System

Abstract

An object of the system according to the embodiment is to find a potential risk in the work of a pharmacist at an early stage and to quickly cope with the potential risk.SOLUTION: A system according to an embodiment includes a flow line tracking unit, a behavior analysis unit, an explanation analysis unit, an interaction analysis unit, and a risk finding unit. The flow line tracking unit tracks a flow line of the pharmacist. The behavior analysis unit analyzes a behavior based on the flow line tracked by the flow line tracking unit. The explanation analysis unit analyzes the explanation based on the behavior analyzed by the behavior analysis unit. The communication analysis unit analyzes communication with another medical worker on the basis of the explanation analyzed by the explanation analysis unit. The risk finding unit finds a potential risk based on the interaction analyzed by the interaction analyzing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has not been able to sufficiently reduce the risk of human error in pharmacists' work, and there is room for improvement.

[0005] The system according to the embodiment aims to detect potential risks in the work of pharmacists early and respond promptly. [Means for solving the problem]

[0006] The system according to the embodiment includes a movement path tracking unit, a behavior analysis unit, an explanation analysis unit, an interaction analysis unit, and a risk detection unit. The movement path tracking unit tracks the movement path of the pharmacist. The behavior analysis unit analyzes behavior based on the movement path tracked by the movement path tracking unit. The explanation analysis unit analyzes explanations based on the behavior analyzed by the behavior analysis unit. The interaction analysis unit analyzes interactions with other medical professionals based on the explanations analyzed by the explanation analysis unit. The risk detection unit detects potential risks based on the interactions analyzed by the interaction analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can detect potential risks in the work of pharmacists early and respond promptly. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The audit system according to an embodiment of the present invention uses AI to support the work of pharmacists and realize a safe dispensing environment. This system tracks the movements of pharmacists and uses AI to analyze their behavior, speech, explanations to patients, and interactions with other medical professionals, thereby enabling early detection of potential risks and prompt response. As a result, the audit system can improve the efficiency of pharmacists' work and realize a safe dispensing environment.

[0029] The audit system according to the embodiment includes a movement trajectory tracking unit, a behavior analysis unit, an explanation analysis unit, an interaction analysis unit, and a risk detection unit. The movement trajectory tracking unit tracks the movement trajectory of a pharmacist. For example, it uses cameras and sensors to record how the pharmacist moves within a dispensing room and which shelf the pharmacist retrieves medications from. This makes it possible to determine whether the pharmacist's movements are efficient and whether the pharmacist is working efficiently. The behavior analysis unit analyzes behavior based on the movement trajectory tracked by the movement trajectory tracking unit. For example, it records and analyzes the steps the pharmacist takes when retrieving medications and the content of explanations given to patients using voice recognition technology. This makes it possible to confirm whether the pharmacist is performing their duties accurately and whether the explanations are appropriate. The explanation analysis unit analyzes explanations based on the behavior analyzed by the behavior analysis unit. For example, it checks whether the explanations about how to take medications and side effects are accurate and whether the patient understands them. This makes it possible to point out areas for improvement if the explanations to patients are insufficient. The interaction analysis unit analyzes interactions with other medical professionals based on the explanations analyzed by the explanation analysis unit. For example, it checks whether prescriptions are received from doctors and whether collaboration with nurses is being carried out appropriately. This allows for early detection and improvement of communication problems. The risk detection unit detects potential risks based on the interactions analyzed by the interaction analysis unit. For example, by issuing an alert if there is waste in a pharmacist's movement or if explanations are insufficient, it is possible to take immediate corrective measures. As a result, the audit system according to the embodiment can improve the efficiency of pharmacist work and realize a safe dispensing environment. For example, optimizing pharmacists' movement can improve work efficiency, and analyzing their behavior and actions can prevent human error. Furthermore, by confirming that explanations to patients are accurate, patients' understanding can be deepened. Furthermore, strengthening collaboration with other medical professionals can improve the efficiency of the entire medical team.

[0030] The flow path tracking unit can perform 3D mapping of flow paths and analyze wasteful flow paths. For example, to 3D map the flow paths of pharmacists, the flow path tracking unit installs multiple cameras and sensors in the dispensing room and records the pharmacist's movements in three dimensions. This allows for the analysis of wasteful and overlapping flow paths and the proposal of efficient flow paths. This allows for the analysis of wasteful flow paths and the proposal of efficient flow paths.

[0031] The movement trajectory tracking unit can estimate the pharmacist's level of fatigue based on the movement trajectory data and identify movement trajectories where fatigue accumulates. For example, the movement trajectory tracking unit analyzes the pharmacist's travel distance and speed based on the movement trajectory data and develops an algorithm to estimate the level of fatigue. For example, long periods of travel or frequent travel can cause fatigue. This makes it possible to estimate the pharmacist's level of fatigue and identify movement trajectories where fatigue is likely to accumulate.

[0032] The flow line tracking unit can share the flow line data with other medical professionals and optimize the overall flow line. The flow line tracking unit, for example, shares the flow line data of pharmacists with other medical professionals and builds a system that optimizes the overall flow line. For example, the flow line tracking unit also collects flow line data of nurses and doctors and optimizes the overall flow line. This allows the flow line data to be shared and the overall flow line to be optimized.

[0033] The flow ray tracking unit can apply the flow ray tracking system to other tasks and manage the flow of patients in operating rooms or wards. For example, the flow ray tracking unit applies the flow ray tracking system to flow management in operating rooms and optimizes the flow of surgical teams. For example, it streamlines the placement of surgical instruments and the flow of patients in operating rooms. This allows the flow ray tracking system to be applied to other tasks and allows overall flow management.

[0034] The behavioral analysis unit can evaluate the work efficiency of pharmacists based on the behavioral data and propose specific procedures to improve efficiency. The behavioral analysis unit, for example, develops an algorithm to evaluate the work efficiency of pharmacists based on the behavioral data. For example, it analyzes the procedures for retrieving and dispensing medicines and proposes efficient work procedures. This makes it possible to evaluate the work efficiency of pharmacists and propose specific procedures to improve efficiency.

[0035] The behavioral analysis unit can analyze the speech and behavior data and develop an index for evaluating the communication skills of pharmacists. The behavioral analysis unit, for example, analyzes the speech and behavior data and develops an index for evaluating the communication skills of pharmacists. For example, speech recognition technology is used to evaluate the clarity and appropriateness of the pharmacist's explanations. This makes it possible to develop an index for evaluating the communication skills of pharmacists.

[0036] The Behavioral Analysis Department reflects the results of the analysis of behavior and speech in educational programs and can use them in training new pharmacists. For example, the Behavioral Analysis Department develops training programs for new pharmacists based on the results of the analysis of behavior and speech. For example, it creates teaching materials for learning efficient work procedures and appropriate communication skills. In this way, the results of the analysis of behavior and speech can be reflected in educational programs and used in training new pharmacists.

[0037] The behavioral analysis unit can propose an arrangement for optimizing the pharmacist's work environment based on the behavioral data. The behavioral analysis unit, for example, proposes an arrangement for optimizing the pharmacist's work environment based on the behavioral data. For example, it designs a layout for efficiently arranging dispensing tables and medicine shelves. This makes it possible to propose an arrangement for optimizing the pharmacist's work environment.

[0038] The explanation analysis unit can analyze the content of the explanation and develop an algorithm for evaluating the patient's level of understanding. The explanation analysis unit, for example, analyzes the content of the explanation and develops an algorithm for evaluating the patient's level of understanding. For example, using voice recognition technology, the content of the pharmacist's explanation is converted into text and the level of understanding is evaluated. This makes it possible to develop an algorithm for analyzing the content of the explanation and evaluating the patient's level of understanding.

[0039] The explanation analysis unit can analyze non-verbal communication during the explanation and perform a comprehensive evaluation of the explanation. The explanation analysis unit, for example, analyzes non-verbal communication (gestures and facial expressions) during the explanation and builds a system that performs a comprehensive evaluation of the explanation. For example, a camera is used to analyze the pharmacist's gestures and facial expressions. This makes it possible to analyze non-verbal communication during the explanation and perform a comprehensive evaluation of the explanation.

[0040] The explanation analysis unit can translate the explanation content into multiple languages ​​and improve the service for foreign patients. For example, the explanation analysis unit can build a system that translates the explanation content into multiple languages ​​and improve the service for foreign patients. For example, it can support multiple languages ​​such as English and Chinese. This allows the explanation content to be translated into multiple languages ​​and improve the service for foreign patients.

[0041] The explanation analysis unit can automatically generate visual aids for explanations to help patients understand. For example, the explanation analysis unit can build a system that automatically generates visual aids (diagrams and videos) for explanations to help patients understand. For example, it can automatically generate videos that explain how to take medicines and their side effects. This allows for the automatic generation of visual aids for explanations to help patients understand.

[0042] The interaction analysis unit can analyze the content of the interaction and develop an algorithm for evaluating the accuracy of information transmission. The interaction analysis unit, for example, analyzes the content of the interaction and develops an algorithm for evaluating the accuracy of information transmission. For example, using voice recognition technology, interactions with doctors and nurses are converted into text and the accuracy is evaluated. This makes it possible to develop an algorithm for analyzing the content of the interaction and evaluating the accuracy of information transmission.

[0043] The interaction analysis unit can visualize communication patterns within a medical team based on the frequency and content of interactions. The interaction analysis unit builds a system that visualizes communication patterns within a medical team based on, for example, the frequency and content of interactions. For example, the frequency and content of interactions are displayed in graphs or charts. This makes it possible to visualize communication patterns within a medical team based on the frequency and content of interactions.

[0044] The interaction analysis unit can optimize the workflow of the entire medical team based on the content of the interactions. The interaction analysis unit, for example, builds a system that optimizes the workflow of the entire medical team based on the content of the interactions. For example, it analyzes the content of the interactions and reduces waste in the workflow. This makes it possible to optimize the workflow of the entire medical team based on the content of the interactions.

[0045] The interaction analysis unit can share the interaction data with other medical institutions and introduce best practices. The interaction analysis unit, for example, shares the interaction data with other medical institutions and builds a system for introducing best practices. For example, the interaction data can be shared on the cloud and collaborated with other medical institutions. This allows the interaction data to be shared with other medical institutions and best practices to be introduced.

[0046] The risk discovery unit can improve the risk discovery algorithm and add a function for predicting the probability of risk occurrence. The risk discovery unit, for example, improves the risk discovery algorithm and adds a function for predicting the probability of risk occurrence. For example, the risk occurrence probability is calculated based on past data. This allows the risk discovery algorithm to be improved and a function for predicting the probability of risk occurrence to be added.

[0047] The risk discovery unit can refer to past data when discovering a risk and propose countermeasures for similar cases. The risk discovery unit, for example, builds a system that refers to past data when discovering a risk and proposes countermeasures for similar cases. For example, it proposes specific countermeasures based on past risk response data. This makes it possible to refer to past data when discovering a risk and propose countermeasures for similar cases.

[0048] The risk detection unit can apply the risk detection system to the medical field to manage operating rooms or hospital wards. For example, the risk detection unit applies the risk detection system to operating room management to detect risks during surgery early. For example, it analyzes the risks of surgical instrument placement and surgical procedures. This allows the risk detection system to be applied to other medical fields to manage operating rooms and hospital wards.

[0049] The risk detection unit notifies other medical professionals in real time when a risk is discovered, allowing the entire team to respond quickly.The risk detection unit, for example, builds a system that notifies other medical professionals in real time when a risk is discovered.For example, when a risk is discovered, it issues an alert and notifies the entire medical team.This allows other medical professionals to be notified in real time when a risk is discovered, allowing the entire team to respond quickly.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The audit system can also be equipped with a health management section that monitors the health status of pharmacists. For example, the system can periodically measure a pharmacist's heart rate and blood pressure, and issue an alert if any abnormalities are detected. This allows the system to grasp the pharmacist's health status in real time and prevent health risks caused by overwork and stress. The health management section can also manage pharmacists' break times and working hours, encouraging them to take appropriate rest. This helps maintain the health of pharmacists and improves work efficiency.

[0052] The audit system can further include an energy management unit that estimates energy consumption based on the pharmacist's movement lines. For example, it can analyze the distance and speed of the pharmacist's movement to calculate the calories burned. This allows the pharmacist's energy consumption to be understood and appropriate nutritional supplementation and rest recommended. The energy management unit can also suggest efficient movement lines based on the pharmacist's energy consumption. This can optimize the pharmacist's energy consumption and improve work efficiency.

[0053] The audit system can also be equipped with an environmental management unit that adjusts the temperature and humidity of the work environment based on the pharmacist's movement patterns. For example, it can monitor the temperature and humidity in areas where pharmacists spend long periods of time and automatically adjust the air conditioning system to maintain a comfortable environment. This optimizes the pharmacist's work environment, allowing them to perform their work in a comfortable environment. The environmental management unit can also suggest improvements to the work environment based on pharmacist movement pattern data. This allows for continuous improvement of the pharmacist's work environment and increases work efficiency.

[0054] The audit system can also be equipped with an automation proposal unit that proposes task automation based on pharmacists' movement patterns. For example, it can propose the introduction of robots or machines to automate tasks frequently performed by pharmacists. This reduces the workload on pharmacists and improves work efficiency. The automation proposal unit can also analyze which tasks should be automated based on pharmacists' movement pattern data and propose optimal automation solutions. This can improve the efficiency of pharmacists' work and prevent human error.

[0055] The audit system can further include a priority suggestion unit that suggests task priorities based on the pharmacist's movement line. For example, the system can analyze the priorities of tasks that pharmacists should perform based on movement line data and suggest an efficient work order. This can improve the work efficiency of pharmacists and reduce unnecessary movements. The priority suggestion unit can also update the work priorities in real time based on the pharmacist's movement line data and suggest an optimal work order. This can improve the efficiency of pharmacists' work and improve business efficiency.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The movement tracking unit tracks the movement of pharmacists. For example, cameras and sensors are used to record how pharmacists move within the dispensing room and which shelves they take medicines from. This makes it possible to determine whether there is any waste in the pharmacists' movements and whether they are working efficiently. Step 2: The behavior analysis unit analyzes behavior based on the movement paths tracked by the movement path tracking unit. For example, the steps a pharmacist takes when dispensing medicine and the content of the explanation given to the patient are recorded and analyzed using voice recognition technology. This makes it possible to confirm whether the pharmacist is performing their duties accurately and whether their explanations are appropriate. Step 3: The explanation analysis unit analyzes the explanation based on the behavior analyzed by the behavior analysis unit. For example, it checks whether the explanation about how to take the medicine and side effects is accurate and whether the patient understands it. This makes it possible to point out areas for improvement if the explanation to the patient is insufficient. Step 4: The interaction analysis unit analyzes interactions with other medical professionals based on the explanations analyzed by the explanation analysis unit. For example, it checks whether the patient is receiving a prescription from a doctor or coordinating with a nurse appropriately. This allows for early detection and improvement of communication problems. Step 5: The risk detection unit detects potential risks based on the interactions analyzed by the interaction analysis unit. For example, if there is unnecessary movement by the pharmacist or if the explanation is insufficient, an alert can be issued, allowing for immediate remedial measures to be taken. As a result, the audit system according to the embodiment can improve the efficiency of pharmacist work and realize a safe dispensing environment.

[0058] (Example 2) The audit system according to an embodiment of the present invention uses AI to support the work of pharmacists and realize a safe dispensing environment. This system tracks the movements of pharmacists and uses AI to analyze their behavior, speech, explanations to patients, and interactions with other medical professionals, thereby enabling early detection of potential risks and prompt response. As a result, the audit system can improve the efficiency of pharmacists' work and realize a safe dispensing environment.

[0059] The audit system according to the embodiment includes a movement trajectory tracking unit, a behavior analysis unit, an explanation analysis unit, an interaction analysis unit, and a risk detection unit. The movement trajectory tracking unit tracks the movement trajectory of a pharmacist. For example, it uses cameras and sensors to record how the pharmacist moves within a dispensing room and which shelf the pharmacist retrieves medications from. This makes it possible to determine whether the pharmacist's movements are efficient and whether the pharmacist is working efficiently. The behavior analysis unit analyzes behavior based on the movement trajectory tracked by the movement trajectory tracking unit. For example, it records and analyzes the steps the pharmacist takes when retrieving medications and the content of explanations given to patients using voice recognition technology. This makes it possible to confirm whether the pharmacist is performing their duties accurately and whether the explanations are appropriate. The explanation analysis unit analyzes explanations based on the behavior analyzed by the behavior analysis unit. For example, it checks whether the explanations about how to take medications and side effects are accurate and whether the patient understands them. This makes it possible to point out areas for improvement if the explanations to patients are insufficient. The interaction analysis unit analyzes interactions with other medical professionals based on the explanations analyzed by the explanation analysis unit. For example, it checks whether prescriptions are received from doctors and whether collaboration with nurses is being carried out appropriately. This allows for early detection and improvement of communication problems. The risk detection unit detects potential risks based on the interactions analyzed by the interaction analysis unit. For example, by issuing an alert if there is waste in a pharmacist's movement or if explanations are insufficient, it is possible to take immediate corrective measures. As a result, the audit system according to the embodiment can improve the efficiency of pharmacist work and realize a safe dispensing environment. For example, optimizing pharmacists' movement can improve work efficiency, and analyzing their behavior and actions can prevent human error. Furthermore, by confirming that explanations to patients are accurate, patients' understanding can be deepened. Furthermore, strengthening collaboration with other medical professionals can improve the efficiency of the entire medical team.

[0060] The flow path tracking unit can perform 3D mapping of flow paths and analyze wasteful flow paths. For example, to 3D map the flow paths of pharmacists, the flow path tracking unit installs multiple cameras and sensors in the dispensing room and records the pharmacist's movements in three dimensions. This allows for the analysis of wasteful and overlapping flow paths and the proposal of efficient flow paths. This allows for the analysis of wasteful flow paths and the proposal of efficient flow paths.

[0061] The movement trajectory tracking unit can estimate the pharmacist's level of fatigue based on the movement trajectory data and identify movement trajectories where fatigue accumulates. For example, the movement trajectory tracking unit analyzes the pharmacist's travel distance and speed based on the movement trajectory data and develops an algorithm to estimate the level of fatigue. For example, long periods of travel or frequent travel can cause fatigue. This makes it possible to estimate the pharmacist's level of fatigue and identify movement trajectories where fatigue is likely to accumulate.

[0062] The flow path tracking unit can use the emotion estimation function to identify stress points in the flow path and propose flow path improvements to reduce stress. The flow path tracking unit, for example, uses the emotion estimation function to identify stress points in the pharmacist's flow path. For example, it analyzes facial expressions and voice tone to identify locations and time periods where stress increases. This makes it possible to identify stress points in the flow path and propose flow path improvements to reduce stress.

[0063] The flow line tracking unit can share the flow line data with other medical professionals and optimize the overall flow line. The flow line tracking unit, for example, shares the flow line data of pharmacists with other medical professionals and builds a system that optimizes the overall flow line. For example, the flow line tracking unit also collects flow line data of nurses and doctors and optimizes the overall flow line. This allows the flow line data to be shared and the overall flow line to be optimized.

[0064] The flow ray tracking unit can apply the flow ray tracking system to other tasks and manage the flow of patients in operating rooms or wards. For example, the flow ray tracking unit applies the flow ray tracking system to flow management in operating rooms and optimizes the flow of surgical teams. For example, it streamlines the placement of surgical instruments and the flow of patients in operating rooms. This allows the flow ray tracking system to be applied to other tasks and allows overall flow management.

[0065] The flow line tracking unit uses the emotion estimation function to monitor the effect of flow line improvements in real time and can perform continuous optimization. The flow line tracking unit, for example, uses the emotion estimation function to build a system that monitors the effect of flow line improvements in real time. For example, the emotion scores of pharmacists are measured periodically to evaluate the effect of flow line improvements. This allows the effect of flow line improvements to be monitored in real time and continuously optimized.

[0066] The behavioral analysis unit can evaluate the work efficiency of pharmacists based on the behavioral data and propose specific procedures to improve efficiency. The behavioral analysis unit, for example, develops an algorithm to evaluate the work efficiency of pharmacists based on the behavioral data. For example, it analyzes the procedures for retrieving and dispensing medicines and proposes efficient work procedures. This makes it possible to evaluate the work efficiency of pharmacists and propose specific procedures to improve efficiency.

[0067] The behavioral analysis unit can analyze the speech and behavior data and develop an index for evaluating the communication skills of pharmacists. The behavioral analysis unit, for example, analyzes the speech and behavior data and develops an index for evaluating the communication skills of pharmacists. For example, speech recognition technology is used to evaluate the clarity and appropriateness of the pharmacist's explanations. This makes it possible to develop an index for evaluating the communication skills of pharmacists.

[0068] The behavior analysis unit uses the emotion estimation function to analyze the patient's emotional response to the pharmacist's words and actions, thereby improving communication. The behavior analysis unit, for example, uses the emotion estimation function to analyze the patient's emotional response to the pharmacist's words and actions. For example, it analyzes the patient's facial expressions and voice and calculates an emotion score. This makes it possible to analyze the patient's emotional response to the pharmacist's words and actions, thereby improving communication.

[0069] The Behavioral Analysis Department reflects the results of the analysis of behavior and speech in educational programs and can use them in training new pharmacists. For example, the Behavioral Analysis Department develops training programs for new pharmacists based on the results of the analysis of behavior and speech. For example, it creates teaching materials for learning efficient work procedures and appropriate communication skills. In this way, the results of the analysis of behavior and speech can be reflected in educational programs and used in training new pharmacists.

[0070] The behavioral analysis unit can propose an arrangement for optimizing the pharmacist's work environment based on the behavioral data. The behavioral analysis unit, for example, proposes an arrangement for optimizing the pharmacist's work environment based on the behavioral data. For example, it designs a layout for efficiently arranging dispensing tables and medicine shelves. This makes it possible to propose an arrangement for optimizing the pharmacist's work environment.

[0071] The behavior analysis unit uses the emotion estimation function to analyze the emotional reactions of other medical professionals to the words and actions of the pharmacist, thereby improving teamwork. The behavior analysis unit, for example, uses the emotion estimation function to analyze the emotional reactions of other medical professionals to the words and actions of the pharmacist. For example, it analyzes the facial expressions and voices of doctors and nurses and calculates an emotion score. This makes it possible to analyze the emotional reactions of other medical professionals to the words and actions of the pharmacist and improve teamwork.

[0072] The explanation analysis unit can analyze the content of the explanation and develop an algorithm for evaluating the patient's level of understanding. The explanation analysis unit, for example, analyzes the content of the explanation and develops an algorithm for evaluating the patient's level of understanding. For example, using voice recognition technology, the content of the pharmacist's explanation is converted into text and the level of understanding is evaluated. This makes it possible to develop an algorithm for analyzing the content of the explanation and evaluating the patient's level of understanding.

[0073] The explanation analysis unit can analyze non-verbal communication during the explanation and perform a comprehensive evaluation of the explanation. The explanation analysis unit, for example, analyzes non-verbal communication (gestures and facial expressions) during the explanation and builds a system that performs a comprehensive evaluation of the explanation. For example, a camera is used to analyze the pharmacist's gestures and facial expressions. This makes it possible to analyze non-verbal communication during the explanation and perform a comprehensive evaluation of the explanation.

[0074] The explanation analysis unit can analyze the emotional response of the patient using the emotion estimation function and suggest improvements to the explanation. The explanation analysis unit, for example, uses the emotion estimation function to analyze the emotional response of the patient. For example, it analyzes the patient's facial expression and voice and calculates an emotion score. This makes it possible to analyze the emotional response of the patient and specifically suggest improvements to the explanation.

[0075] The explanation analysis unit can translate the explanation content into multiple languages ​​and improve the service for foreign patients. For example, the explanation analysis unit can build a system that translates the explanation content into multiple languages ​​and improve the service for foreign patients. For example, it can support multiple languages ​​such as English and Chinese. This allows the explanation content to be translated into multiple languages ​​and improve the service for foreign patients.

[0076] The explanation analysis unit can automatically generate visual aids for explanations to help patients understand. For example, the explanation analysis unit can build a system that automatically generates visual aids (diagrams and videos) for explanations to help patients understand. For example, it can automatically generate videos that explain how to take medicines and their side effects. This allows for the automatic generation of visual aids for explanations to help patients understand.

[0077] The explanation analysis unit uses the emotion estimation function to monitor the emotional changes of the patient during explanations in real time and provide instant feedback. The explanation analysis unit, for example, uses the emotion estimation function to build a system that monitors the emotional changes of the patient during explanations in real time. For example, it analyzes the patient's facial expressions and voice and calculates an emotion score. This makes it possible to monitor the emotional changes of the patient during explanations in real time and provide instant feedback.

[0078] The interaction analysis unit can analyze the content of the interaction and develop an algorithm for evaluating the accuracy of information transmission. The interaction analysis unit, for example, analyzes the content of the interaction and develops an algorithm for evaluating the accuracy of information transmission. For example, using voice recognition technology, interactions with doctors and nurses are converted into text and the accuracy is evaluated. This makes it possible to develop an algorithm for analyzing the content of the interaction and evaluating the accuracy of information transmission.

[0079] The interaction analysis unit can visualize communication patterns within a medical team based on the frequency and content of interactions. The interaction analysis unit builds a system that visualizes communication patterns within a medical team based on, for example, the frequency and content of interactions. For example, the frequency and content of interactions are displayed in graphs or charts. This makes it possible to visualize communication patterns within a medical team based on the frequency and content of interactions.

[0080] The interaction analysis unit uses the emotion estimation function to analyze the emotional tone during an interaction, thereby improving the quality of communication. The interaction analysis unit, for example, uses the emotion estimation function to analyze the emotional tone during an interaction. For example, the interaction analysis unit analyzes the facial expressions and voices of doctors and nurses and calculates an emotion score. This allows the emotional tone during an interaction to be analyzed, thereby improving the quality of communication.

[0081] The interaction analysis unit can optimize the workflow of the entire medical team based on the content of the interactions. The interaction analysis unit, for example, builds a system that optimizes the workflow of the entire medical team based on the content of the interactions. For example, it analyzes the content of the interactions and reduces waste in the workflow. This makes it possible to optimize the workflow of the entire medical team based on the content of the interactions.

[0082] The interaction analysis unit can share the interaction data with other medical institutions and introduce best practices. The interaction analysis unit, for example, shares the interaction data with other medical institutions and builds a system for introducing best practices. For example, the interaction data can be shared on the cloud and collaborated with other medical institutions. This allows the interaction data to be shared with other medical institutions and best practices to be introduced.

[0083] The interaction analysis unit uses the emotion estimation function to monitor the emotional tone during an interaction in real time and provide instantaneous feedback. The interaction analysis unit, for example, uses the emotion estimation function to build a system that monitors the emotional tone during an interaction in real time. For example, it analyzes the facial expressions and voices of doctors and nurses and calculates an emotion score. This makes it possible to monitor the emotional tone during an interaction in real time and provide instantaneous feedback.

[0084] The risk discovery unit can improve the risk discovery algorithm and add a function for predicting the probability of risk occurrence. The risk discovery unit, for example, improves the risk discovery algorithm and adds a function for predicting the probability of risk occurrence. For example, the risk occurrence probability is calculated based on past data. This allows the risk discovery algorithm to be improved and a function for predicting the probability of risk occurrence to be added.

[0085] The risk discovery unit can refer to past data when discovering a risk and propose countermeasures for similar cases. The risk discovery unit, for example, builds a system that refers to past data when discovering a risk and proposes countermeasures for similar cases. For example, it proposes specific countermeasures based on past risk response data. This makes it possible to refer to past data when discovering a risk and propose countermeasures for similar cases.

[0086] The risk detection unit can use the emotion estimation function to analyze emotional reactions when a risk is detected and evaluate the effectiveness of countermeasures. The risk detection unit, for example, uses the emotion estimation function to analyze emotional reactions when a risk is detected. For example, it analyzes the pharmacist's facial expressions and voice and calculates an emotion score. This makes it possible to analyze emotional reactions when a risk is detected and evaluate the effectiveness of countermeasures.

[0087] The risk detection unit can apply the risk detection system to the medical field to manage operating rooms or hospital wards. For example, the risk detection unit applies the risk detection system to operating room management to detect risks during surgery early. For example, it analyzes the risks of surgical instrument placement and surgical procedures. This allows the risk detection system to be applied to other medical fields to manage operating rooms and hospital wards.

[0088] The risk detection unit notifies other medical professionals in real time when a risk is discovered, allowing the entire team to respond quickly.The risk detection unit, for example, builds a system that notifies other medical professionals in real time when a risk is discovered.For example, when a risk is discovered, it issues an alert and notifies the entire medical team.This allows other medical professionals to be notified in real time when a risk is discovered, allowing the entire team to respond quickly.

[0089] The risk detection unit uses the emotion estimation function to monitor emotional reactions when a risk is detected in real time and can provide immediate feedback. The risk detection unit, for example, uses the emotion estimation function to build a system that monitors emotional reactions when a risk is detected in real time. For example, it analyzes the pharmacist's facial expressions and voice and calculates an emotion score. This makes it possible to monitor emotional reactions when a risk is detected in real time and provide immediate feedback.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The audit system can also be equipped with a health management section that monitors the health status of pharmacists. For example, the system can periodically measure a pharmacist's heart rate and blood pressure, and issue an alert if any abnormalities are detected. This allows the system to grasp the pharmacist's health status in real time and prevent health risks caused by overwork and stress. The health management section can also manage pharmacists' break times and working hours, encouraging them to take appropriate rest. This helps maintain the health of pharmacists and improves work efficiency.

[0092] The audit system can further include an energy management unit that estimates energy consumption based on the pharmacist's movement lines. For example, it can analyze the distance and speed of the pharmacist's movement to calculate the calories burned. This allows the pharmacist's energy consumption to be understood and appropriate nutritional supplementation and rest recommended. The energy management unit can also suggest efficient movement lines based on the pharmacist's energy consumption. This can optimize the pharmacist's energy consumption and improve work efficiency.

[0093] The audit system can also be equipped with an environmental management unit that adjusts the temperature and humidity of the work environment based on the pharmacist's movement patterns. For example, it can monitor the temperature and humidity in areas where pharmacists spend long periods of time and automatically adjust the air conditioning system to maintain a comfortable environment. This optimizes the pharmacist's work environment, allowing them to perform their work in a comfortable environment. The environmental management unit can also suggest improvements to the work environment based on pharmacist movement pattern data. This allows for continuous improvement of the pharmacist's work environment and increases work efficiency.

[0094] The audit system can also be equipped with an automation proposal unit that proposes task automation based on pharmacists' movement patterns. For example, it can propose the introduction of robots or machines to automate tasks frequently performed by pharmacists. This reduces the workload on pharmacists and improves work efficiency. The automation proposal unit can also analyze which tasks should be automated based on pharmacists' movement pattern data and propose optimal automation solutions. This can improve the efficiency of pharmacists' work and prevent human error.

[0095] The audit system can further include a priority suggestion unit that suggests task priorities based on the pharmacist's movement line. For example, the system can analyze the priorities of tasks that pharmacists should perform based on movement line data and suggest an efficient work order. This can improve the work efficiency of pharmacists and reduce unnecessary movements. The priority suggestion unit can also update the work priorities in real time based on the pharmacist's movement line data and suggest an optimal work order. This can improve the efficiency of pharmacists' work and improve business efficiency.

[0096] The audit system can also be equipped with a stress management unit that estimates the pharmacist's emotions and manages stress. For example, it can analyze the pharmacist's facial expressions and voice to estimate their stress level. This makes it possible to grasp the pharmacist's stress state in real time and propose appropriate stress reduction measures. The stress management unit can also suggest break timing and relaxation methods based on the pharmacist's stress level. This reduces pharmacist stress and improves work efficiency.

[0097] The audit system can further include a feedback unit that estimates the pharmacist's emotions and provides feedback based on those emotions. For example, the system can analyze the pharmacist's facial expressions and voice to estimate their emotional state. This allows real-time feedback based on the pharmacist's emotions to be provided, and points out areas for improvement in work. The feedback unit can also suggest areas for improving work efficiency and communication based on the pharmacist's emotional data. This allows for feedback based on the pharmacist's emotions to be provided, and work efficiency to be improved.

[0098] The audit system can further include an education unit that estimates the pharmacist's emotions and provides an education program based on those emotions. For example, the system can analyze the pharmacist's facial expressions and voice to estimate their emotional state. This makes it possible to provide an education program based on the pharmacist's emotions and point out areas for improvement in their work. The education unit can also create teaching materials based on the pharmacist's emotional data to teach efficient work procedures and appropriate communication skills. This makes it possible to provide an education program based on the pharmacist's emotions and improve work efficiency.

[0099] The audit system can further include a break suggestion unit that estimates the pharmacist's emotions and suggests break timing based on those emotions. For example, the system can analyze the pharmacist's facial expressions and voice to estimate their emotional state. This makes it possible to suggest break timing based on the pharmacist's emotions and encourage appropriate rest. The break suggestion unit can also suggest relaxation methods and rest locations based on the pharmacist's emotional data. This makes it possible to suggest break timing based on the pharmacist's emotions and improve work efficiency.

[0100] The audit system can further include a team-building module that estimates the emotions of pharmacists and proposes team-building activities based on their emotions. For example, the system can analyze the pharmacists' facial expressions and voices to estimate their emotional state. This makes it possible to propose team-building activities based on the pharmacists' emotions and improve communication throughout the team. The team-building module can also propose activities and training to improve teamwork based on the pharmacists' emotional data. This makes it possible to propose team-building activities based on the pharmacists' emotions and improve work efficiency.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The movement tracking unit tracks the movement of pharmacists. For example, cameras and sensors are used to record how pharmacists move within the dispensing room and which shelves they take medicines from. This makes it possible to determine whether there is any waste in the pharmacists' movements and whether they are working efficiently. Step 2: The behavior analysis unit analyzes behavior based on the movement paths tracked by the movement path tracking unit. For example, the steps a pharmacist takes when dispensing medicine and the content of the explanation given to the patient are recorded and analyzed using voice recognition technology. This makes it possible to confirm whether the pharmacist is performing their duties accurately and whether their explanations are appropriate. Step 3: The explanation analysis unit analyzes the explanation based on the behavior analyzed by the behavior analysis unit. For example, it checks whether the explanation about how to take the medicine and side effects is accurate and whether the patient understands it. This makes it possible to point out areas for improvement if the explanation to the patient is insufficient. Step 4: The interaction analysis unit analyzes interactions with other medical professionals based on the explanations analyzed by the explanation analysis unit. For example, it checks whether the patient is receiving a prescription from a doctor or coordinating with a nurse appropriately. This allows for early detection and improvement of communication problems. Step 5: The risk detection unit detects potential risks based on the interactions analyzed by the interaction analysis unit. For example, if there is unnecessary movement by the pharmacist or if the explanation is insufficient, an alert can be issued, allowing for immediate remedial measures to be taken. As a result, the audit system according to the embodiment can improve the efficiency of pharmacist work and realize a safe dispensing environment.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] 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.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a movement tracking unit that tracks the movement of pharmacists; a behavior analysis unit that analyzes behavior based on the movement line tracked by the movement line tracking unit; an explanation analysis unit that analyzes an explanation based on the behavior analyzed by the behavior analysis unit; an interaction analysis unit that analyzes interactions with other medical professionals based on the explanation analyzed by the explanation analysis unit; a risk detection unit that detects potential risks based on the exchange analyzed by the exchange analysis unit. A system characterized by:

2. The flow line tracking unit 3D mapping of the flow lines and analysis of wasteful flow lines 2. The system of claim 1.

3. The behavior analysis unit Evaluate the work efficiency of the pharmacist based on behavioral data and propose steps to improve efficiency 2. The system of claim 1.

4. The explanation analysis unit Developing algorithms to analyze explanations and assess patients' understanding 2. The system of claim 1.

5. The interaction analysis unit Develop an algorithm to analyze the content of the exchange and evaluate the accuracy of the information transmission.

2. The system of claim 1.

6. The risk detection unit Improve the risk detection algorithm and add functionality to predict the probability of occurrence of said risks.

2. The system of claim 1.

7. The flow line tracking unit Identify stress points in the flow of traffic and propose improvements to reduce stress 2. The system of claim 1.

8. The behavior analysis unit Analyze the patient's emotional response to the pharmacist's words and actions to improve communication 2. The system of claim 1.

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