System

Abstract

An object of the system according to the embodiment is to allow a user to easily know a combination of appropriate medicines.SOLUTION: A system according to an embodiment includes a collection unit, a registration unit, an analysis unit, and a presentation unit. The collection unit collects user information. The registration unit registers information of the medicine. The analysis unit analyzes the information obtained from the collection unit and the registration unit. The presentation unit presents an appropriate combination based on the analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to efficiently provide information about drug interactions, making it difficult for users to know appropriate drug combinations.

[0005] The system according to the embodiment aims to enable a user to easily find out appropriate drug interactions. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a registration unit, an analysis unit, and a presentation unit. The collection unit collects user information. The registration unit registers medication information. The analysis unit analyzes the information obtained from the collection unit and the registration unit. The presentation unit presents appropriate drug combinations based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows the user to easily know appropriate drug interactions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A drug combination recommendation system according to an embodiment of the present invention collects user information, registers the drug information, and analyzes the information to recommend optimal drug combinations. The drug combination recommendation system digitizes the user information and connects with a generation AI via an API. Next, a pharmaceutical company registers the drug information. Based on this information, the generation AI analyzes the interactions between the user's commonly used medications, vitamins, herbal medicines, and other prescription drugs and those prescribed by the hospital, determining whether there are any problems with efficacy. The generation AI then recommends optimal drug combinations. For example, user information may include age, gender, medical history, allergy information, and currently taking medications. This information is input into the generation AI. Next, a pharmaceutical company registers the drug information. The drug information includes ingredients, effects, side effects, and interactions. This information is also input into the generation AI. The generation AI analyzes drug combinations based on the user information and drug information. For example, it compares the ingredients of the drug currently being taken with the drug prescribed by the hospital to determine whether there are any interactions. It also analyzes whether there are any problems with efficacy. For example, if a drug may reduce the effectiveness of another drug, this information is presented. Furthermore, the generative AI suggests optimal drug combinations. For example, if a user is taking multiple medications, it analyzes which combination is most effective and provides that information. This allows users to take highly effective medications. This makes it possible for the drug combination suggestion system to digitize and monetize user information. User information is analyzed by the generative AI, and optimal drug combinations are suggested, allowing users to take their medications with peace of mind. Furthermore, by registering drug information, pharmaceutical companies can provide effective medications to users.

[0029] A drug combination presentation system according to an embodiment includes a collection unit, a registration unit, an analysis unit, and a presentation unit. The collection unit collects user information. The user information includes, but is not limited to, age, gender, medical history, allergy information, and currently taken medications. The collection unit, for example, stores information input by the user in a database. The collection unit can also collect information related to the user's health condition and lifestyle habits. For example, the collection unit monitors the user's health condition and collects data. The collection unit can also analyze the user's social media activities and collect related information. For example, the collection unit analyzes the user's health-related posts on social media and collects related information. The registration unit registers drug information. The drug information includes, but is not limited to, ingredients, effects, side effects, and interactions. The registration unit stores information provided by, for example, a pharmaceutical company in a database. The registration unit can also register drug information in a standardized format. For example, the registration unit adjusts the level of detail of the registration based on the drug's ingredients, effects, and side effects. The registration unit further includes a verification unit for ensuring the accuracy of the medication information. For example, the registration unit verifies the medication information and confirms its accuracy. The analysis unit analyzes the information obtained from the collection unit and the registration unit. For example, the analysis unit compares the ingredients of the medications the user is currently taking with those prescribed by the hospital to determine whether there are any interactions. The analysis unit also analyzes whether there are any problems with efficacy. For example, the analysis unit presents information if there is a possibility that one medication may reduce the effectiveness of another medication. The analysis unit also presents optimal combinations when the user is taking multiple medications. For example, the analysis unit analyzes the combination of medications the user is taking and presents the most effective combination. The presentation unit presents optimal drug combinations based on the analysis results obtained by the analysis unit. For example, the presentation unit provides feedback on the analysis results to the user. The presentation unit can also visually display the analysis results. For example, the presentation unit displays the analysis results as graphs or charts, providing them in a visually easy-to-understand format. The presentation unit can also estimate the user's emotions and adjust the presentation method of the presented information based on the estimated emotions.For example, when the user is feeling stressed, the presenting unit provides a simple and highly visible representation method, thereby enabling the drug combination presentation system according to the embodiment to enable the user to take highly effective drugs.

[0030] The collection unit can digitize user information. Examples of digitization include, but are not limited to, data formats and database structures. For example, the collection unit stores information input by the user in a database. The collection unit can also digitize information related to the user's health condition and lifestyle habits. For example, the collection unit monitors the user's health condition and collects data. Furthermore, the collection unit can analyze the user's social media activities and digitize related information. For example, the collection unit analyzes the user's health-related posts on social media and stores the related information in a database. By digitizing the user information, the accuracy of the analysis is improved. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data monitoring the user's health condition to a generation AI and have the generation AI analyze the data.

[0031] The registration unit can register drug information in a standardized format. The standardized format includes, but is not limited to, data items and format specifications. For example, the registration unit stores information provided by a pharmaceutical company in a database. The registration unit can also adjust the level of detail of the registration based on the drug's ingredients, effects, and side effects. For example, the registration unit registers detailed information when the drug's ingredients are complex. For example, the registration unit can register detailed information about the effects of a highly effective drug. For example, the registration unit can register detailed information about the side effects of a highly effective drug. For example, the registration unit adjusts the level of detail of the registration based on the drug's ingredients, effects, and side effects. Registering drug information in a standardized format ensures consistency of the information. Some or all of the above-described processing in the registration unit may be performed using, or without, AI. For example, the registration unit can input data about the drug's ingredients, effects, and side effects into a generation AI and have the generation AI analyze the data.

[0032] The analysis unit may include an evaluation unit that evaluates interaction criteria and efficacy issues. The interaction criteria include, but are not limited to, the type of interaction and the evaluation method. For example, the analysis unit may compare the ingredients of the medications currently being taken by the user with those prescribed by the hospital to determine whether an interaction exists. The analysis unit also evaluates whether there are any efficacy issues. For example, the analysis unit may present information if one medication may reduce the efficacy of another medication. Furthermore, the analysis unit may present optimal combinations when the user is taking multiple medications. For example, the analysis unit may analyze the combination of medications being taken by the user and present the most effective combination. This improves the accuracy of the analysis by evaluating the interaction criteria and efficacy issues. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit may input ingredient data of the medications being taken by the user into the generation AI and have the generation AI perform interaction determination.

[0033] The presentation unit can feed back the analysis results to the user. Examples of feedback include, but are not limited to, the format and timing of the feedback. For example, the presentation unit visually displays the analysis results to the user. The presentation unit can also display the analysis results as graphs or charts, providing them in a visually easy-to-understand format. Furthermore, the presentation unit can estimate the user's emotions and adjust the presentation method of the presented information based on the estimated emotions. For example, if the user is feeling stressed, the presentation unit provides a simple and highly visible presentation method. By providing the analysis results as feedback to the user, the user can learn appropriate drug interactions. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without AI. For example, the presentation unit can input data of the analysis results to a generation AI and cause the generation AI to generate a visual display.

[0034] The analysis unit can present an optimal combination when a user is taking multiple medications. Examples of optimal combinations include, but are not limited to, interaction evaluation criteria and efficacy evaluation methods. The analysis unit can, for example, analyze the combination of medications the user is taking and present the most effective combination. The analysis unit can also present an optimal combination based on the user's health condition and lifestyle. For example, the analysis unit can monitor the user's health condition and analyze the data to present the optimal combination. Furthermore, the analysis unit can analyze the user's social media activity and present the optimal combination based on related information. For example, the analysis unit can analyze the user's health-related posts on social media and present the optimal combination. This allows the user to take the medications effectively by presenting the optimal combination when taking multiple medications. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the medications the user is taking into the generation AI and have the generation AI analyze the optimal combination.

[0035] The collection unit can digitize and monetize the user information. Monetization includes, but is not limited to, data sales methods and revenue models. For example, the collection unit stores the user information in a database and sells the data. The collection unit can also analyze the user information and build a monetization model. For example, the collection unit can build an advertising model based on the user information and generate revenue. Furthermore, the collection unit can provide a subscription model based on the user information. For example, the collection unit can provide personalized services based on the user information and generate revenue. This enables monetization by digitizing and monetizing the user information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input user information data into a generation AI and have the generation AI build a model for monetization.

[0036] The registration unit may include a verification unit for ensuring the accuracy of the drug information. Methods for ensuring accuracy include, but are not limited to, data verification methods and verification standards. For example, the registration unit verifies the drug information and confirms its accuracy. The registration unit may also use an algorithm for ensuring the accuracy of the drug information. For example, the registration unit verifies information on drug ingredients, effects, and side effects and confirms their accuracy. Furthermore, the registration unit may establish a feedback loop for ensuring the accuracy of the drug information. For example, the registration unit updates the drug information based on feedback from the user and maintains its accuracy. This ensures the accuracy of the drug information, thereby providing highly reliable information. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without AI. For example, the registration unit may input drug information data into a generation AI and have the generation AI verify the accuracy.

[0037] The analysis unit may include an update unit that manages the update frequency of the information. The management of the update frequency includes, but is not limited to, the timing and method of updates. For example, the analysis unit manages the update frequency of medication information, and always provides the latest information. The analysis unit may also manage the update frequency of user information. For example, the analysis unit updates the information in response to changes in the user's health condition or lifestyle. Furthermore, the analysis unit may use an algorithm to optimize the update frequency of the information. For example, the analysis unit adjusts the update frequency based on the importance of the information. In this way, by managing the update frequency of the information, the latest information is always provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input update data of the information to the generation AI and have the generation AI manage the update frequency.

[0038] The collection unit can analyze the user's past medical history and select the optimal collection method. The optimal collection method includes, but is not limited to, the type of collection method and selection criteria. For example, the collection unit selects a collection method appropriate for a specific medical history based on the user's past medical history. The collection unit can also prioritize collecting allergy information based on the user's past medical history. For example, the collection unit analyzes the user's past medical history and focuses on collecting specific medication history. This allows the optimal collection method to be selected by analyzing the past medical history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past medical history data into the generation AI and have the generation AI select the optimal collection method.

[0039] When collecting user information, the collection unit can perform filtering based on the user's current health condition and lifestyle habits. Filtering includes, but is not limited to, filtering conditions and filtering methods, for example. The collection unit, for example, collects only necessary information based on the user's current health condition. The collection unit can also prioritize collecting related information taking into account the user's lifestyle habits (smoking, drinking, etc.). For example, the collection unit filters unnecessary information based on the user's current health condition and lifestyle habits. In this way, filtering based on the current health condition and lifestyle habits can collect only necessary information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's health condition and lifestyle habit data into the generation AI and have the generation AI perform filtering.

[0040] When collecting user information, the collection unit can select the optimal collection means depending on the user's input method. The optimal collection means includes, but is not limited to, the type of collection means and selection criteria. For example, if the user selects voice input, the collection unit can collect information using voice recognition technology. Furthermore, if the user selects text input, the collection unit can also collect information using text analysis technology. For example, if the user selects image input, the collection unit can collect information using image recognition technology. This enables efficient information collection by selecting the optimal collection means depending on the input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's input data into a generation AI and have the generation AI select the optimal collection means.

[0041] When collecting user information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information acquisition methods, etc. For example, the collection unit prioritizes collecting area-specific medical information based on the user's current location. The collection unit can also collect information on nearby medical institutions by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting local health risk information based on the user's geographical location information. This allows highly relevant information to be collected preferentially by taking into account the geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's geographical location data into the generation AI and cause the generation AI to collect highly relevant information.

[0042] When collecting user information, the collection unit can analyze the user's social media activity and collect related information. Social media activity includes, but is not limited to, the content of posts and the frequency of activity. For example, the collection unit can analyze the user's health-related posts on social media and collect related information. The collection unit can also identify health concerns from the user's social media activity and collect information. For example, the collection unit can collect related information by referring to the health information of the user's friends on social media. In this way, related information can be collected by analyzing social media activity. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related information.

[0043] When collecting user information, the collection unit can customize the collection method by reflecting the user's past feedback. Methods for customizing the collection method include, but are not limited to, customization conditions and methods. For example, the collection unit improves the collection method based on the user's past feedback. The collection unit can also adjust the type of information to be collected by reflecting the user's past feedback. For example, the collection unit optimizes the collection timing by referring to the user's past feedback. In this way, the collection method can be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data into the generation AI and have the generation AI customize the collection method.

[0044] When registering drug information, the registration unit can adjust the level of detail of the registration based on the drug's ingredients, effects, and side effects. Methods for adjusting the level of detail of the registration include, but are not limited to, level of detail evaluation criteria and adjustment methods. For example, the registration unit registers detailed information when the drug's ingredients are complex. Furthermore, when the drug is highly effective, the registration unit can also register detailed information regarding the effects. For example, when the drug has many side effects, the registration unit registers detailed information regarding the side effects. In this way, by adjusting the level of detail of the registration based on the ingredients, effects, and side effects, necessary information can be appropriately registered. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input data regarding the drug's ingredients, effects, and side effects into the generation AI and have the generation AI adjust the level of detail of the registration.

[0045] When registering drug information, the registration unit can apply different registration algorithms depending on the drug category. Examples of registration algorithms include, but are not limited to, types of algorithms and application methods. For example, the registration unit applies a simplified registration algorithm for over-the-counter drugs. The registration unit can also apply a detailed registration algorithm for prescription drugs. For example, the registration unit applies a registration algorithm based on specific ingredients and effects for herbal medicines. This enables efficient information registration by applying different registration algorithms depending on the category. Some or all of the above-mentioned processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input drug category data into the generation AI and cause the generation AI to apply the registration algorithm.

[0046] When registering drug information, the registration unit can improve the accuracy of the registration by referring to past registration data. Methods for improving the accuracy of the registration include, but are not limited to, accuracy evaluation criteria and improvement methods. For example, the registration unit automatically complements information on similar drugs based on past registration data. The registration unit can also analyze past registration data to improve the accuracy of the registration. For example, the registration unit maintains consistency of the registration by referring to past registration data. In this way, the accuracy of the registration is improved by referring to the past registration data. Some or all of the above-mentioned processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input past registration data into the generation AI and cause the generation AI to improve the accuracy of the registration.

[0047] When registering drug information, the registration unit can take into consideration information about the drug manufacturer and distributor. Information about the manufacturer and distributor includes, for example, the name of the manufacturer and contact information of the distributor, but is not limited to these examples. The registration unit, for example, registers reliable information based on information about the drug manufacturer. The registration unit can also register detailed information by taking into consideration information about the drug distributor. For example, the registration unit improves the accuracy of registration based on information about the drug manufacturer and distributor. This allows for registration of reliable information by taking into consideration information about the manufacturer and distributor. Some or all of the above-mentioned processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input data about the drug manufacturer and distributor into the generation AI and have the generation AI register the information.

[0048] When registering drug information, the registration unit can improve the accuracy of registration by referring to drug-related literature. Related literature includes, but is not limited to, academic papers and patent documents. For example, the registration unit registers detailed information based on drug-related literature. The registration unit can also improve the accuracy of registration by referring to drug-related literature. For example, the registration unit maintains consistency in registration based on drug-related literature. By referring to the related literature, the accuracy of registration is improved. Some or all of the above-described processing in the registration unit may be performed using, or without, AI. For example, the registration unit can input drug-related literature data into a generation AI and cause the generation AI to improve the accuracy of registration.

[0049] The registration unit can take into consideration the market value of the drug when registering drug information. Market value includes, but is not limited to, market price, demand forecast, etc. For example, the registration unit prioritizes registering information about drugs with high market value. The registration unit can also simplify and register information about drugs with low market value. For example, the registration unit adjusts the level of detail of the registration based on the market value. This allows important information to be registered preferentially by taking market value into consideration. Some or all of the above-mentioned processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input drug market value data into the generation AI and have the generation AI register the information.

[0050] The analysis unit can improve the accuracy of the analysis by taking drug interactions into account during analysis. Interactions include, but are not limited to, drug interactions and effect interactions. The analysis unit can, for example, analyze interactions between drug components in detail to provide highly accurate results. The analysis unit can also improve the accuracy of the analysis by referring to a database on drug interactions. For example, the analysis unit can increase the reliability of the analysis results by taking drug interactions into account. This improves the accuracy of the analysis by taking interactions into account. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input drug interaction data into a generation AI and have the generation AI perform an interaction analysis.

[0051] The analysis unit can perform analysis while taking into account the user's attribute information. Attribute information includes, but is not limited to, for example, age, gender, and medical history. The analysis unit customizes the analysis results, for example, by taking into account the user's age and gender. The analysis unit can also improve the accuracy of the analysis based on the user's medical history. For example, the analysis unit provides analysis results by taking into account the user's allergy information. This allows customized analysis results to be provided by taking into account the attribute information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's attribute data into the generation AI and cause the generation AI to perform an analysis taking into account the attribute information.

[0052] During analysis, the analysis unit can weight the analysis based on the frequency of medication intake. Weighting includes, for example, weighting conditions, methods, etc., but is not limited to these examples. For example, the analysis unit may perform the analysis by prioritizing information about medications that the user takes frequently. The analysis unit may also perform the analysis by disregarding information about medications that the user takes infrequently. For example, the analysis unit adjusts the reliability of the analysis results based on the frequency of medication intake. In this way, weighting based on medication frequency can provide highly reliable analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input medication frequency data into a generation AI and have the generation AI perform a weighting analysis.

[0053] The analysis unit can perform the analysis while taking into account the geographical distribution of the drug. Geographical distribution includes, for example, regional data and distribution evaluation methods, but is not limited to these examples. For example, the analysis unit analyzes region-specific interactions based on the geographical distribution of the drug. The analysis unit can also analyze the effects of the drug for each region while taking into account the geographical distribution. For example, the analysis unit analyzes the side effects of the drug for each region based on the geographical distribution. This makes it possible to provide region-specific analysis results by taking into account the geographical distribution. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input geographical distribution data of the drug into the generation AI and cause the generation AI to perform an analysis taking into account the geographical distribution.

[0054] During analysis, the analysis unit can improve the accuracy of the analysis by referring to drug-related literature. Related literature includes, but is not limited to, academic papers and patent documents. For example, the analysis unit performs a detailed analysis based on drug-related literature. The analysis unit can also improve the accuracy of the analysis by referring to drug-related literature. For example, the analysis unit increases the reliability of the analysis results based on drug-related literature. By referring to the related literature, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input drug-related literature data into the generation AI and cause the generation AI to perform an analysis with reference to the related literature.

[0055] The analysis unit can perform the analysis while taking into account the market value of the drug. Market value includes, but is not limited to, market price, demand forecast, etc. For example, the analysis unit performs the analysis by prioritizing information on drugs with high market value. The analysis unit can also perform the analysis by disregarding information on drugs with low market value. For example, the analysis unit adjusts the reliability of the analysis results based on the market value. By taking market value into account, it is possible to provide highly reliable analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input market value data on the drug into the generation AI and cause the generation AI to perform an analysis taking market value into account.

[0056] The presentation unit can adjust the level of detail of the presentation based on the importance of the medicine when presenting the information. The level of detail includes, for example, evaluation criteria for the level of detail and an adjustment method, but is not limited to such examples. For example, the presentation unit presents detailed information about medicines with high importance. The presentation unit can also present simplified information about medicines with low importance. For example, the presentation unit adjusts the level of detail of the presentation based on the importance. In this way, necessary information can be appropriately presented by adjusting the level of detail based on the importance. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input medicine importance data to the generation AI and cause the generation AI to adjust the level of detail.

[0057] The presentation unit can apply different presentation algorithms depending on the category of the medicine when presenting the information. Presentation algorithms include, but are not limited to, types of algorithms and application methods. For example, the presentation unit can apply a simplified presentation algorithm for over-the-counter medicines. The presentation unit can also apply a detailed presentation algorithm for prescription medicines. For example, the presentation unit can apply a presentation algorithm based on specific ingredients and effects for herbal medicines. By applying different presentation algorithms depending on the category, efficient information presentation becomes possible. Some or all of the above-described processing in the presentation unit can be performed using, or without, AI. For example, the presentation unit can input medicine category data into the generation AI and cause the generation AI to apply the presentation algorithm.

[0058] The presentation unit can improve the accuracy of presentation by referring to the user's past presentation results. Methods for improving the accuracy of presentation include, but are not limited to, accuracy evaluation criteria and improvement methods. For example, the presentation unit automatically complements information on similar drugs based on past presentation results. The presentation unit can also analyze past presentation results to improve the accuracy of presentation. For example, the presentation unit maintains consistency in presentation by referring to past presentation results. This improves the accuracy of presentation by referring to past presentation results. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input past presentation result data into the generation AI and cause the generation AI to improve the accuracy of presentation.

[0059] The presentation unit can determine the presentation priority based on the time of submission of the medication at the time of presentation. The submission time includes, but is not limited to, for example, the timing of submission and the evaluation method. For example, the presentation unit preferentially presents information about medications that have been submitted recently. The presentation unit can also present simplified information about medications that have been submitted recently. For example, the presentation unit adjusts the presentation priority based on the time of submission. In this way, by determining the priority based on the time of submission, the latest information can be presented preferentially. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input medication submission time data into the generation AI and have the generation AI determine the priority.

[0060] The presentation unit can adjust the order of presentation based on the relevance of the drugs when presenting them. Relevance includes, for example, evaluation criteria and evaluation methods for relevance, but is not limited to such examples. For example, the presentation unit preferentially presents information about highly relevant drugs. The presentation unit can also simplify and present information about less relevant drugs. For example, the presentation unit adjusts the order of presentation based on relevance. By adjusting the order of presentation based on relevance, important information can be presented preferentially. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input drug relevance data into a generation AI and have the generation AI adjust the order.

[0061] The presentation unit can adjust the use of technical terminology in the presentation depending on the user's level of expertise. Examples of the level of expertise include, but are not limited to, evaluation criteria and methods for the knowledge level. For example, the presentation unit can present information in simple language to a user with little expertise. The presentation unit can also present information using detailed technical terminology to a user with expertise. For example, the presentation unit can adjust the use of technical terminology in the presentation based on the user's level of expertise. This allows the provision of information appropriate for the user by adjusting the use of technical terminology depending on the level of expertise. Some or all of the above-described processing in the presentation unit can be performed using, for example, AI, or without AI. For example, the presentation unit can input the user's level of expertise data into a generation AI and cause the generation AI to use technical terminology.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The analysis unit can collect the user's dietary information and analyze dietary components that may affect the effectiveness of the medication. For example, the analysis unit can analyze the components of the meals the user ingests and evaluate interactions with specific medications. The analysis unit can also suggest meals to maximize the effectiveness of the medication based on the user's dietary patterns. Furthermore, the analysis unit can adjust the timing of taking the medication based on the user's dietary information. This allows the user to know the meals and timing of taking the medication to maximize the effectiveness of the medication.

[0064] The collection unit can collect the user's exercise information and analyze exercise patterns that may affect the effectiveness of the medication. For example, the collection unit can analyze the user's exercise amount and type and evaluate interactions with specific medications. The collection unit can also suggest exercises to maximize the effectiveness of the medication based on the user's exercise patterns. Furthermore, the collection unit can adjust the timing of taking the medication based on the user's exercise information. This allows the user to know the exercise and dosage timing that will maximize the effectiveness of the medication.

[0065] The analysis unit can collect the user's sleep information and analyze sleep patterns that may affect the effectiveness of the medication. For example, the analysis unit can analyze the user's sleep duration and sleep quality and evaluate interactions with certain medications. The analysis unit can also make sleep suggestions to maximize the effectiveness of the medication based on the user's sleep patterns. Furthermore, the analysis unit can adjust the timing of medication intake based on the user's sleep information. This allows the user to know the sleep and medication timing that will maximize the effectiveness of the medication.

[0066] The collection unit can collect information about the user's environment and analyze environmental factors that may affect the effectiveness of the medication. For example, the collection unit can analyze the user's living environment or work environment and evaluate interactions with specific medications. The collection unit can also suggest environmental improvements to maximize the effectiveness of the medication based on the user's environmental information. Furthermore, the collection unit can adjust the timing of taking the medication based on the user's environmental information. This allows the user to know the environment and timing of taking the medication to maximize the effectiveness of the medication.

[0067] The collection unit can collect the user's genetic information and analyze genetic factors that may affect the effectiveness of medication. For example, the collection unit can analyze the user's genetic information and evaluate interactions with specific medications. The collection unit can also provide genetic advice to maximize the effectiveness of medications based on the user's genetic information. Furthermore, the collection unit can adjust the timing of medication intake based on the user's genetic information. This allows the user to know the genetic factors and the timing of medication intake to maximize the effectiveness of the medication.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The collection unit collects user information, including age, gender, medical history, allergy information, and current medications. The collection unit stores the information entered by the user in a database and also collects information about health conditions and lifestyle habits. In addition, the collection unit analyzes the user's social media activity and collects related information. Step 2: The registration unit registers drug information. Drug information includes ingredients, effects, side effects, and interactions. The registration unit stores the information provided by pharmaceutical companies in a database and registers it in a standardized format. In addition, the registration unit has a verification unit to ensure the accuracy of the drug information. Step 3: The analysis unit analyzes the information obtained from the collection unit and registration unit. The analysis unit compares the ingredients of the medications the user is currently taking with the medications prescribed by the hospital to determine whether there are any interactions. It also analyzes whether there are any problems with effectiveness and presents the optimal combination of medications. Step 4: The presentation unit presents optimal drinking combinations based on the analysis results obtained by the analysis unit. The presentation unit provides feedback on the analysis results to the user and visually displays them. Furthermore, the presentation unit estimates the user's emotions and adjusts the presentation method of the information to be presented based on the estimated emotions.

[0070] (Example 2) A drug combination recommendation system according to an embodiment of the present invention collects user information, registers the drug information, and analyzes the information to recommend optimal drug combinations. The drug combination recommendation system digitizes the user information and connects with a generation AI via an API. Next, a pharmaceutical company registers the drug information. Based on this information, the generation AI analyzes the interactions between the user's commonly used medications, vitamins, herbal medicines, and other prescription drugs and those prescribed by the hospital, determining whether there are any problems with efficacy. The generation AI then recommends optimal drug combinations. For example, user information may include age, gender, medical history, allergy information, and currently taking medications. This information is input into the generation AI. Next, a pharmaceutical company registers the drug information. The drug information includes ingredients, effects, side effects, and interactions. This information is also input into the generation AI. The generation AI analyzes drug combinations based on the user information and drug information. For example, it compares the ingredients of the drug currently being taken with the drug prescribed by the hospital to determine whether there are any interactions. It also analyzes whether there are any problems with efficacy. For example, if a drug may reduce the effectiveness of another drug, this information is presented. Furthermore, the generative AI suggests optimal drug combinations. For example, if a user is taking multiple medications, it analyzes which combination is most effective and provides that information. This allows users to take highly effective medications. This makes it possible for the drug combination suggestion system to digitize and monetize user information. User information is analyzed by the generative AI, and optimal drug combinations are suggested, allowing users to take their medications with peace of mind. Furthermore, by registering drug information, pharmaceutical companies can provide effective medications to users.

[0071] A drug combination presentation system according to an embodiment includes a collection unit, a registration unit, an analysis unit, and a presentation unit. The collection unit collects user information. The user information includes, but is not limited to, age, gender, medical history, allergy information, and currently taken medications. The collection unit, for example, stores information input by the user in a database. The collection unit can also collect information related to the user's health condition and lifestyle habits. For example, the collection unit monitors the user's health condition and collects data. The collection unit can also analyze the user's social media activities and collect related information. For example, the collection unit analyzes the user's health-related posts on social media and collects related information. The registration unit registers drug information. The drug information includes, but is not limited to, ingredients, effects, side effects, and interactions. The registration unit stores information provided by, for example, a pharmaceutical company in a database. The registration unit can also register drug information in a standardized format. For example, the registration unit adjusts the level of detail of the registration based on the drug's ingredients, effects, and side effects. The registration unit further includes a verification unit for ensuring the accuracy of the medication information. For example, the registration unit verifies the medication information and confirms its accuracy. The analysis unit analyzes the information obtained from the collection unit and the registration unit. For example, the analysis unit compares the ingredients of the medications the user is currently taking with those prescribed by the hospital to determine whether there are any interactions. The analysis unit also analyzes whether there are any problems with efficacy. For example, the analysis unit presents information if there is a possibility that one medication may reduce the effectiveness of another medication. The analysis unit also presents optimal combinations when the user is taking multiple medications. For example, the analysis unit analyzes the combination of medications the user is taking and presents the most effective combination. The presentation unit presents optimal drug combinations based on the analysis results obtained by the analysis unit. For example, the presentation unit provides feedback on the analysis results to the user. The presentation unit can also visually display the analysis results. For example, the presentation unit displays the analysis results as graphs or charts, providing them in a visually easy-to-understand format. The presentation unit can also estimate the user's emotions and adjust the presentation method of the presented information based on the estimated emotions.For example, when the user is feeling stressed, the presenting unit provides a simple and highly visible representation method, thereby enabling the drug combination presentation system according to the embodiment to enable the user to take highly effective drugs.

[0072] The collection unit can digitize user information. Examples of digitization include, but are not limited to, data formats and database structures. For example, the collection unit stores information input by the user in a database. The collection unit can also digitize information related to the user's health condition and lifestyle habits. For example, the collection unit monitors the user's health condition and collects data. Furthermore, the collection unit can analyze the user's social media activities and digitize related information. For example, the collection unit analyzes the user's health-related posts on social media and stores the related information in a database. By digitizing the user information, the accuracy of the analysis is improved. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data monitoring the user's health condition to a generation AI and have the generation AI analyze the data.

[0073] The registration unit can register drug information in a standardized format. The standardized format includes, but is not limited to, data items and format specifications. For example, the registration unit stores information provided by a pharmaceutical company in a database. The registration unit can also adjust the level of detail of the registration based on the drug's ingredients, effects, and side effects. For example, the registration unit registers detailed information when the drug's ingredients are complex. For example, the registration unit can register detailed information about the effects of a highly effective drug. For example, the registration unit can register detailed information about the side effects of a highly effective drug. For example, the registration unit adjusts the level of detail of the registration based on the drug's ingredients, effects, and side effects. Registering drug information in a standardized format ensures consistency of the information. Some or all of the above-described processing in the registration unit may be performed using, or without, AI. For example, the registration unit can input data about the drug's ingredients, effects, and side effects into a generation AI and have the generation AI analyze the data.

[0074] The analysis unit may include an evaluation unit that evaluates interaction criteria and efficacy issues. The interaction criteria include, but are not limited to, the type of interaction and the evaluation method. For example, the analysis unit may compare the ingredients of the medications currently being taken by the user with those prescribed by the hospital to determine whether an interaction exists. The analysis unit also evaluates whether there are any efficacy issues. For example, the analysis unit may present information if one medication may reduce the efficacy of another medication. Furthermore, the analysis unit may present optimal combinations when the user is taking multiple medications. For example, the analysis unit may analyze the combination of medications being taken by the user and present the most effective combination. This improves the accuracy of the analysis by evaluating the interaction criteria and efficacy issues. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit may input ingredient data of the medications being taken by the user into the generation AI and have the generation AI perform interaction determination.

[0075] The presentation unit can feed back the analysis results to the user. Examples of feedback include, but are not limited to, the format and timing of the feedback. For example, the presentation unit visually displays the analysis results to the user. The presentation unit can also display the analysis results as graphs or charts, providing them in a visually easy-to-understand format. Furthermore, the presentation unit can estimate the user's emotions and adjust the presentation method of the presented information based on the estimated emotions. For example, if the user is feeling stressed, the presentation unit provides a simple and highly visible presentation method. By providing the analysis results as feedback to the user, the user can learn appropriate drug interactions. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without AI. For example, the presentation unit can input data of the analysis results to a generation AI and cause the generation AI to generate a visual display.

[0076] The analysis unit can present an optimal combination when a user is taking multiple medications. Examples of optimal combinations include, but are not limited to, interaction evaluation criteria and efficacy evaluation methods. The analysis unit can, for example, analyze the combination of medications the user is taking and present the most effective combination. The analysis unit can also present an optimal combination based on the user's health condition and lifestyle. For example, the analysis unit can monitor the user's health condition and analyze the data to present the optimal combination. Furthermore, the analysis unit can analyze the user's social media activity and present the optimal combination based on related information. For example, the analysis unit can analyze the user's health-related posts on social media and present the optimal combination. This allows the user to take the medications effectively by presenting the optimal combination when taking multiple medications. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the medications the user is taking into the generation AI and have the generation AI analyze the optimal combination.

[0077] The collection unit can digitize and monetize the user information. Monetization includes, but is not limited to, data sales methods and revenue models. For example, the collection unit stores the user information in a database and sells the data. The collection unit can also analyze the user information and build a monetization model. For example, the collection unit can build an advertising model based on the user information and generate revenue. Furthermore, the collection unit can provide a subscription model based on the user information. For example, the collection unit can provide personalized services based on the user information and generate revenue. This enables monetization by digitizing and monetizing the user information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input user information data into a generation AI and have the generation AI build a model for monetization.

[0078] The registration unit may include a verification unit for ensuring the accuracy of the drug information. Methods for ensuring accuracy include, but are not limited to, data verification methods and verification standards. For example, the registration unit verifies the drug information and confirms its accuracy. The registration unit may also use an algorithm for ensuring the accuracy of the drug information. For example, the registration unit verifies information on drug ingredients, effects, and side effects and confirms their accuracy. Furthermore, the registration unit may establish a feedback loop for ensuring the accuracy of the drug information. For example, the registration unit updates the drug information based on feedback from the user and maintains its accuracy. This ensures the accuracy of the drug information, thereby providing highly reliable information. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without AI. For example, the registration unit may input drug information data into a generation AI and have the generation AI verify the accuracy.

[0079] The analysis unit may include an update unit that manages the update frequency of the information. The management of the update frequency includes, but is not limited to, the timing and method of updates. For example, the analysis unit manages the update frequency of medication information, and always provides the latest information. The analysis unit may also manage the update frequency of user information. For example, the analysis unit updates the information in response to changes in the user's health condition or lifestyle. Furthermore, the analysis unit may use an algorithm to optimize the update frequency of the information. For example, the analysis unit adjusts the update frequency based on the importance of the information. In this way, by managing the update frequency of the information, the latest information is always provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input update data of the information to the generation AI and have the generation AI manage the update frequency.

[0080] The collection unit can estimate the user's emotions and adjust the timing of collecting user information based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, emotion recognition algorithms and emotion evaluation criteria. The collection unit, for example, analyzes the user's facial expressions and voice to estimate the emotions. The collection unit can also analyze the user's biometric data (heart rate and electrodermal activity) to estimate the emotions. For example, the collection unit estimates the emotions based on fluctuations in the user's heart rate. Furthermore, the collection unit adjusts the collection timing based on the estimated emotions. For example, if the user is feeling stressed, the collection unit delays the collection timing to collect information in a relaxed state. Furthermore, if the user is relaxed, the collection unit can immediately collect information and quickly convert it into data. This enables more appropriate information collection by adjusting the collection timing according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the collection timing.

[0081] The collection unit can analyze the user's past medical history and select the optimal collection method. The optimal collection method includes, but is not limited to, the type of collection method and selection criteria. For example, the collection unit selects a collection method appropriate for a specific medical history based on the user's past medical history. The collection unit can also prioritize collecting allergy information based on the user's past medical history. For example, the collection unit analyzes the user's past medical history and focuses on collecting specific medication history. This allows the optimal collection method to be selected by analyzing the past medical history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past medical history data into the generation AI and have the generation AI select the optimal collection method.

[0082] When collecting user information, the collection unit can perform filtering based on the user's current health condition and lifestyle habits. Filtering includes, but is not limited to, filtering conditions and filtering methods, for example. The collection unit, for example, collects only necessary information based on the user's current health condition. The collection unit can also prioritize collecting related information taking into account the user's lifestyle habits (smoking, drinking, etc.). For example, the collection unit filters unnecessary information based on the user's current health condition and lifestyle habits. In this way, filtering based on the current health condition and lifestyle habits can collect only necessary information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's health condition and lifestyle habit data into the generation AI and have the generation AI perform filtering.

[0083] When collecting user information, the collection unit can select the optimal collection means depending on the user's input method. The optimal collection means includes, but is not limited to, the type of collection means and selection criteria. For example, if the user selects voice input, the collection unit can collect information using voice recognition technology. Furthermore, if the user selects text input, the collection unit can also collect information using text analysis technology. For example, if the user selects image input, the collection unit can collect information using image recognition technology. This enables efficient information collection by selecting the optimal collection means depending on the input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's input data into a generation AI and have the generation AI select the optimal collection means.

[0084] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. Methods for determining the priority of information include, but are not limited to, priority evaluation criteria and determination methods. For example, when the user is stressed, the collection unit prioritizes collecting important information. The collection unit can also collect detailed information when the user is relaxed. For example, when the user is in a hurry, the collection unit prioritizes collecting the minimum amount of information necessary. This allows important information to be collected preferentially by determining the priority of information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of information.

[0085] When collecting user information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information acquisition methods, etc. For example, the collection unit prioritizes collecting area-specific medical information based on the user's current location. The collection unit can also collect information on nearby medical institutions by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting local health risk information based on the user's geographical location information. This allows highly relevant information to be collected preferentially by taking into account the geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's geographical location data into the generation AI and cause the generation AI to collect highly relevant information.

[0086] When collecting user information, the collection unit can analyze the user's social media activity and collect related information. Social media activity includes, but is not limited to, the content of posts and the frequency of activity. For example, the collection unit can analyze the user's health-related posts on social media and collect related information. The collection unit can also identify health concerns from the user's social media activity and collect information. For example, the collection unit can collect related information by referring to the health information of the user's friends on social media. In this way, related information can be collected by analyzing social media activity. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related information.

[0087] When collecting user information, the collection unit can customize the collection method by reflecting the user's past feedback. Methods for customizing the collection method include, but are not limited to, customization conditions and methods. For example, the collection unit improves the collection method based on the user's past feedback. The collection unit can also adjust the type of information to be collected by reflecting the user's past feedback. For example, the collection unit optimizes the collection timing by referring to the user's past feedback. In this way, the collection method can be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data into the generation AI and have the generation AI customize the collection method.

[0088] The registration unit can estimate the user's emotions and adjust the registration method of medication information based on the estimated user emotions. Methods for adjusting the registration method include, but are not limited to, adjustment conditions and methods. For example, when the user is feeling stressed, the registration unit registers medication information using a simple interface. Furthermore, when the user is relaxed, the registration unit can provide detailed input options and register medication information. For example, when the user is in a hurry, the registration unit prioritizes voice input and quickly registers medication information. This enables efficient information registration by adjusting the registration method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the user's emotion data into the generation AI and have the generation AI adjust the registration method.

[0089] When registering drug information, the registration unit can adjust the level of detail of the registration based on the drug's ingredients, effects, and side effects. Methods for adjusting the level of detail of the registration include, but are not limited to, level of detail evaluation criteria and adjustment methods. For example, the registration unit registers detailed information when the drug's ingredients are complex. Furthermore, when the drug is highly effective, the registration unit can also register detailed information regarding the effects. For example, when the drug has many side effects, the registration unit registers detailed information regarding the side effects. In this way, by adjusting the level of detail of the registration based on the ingredients, effects, and side effects, necessary information can be appropriately registered. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input data regarding the drug's ingredients, effects, and side effects into the generation AI and have the generation AI adjust the level of detail of the registration.

[0090] When registering drug information, the registration unit can apply different registration algorithms depending on the drug category. Examples of registration algorithms include, but are not limited to, types of algorithms and application methods. For example, the registration unit applies a simplified registration algorithm for over-the-counter drugs. The registration unit can also apply a detailed registration algorithm for prescription drugs. For example, the registration unit applies a registration algorithm based on specific ingredients and effects for herbal medicines. This enables efficient information registration by applying different registration algorithms depending on the category. Some or all of the above-mentioned processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input drug category data into the generation AI and cause the generation AI to apply the registration algorithm.

[0091] When registering drug information, the registration unit can improve the accuracy of the registration by referring to past registration data. Methods for improving the accuracy of the registration include, but are not limited to, accuracy evaluation criteria and improvement methods. For example, the registration unit automatically complements information on similar drugs based on past registration data. The registration unit can also analyze past registration data to improve the accuracy of the registration. For example, the registration unit maintains consistency of the registration by referring to past registration data. In this way, the accuracy of the registration is improved by referring to the past registration data. Some or all of the above-mentioned processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input past registration data into the generation AI and cause the generation AI to improve the accuracy of the registration.

[0092] The registration unit can estimate the user's emotions and determine the priority of the medication information to be registered based on the estimated user's emotions. Methods for determining the priority of information include, but are not limited to, priority evaluation criteria and determination methods. For example, when the user is feeling stressed, the registration unit prioritizes registering important medication information. The registration unit can also register detailed medication information when the user is relaxed. For example, when the user is in a hurry, the registration unit prioritizes registering the minimum necessary medication information. This allows important information to be registered preferentially by determining the priority of information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the information.

[0093] When registering drug information, the registration unit can take into consideration information about the drug manufacturer and distributor. Information about the manufacturer and distributor includes, for example, the name of the manufacturer and contact information of the distributor, but is not limited to these examples. The registration unit, for example, registers reliable information based on information about the drug manufacturer. The registration unit can also register detailed information by taking into consideration information about the drug distributor. For example, the registration unit improves the accuracy of registration based on information about the drug manufacturer and distributor. This allows for registration of reliable information by taking into consideration information about the manufacturer and distributor. Some or all of the above-mentioned processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input data about the drug manufacturer and distributor into the generation AI and have the generation AI register the information.

[0094] When registering drug information, the registration unit can improve the accuracy of registration by referring to drug-related literature. Related literature includes, but is not limited to, academic papers and patent documents. For example, the registration unit registers detailed information based on drug-related literature. The registration unit can also improve the accuracy of registration by referring to drug-related literature. For example, the registration unit maintains consistency in registration based on drug-related literature. By referring to the related literature, the accuracy of registration is improved. Some or all of the above-described processing in the registration unit may be performed using, or without, AI. For example, the registration unit can input drug-related literature data into a generation AI and cause the generation AI to improve the accuracy of registration.

[0095] The registration unit can take into consideration the market value of the drug when registering drug information. Market value includes, but is not limited to, market price, demand forecast, etc. For example, the registration unit prioritizes registering information about drugs with high market value. The registration unit can also simplify and register information about drugs with low market value. For example, the registration unit adjusts the level of detail of the registration based on the market value. This allows important information to be registered preferentially by taking market value into consideration. Some or all of the above-mentioned processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input drug market value data into the generation AI and have the generation AI register the information.

[0096] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. Methods for adjusting the analysis criteria include, but are not limited to, criteria evaluation and adjustment methods. For example, when the user is stressed, the analysis unit relaxes the analysis criteria to quickly provide results. Furthermore, when the user is relaxed, the analysis unit can perform a detailed analysis to provide highly accurate results. For example, when the user is in a hurry, the analysis unit performs the minimum necessary analysis to quickly provide results. This allows for appropriate analysis results to be provided by adjusting the analysis criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis criteria.

[0097] The analysis unit can improve the accuracy of the analysis by taking drug interactions into account during analysis. Interactions include, but are not limited to, drug interactions and effect interactions. The analysis unit can, for example, analyze interactions between drug components in detail to provide highly accurate results. The analysis unit can also improve the accuracy of the analysis by referring to a database on drug interactions. For example, the analysis unit can increase the reliability of the analysis results by taking drug interactions into account. This improves the accuracy of the analysis by taking interactions into account. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input drug interaction data into a generation AI and have the generation AI perform an interaction analysis.

[0098] The analysis unit can perform analysis while taking into account the user's attribute information. Attribute information includes, but is not limited to, for example, age, gender, and medical history. The analysis unit customizes the analysis results, for example, by taking into account the user's age and gender. The analysis unit can also improve the accuracy of the analysis based on the user's medical history. For example, the analysis unit provides analysis results by taking into account the user's allergy information. This allows customized analysis results to be provided by taking into account the attribute information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's attribute data into the generation AI and cause the generation AI to perform an analysis taking into account the attribute information.

[0099] During analysis, the analysis unit can weight the analysis based on the frequency of medication intake. Weighting includes, for example, weighting conditions, methods, etc., but is not limited to these examples. For example, the analysis unit may perform the analysis by prioritizing information about medications that the user takes frequently. The analysis unit may also perform the analysis by disregarding information about medications that the user takes infrequently. For example, the analysis unit adjusts the reliability of the analysis results based on the frequency of medication intake. In this way, weighting based on medication frequency can provide highly reliable analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input medication frequency data into a generation AI and have the generation AI perform a weighting analysis.

[0100] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. Display methods include, but are not limited to, graph display and text display. For example, if the user is feeling stressed, the analysis unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that focuses on the main points. This allows for adjusting the display method according to the user's emotions to provide highly visible results. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0101] The analysis unit can perform the analysis while taking into account the geographical distribution of the drug. Geographical distribution includes, for example, regional data and distribution evaluation methods, but is not limited to these examples. For example, the analysis unit analyzes region-specific interactions based on the geographical distribution of the drug. The analysis unit can also analyze the effects of the drug for each region while taking into account the geographical distribution. For example, the analysis unit analyzes the side effects of the drug for each region based on the geographical distribution. This makes it possible to provide region-specific analysis results by taking into account the geographical distribution. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input geographical distribution data of the drug into the generation AI and cause the generation AI to perform an analysis taking into account the geographical distribution.

[0102] During analysis, the analysis unit can improve the accuracy of the analysis by referring to drug-related literature. Related literature includes, but is not limited to, academic papers and patent documents. For example, the analysis unit performs a detailed analysis based on drug-related literature. The analysis unit can also improve the accuracy of the analysis by referring to drug-related literature. For example, the analysis unit increases the reliability of the analysis results based on drug-related literature. By referring to the related literature, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input drug-related literature data into the generation AI and cause the generation AI to perform an analysis with reference to the related literature.

[0103] The analysis unit can perform the analysis while taking into account the market value of the drug. Market value includes, but is not limited to, market price, demand forecast, etc. For example, the analysis unit performs the analysis by prioritizing information on drugs with high market value. The analysis unit can also perform the analysis by disregarding information on drugs with low market value. For example, the analysis unit adjusts the reliability of the analysis results based on the market value. By taking market value into account, it is possible to provide highly reliable analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input market value data on the drug into the generation AI and cause the generation AI to perform an analysis taking market value into account.

[0104] The presentation unit can estimate the user's emotion and adjust the presentation method of the information to be presented based on the estimated user's emotion. Examples of presentation methods include, but are not limited to, text presentation and visual presentation. For example, when the user is stressed, the presentation unit provides a simple, highly visible presentation method. Furthermore, when the user is relaxed, the presentation unit can also provide a presentation method including detailed information. For example, when the user is in a hurry, the presentation unit provides a presentation method that focuses on the main points. This allows for highly visible information to be provided by adjusting the presentation method according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the presentation unit may be performed using, for example, an AI. For example, the presentation unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method.

[0105] The presentation unit can adjust the level of detail of the presentation based on the importance of the medicine when presenting the information. The level of detail includes, for example, evaluation criteria for the level of detail and an adjustment method, but is not limited to such examples. For example, the presentation unit presents detailed information about medicines with high importance. The presentation unit can also present simplified information about medicines with low importance. For example, the presentation unit adjusts the level of detail of the presentation based on the importance. In this way, necessary information can be appropriately presented by adjusting the level of detail based on the importance. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input medicine importance data to the generation AI and cause the generation AI to adjust the level of detail.

[0106] The presentation unit can apply different presentation algorithms depending on the category of the medicine when presenting the information. Presentation algorithms include, but are not limited to, types of algorithms and application methods. For example, the presentation unit can apply a simplified presentation algorithm for over-the-counter medicines. The presentation unit can also apply a detailed presentation algorithm for prescription medicines. For example, the presentation unit can apply a presentation algorithm based on specific ingredients and effects for herbal medicines. By applying different presentation algorithms depending on the category, efficient information presentation becomes possible. Some or all of the above-described processing in the presentation unit can be performed using, or without, AI. For example, the presentation unit can input medicine category data into the generation AI and cause the generation AI to apply the presentation algorithm.

[0107] The presentation unit can improve the accuracy of presentation by referring to the user's past presentation results. Methods for improving the accuracy of presentation include, but are not limited to, accuracy evaluation criteria and improvement methods. For example, the presentation unit automatically complements information on similar drugs based on past presentation results. The presentation unit can also analyze past presentation results to improve the accuracy of presentation. For example, the presentation unit maintains consistency in presentation by referring to past presentation results. This improves the accuracy of presentation by referring to past presentation results. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input past presentation result data into the generation AI and cause the generation AI to improve the accuracy of presentation.

[0108] The presentation unit can estimate the user's emotions and adjust the length of the information to be presented based on the estimated user emotions. Methods for adjusting the length of the information include, but are not limited to, information evaluation criteria and adjustment methods. For example, when the user is stressed, the presentation unit presents short, to-the-point information. Furthermore, when the user is relaxed, the presentation unit can present longer information including detailed information. For example, when the user is in a hurry, the presentation unit presents concise, quickly understandable information. This allows appropriate information to be provided by adjusting the length of the information according to the user's emotions. The emotion estimation is realized using, for example, an emotion engine or a generation AI, using an emotion estimation function. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to, such examples. Some or all of the above-described processing in the presentation unit may be performed using, for example, an AI. For example, the presentation unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the information.

[0109] The presentation unit can determine the presentation priority based on the time of submission of the medication at the time of presentation. The submission time includes, but is not limited to, for example, the timing of submission and the evaluation method. For example, the presentation unit preferentially presents information about medications that have been submitted recently. The presentation unit can also present simplified information about medications that have been submitted recently. For example, the presentation unit adjusts the presentation priority based on the time of submission. In this way, by determining the priority based on the time of submission, the latest information can be presented preferentially. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input medication submission time data into the generation AI and have the generation AI determine the priority.

[0110] The presentation unit can adjust the order of presentation based on the relevance of the drugs when presenting them. Relevance includes, for example, evaluation criteria and evaluation methods for relevance, but is not limited to such examples. For example, the presentation unit preferentially presents information about highly relevant drugs. The presentation unit can also simplify and present information about less relevant drugs. For example, the presentation unit adjusts the order of presentation based on relevance. By adjusting the order of presentation based on relevance, important information can be presented preferentially. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input drug relevance data into a generation AI and have the generation AI adjust the order.

[0111] The presentation unit can adjust the use of technical terminology in the presentation depending on the user's level of expertise. Examples of the level of expertise include, but are not limited to, evaluation criteria and methods for the knowledge level. For example, the presentation unit can present information in simple language to a user with little expertise. The presentation unit can also present information using detailed technical terminology to a user with expertise. For example, the presentation unit can adjust the use of technical terminology in the presentation based on the user's level of expertise. This allows the provision of information appropriate for the user by adjusting the use of technical terminology depending on the level of expertise. Some or all of the above-described processing in the presentation unit can be performed using, for example, AI, or without AI. For example, the presentation unit can input the user's level of expertise data into a generation AI and cause the generation AI to use technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, registration unit, analysis unit, and presentation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects user information using the camera 42 and microphone 38B of the smart device 14 and stores the collected information in a database using the control unit 46A. The registration unit registers drug information using the specific processing unit 290 of the data processing device 12 and verifies its accuracy using the verification unit. The analysis unit analyzes the user information and drug information using the specific processing unit 290 of the data processing device 12 to determine interactions and efficacy issues. The presentation unit visually displays the analysis results using the control unit 46A of the smart device 14 and provides feedback to the user. The collection unit can also estimate emotions using the specific processing unit 290 of the data processing device 12 and adjust the collection timing. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, registration unit, analysis unit, and presentation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the smart glasses 214 and stores the collected information in a database using the control unit 46A. The registration unit registers medication information using the specific processing unit 290 of the data processing device 12 and verifies its accuracy using the verification unit. The analysis unit analyzes the user information and medication information using the specific processing unit 290 of the data processing device 12 to determine interactions and efficacy issues. The presentation unit visually displays the analysis results using the control unit 46A of the smart glasses 214 and provides feedback to the user. The collection unit can also estimate emotions using the specific processing unit 290 of the data processing device 12 and adjust the collection timing. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, registration unit, analysis unit, and presentation unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the headset-type terminal 314 and stores the collected information in a database using the control unit 46A. The registration unit registers drug information using the specific processing unit 290 of the data processing device 12 and verifies its accuracy using the verification unit. The analysis unit analyzes the user information and drug information using the specific processing unit 290 of the data processing device 12 to determine interactions and problems with efficacy. The presentation unit visually displays the analysis results using the control unit 46A of the headset-type terminal 314 and provides feedback to the user. The collection unit can also estimate emotions using the specific processing unit 290 of the data processing device 12 and adjust the collection timing. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, registration unit, analysis unit, and presentation unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the robot 414 and stores the collected information in a database using the control unit 46A. The registration unit registers drug information using the specific processing unit 290 of the data processing device 12 and verifies its accuracy using the verification unit. The analysis unit analyzes the user information and drug information using the specific processing unit 290 of the data processing device 12 to determine any interactions or problems with efficacy. The presentation unit visually displays the analysis results using the control unit 46A of the robot 414 and provides feedback to the user. The collection unit can also estimate emotions using the specific processing unit 290 of the data processing device 12 and adjust the collection timing.

[0112] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0113] The analysis unit can collect the user's dietary information and analyze dietary components that may affect the effectiveness of the medication. For example, the analysis unit can analyze the components of the meals the user ingests and evaluate interactions with specific medications. The analysis unit can also suggest meals to maximize the effectiveness of the medication based on the user's dietary patterns. Furthermore, the analysis unit can adjust the timing of taking the medication based on the user's dietary information. This allows the user to know the meals and timing of taking the medication to maximize the effectiveness of the medication.

[0114] The reminder can estimate the user's emotions and adjust the medication reminder based on the estimated emotions. For example, if the user is stressed, the reminder can be provided in a soft tone. If the user is relaxed, the reminder can be detailed. If the user is busy, the reminder can be brief and to the point. This allows the user to receive appropriate reminders according to their emotions.

[0115] The collection unit can collect the user's exercise information and analyze exercise patterns that may affect the effectiveness of the medication. For example, the collection unit can analyze the user's exercise amount and type and evaluate interactions with specific medications. The collection unit can also suggest exercises to maximize the effectiveness of the medication based on the user's exercise patterns. Furthermore, the collection unit can adjust the timing of taking the medication based on the user's exercise information. This allows the user to know the exercise and dosage timing that will maximize the effectiveness of the medication.

[0116] The analysis unit can collect the user's sleep information and analyze sleep patterns that may affect the effectiveness of the medication. For example, the analysis unit can analyze the user's sleep duration and sleep quality and evaluate interactions with certain medications. The analysis unit can also make sleep suggestions to maximize the effectiveness of the medication based on the user's sleep patterns. Furthermore, the analysis unit can adjust the timing of medication intake based on the user's sleep information. This allows the user to know the sleep and medication timing that will maximize the effectiveness of the medication.

[0117] The presentation unit can estimate the user's emotions and provide information about drug side effects based on the estimated emotions. For example, if the user feels anxious, advice on reducing the risk of side effects can be provided. If the user feels relaxed, detailed information about side effects can be provided. Furthermore, if the user feels stressed, brief information about side effects can be provided. This allows the user to receive appropriate side effect information according to their emotions.

[0118] The collection unit can collect information about the user's environment and analyze environmental factors that may affect the effectiveness of the medication. For example, the collection unit can analyze the user's living environment or work environment and evaluate interactions with specific medications. The collection unit can also suggest environmental improvements to maximize the effectiveness of the medication based on the user's environmental information. Furthermore, the collection unit can adjust the timing of taking the medication based on the user's environmental information. This allows the user to know the environment and timing of taking the medication to maximize the effectiveness of the medication.

[0119] The analysis unit can estimate the user's emotions and evaluate the effectiveness of the medication based on the estimated emotions. For example, if the user is feeling stressed, the medication's effectiveness may be reduced, so the analysis unit can provide that information. Also, if the user is relaxed, the medication's effectiveness may be maximized, so the analysis unit can provide that information. Furthermore, if the user is feeling anxious, the analysis unit can provide concise information about the medication's effectiveness. This allows the user to receive medication effect information according to their emotions.

[0120] The collection unit can collect the user's genetic information and analyze genetic factors that may affect the effectiveness of medication. For example, the collection unit can analyze the user's genetic information and evaluate interactions with specific medications. The collection unit can also provide genetic advice to maximize the effectiveness of medications based on the user's genetic information. Furthermore, the collection unit can adjust the timing of medication intake based on the user's genetic information. This allows the user to know the genetic factors and the timing of medication intake to maximize the effectiveness of the medication.

[0121] The presentation unit can estimate the user's emotions and provide educational content related to taking medicine based on the estimated emotions. For example, if the user feels anxious, educational content that gives a sense of security can be provided. If the user feels relaxed, detailed educational content can be provided. Furthermore, if the user feels stressed, concise and highly visible educational content can be provided. This allows the user to receive appropriate educational content according to their emotions.

[0122] The analysis unit can estimate the user's emotions and evaluate the risk of drug interactions based on the estimated emotions. For example, if the user is feeling stressed, the risk of drug interactions may increase, and information about this is provided. Also, if the user is relaxed, the risk of drug interactions may decrease, and information about this can be provided. Furthermore, if the user is feeling anxious, concise information about the risk of drug interactions can be provided. This allows the user to receive information about the risk of drug interactions according to their emotions.

[0123] The processing flow of the second embodiment will be briefly explained below.

[0124] Step 1: The collection unit collects user information, including age, gender, medical history, allergy information, and current medications. The collection unit stores the information entered by the user in a database and also collects information about health conditions and lifestyle habits. In addition, the collection unit analyzes the user's social media activity and collects related information. Step 2: The registration unit registers drug information. Drug information includes ingredients, effects, side effects, and interactions. The registration unit stores the information provided by pharmaceutical companies in a database and registers it in a standardized format. In addition, the registration unit has a verification unit to ensure the accuracy of the drug information. Step 3: The analysis unit analyzes the information obtained from the collection unit and registration unit. The analysis unit compares the ingredients of the medications the user is currently taking with the medications prescribed by the hospital to determine whether there are any interactions. It also analyzes whether there are any problems with effectiveness and presents the optimal combination of medications. Step 4: The presentation unit presents optimal drinking combinations based on the analysis results obtained by the analysis unit. The presentation unit provides feedback on the analysis results to the user and visually displays them. Furthermore, the presentation unit estimates the user's emotions and adjusts the presentation method of the information to be presented based on the estimated emotions.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0130] 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.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0146] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0149] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0152] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0153] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0154] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0155] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0156] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0159] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0162] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0165] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0167] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0168] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0169] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0170] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0171] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0172] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0173] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0174] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0175] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0176] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0177] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0178] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0180] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0181] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0182] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0186] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0187] 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.

[0188] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0189] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0190] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0191] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0193] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0194] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0195] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0196] [Explanation of symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects user information; a registration unit for registering drug information; an analysis unit that analyzes information obtained from the collection unit and the registration unit; a presentation unit that presents appropriate drug combinations based on the analysis results obtained by the analysis unit; Equipped with A system characterized by:

2. The collecting unit Convert user information into data 2. The system of claim 1.

3. The registration unit Register drug information in a standardized format 2. The system of claim 1.

4. The analysis unit Equipped with an evaluation section to evaluate interaction criteria and effectiveness issues 2. The system of claim 1.

5. The presentation unit Provide analysis results as feedback to the user 2. The system of claim 1.

6. The analysis unit Suggesting optimal combinations when a user is taking multiple medications 2. The system of claim 1.

7. The collecting unit Convert user information into data and monetize it 2. The system of claim 1.

8. The registration unit Equipped with a verification department to ensure the accuracy of drug information 2. The system of claim 1.

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