System

Abstract

To provide a system which automates the steps from prescription analysis and pharmaceutical compatibility confirmation to pickup and delivery of medicine, thereby reducing errors.SOLUTION: A system includes an analysis section, a retrieval section, a confirmation section, a pickup section, and a delivery section. The analysis section analyzes a prescription image. The retrieval section retrieves information on a contradiction drug amount or on a new drug and generic drugs on the basis of medicine information extracted by the analysis section. The confirmation section confirms compatibility of medicine in accordance with the information obtained by the retrieval section. The pickup section picks up the medicine for which compatibility has been confirmed by the confirmation section. The delivery section delivers the medicine which has been picked up by the pickup section.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] With conventional technology, there was a high risk of errors when analyzing prescriptions and checking drug compatibility, and the cost of training people with the specialized skills was high.

[0005] The system of the embodiment aims to reduce errors by automating the process from prescription analysis to drug compatibility check, pickup, and delivery. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a search unit, a confirmation unit, a pickup unit, and a delivery unit. The analysis unit analyzes a prescription image. The search unit searches for information on contraindicated drug amounts or new drugs and generic drugs based on the drug information extracted by the analysis unit. The confirmation unit confirms the suitability of the drug based on the information obtained by the search unit. The pickup unit picks up the drug whose suitability has been confirmed by the confirmation unit. The delivery unit delivers the drug picked up by the pickup unit. Effect of the Invention

[0007] The system according to the embodiment automates the process from prescription analysis to drug compatibility check, pickup, and delivery, reducing errors. [Brief description of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The drug delivery system according to the embodiment of the present invention is a system for dealing with the high risk of errors occurring when pharmacists use their knowledge and manual work, since the format of prescriptions issued by hospitals is not standardized. This system uses AI to search for matching drugs from prescription images, and refers to information on contraindicated drug amounts, new drugs, generics, and a database of other medical history and medication information of the individual. This system can pick up the required number of drugs using a robot and deliver them to the store or the patient's home. For example, a prescription image is input into AI. The AI ​​analyzes the image and extracts the drug information written on the prescription. Next, based on the extracted drug information, information on contraindicated drug amounts, new drugs, and generics is searched from the database. Furthermore, the database of other medical history and medication information of the individual is referred to to confirm the suitability of the drug. After that, the robot picks up the required number of drugs. The picked-up drugs can be picked up at the store or delivered to the patient's home. This reduces manual errors by pharmacists and reduces the cost of training people with specialized skills. For example, if a prescription says "Drug A 10 mg three times a day," the AI ​​extracts this information from the prescription image and searches the database for information on contraindicated dosages of Drug A, as well as new drugs and generics. It also references the individual's other medical history and medication information database to check whether Drug A is suitable. If it is suitable, the robot picks up the required number of Drug A and delivers it to the store or to the patient's home. This system reduces manual errors by pharmacists and reduces the training costs for people with specialized skills. It also enables patients to receive medication quickly and accurately. As a result, the drug delivery system can reduce manual errors by pharmacists and reduce the training costs for people with specialized skills.

[0029] The medicine provision system according to the embodiment includes an analysis unit, a search unit, a confirmation unit, a pickup unit, and a delivery unit. The analysis unit analyzes a prescription image. The analysis unit extracts medicine information from the prescription image using, for example, an image analysis algorithm. For example, the analysis unit extracts information such as the medicine name, dosage, and administration method from the prescription image using an image analysis algorithm. The analysis unit can also extract medicine information from a handwritten prescription using handwritten character recognition technology. For example, the handwritten character recognition technology is used to extract the medicine name and dosage from the handwritten prescription with high accuracy. The search unit searches for information on prohibited drug amounts, new drugs, and generics based on the medicine information extracted by the analysis unit. The search unit searches for information on prohibited drug amounts, new drugs, and generics from a database, for example. For example, the search unit searches for prohibited drug amounts of the medicine from a database and confirms the appropriate dosage. The search unit can also search for information on new drugs and generics to provide the latest medicine information. The confirmation unit confirms the suitability of the medicine based on the information obtained by the search unit. The confirmation unit, for example, refers to the individual's other medical history and medication information database and confirms the suitability of the medicine. For example, the confirmation unit refers to the individual's other medical history and medication information database and confirms the suitability of the medicine based on past prescription history and medical history information. The pick-up unit picks up the medicine whose suitability has been confirmed by the confirmation unit. The pick-up unit, for example, uses a robot to pick up the required number of medicines. For example, the pick-up unit uses a robot to accurately and quickly pick up the medicines. The delivery unit delivers the medicines picked up by the pick-up unit. The delivery unit, for example, picks up the medicine at a store or delivers it to the patient's home. For example, the delivery unit can select whether the medicine is to be picked up at a store or delivered to the patient's home. This allows the medicine provision system according to the embodiment to reduce manual errors by pharmacists and reduce the training costs of people with specialized skills. For example, the analysis unit analyzes a prescription image and extracts medicine information. The search unit searches for information on contraindicated drug amounts, new drugs, and generics. The confirmation unit refers to the individual's other medical history and medication information database and confirms the suitability of the medicine. The pickup unit uses a robot to pick up the medicine, and the delivery unit delivers the medicine to the store or to the patient's home.This enables the medication delivery system to reduce manual errors made by pharmacists and cut the costs of training people with specialized skills.

[0030] The analysis unit analyzes the prescription image. The analysis unit extracts drug information from the prescription image, for example, using an image analysis algorithm. Specifically, the prescription image is scanned at high resolution and characters and symbols are recognized using image processing technology. The image analysis algorithm utilizes optical character recognition (OCR) technology to convert printed characters and handwritten characters into digital data. Furthermore, the handwritten character recognition technology is used to extract drug names and dosages from handwritten prescriptions with high accuracy. The handwritten character recognition technology trains a model using deep learning to accommodate various handwriting and fonts. For example, even if the drug name is unclear in a handwritten prescription, the exact drug name can be identified by performing context analysis and comparing it with other information. By combining these technologies, the analysis unit can quickly and accurately extract information such as drug names, dosages, and administration methods from prescription images.

[0031] The search unit searches for information on contraindicated drug amounts, new drugs, and generic drugs based on the drug information extracted by the analysis unit. The search unit searches for information on contraindicated drug amounts, new drugs, and generic drugs from a database, for example. Specifically, the search unit accesses a drug information database and acquires related information based on the extracted drug name and dosage. The search unit searches for contraindicated drug amounts and confirms appropriate dosages. For example, if a specific drug is contraindicated for a specific patient, the search unit promptly provides that information. The search unit can also search for information on new drugs and generic drugs and provide the latest drug information. The new drug information includes the latest clinical trial results and approval information, and the generic drug information provides options that take into account cost efficiency and supply status. The search unit integrates this information to support optimal drug selection. In addition, the search unit also provides information on drug interactions and side effects, playing an important role in ensuring patient safety.

[0032] The confirmation unit confirms the suitability of the drug based on the information obtained by the search unit. The confirmation unit, for example, refers to a database of other medical history and medication information of an individual to confirm the suitability of the drug. Specifically, the confirmation unit evaluates whether the current prescription is appropriate based on the patient's past prescription history and medical history information. For example, the confirmation unit prevents a patient who has had an allergic reaction to a specific drug in the past from being prescribed that drug again. In addition, when multiple drugs are prescribed at the same time, the confirmation unit checks whether those drugs do not interact with each other. For example, if the effectiveness of a specific drug decreases or side effects increase when used in combination with other drugs, the confirmation unit provides that information and suggests an appropriate alternative drug. Based on this information, the confirmation unit plays an important role in selecting the safest and most effective drug for the patient. Furthermore, the confirmation unit takes into account the patient's lifestyle and allergy information, etc., to provide personalized medicine.

[0033] The pickup unit picks up the medicines whose suitability has been confirmed by the confirmation unit. The pickup unit, for example, uses a robot to pick up the required number of medicines. Specifically, the robot uses a robot arm to accurately pick up the specified medicines from the shelves or automated warehouses where the medicines are stored. The robot identifies the medicines using barcodes or RFID tags to improve the accuracy of the pickup. For example, the robot quickly and accurately picks up the medicines based on the position information of the specified medicines and places them on a pickup tray. This prevents the pickup unit from picking up the wrong medicines or making mistakes in the quantity, and realizes efficient medicine supply. Furthermore, the pickup unit also manages the inventory of medicines and has a function of automatically replenishing the inventory when there is a shortage. This ensures that the necessary medicines are always supplied appropriately.

[0034] The delivery unit delivers the medicine picked up by the pickup unit. The delivery unit, for example, delivers the medicine to a store or to the patient's home. Specifically, after the medicine is picked up, the delivery unit calculates the optimal delivery route based on the information of the delivery destination and delivers the medicine quickly. The delivery unit can also deliver the medicine using, for example, a drone or an autonomous vehicle. This avoids traffic congestion and delivery delays and realizes prompt delivery of medicine. Furthermore, the delivery unit has a function of tracking the delivery status in real time and notifying the recipient. For example, the delivery unit notifies the recipient of the progress until delivery is completed via a smartphone app or email, allowing the recipient to receive the medicine with peace of mind. The delivery unit thereby efficiently and reliably performs the final step of medicine delivery, improving patient convenience.

[0035] The analysis unit can extract drug information from the prescription image. The analysis unit extracts drug information from the prescription image using, for example, an image analysis algorithm. For example, the analysis unit extracts information such as drug name, dosage, and administration method from the prescription image using an image analysis algorithm. The analysis unit can also extract drug information from a handwritten prescription using handwritten character recognition technology. For example, the handwritten character recognition technology is used to extract drug name and dosage from a handwritten prescription with high accuracy. As a result, drug compatibility can be quickly confirmed by extracting drug information from a prescription image.

[0036] The search unit can search the database for information on contraindicated drug amounts, new drugs, and generic drugs. The search unit, for example, searches the database for information on contraindicated drug amounts, new drugs, and generic drugs. For example, the search unit searches the database for contraindicated drug amounts of a drug and confirms the appropriate dosage. The search unit can also search for information on new drugs and generic drugs and provide the latest drug information. This makes it possible to select an appropriate drug by searching for information on contraindicated drug amounts, new drugs, and generic drugs.

[0037] The confirmation unit can confirm the suitability of the drug by referring to the individual's other medical history and medication information database. The confirmation unit, for example, refers to the individual's other medical history and medication information database to confirm the suitability of the drug. For example, the confirmation unit refers to the individual's other medical history and medication information database to confirm the suitability of the drug based on past prescription history and medical history information. As a result, by referring to the individual's other medical history and medication information database, the suitability of the drug can be confirmed with high accuracy.

[0038] The pickup unit can pick up the required number of medicines using a robot. The pickup unit, for example, picks up the required number of medicines using a robot. For example, the pickup unit picks up the medicines accurately and quickly using a robot. As a result, by using the robot, the medicines can be picked up accurately and quickly.

[0039] The delivery unit can perform store pick-up or delivery to the patient's home. The delivery unit, for example, performs store pick-up or delivery to the patient's home. For example, the delivery unit can select whether to pick up the medicine at the store or to have it delivered to the patient's home. This allows for quick and accurate provision of medicine to the patient by having the medicine picked up at the store or delivered to the patient's home.

[0040] When analyzing the prescription image, the analysis unit can apply different analysis algorithms depending on the resolution and quality of the image. For example, the analysis unit applies a detailed analysis algorithm to a high-resolution image. Also, the analysis unit can apply a noise removal algorithm to a low-resolution image before performing analysis. Furthermore, when the image quality is uneven, different analysis algorithms can be applied to each part. This improves the analysis accuracy by applying an analysis algorithm depending on the resolution and quality of the image.

[0041] The analysis unit can perform preprocessing to improve the accuracy of handwritten character recognition when analyzing the prescription image. For example, the analysis unit can perform preprocessing to emphasize the contours of handwritten characters. The analysis unit can also perform preprocessing to correct the inclination of handwritten characters. Furthermore, the analysis unit can perform preprocessing to remove background noise from handwritten characters. Thus, by performing preprocessing to improve the accuracy of handwritten character recognition, the analysis accuracy is improved.

[0042] When analyzing a prescription image, the analysis unit can improve the analysis accuracy by taking into account the shooting environment of the image. For example, when the light intensity is non-uniform, the analysis unit performs pre-processing to make the brightness of the image uniform. Also, when the image is shot at an oblique angle, the analysis unit can perform angle correction before performing analysis. Furthermore, when there is light reflection, pre-processing can be performed to remove the reflected portion. In this way, the analysis accuracy is improved by taking into account the shooting environment of the image.

[0043] When analyzing a prescription image, the analysis unit can improve the accuracy of the analysis by integrating multiple images. For example, the analysis unit integrates images taken from multiple angles and performs the analysis. The analysis unit can also integrate images with multiple resolutions and perform the analysis. Furthermore, images taken in multiple time periods can be integrated and analyzed. In this way, the analysis accuracy is improved by integrating multiple images.

[0044] During a search, the search unit can adjust the priority of search results based on the effects and side effects of the drugs. For example, the search unit can preferentially display highly effective drugs. It can also preferentially display drugs with fewer side effects. It can also preferentially display drugs with a good balance between effects and side effects. This makes it possible to select an appropriate drug by adjusting the priority based on the effects and side effects of the drug.

[0045] During a search, the search unit can propose the most suitable drug by taking into consideration drug price information. The search unit, for example, can preferentially display low-priced drugs. It can also preferentially display drugs with a good balance between price and effectiveness. It can also preferentially display drugs that are expensive but highly effective. This makes it possible to select a drug with high cost performance by taking into consideration drug price information.

[0046] During a search, the search unit can adjust the search results by taking into account the drug supply situation. For example, the search unit can preferentially display drugs with stable supply. It can also not display drugs with unstable supply. Furthermore, it can also suggest alternative drugs depending on the supply situation. This makes it possible to select a drug that can be stably supplied by taking into account the drug supply situation.

[0047] During a search, the search unit can adjust search results by taking into account drug manufacturer information. For example, the search unit can preferentially display drugs from highly reliable manufacturers. It can also not display drugs from manufacturers with poor reputations. Furthermore, it can also suggest alternative drugs based on the manufacturer information. This makes it possible to select highly reliable drugs by taking into account drug manufacturer information.

[0048] The confirmation unit can improve the accuracy of compatibility by referring to past medication history when confirming compatibility. The confirmation unit confirms compatibility based on, for example, past medication history. In addition, the confirmation unit can identify contraindicated drugs from the past medication history and confirm compatibility. Furthermore, the confirmation unit can analyze the past medication history and suggest the most suitable drug. In this way, the accuracy of compatibility confirmation is improved by referring to the past medication history.

[0049] The confirmation unit can take into consideration the patient's allergy information when confirming compatibility. The confirmation unit confirms compatibility, for example, based on the patient's allergy information. It can also identify contraindicated drugs from the allergy information and confirm compatibility. It can also analyze the allergy information and suggest the most suitable drug. This improves the accuracy of compatibility confirmation by taking the patient's allergy information into consideration.

[0050] The confirmation unit can take into consideration the lifestyle habit information of the patient when confirming compatibility. The confirmation unit confirms compatibility, for example, based on the lifestyle habit information of the patient. The confirmation unit can also identify contraindicated drugs from the lifestyle habit information and confirm compatibility. Furthermore, the lifestyle habit information can be analyzed to suggest optimal medications. As a result, the accuracy of compatibility confirmation is improved by taking into consideration the lifestyle habit information of the patient.

[0051] The confirmation unit can take into consideration the genetic information of the patient when confirming compatibility. The confirmation unit confirms compatibility based on, for example, the genetic information of the patient. The confirmation unit can also identify contraindicated drugs from the genetic information and confirm compatibility. Furthermore, the genetic information can be analyzed to suggest the most suitable drug. In this way, by taking the genetic information of the patient into consideration, the accuracy of compatibility confirmation is improved.

[0052] The pickup unit can select the optimal pickup method in consideration of the storage conditions of the medicines when picking them up. For example, the pickup unit preferentially picks up medicines for which the storage temperature is important. The pickup unit can also efficiently pick up medicines stored in different locations. Furthermore, the pickup unit can also preferentially pick up medicines whose storage expiration date is approaching. This allows appropriate picking up by considering the storage conditions of the medicines.

[0053] The pickup unit can select the optimal pickup method in consideration of the size and shape of the medicine when picking up the medicine. For example, the pickup unit efficiently picks up large-sized medicines. It can also carefully pick up small-sized medicines. Furthermore, it can properly pick up medicines with special shapes. This allows for proper pickup by considering the size and shape of the medicine.

[0054] The pickup unit can take the medicine packaging information into consideration when picking up the medicine. For example, the pickup unit carefully picks up medicines whose packaging is easily damaged. It can also efficiently pick up medicines whose packaging is large. Furthermore, it can appropriately pick up medicines whose packaging is special. This allows appropriate picking up by taking the medicine packaging information into consideration.

[0055] At the time of delivery, the delivery unit can select the optimal delivery method by taking into consideration the environmental information of the delivery destination. For example, if the delivery destination is in a hot and humid area, the delivery unit will deliver using an appropriate storage method. Also, if the delivery destination is in a cold area, the delivery unit can deliver the product by taking measures to keep the product warm. Furthermore, if the delivery destination is in a remote area, the delivery unit can select the optimal delivery route. This allows for appropriate delivery by taking into consideration the environmental information of the delivery destination.

[0056] The delivery unit can deliver medicines taking into consideration the storage temperature of the medicine. For example, the delivery unit delivers medicines with low storage temperatures at an appropriate temperature. It can also deliver medicines with high storage temperatures at an appropriate temperature. It can also deliver medicines whose storage temperatures are prone to change at an appropriate temperature. This makes it possible to deliver medicines at an appropriate temperature by taking into consideration the storage temperature of the medicine.

[0057] At the time of delivery, the delivery unit can select the optimal delivery route by taking into consideration the geographic information of the delivery destination. For example, when the delivery destination is in an urban area, the delivery unit selects the shortest route. Also, when the delivery destination is in a suburban area, the delivery unit can select an efficient route. Furthermore, when the delivery destination is in a mountainous area, the delivery unit can select a safe route. In this way, efficient delivery is possible by taking into consideration the geographic information of the delivery destination.

[0058] The delivery unit can take into consideration the recipient information of the delivery destination when making delivery. For example, if the recipient is elderly, the delivery unit can deliver at a time that is convenient for the recipient to receive the parcel. Also, if the recipient is busy, the delivery unit can deliver at a time that is convenient for the recipient to receive the parcel. Furthermore, if the recipient is not at home, the delivery unit can arrange for redelivery. This allows for appropriate delivery by taking into consideration the recipient information of the delivery destination.

[0059] At the time of delivery, the delivery unit can select the optimal delivery route by taking into consideration the geographic information of the delivery destination. For example, when the delivery destination is in an urban area, the delivery unit selects the shortest route. Also, when the delivery destination is in a suburban area, the delivery unit can select an efficient route. Furthermore, when the delivery destination is in a mountainous area, the delivery unit can select a safe route. In this way, efficient delivery is possible by taking into consideration the geographic information of the delivery destination.

[0060] The delivery unit can take into consideration the recipient information of the delivery destination when making delivery. For example, if the recipient is elderly, the delivery unit can deliver at a time that is convenient for the recipient to receive the parcel. Also, if the recipient is busy, the delivery unit can deliver at a time that is convenient for the recipient to receive the parcel. Furthermore, if the recipient is not at home, the delivery unit can arrange for redelivery. This allows for appropriate delivery by taking into consideration the recipient information of the delivery destination.

[0061] When standardizing formats, the format unification unit can select the optimal format by taking into consideration the characteristics of each hospital. The format unification unit unifies formats based on the format of a large hospital, for example. It can also unify formats based on the format of a small hospital. Furthermore, it can select the optimal format by taking into consideration the characteristics of each hospital. This makes it possible to select the optimal format by taking into consideration the characteristics of each hospital.

[0062] When standardizing formats, the format unification unit can select the optimal format by taking into consideration the system information of each hospital. The format unification unit unifies the formats based on the system information of a large hospital, for example. It can also unify based on the system information of a small hospital. Furthermore, it can also select the optimal format by taking into consideration the system information of each hospital. This makes it possible to select the optimal format by taking into consideration the system information of each hospital.

[0063] When strengthening security, the security department can select the most appropriate countermeasure by referring to past security incidents. For example, the security department analyzes past security incidents and selects the most appropriate countermeasure. It is also possible to select measures to prevent recurrence from past security incidents. Furthermore, it is also possible to strengthen security countermeasures based on past security incidents. In this way, it is possible to take the most appropriate security countermeasure by referring to past security incidents.

[0064] When strengthening security, the security unit can select the optimal countermeasure by taking into consideration the characteristics of each database. For example, the security unit selects security countermeasures based on the characteristics of a large-scale database. It can also select security countermeasures based on the characteristics of a small-scale database. It can also select the optimal security countermeasure by taking into consideration the characteristics of each database. This makes it possible to take optimal security countermeasures by taking into consideration the characteristics of each database.

[0065] When controlling the motion of the robot, the control unit can select an optimal motion pattern by referring to past motion history. The control unit selects an optimal motion pattern based on, for example, past motion history. Also, the control unit can select an efficient motion pattern from the past motion history. Furthermore, the control unit can analyze the past motion history and select an optimal motion pattern. In this way, it becomes possible to select an optimal motion pattern by referring to the past motion history.

[0066] The control unit can select the optimal motion pattern taking into consideration the characteristics of each drug when controlling the motion of the robot. For example, the control unit selects a motion pattern that efficiently picks up drugs with different characteristics. It can also select a motion pattern that collectively picks up drugs with similar characteristics. It can also select a motion pattern that carefully picks up drugs with special characteristics. This makes it possible to select the optimal motion pattern by taking into consideration the characteristics of each drug.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] When analyzing a prescription image, the analysis unit can improve the analysis accuracy by taking into account the shooting environment of the image. For example, if the light intensity is non-uniform, pre-processing is performed to make the brightness of the image uniform. Also, if the image is shot at an oblique angle, the angle can be corrected before analysis. Furthermore, if there is light reflection, pre-processing can be performed to remove the reflected portion. In this way, the analysis accuracy is improved by taking into account the shooting environment of the image.

[0069] During a search, the search unit can adjust the priority of search results based on the effects and side effects of drugs. For example, highly effective drugs can be preferentially displayed. Drugs with fewer side effects can also be preferentially displayed. Furthermore, drugs with a good balance between effects and side effects can also be preferentially displayed. This makes it possible to select an appropriate drug by adjusting the priority based on the effects and side effects of the drug.

[0070] When checking compatibility, the confirmation unit can take into consideration the patient's allergy information. For example, compatibility is confirmed based on the patient's allergy information. Contraindicated drugs can also be identified from the allergy information and compatibility can be confirmed. Furthermore, the allergy information can be analyzed and the most suitable drug can be suggested. In this way, the accuracy of compatibility confirmation is improved by taking the patient's allergy information into consideration.

[0071] The pickup unit can select the optimal pickup method by taking into consideration the storage conditions of the medicine when picking up the medicine. For example, it can prioritize picking up medicines for which the storage temperature is important. It can also efficiently pick up medicines stored in different locations. It can also prioritize picking up medicines whose storage expiration date is approaching. This makes it possible to appropriately pick up medicines by taking into consideration the storage conditions of the medicines.

[0072] At the time of delivery, the delivery unit can select the optimal delivery method by taking into consideration the environmental information of the delivery destination. For example, if the delivery destination is in a hot and humid area, delivery will be made using an appropriate storage method. Also, if the delivery destination is in a cold area, delivery can be made by taking measures to keep the item warm. Furthermore, if the delivery destination is in a remote area, the optimal delivery route can be selected. This makes it possible to make appropriate deliveries by taking into consideration the environmental information of the delivery destination.

[0073] The process flow of the first embodiment will be briefly described below.

[0074] Step 1: The analysis unit analyzes the prescription image and extracts drug information. For example, image analysis algorithms and handwritten character recognition technology are used to extract information such as drug name, dosage, and administration method from the prescription image with high accuracy. Step 2: The search unit searches the database for information on contraindicated drug dosages, new drugs, and generic drugs based on the drug information extracted by the analysis unit. This provides appropriate dosages and the latest drug information. Step 3: The confirmation unit confirms the suitability of the drug based on the information obtained by the search unit. For example, the confirmation unit refers to a database of other medical history and medication information of the individual, and confirms the suitability of the drug based on the past prescription history and medical history information. Step 4: The pick-up unit picks up the medicines whose compatibility has been confirmed by the confirmation unit. For example, a robot may be used to accurately and quickly pick up the required number of medicines. Step 5: The delivery department delivers the medicines picked up by the pickup department, for example, by having them collected at a store or by delivering them to the patient's home.

[0075] (Example 2) The drug delivery system according to the embodiment of the present invention is a system for dealing with the high risk of errors occurring when pharmacists use their knowledge and manual work, since the format of prescriptions issued by hospitals is not standardized. This system uses AI to search for matching drugs from prescription images, and refers to information on contraindicated drug amounts, new drugs, generics, and a database of other medical history and medication information of the individual. This system can pick up the required number of drugs using a robot and deliver them to the store or the patient's home. For example, a prescription image is input into AI. The AI ​​analyzes the image and extracts the drug information written on the prescription. Next, based on the extracted drug information, information on contraindicated drug amounts, new drugs, and generics is searched from the database. Furthermore, the database of other medical history and medication information of the individual is referred to to confirm the suitability of the drug. After that, the robot picks up the required number of drugs. The picked-up drugs can be picked up at the store or delivered to the patient's home. This reduces manual errors by pharmacists and reduces the cost of training people with specialized skills. For example, if a prescription says "Drug A 10 mg three times a day," the AI ​​extracts this information from the prescription image and searches the database for information on contraindicated dosages of Drug A, as well as new drugs and generics. It also references the individual's other medical history and medication information database to check whether Drug A is suitable. If it is suitable, the robot picks up the required number of Drug A and delivers it to the store or to the patient's home. This system reduces manual errors by pharmacists and reduces the training costs for people with specialized skills. It also enables patients to receive medication quickly and accurately. As a result, the drug delivery system can reduce manual errors by pharmacists and reduce the training costs for people with specialized skills.

[0076] The medicine provision system according to the embodiment includes an analysis unit, a search unit, a confirmation unit, a pickup unit, and a delivery unit. The analysis unit analyzes a prescription image. The analysis unit extracts medicine information from the prescription image using, for example, an image analysis algorithm. For example, the analysis unit extracts information such as the medicine name, dosage, and administration method from the prescription image using an image analysis algorithm. The analysis unit can also extract medicine information from a handwritten prescription using handwritten character recognition technology. For example, the handwritten character recognition technology is used to extract the medicine name and dosage from the handwritten prescription with high accuracy. The search unit searches for information on prohibited drug amounts, new drugs, and generics based on the medicine information extracted by the analysis unit. The search unit searches for information on prohibited drug amounts, new drugs, and generics from a database, for example. For example, the search unit searches for prohibited drug amounts of the medicine from a database and confirms the appropriate dosage. The search unit can also search for information on new drugs and generics to provide the latest medicine information. The confirmation unit confirms the suitability of the medicine based on the information obtained by the search unit. The confirmation unit, for example, refers to the individual's other medical history and medication information database and confirms the suitability of the medicine. For example, the confirmation unit refers to the individual's other medical history and medication information database and confirms the suitability of the medicine based on past prescription history and medical history information. The pick-up unit picks up the medicine whose suitability has been confirmed by the confirmation unit. The pick-up unit, for example, uses a robot to pick up the required number of medicines. For example, the pick-up unit uses a robot to accurately and quickly pick up the medicines. The delivery unit delivers the medicines picked up by the pick-up unit. The delivery unit, for example, picks up the medicine at a store or delivers it to the patient's home. For example, the delivery unit can select whether the medicine is to be picked up at a store or delivered to the patient's home. This allows the medicine provision system according to the embodiment to reduce manual errors by pharmacists and reduce the training costs of people with specialized skills. For example, the analysis unit analyzes a prescription image and extracts medicine information. The search unit searches for information on contraindicated drug amounts, new drugs, and generics. The confirmation unit refers to the individual's other medical history and medication information database and confirms the suitability of the medicine. The pickup unit uses a robot to pick up the medicine, and the delivery unit delivers the medicine to the store or to the patient's home.This enables the medication delivery system to reduce manual errors made by pharmacists and cut the costs of training people with specialized skills.

[0077] The analysis unit analyzes the prescription image. The analysis unit extracts drug information from the prescription image, for example, using an image analysis algorithm. Specifically, the prescription image is scanned at high resolution and characters and symbols are recognized using image processing technology. The image analysis algorithm utilizes optical character recognition (OCR) technology to convert printed characters and handwritten characters into digital data. Furthermore, the handwritten character recognition technology is used to extract drug names and dosages from handwritten prescriptions with high accuracy. The handwritten character recognition technology trains a model using deep learning to accommodate various handwriting and fonts. For example, even if the drug name is unclear in a handwritten prescription, the exact drug name can be identified by performing context analysis and comparing it with other information. By combining these technologies, the analysis unit can quickly and accurately extract information such as drug names, dosages, and administration methods from prescription images.

[0078] The search unit searches for information on contraindicated drug amounts, new drugs, and generic drugs based on the drug information extracted by the analysis unit. The search unit searches for information on contraindicated drug amounts, new drugs, and generic drugs from a database, for example. Specifically, the search unit accesses a drug information database and acquires related information based on the extracted drug name and dosage. The search unit searches for contraindicated drug amounts and confirms appropriate dosages. For example, if a specific drug is contraindicated for a specific patient, the search unit promptly provides that information. The search unit can also search for information on new drugs and generic drugs and provide the latest drug information. The new drug information includes the latest clinical trial results and approval information, and the generic drug information provides options that take into account cost efficiency and supply status. The search unit integrates this information to support optimal drug selection. In addition, the search unit also provides information on drug interactions and side effects, playing an important role in ensuring patient safety.

[0079] The confirmation unit confirms the suitability of the drug based on the information obtained by the search unit. The confirmation unit, for example, refers to a database of other medical history and medication information of an individual to confirm the suitability of the drug. Specifically, the confirmation unit evaluates whether the current prescription is appropriate based on the patient's past prescription history and medical history information. For example, the confirmation unit prevents a patient who has had an allergic reaction to a specific drug in the past from being prescribed that drug again. In addition, when multiple drugs are prescribed at the same time, the confirmation unit checks whether those drugs do not interact with each other. For example, if the effectiveness of a specific drug decreases or side effects increase when used in combination with other drugs, the confirmation unit provides that information and suggests an appropriate alternative drug. Based on this information, the confirmation unit plays an important role in selecting the safest and most effective drug for the patient. Furthermore, the confirmation unit takes into account the patient's lifestyle and allergy information, etc., to provide personalized medicine.

[0080] The pickup unit picks up the medicines whose suitability has been confirmed by the confirmation unit. The pickup unit, for example, uses a robot to pick up the required number of medicines. Specifically, the robot uses a robot arm to accurately pick up the specified medicines from the shelves or automated warehouses where the medicines are stored. The robot identifies the medicines using barcodes or RFID tags to improve the accuracy of the pickup. For example, the robot quickly and accurately picks up the medicines based on the position information of the specified medicines and places them on a pickup tray. This prevents the pickup unit from picking up the wrong medicines or making mistakes in the quantity, and realizes efficient medicine supply. Furthermore, the pickup unit also manages the inventory of medicines and has a function of automatically replenishing the inventory when there is a shortage. This ensures that the necessary medicines are always supplied appropriately.

[0081] The delivery unit delivers the medicine picked up by the pickup unit. The delivery unit, for example, delivers the medicine to a store or to the patient's home. Specifically, after the medicine is picked up, the delivery unit calculates the optimal delivery route based on the information of the delivery destination and delivers the medicine quickly. The delivery unit can also deliver the medicine using, for example, a drone or an autonomous vehicle. This avoids traffic congestion and delivery delays and realizes prompt delivery of medicine. Furthermore, the delivery unit has a function of tracking the delivery status in real time and notifying the recipient. For example, the delivery unit notifies the recipient of the progress until delivery is completed via a smartphone app or email, allowing the recipient to receive the medicine with peace of mind. The delivery unit thereby efficiently and reliably performs the final step of medicine delivery, improving patient convenience.

[0082] The analysis unit can extract drug information from the prescription image. The analysis unit extracts drug information from the prescription image using, for example, an image analysis algorithm. For example, the analysis unit extracts information such as drug name, dosage, and administration method from the prescription image using an image analysis algorithm. The analysis unit can also extract drug information from a handwritten prescription using handwritten character recognition technology. For example, the handwritten character recognition technology is used to extract drug name and dosage from a handwritten prescription with high accuracy. As a result, drug compatibility can be quickly confirmed by extracting drug information from a prescription image.

[0083] The search unit can search the database for information on contraindicated drug amounts, new drugs, and generic drugs. The search unit, for example, searches the database for information on contraindicated drug amounts, new drugs, and generic drugs. For example, the search unit searches the database for contraindicated drug amounts of a drug and confirms the appropriate dosage. The search unit can also search for information on new drugs and generic drugs and provide the latest drug information. This makes it possible to select an appropriate drug by searching for information on contraindicated drug amounts, new drugs, and generic drugs.

[0084] The confirmation unit can confirm the suitability of the drug by referring to the individual's other medical history and medication information database. The confirmation unit, for example, refers to the individual's other medical history and medication information database to confirm the suitability of the drug. For example, the confirmation unit refers to the individual's other medical history and medication information database to confirm the suitability of the drug based on past prescription history and medical history information. As a result, by referring to the individual's other medical history and medication information database, the suitability of the drug can be confirmed with high accuracy.

[0085] The pickup unit can pick up the required number of medicines using a robot. The pickup unit, for example, picks up the required number of medicines using a robot. For example, the pickup unit picks up the medicines accurately and quickly using a robot. As a result, by using the robot, the medicines can be picked up accurately and quickly.

[0086] The delivery unit can perform store pick-up or delivery to the patient's home. The delivery unit, for example, performs store pick-up or delivery to the patient's home. For example, the delivery unit can select whether to pick up the medicine at the store or to have it delivered to the patient's home. This allows for quick and accurate provision of medicine to the patient by having the medicine picked up at the store or delivered to the patient's home.

[0087] The analysis unit can estimate the user's emotion and adjust the analysis accuracy of the prescription image based on the estimated user's emotion. For example, when the user is stressed, the analysis unit increases the analysis accuracy and quickly analyzes the prescription image. Also, when the user is relaxed, the analysis unit can analyze the prescription image with normal analysis accuracy. Furthermore, when the user is in a hurry, the analysis speed can be prioritized and the prescription image can be analyzed with the minimum necessary accuracy. This allows for more appropriate analysis by adjusting the analysis accuracy according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0088] When analyzing the prescription image, the analysis unit can apply different analysis algorithms depending on the resolution and quality of the image. For example, the analysis unit applies a detailed analysis algorithm to a high-resolution image. Also, the analysis unit can apply a noise removal algorithm to a low-resolution image before performing analysis. Furthermore, when the image quality is uneven, different analysis algorithms can be applied to each part. This improves the analysis accuracy by applying an analysis algorithm depending on the resolution and quality of the image.

[0089] The analysis unit can perform preprocessing to improve the accuracy of handwritten character recognition when analyzing the prescription image. For example, the analysis unit can perform preprocessing to emphasize the contours of handwritten characters. The analysis unit can also perform preprocessing to correct the inclination of handwritten characters. Furthermore, the analysis unit can perform preprocessing to remove background noise from handwritten characters. Thus, by performing preprocessing to improve the accuracy of handwritten character recognition, the analysis accuracy is improved.

[0090] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. For example, when the user is nervous, the analysis unit provides a simple and highly visible display method. When the user is relaxed, the analysis unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This makes it possible to provide more appropriate information by adjusting the display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0091] When analyzing a prescription image, the analysis unit can improve the analysis accuracy by taking into account the shooting environment of the image. For example, when the light intensity is non-uniform, the analysis unit performs pre-processing to make the brightness of the image uniform. Also, when the image is shot at an oblique angle, the analysis unit can perform angle correction before performing analysis. Furthermore, when there is light reflection, pre-processing can be performed to remove the reflected portion. In this way, the analysis accuracy is improved by taking into account the shooting environment of the image.

[0092] When analyzing a prescription image, the analysis unit can improve the accuracy of the analysis by integrating multiple images. For example, the analysis unit integrates images taken from multiple angles and performs the analysis. The analysis unit can also integrate images with multiple resolutions and perform the analysis. Furthermore, images taken in multiple time periods can be integrated and analyzed. In this way, the analysis accuracy is improved by integrating multiple images.

[0093] The search unit can estimate the user's emotion and adjust the display method of the search results based on the estimated user's emotion. For example, when the user is nervous, the search unit provides a simple and highly visible display method. When the user is relaxed, the search unit can also provide a display method including detailed information. When the user is in a hurry, the search unit can also provide a display method that focuses on the main points. This makes it possible to provide more appropriate information by adjusting the display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0094] During a search, the search unit can adjust the priority of search results based on the effects and side effects of the drugs. For example, the search unit can preferentially display highly effective drugs. It can also preferentially display drugs with fewer side effects. It can also preferentially display drugs with a good balance between effects and side effects. This makes it possible to select an appropriate drug by adjusting the priority based on the effects and side effects of the drug.

[0095] During a search, the search unit can propose the most suitable drug by taking into consideration drug price information. The search unit, for example, can preferentially display low-priced drugs. It can also preferentially display drugs with a good balance between price and effectiveness. It can also preferentially display drugs that are expensive but highly effective. This makes it possible to select a drug with high cost performance by taking into consideration drug price information.

[0096] The search unit can estimate the user's emotion and filter search results based on the estimated user's emotion. For example, when the user is nervous, the search unit provides simple and highly visible search results. When the user is relaxed, the search unit can also provide search results including detailed information. Furthermore, when the user is in a hurry, the search unit can also provide search results that focus on the main points. This makes it possible to provide more appropriate information by filtering according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0097] During a search, the search unit can adjust the search results by taking into account the drug supply situation. For example, the search unit can preferentially display drugs with stable supply. It can also not display drugs with unstable supply. Furthermore, it can also suggest alternative drugs depending on the supply situation. This makes it possible to select a drug that can be stably supplied by taking into account the drug supply situation.

[0098] During a search, the search unit can adjust search results by taking into account drug manufacturer information. For example, the search unit can preferentially display drugs from highly reliable manufacturers. It can also not display drugs from manufacturers with poor reputations. Furthermore, it can also suggest alternative drugs based on the manufacturer information. This makes it possible to select highly reliable drugs by taking into account drug manufacturer information.

[0099] The confirmation unit can estimate the user's emotion and adjust the compatibility confirmation criteria based on the estimated user's emotion. For example, when the user is nervous, the confirmation unit confirms compatibility with strict criteria. Also, when the user is relaxed, the confirmation unit can confirm compatibility with normal criteria. Furthermore, when the user is in a hurry, the confirmation unit can quickly confirm compatibility. This allows for more appropriate confirmation by adjusting the compatibility confirmation criteria according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0100] The confirmation unit can improve the accuracy of compatibility by referring to past medication history when confirming compatibility. The confirmation unit confirms compatibility based on, for example, past medication history. In addition, the confirmation unit can identify contraindicated drugs from the past medication history and confirm compatibility. Furthermore, the confirmation unit can analyze the past medication history and suggest the most suitable drug. In this way, the accuracy of compatibility confirmation is improved by referring to the past medication history.

[0101] The confirmation unit can take into consideration the patient's allergy information when confirming compatibility. The confirmation unit confirms compatibility, for example, based on the patient's allergy information. It can also identify contraindicated drugs from the allergy information and confirm compatibility. It can also analyze the allergy information and suggest the most suitable drug. This improves the accuracy of compatibility confirmation by taking the patient's allergy information into consideration.

[0102] The confirmation unit can estimate the user's emotion and adjust the order of displaying the compatibility confirmation results based on the estimated user's emotion. For example, when the user is nervous, the confirmation unit can display important information first. Also, when the user is relaxed, detailed information can be displayed in order. Furthermore, when the user is in a hurry, information with the main points can be displayed first. This allows more appropriate information to be provided by adjusting the display order according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI.

[0103] The confirmation unit can take into consideration the lifestyle habit information of the patient when confirming compatibility. The confirmation unit confirms compatibility, for example, based on the lifestyle habit information of the patient. The confirmation unit can also identify contraindicated drugs from the lifestyle habit information and confirm compatibility. Furthermore, the lifestyle habit information can be analyzed to suggest optimal medications. As a result, the accuracy of compatibility confirmation is improved by taking into consideration the lifestyle habit information of the patient.

[0104] The confirmation unit can take into consideration the genetic information of the patient when confirming compatibility. The confirmation unit confirms compatibility based on, for example, the genetic information of the patient. The confirmation unit can also identify contraindicated drugs from the genetic information and confirm compatibility. Furthermore, the genetic information can be analyzed to suggest the most suitable drug. In this way, by taking the genetic information of the patient into consideration, the accuracy of compatibility confirmation is improved.

[0105] The pickup unit can estimate the user's emotion and adjust the pickup order based on the estimated user's emotion. For example, when the user is nervous, the pickup unit picks up important medications first. Also, when the user is relaxed, the pickup unit can pick up medications in the normal order. Furthermore, when the user is in a hurry, the pickup unit can quickly pick up medications. This makes it possible to provide more appropriate medications by adjusting the pickup order according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0106] The pickup unit can select the optimal pickup method in consideration of the storage conditions of the medicines when picking them up. For example, the pickup unit preferentially picks up medicines for which the storage temperature is important. The pickup unit can also efficiently pick up medicines stored in different locations. Furthermore, the pickup unit can also preferentially pick up medicines whose storage expiration date is approaching. This allows appropriate picking up by considering the storage conditions of the medicines.

[0107] The pickup unit can estimate the user's emotions and determine the priority order of pickup based on the estimated user's emotions. For example, when the user is nervous, the pickup unit prioritizes picking up important medications. Also, when the user is relaxed, the pickup unit can pick up medications in the normal order. Furthermore, when the user is in a hurry, the pickup unit can quickly pick up medications. This makes it possible to provide more appropriate medications by determining the priority order of pickup according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0108] The pickup unit can select the optimal pickup method in consideration of the size and shape of the medicine when picking up the medicine. For example, the pickup unit efficiently picks up large-sized medicines. It can also carefully pick up small-sized medicines. Furthermore, it can properly pick up medicines with special shapes. This allows for proper pickup by considering the size and shape of the medicine.

[0109] The pickup unit can take the medicine packaging information into consideration when picking up the medicine. For example, the pickup unit carefully picks up medicines whose packaging is easily damaged. It can also efficiently pick up medicines whose packaging is large. Furthermore, it can appropriately pick up medicines whose packaging is special. This allows appropriate picking up by taking the medicine packaging information into consideration.

[0110] The delivery unit can estimate the user's emotion and adjust the timing of delivery based on the estimated user's emotion. For example, when the user is nervous, the delivery unit performs delivery quickly. Also, when the user is relaxed, delivery can be performed at a normal delivery timing. Furthermore, when the user is in a hurry, delivery can be performed in the shortest time. This allows more appropriate delivery by adjusting the delivery timing according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0111] At the time of delivery, the delivery unit can select the optimal delivery method by taking into consideration the environmental information of the delivery destination. For example, if the delivery destination is in a hot and humid area, the delivery unit will deliver using an appropriate storage method. Also, if the delivery destination is in a cold area, the delivery unit can deliver the product by taking measures to keep the product warm. Furthermore, if the delivery destination is in a remote area, the delivery unit can select the optimal delivery route. This allows for appropriate delivery by taking into consideration the environmental information of the delivery destination.

[0112] The delivery unit can deliver medicines taking into consideration the storage temperature of the medicine. For example, the delivery unit delivers medicines with low storage temperatures at an appropriate temperature. It can also deliver medicines with high storage temperatures at an appropriate temperature. It can also deliver medicines whose storage temperatures are prone to change at an appropriate temperature. This makes it possible to deliver medicines at an appropriate temperature by taking into consideration the storage temperature of the medicine.

[0113] The delivery unit can estimate the user's emotions and determine delivery priorities based on the estimated user's emotions. For example, when the user is nervous, the delivery unit prioritizes delivery of important medicines. Also, when the user is relaxed, delivery can be performed in the normal order. Furthermore, when the user is in a hurry, delivery can be performed quickly. This enables more appropriate delivery by determining the delivery priorities according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0114] At the time of delivery, the delivery unit can select the optimal delivery route by taking into consideration the geographic information of the delivery destination. For example, when the delivery destination is in an urban area, the delivery unit selects the shortest route. Also, when the delivery destination is in a suburban area, the delivery unit can select an efficient route. Furthermore, when the delivery destination is in a mountainous area, the delivery unit can select a safe route. In this way, efficient delivery is possible by taking into consideration the geographic information of the delivery destination.

[0115] The delivery unit can take into consideration the recipient information of the delivery destination when making delivery. For example, if the recipient is elderly, the delivery unit can deliver at a time that is convenient for the recipient to receive the parcel. Also, if the recipient is busy, the delivery unit can deliver at a time that is convenient for the recipient to receive the parcel. Furthermore, if the recipient is not at home, the delivery unit can arrange for redelivery. This allows for appropriate delivery by taking into consideration the recipient information of the delivery destination.

[0116] The delivery unit can estimate the user's emotions and determine delivery priorities based on the estimated user's emotions. For example, when the user is nervous, the delivery unit prioritizes delivery of important medicines. Also, when the user is relaxed, delivery can be performed in the normal order. Furthermore, when the user is in a hurry, delivery can be performed quickly. This enables more appropriate delivery by determining the delivery priorities according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0117] At the time of delivery, the delivery unit can select the optimal delivery route by taking into consideration the geographic information of the delivery destination. For example, when the delivery destination is in an urban area, the delivery unit selects the shortest route. Also, when the delivery destination is in a suburban area, the delivery unit can select an efficient route. Furthermore, when the delivery destination is in a mountainous area, the delivery unit can select a safe route. In this way, efficient delivery is possible by taking into consideration the geographic information of the delivery destination.

[0118] The delivery unit can take into consideration the recipient information of the delivery destination when making delivery. For example, if the recipient is elderly, the delivery unit can deliver at a time that is convenient for the recipient to receive the parcel. Also, if the recipient is busy, the delivery unit can deliver at a time that is convenient for the recipient to receive the parcel. Furthermore, if the recipient is not at home, the delivery unit can arrange for redelivery. This allows for appropriate delivery by taking into consideration the recipient information of the delivery destination.

[0119] The format unification unit can estimate the user's emotion and adjust the format display method based on the estimated user's emotion. For example, when the user is nervous, the format unification unit provides a simple and highly visible display method. When the user is relaxed, the format unification unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the format unification unit can also provide a display method that focuses on the main points. This makes it possible to provide more appropriate information by adjusting the display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0120] When standardizing formats, the format unification unit can select the optimal format by taking into consideration the characteristics of each hospital. The format unification unit unifies formats based on the format of a large hospital, for example. It can also unify formats based on the format of a small hospital. Furthermore, it can select the optimal format by taking into consideration the characteristics of each hospital. This makes it possible to select the optimal format by taking into consideration the characteristics of each hospital.

[0121] The format unification unit can estimate the user's emotions and determine the priority of the format based on the estimated user's emotions. For example, when the user is nervous, the format unification unit can display important information first. Also, when the user is relaxed, the format unification unit can display detailed information in order. Furthermore, when the user is in a hurry, the format unification unit can display information that covers the main points first. This makes it possible to provide more appropriate information by determining the priority according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0122] When standardizing formats, the format unification unit can select the optimal format by taking into consideration the system information of each hospital. The format unification unit unifies the formats based on the system information of a large hospital, for example. It can also unify based on the system information of a small hospital. Furthermore, it can also select the optimal format by taking into consideration the system information of each hospital. This makes it possible to select the optimal format by taking into consideration the system information of each hospital.

[0123] The security unit can estimate the user's emotion and adjust the security level based on the estimated user's emotion. For example, the security unit increases the security level when the user is nervous. Also, when the user is relaxed, the security unit can maintain the normal security level. Furthermore, when the user is in a hurry, the security check can be performed quickly. This allows more appropriate security measures to be taken by adjusting the security level according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0124] When strengthening security, the security department can select the most appropriate countermeasure by referring to past security incidents. For example, the security department analyzes past security incidents and selects the most appropriate countermeasure. It is also possible to select measures to prevent recurrence from past security incidents. Furthermore, it is also possible to strengthen security countermeasures based on past security incidents. In this way, it is possible to take the most appropriate security countermeasure by referring to past security incidents.

[0125] The security unit can estimate the user's emotions and determine the priority of security measures based on the estimated user's emotions. For example, when the user is nervous, the security unit prioritizes important security measures. When the user is relaxed, the security unit can also implement normal security measures. Furthermore, when the user is in a hurry, the security unit can also implement security measures quickly. This enables more appropriate security measures by determining the priority of security measures according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0126] When strengthening security, the security unit can select the optimal countermeasure by taking into consideration the characteristics of each database. For example, the security unit selects security countermeasures based on the characteristics of a large-scale database. It can also select security countermeasures based on the characteristics of a small-scale database. It can also select the optimal security countermeasure by taking into consideration the characteristics of each database. This makes it possible to take optimal security countermeasures by taking into consideration the characteristics of each database.

[0127] The control unit can estimate the user's emotion and adjust the robot's behavior based on the estimated user's emotion. For example, when the user is nervous, the control unit can make the robot move quickly. Also, when the user is relaxed, the control unit can move the robot at a normal movement speed. Furthermore, when the user is in a hurry, the control unit can make the robot move at the fastest speed. This allows the robot to move more appropriately by adjusting the robot's behavior according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0128] When controlling the motion of the robot, the control unit can select an optimal motion pattern by referring to past motion history. The control unit selects an optimal motion pattern based on, for example, past motion history. Also, the control unit can select an efficient motion pattern from the past motion history. Furthermore, the control unit can analyze the past motion history and select an optimal motion pattern. In this way, it becomes possible to select an optimal motion pattern by referring to the past motion history.

[0129] The control unit can estimate the user's emotions and determine the robot's operation priority order based on the estimated user's emotions. For example, when the user is nervous, the control unit can prioritize important operations. Also, when the user is relaxed, the control unit can perform operations in the normal order. Furthermore, when the user is in a hurry, the control unit can perform operations quickly. This allows the robot to perform more appropriate operations by determining the robot's operation priority order according to the user's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0130] The control unit can select the optimal motion pattern taking into consideration the characteristics of each drug when controlling the motion of the robot. For example, the control unit selects a motion pattern that efficiently picks up drugs with different characteristics. It can also select a motion pattern that collectively picks up drugs with similar characteristics. It can also select a motion pattern that carefully picks up drugs with special characteristics. This makes it possible to select the optimal motion pattern by taking into consideration the characteristics of each drug.

[0131] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0132] When analyzing a prescription image, the analysis unit can improve the analysis accuracy by taking into account the shooting environment of the image. For example, if the light intensity is non-uniform, pre-processing is performed to make the brightness of the image uniform. Also, if the image is shot at an oblique angle, the angle can be corrected before analysis. Furthermore, if there is light reflection, pre-processing can be performed to remove the reflected portion. In this way, the analysis accuracy is improved by taking into account the shooting environment of the image.

[0133] During a search, the search unit can adjust the priority of search results based on the effects and side effects of drugs. For example, highly effective drugs can be preferentially displayed. Drugs with fewer side effects can also be preferentially displayed. Furthermore, drugs with a good balance between effects and side effects can also be preferentially displayed. This makes it possible to select an appropriate drug by adjusting the priority based on the effects and side effects of the drug.

[0134] When checking compatibility, the confirmation unit can take into consideration the patient's allergy information. For example, compatibility is confirmed based on the patient's allergy information. Contraindicated drugs can also be identified from the allergy information and compatibility can be confirmed. Furthermore, the allergy information can be analyzed and the most suitable drug can be suggested. In this way, the accuracy of compatibility confirmation is improved by taking the patient's allergy information into consideration.

[0135] The pickup unit can select the optimal pickup method by taking into consideration the storage conditions of the medicine when picking up the medicine. For example, it can prioritize picking up medicines for which the storage temperature is important. It can also efficiently pick up medicines stored in different locations. It can also prioritize picking up medicines whose storage expiration date is approaching. This makes it possible to appropriately pick up medicines by taking into consideration the storage conditions of the medicines.

[0136] At the time of delivery, the delivery unit can select the optimal delivery method by taking into consideration the environmental information of the delivery destination. For example, if the delivery destination is in a hot and humid area, delivery will be made using an appropriate storage method. Also, if the delivery destination is in a cold area, delivery can be made by taking measures to keep the item warm. Furthermore, if the delivery destination is in a remote area, the optimal delivery route can be selected. This makes it possible to make appropriate deliveries by taking into consideration the environmental information of the delivery destination.

[0137] The analysis unit can estimate the user's emotions and adjust the analysis accuracy of the prescription image based on the estimated user's emotions. For example, when the user is stressed, the analysis accuracy is increased to quickly analyze the prescription image. When the user is relaxed, the prescription image can be analyzed with normal analysis accuracy. Furthermore, when the user is in a hurry, the analysis speed can be prioritized and the prescription image can be analyzed with the minimum necessary accuracy. This allows for more appropriate analysis by adjusting the analysis accuracy according to the user's emotions.

[0138] The search unit can estimate the user's emotions and adjust the display method of the search results based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to provide more appropriate information by adjusting the display method according to the user's emotions.

[0139] The confirmation unit can estimate the user's emotions and adjust the compatibility confirmation criteria based on the estimated user's emotions. For example, if the user is nervous, compatibility can be confirmed with strict criteria. Also, if the user is relaxed, compatibility can be confirmed with normal criteria. Furthermore, if the user is in a hurry, compatibility can be confirmed quickly. In this way, by adjusting the compatibility confirmation criteria according to the user's emotions, more appropriate confirmation is possible.

[0140] The pickup unit can estimate the user's emotions and adjust the pickup order based on the estimated user's emotions. For example, if the user is nervous, important medications are picked up first. If the user is relaxed, medications can be picked up in the normal order. Furthermore, if the user is in a hurry, medications can be picked up quickly. This makes it possible to provide more appropriate medications by adjusting the pickup order according to the user's emotions.

[0141] The delivery unit can estimate the user's emotions and adjust the timing of delivery based on the estimated user's emotions. For example, if the user is nervous, delivery can be made quickly. If the user is relaxed, delivery can be made at the normal timing. Furthermore, if the user is in a hurry, delivery can be made in the shortest time possible. This allows more appropriate delivery by adjusting the timing of delivery according to the user's emotions.

[0142] The process flow of the second embodiment will be briefly described below.

[0143] Step 1: The analysis unit analyzes the prescription image and extracts drug information. For example, image analysis algorithms and handwritten character recognition technology are used to extract information such as drug name, dosage, and administration method from the prescription image with high accuracy. Step 2: The search unit searches the database for information on contraindicated drug dosages, new drugs, and generic drugs based on the drug information extracted by the analysis unit. This provides appropriate dosages and the latest drug information. Step 3: The confirmation unit confirms the suitability of the drug based on the information obtained by the search unit. For example, the confirmation unit refers to a database of other medical history and medication information of the individual, and confirms the suitability of the drug based on the past prescription history and medical history information. Step 4: The pick-up unit picks up the medicines whose compatibility has been confirmed by the confirmation unit. For example, a robot may be used to accurately and quickly pick up the required number of medicines. Step 5: The delivery department delivers the medicines picked up by the pickup department, for example, by having them collected at a store or by delivering them to the patient's home.

[0144] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0145] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0146] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements including the above-mentioned analysis unit, search unit, confirmation unit, pickup unit, and delivery unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit analyzes a prescription image by the control unit 46A of the smart device 14 and extracts drug information. The search unit searches for information on contraindicated drug amounts, new drugs, and generics by the specific processing unit 290 of the data processing device 12. The confirmation unit refers to the individual's other medical history and medication information database by the specific processing unit 290 of the data processing device 12 and confirms the suitability of the drug. The pickup unit controls a robot by the control unit 46A of the smart device 14 and picks up the required number of drugs. The delivery unit delivers the drugs to the store or to the person's home by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0148] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0149] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0150] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0151] 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.

[0152] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0154] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0155] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0156] 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.

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0158] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0159] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0160] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0162] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements including the above-mentioned analysis unit, search unit, confirmation unit, pickup unit, and delivery unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit analyzes a prescription image by the control unit 46A of the smart glasses 214 and extracts drug information. The search unit searches for information on contraindicated drug amounts, new drugs, and generics by the specific processing unit 290 of the data processing device 12. The confirmation unit refers to an individual's other medical history and medication information database by the specific processing unit 290 of the data processing device 12 and confirms the suitability of the drug. The pickup unit controls a robot by the control unit 46A of the smart glasses 214 and picks up the required number of drugs. The delivery unit delivers the drugs to the store or the person's home by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0164] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0165] 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.

[0166] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0167] 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.

[0168] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0169] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0170] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0171] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0172] 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.

[0173] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0174] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0175] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0176] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0177] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0178] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0179] Each of the multiple elements including the above-mentioned analysis unit, search unit, confirmation unit, pick-up unit, and delivery unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit analyzes a prescription image by the control unit 46A of the headset type terminal 314 and extracts drug information. The search unit searches for information on contraindicated drug amounts, new drugs, and generics by the specific processing unit 290 of the data processing device 12. The confirmation unit refers to the individual's other medical history and medication information database by the specific processing unit 290 of the data processing device 12 and confirms the suitability of the drug. The pickup unit controls a robot by the control unit 46A of the headset type terminal 314 and picks up the required number of drugs. The delivery unit delivers the drugs to the store or the person's home by the control unit 46A of the headset type terminal 314. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0180] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0181] 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.

[0182] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0183] 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.

[0184] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0185] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0186] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0187] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0188] 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.

[0189] 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.

[0190] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0191] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0192] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0193] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0194] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0195] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0196] Each of the multiple elements including the above-mentioned analysis unit, search unit, confirmation unit, pick-up unit, and delivery unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit analyzes a prescription image by the control unit 46A of the robot 414 and extracts drug information. The search unit searches for information on contraindicated drug amounts, new drugs, and generics by the specific processing unit 290 of the data processing device 12. The confirmation unit refers to the individual's other medical history and medication information database by the specific processing unit 290 of the data processing device 12 and confirms the suitability of the drug. The pickup unit controls the robot by the control unit 46A of the robot 414 and picks up the required number of drugs. The delivery unit delivers the drugs to the store or the person's home by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0197] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0198] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0199] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0200] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0201] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0202] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0203] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0204] 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.

[0205] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0206] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0207] 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.

[0208] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0209] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0210] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0211] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0212] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0213] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0214] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0215] (Appendix 1) An analysis unit that analyzes the prescription image; a search unit that searches for information on contraindicated drug amounts, new drugs, or generic drugs based on the drug information extracted by the analysis unit; a confirmation unit that confirms the suitability of a drug based on the information obtained by the search unit; a pickup unit that picks up the medicine whose compatibility has been confirmed by the confirmation unit; A delivery unit that delivers the medicine picked up by the pickup unit is provided. A system characterized by: (Appendix 2) Equipping a format unification department to standardize prescription formats 2. The system of claim 1, (Appendix 3) Equipping a security department to strengthen database security 2. The system of claim 1, (Appendix 4) Equipped with a control unit that controls the robot's movements 2. The system of claim 1, (Appendix 5) The analysis unit is Extracting drug information from prescription images 2. The system of claim 1, (Appendix 6) The search unit is Search the database for contraindicated dosages or new and generic drug information 2. The system of claim 1, (Appendix 7) The confirmation unit is Check the suitability of medications by checking the individual's database of other medical history and medication information 2. The system of claim 1, (Appendix 8) The pickup unit includes: Use a robot to pick up the required number of medications 2. The system of claim 1, (Appendix 9) The delivery unit includes: Pick up in store or deliver to your home 2. The system of claim 1, (Appendix 10) The analysis unit is Estimating a user's emotions and adjusting the analysis accuracy of the prescription image based on the estimated user's emotions 2. The system of claim 1, (Appendix 11) The analysis unit is When analyzing prescription images, different analysis algorithms are applied depending on the image resolution and quality 2. The system of claim 1, (Appendix 12) The analysis unit is When analyzing prescription images, preprocessing is performed to improve the accuracy of handwritten character recognition. 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is Improve accuracy of prescription image analysis by considering the shooting environment of the image 2. The system of claim 1, (Appendix 15) The analysis unit is When analyzing prescription images, multiple images can be integrated to improve analysis accuracy. 2. The system of claim 1, (Appendix 16) The search unit is Infer user sentiment and adjust how search results are displayed based on the inferred user sentiment 2. The system of claim 1, (Appendix 17) The search unit is During a search, results are prioritized based on drug effects and side effects 2. The system of claim 1, (Appendix 18) The search unit is When searching, the system will suggest the most suitable drug based on drug price information. 2. The system of claim 1, (Appendix 19) The search unit is Infer user sentiment and filter search results based on the estimated user sentiment 2. The system of claim 1, (Appendix 20) The search unit is When searching, search results are adjusted to account for drug availability 2. The system of claim 1, (Appendix 21) The search unit is When searching, drug manufacturer information is taken into account to tailor search results 2. The system of claim 1, (Appendix 22) The confirmation unit is Inferring a user's emotion and adjusting a compatibility check criterion based on the estimated user's emotion 2. The system of claim 1, (Appendix 23) The confirmation unit is When checking compatibility, refer to past medication history to improve compatibility accuracy. 2. The system of claim 1, (Appendix 24) The confirmation unit is When checking compatibility, the patient's allergy information is taken into consideration. 2. The system of claim 1, (Appendix 25) The confirmation unit is Inferring user emotion and adjusting the order in which compatibility check results are displayed based on the estimated user emotion 2. The system of claim 1, (Appendix 26) The confirmation unit is When checking compatibility, the patient's lifestyle information is taken into consideration. 2. The system of claim 1, (Appendix 27) The confirmation unit is When checking compatibility, the patient's genetic information is taken into consideration. 2. The system of claim 1, (Appendix 28) The pickup unit includes: Estimate user emotions and adjust pickup order based on the estimated user emotions 2. The system of claim 1, (Appendix 29) The pickup unit includes: When picking up, the optimal pick-up method is selected taking into account the storage conditions of the medicine. 2. The system of claim 1, (Appendix 30) The pickup unit includes: Estimate user emotions and prioritize pickups based on the estimated user emotions 2. The system of claim 1, (Appendix 31) The pickup unit includes: When picking up medicine, the optimal pick-up method is selected taking into consideration the size and shape of the medicine. 2. The system of claim 1, (Appendix 32) The pickup unit includes: When picking up medicines, the medicine packaging information is taken into consideration. 2. The system of claim 1, (Appendix 33) The delivery unit includes: Estimate user emotions and adjust delivery timing based on the estimated user emotions 2. The system of claim 1, (Appendix 34) The delivery unit includes: When shipping, the optimal shipping method is selected taking into account the environmental information of the delivery destination. 2. The system of claim 1, (Appendix 35) The delivery unit includes: When shipping, take into consideration the storage temperature of the medicine. 2. The system of claim 1, (Appendix 36) The delivery unit includes: Estimate user emotions and prioritize delivery based on the estimated user emotions 2. The system of claim 1, (Appendix 37) The delivery unit includes: When delivering, the optimal delivery route is selected taking into account the geographical information of the delivery destination. 2. The system of claim 1, (Appendix 38) The delivery unit includes: When delivering, the delivery is made taking into account the recipient's information. 2. The system of claim 1, (Appendix 39) The format unification unit includes: Inferring user sentiment and adjusting formatting display based on the inferred user sentiment 3. The system of claim 2, (Appendix 40) The format unification unit includes: When standardizing formats, select the most appropriate format taking into account the characteristics of each hospital. 3. The system of claim 2, (Appendix 41) The format unification unit includes: Inferring user sentiment and prioritizing formats based on the estimated user sentiment 3. The system of claim 2, (Appendix 42) The format unification unit includes: When standardizing formats, select the optimal format taking into consideration each hospital's system information. 3. The system of claim 2, (Appendix 43) The security unit includes: Estimate user emotions and adjust security levels based on the estimated user emotions 4. The system of claim 3. (Appendix 44) The security unit includes: When strengthening security, select the most appropriate measures by referring to past security incidents 4. The system of claim 3. (Appendix 45) The security unit includes: Inferring user emotions and prioritizing security measures based on the inferred user emotions 4. The system of claim 3. (Appendix 46) The security unit includes: When strengthening security, select the most appropriate measures taking into account the characteristics of each database. 4. The system of claim 3. (Appendix 47) The control unit is Estimate the user's emotions and adjust the robot's behavior based on the estimated user's emotions 5. The system of claim 4. (Appendix 48) The control unit is When controlling the robot's movements, the optimal movement pattern is selected by referring to past movement history. 5. The system of claim 4. (Appendix 49) The control unit is Estimate the user's emotions and determine the robot's operational priorities based on the estimated user's emotions. 5. The system of claim 4. (Appendix 50) The control unit is When controlling the robot's movements, the optimal movement pattern is selected taking into account the characteristics of each drug. 5. The system of claim 4. [Explanation of symbols]

[0216] 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. An analysis unit that analyzes the prescription image; a search unit that searches for information on contraindicated drug amounts, new drugs, or generic drugs based on the drug information extracted by the analysis unit; a confirmation unit that confirms the suitability of a drug based on the information obtained by the search unit; a pickup unit that picks up the medicine whose compatibility has been confirmed by the confirmation unit; A delivery unit that delivers the medicine picked up by the pickup unit is provided. A system characterized by:

2. Equipping a format unification department to standardize prescription formats 2. The system of claim 1 .

3. Equipping a security department to strengthen database security 2. The system of claim 1 .

4. Equipped with a control unit that controls the robot's movements 2. The system of claim 1 .

5. The analysis unit is Extracting drug information from prescription images 2. The system of claim 1 .

6. The search unit is Search the database for contraindicated dosages or new and generic drug information 2. The system of claim 1 .

7. The confirmation unit is Check the suitability of medications by checking the individual's database of other medical history and medication information 2. The system of claim 1 .

8. The pickup unit includes: Use a robot to pick up the required number of medications 2. The system of claim 1 .

9. The delivery unit includes: Pick up at store or deliver to home 2. The system of claim 1 .

10. The analysis unit is Estimating user emotions and adjusting the analysis accuracy of prescription images based on the estimated user emotions 2. The system of claim 1 .

Images