system

Abstract

The system according to this embodiment aims to avoid human error regarding drug dosage. [Solution] The system according to the embodiment comprises a surveillance camera, an analysis unit, and a warning unit. The surveillance camera captures the drug administration scene in real time. The analysis unit analyzes the video captured by the surveillance camera and recognizes the type and amount of drug. The warning unit issues a warning based on the information recognized by the analysis unit if the administered amount exceeds a standard value.

Description

Technical Field

[0004] ,

[0006] , , ,

[0005] , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a risk of human error regarding the dosage of drugs, and there is room for improvement.

[0005] The system according to the embodiment aims to avoid human error regarding the dosage of drugs. [[ID=四十]]

Means for Solving the Problems

[0006] The system according to the embodiment includes a monitoring camera, an analysis unit, and a warning unit. The monitoring camera captures the drug administration scene in real time. The analysis unit analyzes the video captured by the monitoring camera and recognizes the type and amount of the drug. The warning unit issues a warning when the administered dose exceeds the reference value based on the information recognized by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can avoid human error regarding drug dosage. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The drug dosage monitoring system according to an embodiment of the present invention is a system that uses AI to monitor drug dosages and prevent human errors in medical settings. This drug dosage monitoring system monitors the type and amount of drug through a surveillance camera, and the AI ​​analyzes this information. If the dosage is incorrect, the AI ​​immediately issues a warning and notifies healthcare professionals. This prevents drug misadministration and ensures the safety of healthcare professionals and patients. For example, a surveillance camera captures the drug administration scene in real time. This video is sent to the AI, which analyzes the type and amount of drug. For example, the AI ​​recognizes the drug label, color, and shape and calculates the dosage. Next, the AI ​​compares the analysis result with a pre-set correct dosage. If the dosage is incorrect, the AI ​​immediately issues a warning and notifies healthcare professionals. For example, it may display a warning message on a monitor or sound an alarm. This system allows healthcare professionals to prevent drug administration errors and ensures patient safety. It also reduces the burden on healthcare professionals and improves work efficiency. For example, even when nurses on night shifts are tired, they can perform their duties with peace of mind because the AI ​​is monitoring them. Furthermore, this system can be used not only in medical settings but also in pharmacies and nursing homes. For instance, the AI ​​can monitor the dispensing of medications in pharmacies to prevent incorrect prescriptions. Similarly, the AI ​​can monitor the administration of medications in nursing homes to prevent misadministration. In this way, an AI-powered medication dosage monitoring system is an effective means of preventing human error in medical settings and ensuring the safety of healthcare professionals and patients. Thus, a medication dosage monitoring system can ensure the safety of healthcare professionals and patients and prevent human error in medical settings.

[0029] The drug dosage monitoring system according to this embodiment comprises a surveillance camera, an analysis unit, and a warning unit. The surveillance camera captures the drug administration scene in real time. For example, the surveillance camera captures the drug administration scene in high resolution and generates video data. The surveillance camera can also be used in conjunction with an infrared camera that can shoot in dark places. For example, the surveillance camera can accurately capture the drug administration scene even in dark places using an infrared camera. Furthermore, the surveillance camera can acquire images from different angles by linking multiple cameras. For example, the surveillance camera can acquire images from different angles simultaneously by linking multiple cameras to improve analysis accuracy. The analysis unit uses AI to analyze the images captured by the surveillance camera and recognize the type and amount of drug. For example, the analysis unit recognizes the drug label, color, and shape and calculates the dosage. For example, the analysis unit reads the drug label using OCR technology to identify the type of drug. The analysis unit can also identify the type of drug by analyzing the color and shape of the drug using image recognition technology. Furthermore, the analysis unit can also recognize the smell and texture of the drug. For example, the analysis unit uses an odor sensor to recognize the smell of the drug and improve the accuracy of the analysis. The warning unit issues a warning based on the information recognized by the analysis unit if the dosage exceeds the standard value. The warning unit can, for example, display a warning message on a monitor. For example, if the dosage exceeds the standard value, the warning unit displays a warning message on the monitor and notifies healthcare workers. The warning unit can also sound an alarm. For example, if the dosage exceeds the standard value, the warning unit sounds an alarm to warn healthcare workers. As a result, the drug dosage monitoring system according to the embodiment can prevent drug administration errors and ensure the safety of healthcare workers and patients. Some or all of the above-described processes in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can issue a warning using an AI model that takes the information recognized by the analysis unit as input and outputs a warning message. Furthermore, the warning unit can send notifications to the smartphones or tablets of healthcare workers. For example, the warning unit can send a warning message about an administration error to the smartphone of a healthcare worker so that they can receive the warning immediately.This allows healthcare workers to respond quickly.

[0030] Surveillance cameras can simultaneously capture the drug administration scene and the surrounding environment, enabling the identification of the cause of administration errors. For example, surveillance cameras can capture the actions and facial expressions of healthcare workers simultaneously with the drug administration scene to identify the cause of administration errors. Surveillance cameras can also capture the patient's reactions and actions simultaneously with the drug administration scene to identify the cause of administration errors. Furthermore, surveillance cameras can capture the surrounding environmental sounds and background simultaneously with the drug administration scene to identify the cause of administration errors. This makes it easier to identify the cause of administration errors. Some or all of the above processing in surveillance cameras may be performed using AI, for example, or without AI. For example, the surveillance camera can input the captured video data into a generating AI, which can then perform the task of identifying the cause of the administration error.

[0031] Surveillance cameras can improve analysis accuracy by coordinating multiple cameras to acquire images from different angles. For example, surveillance cameras can improve analysis accuracy by coordinating multiple cameras to simultaneously acquire images from different angles. Furthermore, surveillance cameras can improve analysis accuracy by coordinating multiple cameras to simultaneously acquire images from different heights. Furthermore, surveillance cameras can improve analysis accuracy by coordinating multiple cameras to simultaneously acquire images from different distances. This improves analysis accuracy. Some or all of the above processing in surveillance cameras may be performed using AI, for example, or without AI. For example, a surveillance camera can input video data acquired from multiple cameras into a generating AI and have the generating AI perform the task of improving analysis accuracy.

[0032] Surveillance cameras can simultaneously capture the drug administration scene and the actions of medical personnel, enabling the identification of the cause of administration errors. For example, a surveillance camera can capture the hand movements of medical personnel simultaneously with the drug administration scene to identify the cause of administration errors. Furthermore, a surveillance camera can also capture the facial expressions of medical personnel simultaneously with the drug administration scene to identify the cause of administration errors. Additionally, a surveillance camera can capture the body movements of medical personnel simultaneously with the drug administration scene to identify the cause of administration errors. This makes it easier to identify the cause of administration errors. Some or all of the above processing in surveillance cameras may be performed using AI, for example, or without AI. For example, the surveillance camera can input the captured video data into a generating AI, which can then perform the task of identifying the cause of the administration error.

[0033] Surveillance cameras can be equipped with infrared cameras to capture images accurately even in dark places. For example, a surveillance camera can use an infrared camera to accurately capture drug administration scenes even in dark places. It can also use an infrared camera to accurately capture the actions of medical personnel even in dark places. Furthermore, it can use an infrared camera to accurately capture patient reactions even in dark places. This enables accurate capture even in dark environments. Some or all of the above processing in surveillance cameras may be performed using AI, for example, or without AI. For example, a surveillance camera can input video data acquired by an infrared camera into a generating AI to improve the accuracy of shooting in dark places.

[0034] The analysis unit can improve analysis accuracy by recognizing not only the label, color, and shape of the drug, but also its smell and texture. For example, the analysis unit can improve analysis accuracy by recognizing the smell of the drug in addition to its label, color, and shape. Furthermore, the analysis unit can improve analysis accuracy by recognizing the texture of the drug in addition to its label, color, and shape. Also, the analysis unit can improve analysis accuracy by recognizing the temperature of the drug in addition to its label, color, and shape. This improves analysis accuracy. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the smell and texture of the drug into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0035] The analysis unit can prevent overall administration errors by analyzing not only the drug dosage but also the administration rate and timing. For example, the analysis unit can prevent overall administration errors by analyzing the administration rate in addition to the drug dosage. Furthermore, the analysis unit can prevent overall administration errors by analyzing the administration timing in addition to the drug dosage. Furthermore, the analysis unit can prevent overall administration errors by analyzing the administration method in addition to the drug dosage. This makes it possible to prevent overall administration errors. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on administration rate and timing into a generating AI and have the generating AI perform the prevention of overall administration errors.

[0036] The analysis unit can improve analysis accuracy by recognizing not only the drug label, color, and shape, but also the shape of the drug packaging and container. For example, the analysis unit can improve analysis accuracy by recognizing the drug packaging in addition to the drug label, color, and shape. Furthermore, the analysis unit can improve analysis accuracy by recognizing the shape of the drug container in addition to the drug label, color, and shape. Furthermore, the analysis unit can improve analysis accuracy by recognizing the material of the drug packaging in addition to the drug label, color, and shape. This improves analysis accuracy. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the shape of the drug packaging and container into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0037] The analysis unit can analyze not only the dosage of a drug but also the method of administration and propose the optimal method of administration. For example, the analysis unit can analyze the method of administration by injection in addition to the dosage of the drug and propose an appropriate method of administration. The analysis unit can also analyze the method of administration by oral administration in addition to the dosage of the drug and propose an appropriate method of administration. Furthermore, the analysis unit can analyze the method of administration of topical medications in addition to the dosage of the drug and propose an appropriate method of administration. This enables the proposal of an appropriate method of administration. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the method of administration into a generating AI and have the generating AI propose an optimal method of administration.

[0038] The warning unit can display not only warning messages but also specific countermeasures simultaneously, thereby supporting the response of healthcare professionals. For example, the warning unit can display the correct administration method along with a warning message for an administration error. It can also display reconfirmation procedures along with a warning message for an administration error. Furthermore, it can display procedures for reporting to a supervisor along with a warning message for an administration error. This supports the response of healthcare professionals. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input warning message and countermeasure data into a generating AI, and have the generating AI perform the support for healthcare professionals.

[0039] The warning unit can improve the accuracy of warnings not only by providing warning messages but also by referring to data on past administration errors. For example, the warning unit can refer to data on past administration errors and issue a warning if a similar error occurs. The warning unit can also refer to data on past administration errors and strengthen warnings for specific drugs. Furthermore, the warning unit can refer to data on past administration errors and strengthen warnings for specific healthcare professionals. This improves the accuracy of warnings. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input data on past administration errors into a generating AI and have the generating AI perform the task of improving the accuracy of warnings.

[0040] The warning unit can send not only warning messages but also notifications to healthcare workers' smartphones and tablets. For example, the warning unit can send a warning message about an administration error to a healthcare worker's smartphone. It can also send a warning message about an administration error to a healthcare worker's tablet. It can also send a warning message about an administration error to a healthcare worker's computer. This allows healthcare workers to receive warnings immediately. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input warning message data into a generating AI and send a notification to the healthcare worker's device.

[0041] The warning unit can not only issue a warning message, but also identify the cause of the administration error and propose measures to prevent recurrence. For example, the warning unit can identify the cause of the error and propose measures to prevent recurrence along with a warning message for the administration error. Furthermore, the warning unit can analyze the cause of the error and propose specific improvement measures along with a warning message for the administration error. It can also identify the cause of the error and propose a training program along with a warning message for the administration error. This allows for the proposal of measures to prevent recurrence. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input data on the cause of the error and measures to prevent recurrence into a generating AI, and have the generating AI execute the proposal of measures to prevent recurrence.

[0042] The system can be customized for use in multiple locations, not just medical settings. For example, when used in a medical setting, the system can be modified to include features for integrating with medical devices. Similarly, when used in a pharmacy, the system can be modified to include features corresponding to the drug dispensing process. Furthermore, when used in a nursing home, the system can be modified to include features corresponding to the tasks of care staff. This makes the system usable in a variety of locations. Some or all of the processes described above in the system may be performed using AI, or not. For example, the system can input customized data for each location into a generating AI, which can then execute the optimal customization.

[0043] The system can provide a simplified version for home use, so that it can also be used when administering medication at home. For example, when used at home, the system provides a simplified interface. The system can also add a home-use dosage record function when used at home. The system can also add a home-use alarm function when used at home. This makes it usable at home. Some or all of the above processes in the system may be performed using AI, for example, or not using AI. For example, the system can input data from the simplified home version into a generating AI and have the generating AI perform the task of providing the optimal simplified version.

[0044] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0045] The analysis unit can prevent overall administration errors by analyzing not only the drug dosage but also the administration rate and timing. For example, the analysis unit can prevent overall administration errors by analyzing the administration rate in addition to the drug dosage. Furthermore, the analysis unit can prevent overall administration errors by analyzing the administration timing in addition to the drug dosage. Furthermore, the analysis unit can prevent overall administration errors by analyzing the administration method in addition to the drug dosage. This makes it possible to prevent overall administration errors. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on administration rate and timing into a generating AI and have the generating AI perform the prevention of overall administration errors.

[0046] The analysis unit can improve analysis accuracy by recognizing not only the label, color, and shape of the drug, but also its smell and texture. For example, the analysis unit can improve analysis accuracy by recognizing the smell of the drug in addition to its label, color, and shape. Furthermore, the analysis unit can improve analysis accuracy by recognizing the texture of the drug in addition to its label, color, and shape. Also, the analysis unit can improve analysis accuracy by recognizing the temperature of the drug in addition to its label, color, and shape. This improves analysis accuracy. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the smell and texture of the drug into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0047] The warning unit can display not only warning messages but also specific countermeasures simultaneously, thereby supporting the response of healthcare professionals. For example, the warning unit can display the correct administration method along with a warning message for an administration error. It can also display reconfirmation procedures along with a warning message for an administration error. Furthermore, it can display procedures for reporting to a supervisor along with a warning message for an administration error. This supports the response of healthcare professionals. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input warning message and countermeasure data into a generating AI, and have the generating AI perform the support for healthcare professionals.

[0048] The warning unit can improve the accuracy of warnings not only by providing warning messages but also by referring to data on past administration errors. For example, the warning unit can refer to data on past administration errors and issue a warning if a similar error occurs. The warning unit can also refer to data on past administration errors and strengthen warnings for specific drugs. Furthermore, the warning unit can refer to data on past administration errors and strengthen warnings for specific healthcare professionals. This improves the accuracy of warnings. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input data on past administration errors into a generating AI and have the generating AI perform the task of improving the accuracy of warnings.

[0049] The analysis unit can analyze not only the dosage of a drug but also the method of administration and propose the optimal method of administration. For example, the analysis unit can analyze the method of administration by injection in addition to the dosage of the drug and propose an appropriate method of administration. The analysis unit can also analyze the method of administration by oral administration in addition to the dosage of the drug and propose an appropriate method of administration. Furthermore, the analysis unit can analyze the method of administration of topical medications in addition to the dosage of the drug and propose an appropriate method of administration. This enables the proposal of an appropriate method of administration. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the method of administration into a generating AI and have the generating AI propose an optimal method of administration.

[0050] The analysis unit can improve analysis accuracy by recognizing not only the drug label, color, and shape, but also the shape of the drug packaging and container. For example, the analysis unit can improve analysis accuracy by recognizing the drug packaging in addition to the drug label, color, and shape. Furthermore, the analysis unit can improve analysis accuracy by recognizing the shape of the drug container in addition to the drug label, color, and shape. Furthermore, the analysis unit can improve analysis accuracy by recognizing the material of the drug packaging in addition to the drug label, color, and shape. This improves analysis accuracy. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the shape of the drug packaging and container into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0051] The following briefly describes the processing flow for example form 1.

[0052] Step 1: Surveillance cameras capture the drug administration scene in real time. The surveillance cameras generate high-resolution video data and can be used in conjunction with infrared cameras that can shoot in dark places. It is also possible to coordinate multiple cameras to obtain images from different angles. Step 2: The analysis unit uses AI to analyze the video footage captured by the surveillance camera and recognize the type and quantity of medication. The analysis unit recognizes the medication's label, color, and shape, and calculates the dosage. Furthermore, it can also recognize the medication's scent using an odor sensor. Step 3: The warning unit issues a warning if the dosage exceeds the standard value, based on the information recognized by the analysis unit. The warning unit can display a warning message on the monitor and sound an alarm. It can also send notifications to the healthcare professional's smartphone or tablet.

[0053] (Example of form 2) The drug dosage monitoring system according to an embodiment of the present invention is a system that uses AI to monitor drug dosages and prevent human errors in medical settings. This drug dosage monitoring system monitors the type and amount of drug through a surveillance camera, and the AI ​​analyzes this information. If the dosage is incorrect, the AI ​​immediately issues a warning and notifies healthcare professionals. This prevents drug misadministration and ensures the safety of healthcare professionals and patients. For example, a surveillance camera captures the drug administration scene in real time. This video is sent to the AI, which analyzes the type and amount of drug. For example, the AI ​​recognizes the drug label, color, and shape and calculates the dosage. Next, the AI ​​compares the analysis result with a pre-set correct dosage. If the dosage is incorrect, the AI ​​immediately issues a warning and notifies healthcare professionals. For example, it may display a warning message on a monitor or sound an alarm. This system allows healthcare professionals to prevent drug administration errors and ensures patient safety. It also reduces the burden on healthcare professionals and improves work efficiency. For example, even when nurses on night shifts are tired, they can perform their duties with peace of mind because the AI ​​is monitoring them. Furthermore, this system can be used not only in medical settings but also in pharmacies and nursing homes. For instance, the AI ​​can monitor the dispensing of medications in pharmacies to prevent incorrect prescriptions. Similarly, the AI ​​can monitor the administration of medications in nursing homes to prevent misadministration. In this way, an AI-powered medication dosage monitoring system is an effective means of preventing human error in medical settings and ensuring the safety of healthcare professionals and patients. Thus, a medication dosage monitoring system can ensure the safety of healthcare professionals and patients and prevent human error in medical settings.

[0054] The drug dosage monitoring system according to this embodiment comprises a surveillance camera, an analysis unit, and a warning unit. The surveillance camera captures the drug administration scene in real time. For example, the surveillance camera captures the drug administration scene in high resolution and generates video data. The surveillance camera can also be used in conjunction with an infrared camera that can shoot in dark places. For example, the surveillance camera can accurately capture the drug administration scene even in dark places using an infrared camera. Furthermore, the surveillance camera can acquire images from different angles by linking multiple cameras. For example, the surveillance camera can acquire images from different angles simultaneously by linking multiple cameras to improve analysis accuracy. The analysis unit uses AI to analyze the images captured by the surveillance camera and recognize the type and amount of drug. For example, the analysis unit recognizes the drug label, color, and shape and calculates the dosage. For example, the analysis unit reads the drug label using OCR technology to identify the type of drug. The analysis unit can also identify the type of drug by analyzing the color and shape of the drug using image recognition technology. Furthermore, the analysis unit can also recognize the smell and texture of the drug. For example, the analysis unit uses an odor sensor to recognize the smell of the drug and improve the accuracy of the analysis. The warning unit issues a warning based on the information recognized by the analysis unit if the dosage exceeds the standard value. The warning unit can, for example, display a warning message on a monitor. For example, if the dosage exceeds the standard value, the warning unit displays a warning message on the monitor and notifies healthcare workers. The warning unit can also sound an alarm. For example, if the dosage exceeds the standard value, the warning unit sounds an alarm to warn healthcare workers. As a result, the drug dosage monitoring system according to the embodiment can prevent drug administration errors and ensure the safety of healthcare workers and patients. Some or all of the above-described processes in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can issue a warning using an AI model that takes the information recognized by the analysis unit as input and outputs a warning message. Furthermore, the warning unit can send notifications to the smartphones or tablets of healthcare workers. For example, the warning unit can send a warning message about an administration error to the smartphone of a healthcare worker so that they can receive the warning immediately.This allows healthcare workers to respond quickly.

[0055] A surveillance camera can estimate a user's emotions and adjust the shooting angle to an appropriate level based on the estimated emotions. For example, if the user is tense, the camera can widen the shooting angle to make it easier to grasp the overall situation. Conversely, if the user is relaxed, the camera can narrow the shooting angle to focus on a specific area. Furthermore, if the user is tired, the camera can automatically adjust the shooting angle to prioritize capturing important scenes. This enables optimal shooting tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in a surveillance camera may be performed using AI or not. For example, the surveillance camera can input user facial expression data into a generative AI and have the generative AI perform the user's emotion estimation.

[0056] Surveillance cameras can simultaneously capture the drug administration scene and the surrounding environment, enabling the identification of the cause of administration errors. For example, surveillance cameras can capture the actions and facial expressions of healthcare workers simultaneously with the drug administration scene to identify the cause of administration errors. Surveillance cameras can also capture the patient's reactions and actions simultaneously with the drug administration scene to identify the cause of administration errors. Furthermore, surveillance cameras can capture the surrounding environmental sounds and background simultaneously with the drug administration scene to identify the cause of administration errors. This makes it easier to identify the cause of administration errors. Some or all of the above processing in surveillance cameras may be performed using AI, for example, or without AI. For example, the surveillance camera can input the captured video data into a generating AI, which can then perform the task of identifying the cause of the administration error.

[0057] Surveillance cameras can improve analysis accuracy by coordinating multiple cameras to acquire images from different angles. For example, surveillance cameras can improve analysis accuracy by coordinating multiple cameras to simultaneously acquire images from different angles. Furthermore, surveillance cameras can improve analysis accuracy by coordinating multiple cameras to simultaneously acquire images from different heights. Furthermore, surveillance cameras can improve analysis accuracy by coordinating multiple cameras to simultaneously acquire images from different distances. This improves analysis accuracy. Some or all of the above processing in surveillance cameras may be performed using AI, for example, or without AI. For example, a surveillance camera can input video data acquired from multiple cameras into a generating AI and have the generating AI perform the task of improving analysis accuracy.

[0058] A surveillance camera can estimate a user's emotions and adjust the recording frequency based on the estimated emotions. For example, if the user is tense, the camera can increase the recording frequency to capture detailed footage. Conversely, if the user is relaxed, the camera can decrease the recording frequency to capture only the necessary scenes. Furthermore, if the user is tired, the camera can automatically adjust the recording frequency to prioritize capturing important scenes. This enables an optimal recording frequency tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the surveillance camera may be performed using AI or not. For example, the surveillance camera can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0059] Surveillance cameras can simultaneously capture the drug administration scene and the actions of medical personnel, enabling the identification of the cause of administration errors. For example, a surveillance camera can capture the hand movements of medical personnel simultaneously with the drug administration scene to identify the cause of administration errors. Furthermore, a surveillance camera can also capture the facial expressions of medical personnel simultaneously with the drug administration scene to identify the cause of administration errors. Additionally, a surveillance camera can capture the body movements of medical personnel simultaneously with the drug administration scene to identify the cause of administration errors. This makes it easier to identify the cause of administration errors. Some or all of the above processing in surveillance cameras may be performed using AI, for example, or without AI. For example, the surveillance camera can input the captured video data into a generating AI, which can then perform the task of identifying the cause of the administration error.

[0060] Surveillance cameras can be equipped with infrared cameras to capture images accurately even in dark places. For example, a surveillance camera can use an infrared camera to accurately capture drug administration scenes even in dark places. It can also use an infrared camera to accurately capture the actions of medical personnel even in dark places. Furthermore, it can use an infrared camera to accurately capture patient reactions even in dark places. This enables accurate capture even in dark environments. Some or all of the above processing in surveillance cameras may be performed using AI, for example, or without AI. For example, a surveillance camera can input video data acquired by an infrared camera into a generating AI to improve the accuracy of shooting in dark places.

[0061] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. Alternatively, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. This enables the optimal display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input user facial expression data into the generative AI and have the generative AI perform the user's emotion estimation.

[0062] The analysis unit can improve analysis accuracy by recognizing not only the label, color, and shape of the drug, but also its smell and texture. For example, the analysis unit can improve analysis accuracy by recognizing the smell of the drug in addition to its label, color, and shape. Furthermore, the analysis unit can improve analysis accuracy by recognizing the texture of the drug in addition to its label, color, and shape. Also, the analysis unit can improve analysis accuracy by recognizing the temperature of the drug in addition to its label, color, and shape. This improves analysis accuracy. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the smell and texture of the drug into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0063] The analysis unit can prevent overall administration errors by analyzing not only the drug dosage but also the administration rate and timing. For example, the analysis unit can prevent overall administration errors by analyzing the administration rate in addition to the drug dosage. Furthermore, the analysis unit can prevent overall administration errors by analyzing the administration timing in addition to the drug dosage. Furthermore, the analysis unit can prevent overall administration errors by analyzing the administration method in addition to the drug dosage. This makes it possible to prevent overall administration errors. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on administration rate and timing into a generating AI and have the generating AI perform the prevention of overall administration errors.

[0064] The analysis unit can estimate the user's emotions and determine the priority of appropriate analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit will prioritize displaying important analysis results. It can also prioritize displaying detailed analysis results if the user is relaxed, or prioritize displaying concise analysis results if the user is in a hurry. This allows for the determination of the optimal priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user facial expression data into the generative AI and have the generative AI perform the user's emotion estimation.

[0065] The analysis unit can improve analysis accuracy by recognizing not only the drug label, color, and shape, but also the shape of the drug packaging and container. For example, the analysis unit can improve analysis accuracy by recognizing the drug packaging in addition to the drug label, color, and shape. Furthermore, the analysis unit can improve analysis accuracy by recognizing the shape of the drug container in addition to the drug label, color, and shape. Furthermore, the analysis unit can improve analysis accuracy by recognizing the material of the drug packaging in addition to the drug label, color, and shape. This improves analysis accuracy. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the shape of the drug packaging and container into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0066] The analysis unit can analyze not only the dosage of a drug but also the method of administration and propose the optimal method of administration. For example, the analysis unit can analyze the method of administration by injection in addition to the dosage of the drug and propose an appropriate method of administration. The analysis unit can also analyze the method of administration by oral administration in addition to the dosage of the drug and propose an appropriate method of administration. Furthermore, the analysis unit can analyze the method of administration of topical medications in addition to the dosage of the drug and propose an appropriate method of administration. This enables the proposal of an appropriate method of administration. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the method of administration into a generating AI and have the generating AI propose an optimal method of administration.

[0067] The warning unit can estimate the user's emotions and adjust the display method of the appropriate warning message based on the estimated emotions. For example, if the user is tense, the warning unit can display a simple and highly visible warning message. Alternatively, if the user is relaxed, the warning unit can display a warning message containing detailed information. Furthermore, if the user is in a hurry, the warning unit can display a concise warning message. This allows for the display of the most appropriate warning message according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the warning unit may be performed using AI or not. For example, the warning unit can input user facial expression data into the generative AI and have the generative AI perform the user's emotion estimation.

[0068] The warning unit can display not only warning messages but also specific countermeasures simultaneously, thereby supporting the response of healthcare professionals. For example, the warning unit can display the correct administration method along with a warning message for an administration error. It can also display reconfirmation procedures along with a warning message for an administration error. Furthermore, it can display procedures for reporting to a supervisor along with a warning message for an administration error. This supports the response of healthcare professionals. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input warning message and countermeasure data into a generating AI, and have the generating AI perform the support for healthcare professionals.

[0069] The warning unit can improve the accuracy of warnings not only by providing warning messages but also by referring to data on past administration errors. For example, the warning unit can refer to data on past administration errors and issue a warning if a similar error occurs. The warning unit can also refer to data on past administration errors and strengthen warnings for specific drugs. Furthermore, the warning unit can refer to data on past administration errors and strengthen warnings for specific healthcare professionals. This improves the accuracy of warnings. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input data on past administration errors into a generating AI and have the generating AI perform the task of improving the accuracy of warnings.

[0070] The warning unit can estimate the user's emotions and adjust the type and volume of the appropriate warning sound based on the estimated emotions. For example, if the user is tense, the warning unit will issue a warning with a calm sound. Alternatively, if the user is relaxed, the warning unit can issue a warning with a bright sound. Furthermore, if the user is in a hurry, the warning unit can issue a warning with a quick and concise sound. This ensures that the optimal warning sound is emitted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the warning unit may be performed using AI, or not. For example, the warning unit can input user facial expression data into the generative AI and have the generative AI perform the user's emotion estimation.

[0071] The warning unit can send not only warning messages but also notifications to healthcare workers' smartphones and tablets. For example, the warning unit can send a warning message about an administration error to a healthcare worker's smartphone. It can also send a warning message about an administration error to a healthcare worker's tablet. It can also send a warning message about an administration error to a healthcare worker's computer. This allows healthcare workers to receive warnings immediately. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input warning message data into a generating AI and send a notification to the healthcare worker's device.

[0072] The warning unit can not only issue a warning message, but also identify the cause of the administration error and propose measures to prevent recurrence. For example, the warning unit can identify the cause of the error and propose measures to prevent recurrence along with a warning message for the administration error. Furthermore, the warning unit can analyze the cause of the error and propose specific improvement measures along with a warning message for the administration error. It can also identify the cause of the error and propose a training program along with a warning message for the administration error. This allows for the proposal of measures to prevent recurrence. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input data on the cause of the error and measures to prevent recurrence into a generating AI, and have the generating AI execute the proposal of measures to prevent recurrence.

[0073] The system can be customized for use in multiple locations, not just medical settings. For example, when used in a medical setting, the system can be modified to include features for integrating with medical devices. Similarly, when used in a pharmacy, the system can be modified to include features corresponding to the drug dispensing process. Furthermore, when used in a nursing home, the system can be modified to include features corresponding to the tasks of care staff. This makes the system usable in a variety of locations. Some or all of the processes described above in the system may be performed using AI, or not. For example, the system can input customized data for each location into a generating AI, which can then execute the optimal customization.

[0074] The system can provide a simplified version for home use, so that it can also be used when administering medication at home. For example, when used at home, the system provides a simplified interface. The system can also add a home-use dosage record function when used at home. The system can also add a home-use alarm function when used at home. This makes it usable at home. Some or all of the above processes in the system may be performed using AI, for example, or not using AI. For example, the system can input data from the simplified home version into a generating AI and have the generating AI perform the task of providing the optimal simplified version. === Hard Collateral 1-1 === Each of the multiple elements described above, including the surveillance camera, analysis unit, and warning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the surveillance camera is implemented by the camera 42 of the smart device 14, which captures the drug administration scene in real time. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which uses AI to analyze the video and recognize the type and amount of drug. The warning unit is implemented by the control unit 46A of the smart device 14, which displays a warning message if the administered amount exceeds a standard value. === Hard Collateral 1-2 === Each of the multiple elements described above, including the surveillance camera, analysis unit, and warning unit, is implemented in, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the surveillance camera is implemented by the camera 42 of the smart glasses 214, which captures the drug administration scene in real time. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the video using AI to recognize the type and amount of drug. The warning unit is implemented by, for example, the control unit 46A of the smart glasses 214, which displays a warning message if the administered amount exceeds a standard value. === Hard Collateral 1-3 === Each of the multiple elements described above, including the surveillance camera, analysis unit, and warning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the surveillance camera is implemented by the camera 42 of the headset terminal 314, which captures the drug administration scene in real time. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which uses AI to analyze the video and recognize the type and amount of drug. The warning unit is implemented by the control unit 46A of the headset terminal 314, which displays a warning message if the administered amount exceeds a standard value. === Hard Collateral 1-4 === Each of the multiple elements described above, including the surveillance camera, analysis unit, and warning unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the surveillance camera is implemented by the camera 42 of the robot 414, which captures the drug administration scene in real time. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which uses AI to analyze the video and recognize the type and amount of drug. The warning unit is implemented by, for example, the control unit 46A of the robot 414, which displays a warning message if the administered amount exceeds a standard value.

[0075] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0076] The analysis unit can prevent overall administration errors by analyzing not only the drug dosage but also the administration rate and timing. For example, the analysis unit can prevent overall administration errors by analyzing the administration rate in addition to the drug dosage. Furthermore, the analysis unit can prevent overall administration errors by analyzing the administration timing in addition to the drug dosage. Furthermore, the analysis unit can prevent overall administration errors by analyzing the administration method in addition to the drug dosage. This makes it possible to prevent overall administration errors. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on administration rate and timing into a generating AI and have the generating AI perform the prevention of overall administration errors.

[0077] The warning unit can estimate the user's emotions and adjust the type and volume of the appropriate warning sound based on the estimated emotions. For example, if the user is tense, the warning unit will issue a warning with a calm sound. Alternatively, if the user is relaxed, the warning unit can issue a warning with a bright sound. Furthermore, if the user is in a hurry, the warning unit can issue a warning with a quick and concise sound. This ensures that the optimal warning sound is emitted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the warning unit may be performed using AI, or not. For example, the warning unit can input user facial expression data into the generative AI and have the generative AI perform the user's emotion estimation.

[0078] The analysis unit can improve analysis accuracy by recognizing not only the label, color, and shape of the drug, but also its smell and texture. For example, the analysis unit can improve analysis accuracy by recognizing the smell of the drug in addition to its label, color, and shape. Furthermore, the analysis unit can improve analysis accuracy by recognizing the texture of the drug in addition to its label, color, and shape. Also, the analysis unit can improve analysis accuracy by recognizing the temperature of the drug in addition to its label, color, and shape. This improves analysis accuracy. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the smell and texture of the drug into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0079] The warning unit can display not only warning messages but also specific countermeasures simultaneously, thereby supporting the response of healthcare professionals. For example, the warning unit can display the correct administration method along with a warning message for an administration error. It can also display reconfirmation procedures along with a warning message for an administration error. Furthermore, it can display procedures for reporting to a supervisor along with a warning message for an administration error. This supports the response of healthcare professionals. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input warning message and countermeasure data into a generating AI, and have the generating AI perform the support for healthcare professionals.

[0080] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. Alternatively, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. This enables the optimal display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input user facial expression data into the generative AI and have the generative AI perform the user's emotion estimation.

[0081] The warning unit can improve the accuracy of warnings not only by providing warning messages but also by referring to data on past administration errors. For example, the warning unit can refer to data on past administration errors and issue a warning if a similar error occurs. The warning unit can also refer to data on past administration errors and strengthen warnings for specific drugs. Furthermore, the warning unit can refer to data on past administration errors and strengthen warnings for specific healthcare professionals. This improves the accuracy of warnings. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input data on past administration errors into a generating AI and have the generating AI perform the task of improving the accuracy of warnings.

[0082] The analysis unit can analyze not only the dosage of a drug but also the method of administration and propose the optimal method of administration. For example, the analysis unit can analyze the method of administration by injection in addition to the dosage of the drug and propose an appropriate method of administration. The analysis unit can also analyze the method of administration by oral administration in addition to the dosage of the drug and propose an appropriate method of administration. Furthermore, the analysis unit can analyze the method of administration of topical medications in addition to the dosage of the drug and propose an appropriate method of administration. This enables the proposal of an appropriate method of administration. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the method of administration into a generating AI and have the generating AI propose an optimal method of administration.

[0083] The warning unit can estimate the user's emotions and adjust the display method of the appropriate warning message based on the estimated emotions. For example, if the user is tense, the warning unit can display a simple and highly visible warning message. Alternatively, if the user is relaxed, the warning unit can display a warning message containing detailed information. Furthermore, if the user is in a hurry, the warning unit can display a concise warning message. This allows for the display of the most appropriate warning message according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the warning unit may be performed using AI or not. For example, the warning unit can input user facial expression data into the generative AI and have the generative AI perform the user's emotion estimation.

[0084] The analysis unit can improve analysis accuracy by recognizing not only the drug label, color, and shape, but also the shape of the drug packaging and container. For example, the analysis unit can improve analysis accuracy by recognizing the drug packaging in addition to the drug label, color, and shape. Furthermore, the analysis unit can improve analysis accuracy by recognizing the shape of the drug container in addition to the drug label, color, and shape. Furthermore, the analysis unit can improve analysis accuracy by recognizing the material of the drug packaging in addition to the drug label, color, and shape. This improves analysis accuracy. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the shape of the drug packaging and container into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0085] The warning unit can estimate the user's emotions and adjust the display method of the appropriate warning message based on the estimated emotions. For example, if the user is tense, the warning unit can display a simple and highly visible warning message. Alternatively, if the user is relaxed, the warning unit can display a warning message containing detailed information. Furthermore, if the user is in a hurry, the warning unit can display a concise warning message. This allows for the display of the most appropriate warning message according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the warning unit may be performed using AI or not. For example, the warning unit can input user facial expression data into the generative AI and have the generative AI perform the user's emotion estimation.

[0086] The following briefly describes the processing flow for example form 2.

[0087] Step 1: Surveillance cameras capture the drug administration scene in real time. The surveillance cameras generate high-resolution video data and can be used in conjunction with infrared cameras that can shoot in dark places. It is also possible to coordinate multiple cameras to obtain images from different angles. Step 2: The analysis unit uses AI to analyze the video footage captured by the surveillance camera and recognize the type and quantity of medication. The analysis unit recognizes the medication's label, color, and shape, and calculates the dosage. Furthermore, it can also recognize the medication's scent using an odor sensor. Step 3: The warning unit issues a warning if the dosage exceeds the standard value, based on the information recognized by the analysis unit. The warning unit can display a warning message on the monitor and sound an alarm. It can also send notifications to the healthcare professional's smartphone or tablet.

[0088] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0089] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0090] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0091] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0092] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0093] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0094] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0095] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0096] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0097] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0098] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0099] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0100] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0101] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0102] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0103] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0106] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0108] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0109] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0110] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0111] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0112] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0114] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0115] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0116] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0117] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0118] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0120] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0122] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0124] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0125] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0126] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0127] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0128] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0130] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0131] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0132] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0141] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0142] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0143] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0144] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0145] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0146] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0147] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0148] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0149] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0150] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0151] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0152] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0153] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0154] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0155] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0156] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0157] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0158] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0159] [Explanation of symbols]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A surveillance camera that films the drug administration scene in real time, An analysis unit analyzes the video footage captured by the aforementioned surveillance camera to recognize the type and quantity of the drug, The system includes a warning unit that issues a warning if the dosage exceeds a standard value based on the information recognized by the analysis unit. A system characterized by the following features.

2. The aforementioned surveillance camera is It estimates the user's emotions and adjusts the appropriate shooting angle based on the estimated emotions. The system according to feature 1.

3. The aforementioned surveillance camera is The drug administration scene and the surrounding environment are filmed simultaneously to identify the cause of the administration error. The system according to feature 1.

4. The aforementioned surveillance camera is By coordinating multiple cameras to acquire images from different angles, the accuracy of the analysis is improved. The system according to feature 1.

5. The aforementioned surveillance camera is It estimates the user's emotions and adjusts the appropriate shooting frequency based on the estimated user emotions. The system according to feature 1.

6. The aforementioned surveillance camera is By simultaneously filming the drug administration scene and the actions of medical professionals, the cause of the administration error can be identified. The system according to feature 1.

7. The aforementioned surveillance camera is By using an infrared camera in conjunction with the system, accurate shooting is possible even in dark places. The system according to feature 1.

8. The aforementioned analysis unit, It estimates the user's emotions and adjusts the display method of the analysis results appropriately based on the estimated user emotions. The system according to feature 1.

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