The reference unit applies the optimal reference algorithm by referring to past research results when information is referenced. For example, the reference unit can apply the optimal reference algorithm based on past research results. Furthermore, the reference unit can prioritize the application of a reference algorithm related to a specific drug based on past research results. For example, the reference unit applies the optimal reference algorithm based on past research results and prioritizes the application of a reference algorithm related to a specific drug based on past research results. In addition, the reference unit can analyze past research results using generative AI and apply the optimal reference algorithm based on the results. This allows the application of the optimal reference algorithm by referring to past research results. For example, it applies the optimal reference algorithm based on past research results. It can also prioritize the application of a reference algorithm related to a specific drug based on past research results. Furthermore, it can analyze past research results and apply the most efficient reference algorithm.

[0107] The drug information provision system includes a reference unit that applies different reference methods to each drug category during retrieval. The reference unit can, for example, classify drug categories based on therapeutic classification and apply different reference methods according to that category. It can also classify drug categories based on ingredient classification and apply different reference methods according to that category. For example, the reference unit can apply a specific reference method to antibiotics based on therapeutic classification, and a different reference method to analgesics based on ingredient classification. Furthermore, the reference unit can classify drug categories using generating AI and apply different reference methods based on the results. For example, the reference unit can have generating AI analyze drug categories and apply different reference methods based on the results. This allows for more appropriate information provision by applying different reference methods to each drug category. For example, a specific reference method can be applied to antibiotics. A different reference method can also be applied to analgesics. And yet another different reference method can be applied to vitamins.

[0108] The drug information provision system includes a reference unit that estimates the patient's emotions and determines the priority of references based on the estimated emotions. The reference unit can estimate the patient's emotions using, for example, facial recognition technology. The reference unit can also estimate the patient's emotions using voice analysis technology. For example, the reference unit can estimate emotions from the patient's facial expressions using facial recognition technology, and estimate emotions from the tone and speed of the patient's voice using voice analysis technology. Furthermore, the reference unit can also estimate the patient's emotions using generative AI. For example, the reference unit can have generative AI analyze the patient's emotions and determine the priority of references based on the results. This makes it possible to provide more appropriate information by determining the priority of references according to the patient's emotions. For example, if the patient is feeling anxious, important research results can be prioritized. If the patient is relaxed, detailed research results can be prioritized. If the patient is in a hurry, only the minimum necessary research results can be prioritized.

[0109] The drug information provision system includes a reference unit that weights references based on the timing of drug prescriptions. The reference unit can, for example, evaluate the timing of drug prescriptions based on the prescription date and weight the references based on the result. Alternatively, the reference unit can evaluate the timing of drug prescriptions based on the prescription period and weight the references based on the result. For example, the reference unit can prioritize referencing recently prescribed drugs based on the prescription date, and reference previously prescribed drugs as needed based on the prescription period. Furthermore, the reference unit can use generating AI to evaluate the timing of drug prescriptions and weight the references based on the result. For example, the reference unit can use generating AI to analyze the timing of drug prescriptions and weight the references based on the result. This allows for more appropriate information provision by weighting references based on the timing of drug prescriptions. For example, recently prescribed drugs are prioritized for reference. Previously prescribed drugs can also be referenced as needed. Additionally, drugs prescribed within a specific period can be referenced with weights corresponding to that period.

[0110] The drug information provision system includes a reference unit that improves the accuracy of references by referencing drug-related literature during the referencing process. The reference unit can, for example, reference drug-related literature using a literature database. It can also reference drug-related literature based on citation relationships. For example, the reference unit can reference drug-related literature using a literature database and also reference drug-related literature based on citation relationships. Furthermore, the reference unit can analyze drug-related literature using generative AI and improve the accuracy of references based on the results. For example, the reference unit uses generative AI to analyze drug-related literature and improves the accuracy of references based on the results. This allows for improved accuracy of references by referencing drug-related literature. For example, it can improve accuracy based on drug-related literature. It can also prioritize improving the accuracy of references for specific drugs from drug-related literature. Additionally, it can analyze drug-related literature and improve the most efficient reference accuracy. === Hard Collateral 1-1 === Each of the multiple elements described above, including the acquisition unit, analysis unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit is implemented by the control unit 46A of the smart device 14 and acquires drug component information, efficacy, side effects, etc. from the database 24. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and evaluates the drug's effects and the risk of side effects using data mining techniques and statistical analysis. The provision unit is implemented by the control unit 46A of the smart device 14 and displays the analysis results through a user interface and provides the analysis results as a report using a report generation function. === Hard Collateral 1-2 === Each of the multiple elements described above, including the acquisition unit, analysis unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit is implemented by the control unit 46A of the smart glasses 214 and acquires drug ingredient information, efficacy, side effects, etc. from the database 24. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and evaluates the drug's effects and the risk of side effects using data mining techniques and statistical analysis. The provision unit is implemented by the control unit 46A of the smart glasses 214 and displays the analysis results through a user interface and provides the analysis results as a report using a report generation function. === Hard Collateral 1-3 === Each of the multiple elements described above, including the acquisition unit, analysis unit, and provision unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the acquisition unit is implemented by the control unit 46A of the headset terminal 314 and acquires drug component information, efficacy, side effects, etc., from the database 24. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and evaluates the drug's effects and the risk of side effects using data mining techniques and statistical analysis. The provision unit is implemented by the control unit 46A of the headset terminal 314 and displays the analysis results through a user interface and provides the analysis results as a report using a report generation function. === Hard Collateral 1-4 === Each of the multiple elements described above, including the acquisition unit, analysis unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit is implemented by the control unit 46A of the robot 414 and acquires drug component information, efficacy, side effects, etc. from the database 24. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and evaluates the drug's effects and the risk of side effects using data mining techniques and statistical analysis. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and displays the analysis results through a user interface and provides the analysis results as a report using a report generation function.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The data acquisition unit can also consider the patient's allergy information when acquiring drug information. For example, the unit can acquire the patient's allergy information from a database and filter drug ingredients and side effects based on that information. Furthermore, the unit can exclude drugs that may cause allergic reactions based on the patient's allergy information. In addition, the unit can analyze the patient's allergy information using generating AI and acquire drug information based on the results. This allows for the provision of safer drug information by considering the patient's allergy information. For example, if a patient is allergic to a specific ingredient, drugs containing that ingredient can be excluded. The unit can also provide information on alternative medications based on the patient's allergy information. Furthermore, it can provide advice on preventing allergic reactions based on the patient's allergy information.

[0113] The input unit allows users to input information about the patient's lifestyle when entering their symptoms and physical condition. For example, it can input information about the patient's diet, exercise, sleep, and other lifestyle habits. Furthermore, the input unit can evaluate symptoms and physical condition based on this lifestyle information. It can also use a generation AI to analyze the patient's lifestyle information and evaluate symptoms and physical condition based on the results. This allows for the provision of more detailed information by considering the patient's lifestyle. For example, it can evaluate the effectiveness and side effects of medication based on the patient's diet. It can also provide advice to maximize the effectiveness of medication based on the patient's exercise habits. Furthermore, it can adjust the timing of medication administration based on the patient's sleep habits.

[0114] The evaluation unit can also consider the patient's stress level when evaluating the information input by the input unit. For example, the evaluation unit can assess the stress level using a sensor that measures the patient's stress level. Furthermore, the evaluation unit can evaluate the patient's symptoms and physical condition based on the patient's stress level. In addition, the evaluation unit can analyze the patient's stress level using generative AI and evaluate the symptoms and physical condition based on the results. This allows for a more appropriate evaluation by considering the patient's stress level. For example, if the patient's stress level is high, advice for stress reduction can be provided. The effectiveness and side effects of medication can also be evaluated based on the patient's stress level. Furthermore, the timing of medication administration can be adjusted based on the patient's stress level.

[0115] The reference section can also provide information tailored to the patient's age and gender when basing its information on the latest research findings. For example, the reference section can refer to relevant research findings while considering the patient's age and gender. Furthermore, the reference section can provide information on appropriate treatments and medications based on the patient's age and gender. In addition, the reference section can use generative AI to analyze the patient's age and gender and provide information based on the results. This enables the provision of information tailored to the patient's age and gender. For example, it can provide information on the effects and side effects of medications appropriate for their age. It can also provide information on treatments and medications appropriate for their gender. Furthermore, it can assess specific risks based on age and gender and provide appropriate advice.

[0116] The data acquisition unit can also consider the patient's genetic information when basing its analysis on past prescription data. For example, the unit can acquire the patient's genetic information from a database and use that information to evaluate the drug's effectiveness and side effects. Furthermore, the unit can predict the patient's response to a specific drug based on the patient's genetic information. In addition, the unit can analyze the patient's genetic information using generative AI and provide drug information based on the results. This allows for more personalized information provision by considering the patient's genetic information. For example, it can predict the effectiveness and side effects of a drug based on genetic information. It can also provide information on alternative drugs based on genetic information. Furthermore, it can assess specific risks based on genetic information and provide appropriate advice.

[0117] The information delivery system can also estimate the patient's emotions when providing information in a conversational format and adjust the tone of the conversation based on those estimated emotions. For example, the system can use facial recognition technology to estimate the patient's emotions and adjust the tone of the conversation based on the results. It can also use voice analysis technology to estimate the patient's emotions and adjust the tone of the conversation based on the results. Furthermore, it can use generative AI to analyze the patient's emotions and adjust the tone of the conversation based on the results. This makes it possible to provide information in a conversational format that is tailored to the patient's emotions. For example, if the patient is feeling anxious, the information can be delivered in a gentle tone. If the patient is relaxed, the information can be delivered in a friendly tone. If the patient is in a hurry, the information can be delivered in a concise and efficient tone.

[0118] The drug information provision system includes an acquisition unit that estimates the patient's emotions and adjusts the timing of drug information acquisition based on the estimated emotions. The acquisition unit can estimate the patient's emotions using, for example, facial recognition technology. It can also estimate the patient's emotions using voice analysis technology. For example, the acquisition unit can estimate emotions from the patient's facial expressions using facial recognition technology, and estimate emotions from the tone and speed of the patient's voice using voice analysis technology. Furthermore, the acquisition unit can estimate the patient's emotions using generative AI. For example, the acquisition unit can have the generative AI analyze the patient's emotions and adjust the information acquisition timing based on the results. This allows for more appropriate information provision by adjusting the information acquisition timing according to the patient's emotions. For example, if the patient is feeling anxious, drug information can be acquired and provided immediately. If the patient is relaxed, drug information can be acquired and provided at an appropriate time. If the patient is in a hurry, drug information can be acquired and provided quickly.

[0119] The drug information provision system includes an acquisition unit that analyzes a patient's past drug use history and selects the optimal information acquisition method. For example, the acquisition unit can select the optimal information acquisition method based on information about medications used in the past. Furthermore, the acquisition unit can prioritize the acquisition of information about specific medications from the patient's past drug use history. For instance, the acquisition unit selects the optimal information acquisition method based on information about medications used in the past and prioritizes the acquisition of information about specific medications from the patient's past drug use history. In addition, the acquisition unit can analyze the patient's past drug use history using a generating AI. For example, the acquisition unit uses a generating AI to analyze the patient's past drug use history and selects the optimal information acquisition method based on the results. This allows for the selection of the optimal information acquisition method by analyzing past usage history. For example, the optimal information acquisition method is selected based on information about medications the patient has used in the past. It can also prioritize the acquisition of information about specific medications from the patient's past drug use history. Furthermore, it can analyze the patient's past drug use history and select the most efficient information acquisition method.

[0120] The drug information provision system includes an acquisition unit that filters drug information based on the patient's current health status and lifestyle. For example, the acquisition unit can filter relevant drug information considering the patient's current health status. It can also filter appropriate drug information based on the patient's lifestyle. For instance, the acquisition unit filters relevant drug information considering the patient's current health status and filters appropriate drug information based on the patient's lifestyle. Furthermore, the acquisition unit can analyze the patient's health status and lifestyle using a generating AI and filter information based on the results. This allows for more appropriate information provision by filtering information based on the patient's health status and lifestyle. For example, it can filter relevant drug information considering the patient's current health status. It can also filter appropriate drug information based on the patient's lifestyle. Additionally, it can provide drug information while excluding unnecessary information based on the patient's health status and lifestyle.

[0121] The drug information provision system includes an acquisition unit that estimates the patient's emotions and determines the priority of drug information to be acquired based on the estimated emotions. The acquisition unit can estimate the patient's emotions using, for example, facial recognition technology. It can also estimate the patient's emotions using voice analysis technology. For example, the acquisition unit can estimate emotions from the patient's facial expressions using facial recognition technology, and estimate emotions from the tone and speed of the patient's voice using voice analysis technology. Furthermore, the acquisition unit can estimate the patient's emotions using generative AI. For example, the acquisition unit uses generative AI to analyze the patient's emotions and determines the priority of information based on the results. This makes it possible to provide more appropriate information by determining the priority of information according to the patient's emotions. For example, if the patient is feeling anxious, important drug information will be prioritized. If the patient is relaxed, detailed drug information will be prioritized. If the patient is in a hurry, only the minimum necessary drug information will be prioritized.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The acquisition unit acquires drug information. The acquisition unit can acquire information such as drug ingredients, efficacy, and side effects from a database. The acquisition unit can also acquire drug information using sensors. Furthermore, the acquisition unit can refer to drug usage history and past prescription data of patients. Step 2: The analysis unit analyzes the information acquired by the acquisition unit. The analysis unit can, for example, analyze the effects of a drug using data mining techniques. The analysis unit can also evaluate the risk of drug side effects using statistical analysis. Furthermore, the analysis unit can analyze drug information using generative AI. Step 3: The service provider provides the results analyzed by the analysis unit. The service provider can, for example, display the analysis results through a user interface. Alternatively, the service provider can provide the analysis results as a report using a report generation function.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0126] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0168] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0169] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0170] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0171] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0172] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0173] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0174] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0175] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0177] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0179] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0180] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0181] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0185] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0186] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0187] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0188] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0189] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0190] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0192] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0193] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0194] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0195] [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A unit that acquires drug information, An analysis unit analyzes the information acquired by the acquisition unit, A providing unit that provides the results of the analysis performed by the aforementioned analysis unit, Equipped with A system characterized by the following features.

2. It includes an input section for entering the patient's symptoms or physical condition. The system according to feature 1.

3. The system includes an evaluation unit that evaluates the information input by the input unit. The system according to feature 2.

4. It includes a reference section based on the latest research findings. The system according to feature 1.

5. The acquisition unit is, Based on past prescription data The system according to feature 1.

6. The aforementioned supply unit is, Provide information in a conversational format. The system according to feature 1.

7. The acquisition unit is, The system estimates the patient's emotions and adjusts the timing of medication information acquisition based on those estimated emotions. The system according to feature 1.

8. The acquisition unit is, Analyze the patient's past medication history and select the most appropriate method for obtaining information. The system according to feature 1.

9. The acquisition unit is, When obtaining drug information, filtering is performed based on the patient's current health status and lifestyle. The system according to feature 1.

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