System

Abstract

An object of the system according to the embodiment is to analyze action data of an employee and make a proposal or a warning for protecting safety or health.SOLUTION: A system according to an embodiment includes a work assistance unit, an action record accumulation unit, an analysis unit, and a proposal unit. The work assistance unit assists the work using the AI assistant. The action record storage unit stores an action record of an employee. The analysis unit analyzes the action record accumulated by the action record accumulation unit. The proposal unit makes a proposal or a warning for protecting the safety or health of the employee based on the result analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Previous technologies had the problem of not being able to effectively utilize employee behavioral data to provide suggestions and warnings to protect safety and health.

[0005] The system according to the embodiment aims to analyze employee behavioral data and provide suggestions and warnings to protect safety and health. [Means for solving the problem]

[0006] The system according to the embodiment includes a work support unit, an action record storage unit, an analysis unit, and a suggestion unit. The work support unit uses an AI assistant to assist with work. The action record storage unit stores employee action records. The analysis unit analyzes the action records stored by the action record storage unit. The suggestion unit makes suggestions and warnings to protect the safety and health of employees based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze employee behavioral data and provide suggestions and warnings to protect safety and health. [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) A system according to an embodiment of the present invention is a system that uses an AI assistant to assist with work in medical and nursing care settings and protects employees by utilizing accumulated behavioral records. This system not only uses the AI ​​assistant to improve work efficiency and reduce the burden on employees, but also analyzes the behavioral records to make suggestions and warnings to protect the safety and health of employees. As a result, the system can improve work efficiency, reduce the burden on employees, and protect their safety and health.

[0029] The system according to the embodiment includes a task support unit, an action record storage unit, an analysis unit, and a suggestion unit. The task support unit uses an AI assistant to assist with tasks. For example, the task support unit monitors patients' vital signs. The task support unit can also manage medication administration schedules. The task support unit can also provide transportation support for patients. The action record storage unit stores employee action records. For example, the action record storage unit records which tasks were performed and when. The action record storage unit can also record what care was provided to which patient. The action record storage unit can also record the movement history of employees. The analysis unit analyzes the action records stored by the action record storage unit. For example, the analysis unit analyzes the work patterns of employees. The analysis unit can also analyze the workload of employees. The analysis unit can also analyze health data of employees. The suggestion unit makes suggestions and warnings to protect the safety and health of employees based on the results of the analysis by the analysis unit. For example, the suggestion unit may suggest taking a break if signs of overwork are observed. The suggestion unit may also suggest reviewing the allocation of work if a particular task is too burdensome for an employee. The suggestion unit may also warn of health risks to employees. This allows the system according to the embodiment to assist with work using an AI assistant and protect the safety and health of employees by analyzing behavioral records. For example, the system may analyze an employee's work patterns and suggest an efficient work schedule. The system may also analyze an employee's health data and warn of health risks. The system may also analyze an employee's behavioral records to detect signs of overwork and suggest taking a break.

[0030] The task support unit can monitor a patient's vital signs, manage a medication administration schedule, and assist with patient transportation. The task support unit, for example, monitors a patient's vital signs. For example, the task support unit monitors a patient's heart rate. The task support unit can also monitor a patient's blood pressure. The task support unit can also monitor a patient's body temperature. The task support unit, for example, manages a medication administration schedule. For example, the task support unit manages a schedule of medications to be administered to a patient. The task support unit can also record the time of medication administration. The task support unit can also manage the dosage of medication. The task support unit, for example, supports patient transportation. For example, the task support unit assists in operating a wheelchair. The task support unit can also guide the patient's travel route. The task support unit can also support patient transportation. In this way, by monitoring a patient's vital signs, managing a medication administration schedule, and assisting with patient transportation, it is possible to improve work efficiency and the quality of patient care.

[0031] The analysis unit can analyze the behavioral records to understand the work patterns and workload of employees. The analysis unit, for example, analyzes the behavioral records to understand the work patterns of employees. For example, the analysis unit analyzes which tasks the employees performed at what time. The analysis unit can also analyze what kind of care the employees provided to which patients. The analysis unit can also analyze the movement history of employees. The analysis unit, for example, analyzes the behavioral records to understand the workload of employees. For example, the analysis unit analyzes how much time the employees spent on each task. The analysis unit can also analyze how much effort the employees spent on each task. The analysis unit can also analyze the health data of employees to understand the workload of employees. In this way, by understanding the work patterns and workload of employees, it is possible to improve work efficiency and reduce the workload of employees.

[0032] The suggestion unit can suggest that an employee take a break when signs of overwork are observed. The suggestion unit, for example, suggests that an employee take a break when signs of overwork are observed. For example, the suggestion unit analyzes the employee's behavioral records to detect signs of overwork. The suggestion unit can also analyze the employee's health data to detect signs of overwork. The suggestion unit can also analyze the employee's stress level to detect signs of overwork. This makes it possible to prevent employees from overworking and protect their health.

[0033] The suggestion unit can automatically set the priority of work and propose a schedule for efficiently progressing the work. The suggestion unit, for example, automatically sets the priority of work and proposes a schedule for efficiently progressing the work. For example, the suggestion unit analyzes employee behavior records to set the priority of work. The suggestion unit can also analyze employee work content and propose an efficient schedule. The suggestion unit can also analyze employee work patterns and propose an efficient schedule. This can improve work efficiency and reduce the burden on employees.

[0034] The suggestion unit can monitor the patient's condition in real time and immediately issue a warning if an abnormality occurs. The suggestion unit, for example, monitors the patient's condition in real time and immediately issue a warning if an abnormality occurs. For example, the suggestion unit monitors the patient's vital signs and detects abnormalities. The suggestion unit can also monitor the patient's behavior and detect abnormalities. The suggestion unit can also monitor the patient's health data and detect abnormalities. This makes it possible to monitor the patient's condition in real time and respond immediately if an abnormality occurs.

[0035] The suggestion unit can analyze the stress level of an employee and make suggestions for stress reduction. The suggestion unit can, for example, analyze the stress level of an employee and make suggestions for stress reduction. For example, the suggestion unit can analyze the employee's behavioral record and estimate the stress level. The suggestion unit can also analyze the employee's health data and estimate the stress level. The suggestion unit can also analyze the employee's emotional data and estimate the stress level. This can reduce the employee's stress and protect their health.

[0036] The suggestion unit can estimate an employee's level of fatigue and suggest appropriate timing for taking a break. The suggestion unit, for example, estimates an employee's level of fatigue and suggests appropriate timing for taking a break. For example, the suggestion unit analyzes the employee's behavioral record to estimate the level of fatigue. The suggestion unit can also analyze the employee's health data to estimate the level of fatigue. The suggestion unit can also analyze the employee's self-reported data to estimate the level of fatigue. This makes it possible to appropriately manage employee fatigue and protect their health.

[0037] The suggestion unit can analyze the behavioral records to identify bottlenecks in operations and propose improvement measures. The suggestion unit, for example, analyzes the behavioral records to identify bottlenecks in operations and propose improvement measures. For example, the suggestion unit analyzes the behavioral records of employees to identify bottlenecks in operations. The suggestion unit can also analyze the content of employees' work to identify bottlenecks in operations. The suggestion unit can also analyze employees' work patterns to identify bottlenecks in operations. This makes it possible to identify bottlenecks in operations and improve efficiency.

[0038] The suggestion unit can evaluate the skill level of an employee based on the behavioral record and suggest an appropriate training program. The suggestion unit, for example, evaluates the skill level of an employee based on the behavioral record and suggests an appropriate training program. For example, the suggestion unit analyzes the behavioral record of an employee and evaluates the skill level. The suggestion unit can also analyze the work content of an employee and evaluate the skill level. The suggestion unit can also analyze the work pattern of an employee and evaluate the skill level. In this way, the quality of work can be improved by evaluating the skill level of an employee and providing appropriate training.

[0039] The proposal unit can design a career path for an employee based on the behavioral record and support long-term growth. The proposal unit can, for example, design a career path for an employee based on the behavioral record and support long-term growth. For example, the proposal unit can analyze the behavioral record of an employee and design a career path. The proposal unit can also analyze the work content of an employee and design a career path. The proposal unit can also analyze the work patterns of an employee and design a career path. In this way, by designing a career path for an employee and supporting their long-term growth, it is possible to improve employee motivation and the quality of work.

[0040] The proposal unit can share behavioral records with other medical institutions and introduce best practices. For example, the proposal unit can share business procedures that have been successful at other medical institutions and introduce them at its own facility. The proposal unit can also propose business improvement measures based on successful cases at other medical institutions. The proposal unit can also formulate and introduce best practices in collaboration with other medical institutions. In this way, the quality of business operations can be improved by sharing behavioral records and introducing best practices.

[0041] The suggestion unit can analyze employee motivation using the emotion estimation function and propose measures to improve motivation. The suggestion unit, for example, uses the emotion estimation function to analyze employee motivation and propose measures to improve motivation. For example, the suggestion unit analyzes employee behavior records and emotion data to estimate motivation. The suggestion unit can also analyze employee work content and estimate motivation. The suggestion unit can also analyze employee work patterns and estimate motivation. In this way, the quality of work can be improved by analyzing employee motivation and proposing measures to improve it.

[0042] The suggestion unit can monitor employee health data in real time and immediately issue a warning if an abnormality is detected. The suggestion unit, for example, monitors employee health data in real time and immediately issue a warning if an abnormality is detected. For example, the suggestion unit monitors an employee's heart rate and blood pressure and detects abnormalities. The suggestion unit can also monitor an employee's body temperature and detect abnormalities. The suggestion unit can also monitor an employee's health check results and detect abnormalities. This makes it possible to monitor employee health data in real time and respond immediately if an abnormality occurs.

[0043] The suggestion unit can analyze employee behavior patterns, predict the risk of accidents, and suggest preventive measures. The suggestion unit, for example, analyzes employee behavior patterns, predicts the risk of accidents, and suggests preventive measures. For example, the suggestion unit analyzes employee behavior records and predicts the risk of accidents. The suggestion unit can also analyze employee work content and predict the risk of accidents. The suggestion unit can also analyze employee work patterns and predict the risk of accidents. In this way, by analyzing employee behavior patterns, predicting the risk of accidents, and suggesting preventive measures, it is possible to prevent accidents from occurring.

[0044] The suggestion unit can analyze the stress level of an employee and suggest counseling for stress reduction. The suggestion unit, for example, analyzes the stress level of an employee and suggests counseling for stress reduction. For example, the suggestion unit analyzes the employee's behavioral record and emotional data to estimate the stress level. The suggestion unit can also analyze the employee's health data to estimate the stress level. The suggestion unit can also analyze the employee's self-reported data to estimate the stress level. In this way, the employee's health can be protected by analyzing the employee's stress level and suggesting appropriate counseling.

[0045] The proposal unit can create an individual health plan based on the employee's health data and support the maintenance of health. The proposal unit, for example, creates an individual health plan based on the employee's health data and supports the maintenance of health. For example, the proposal unit analyzes the employee's health data and creates an individual health plan. The proposal unit can also analyze the employee's health checkup results and create an individual health plan. The proposal unit can also analyze the employee's self-reported data and create an individual health plan. In this way, the health of employees can be protected by creating an individual health plan based on the employee's health data and supporting the maintenance of health.

[0046] The suggestion unit can analyze employee behavior records, automatically generate a safety training program, and periodically update it. The suggestion unit, for example, analyzes employee behavior records, automatically generate a safety training program, and periodically update it. For example, the suggestion unit analyzes employee behavior records and creates a training program for high-risk behavior. The suggestion unit can also analyze past accident data and create a safety training program. The suggestion unit can also analyze employee work content and create a safety training program. In this way, by analyzing employee behavior records, automatically generating a safety training program, and periodically updating it, employee safety can be protected.

[0047] The suggestion unit can use the emotion estimation function to analyze the emotional state of an employee and provide emotional support. The suggestion unit, for example, uses the emotion estimation function to analyze the emotional state of an employee and provide emotional support. For example, the suggestion unit analyzes the employee's behavioral record and emotion data to estimate the emotional state. The suggestion unit can also analyze the employee's work content and estimate the emotional state. The suggestion unit can also analyze the employee's work pattern and estimate the emotional state. In this way, by analyzing the employee's emotional state and providing emotional support, it is possible to improve the health and motivation of employees.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The system can further include a voice recognition unit. The voice recognition unit recognizes voice instructions from employees and transmits the instructions to the work support unit. For example, an employee can give voice instructions to start monitoring a patient's vital signs. The voice recognition unit can also accept voice instructions to change a medication administration schedule. Furthermore, the voice recognition unit can also provide voice instructions to assist with patient transportation. This allows employees to give instructions without using their hands, improving work efficiency.

[0050] The system may further include an environmental monitoring unit. The environmental monitoring unit monitors the temperature, humidity, and lighting conditions in the patient room and makes suggestions to maintain an appropriate environment. For example, the environmental monitoring unit may suggest turning on the air conditioner if the temperature in the room is too high, or suggest using a humidifier if the humidity is too low, or suggest adjusting the lighting if the lighting is too dim. This helps maintain a comfortable environment for the patient and improves the quality of care.

[0051] The system can further include a prediction unit. The prediction unit predicts future workloads based on employee behavior records and proposes countermeasures in advance. For example, the prediction unit can analyze employees' past work patterns and predict that workloads will be concentrated at specific times. The prediction unit can also predict future health risks based on employees' health data. Furthermore, the prediction unit can propose efficient travel routes based on employees' movement history. This reduces employees' workloads in advance and enables efficient business operations.

[0052] The system may further include a feedback section. The feedback section collects feedback from employees and uses it to improve the system. For example, the feedback section allows employees to submit opinions about the functions of the business support section. The feedback section also allows employees to evaluate the proposals made by the proposal section. The feedback section also allows employees to give their opinions about the analysis results of the analysis section. This allows the system to reflect employee opinions and evolve into a more user-friendly and effective system.

[0053] The system can further include a learning unit. The learning unit provides individual learning programs based on employee behavior records. For example, the learning unit can analyze an employee's work patterns and suggest training to improve necessary skills. The learning unit can also provide knowledge for maintaining health based on the employee's health data. Furthermore, the learning unit can teach efficient transportation methods based on the employee's movement history. This can improve the employee's skills and knowledge and increase the quality of work.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The operational support department uses an AI assistant to assist with operations. For example, the operational support department monitors patients' vital signs, manages medication schedules, and assists patients with transportation. Step 2: The behavior record storage unit stores employee behavior records, such as which tasks were performed at what times, what kind of care was provided to which patients, and the employee's movement history. Step 3: The analysis unit analyzes the behavioral records stored by the behavioral record storage unit, for example, analyzing the employee's work patterns, workload, and health data. Step 4: The suggestion unit makes suggestions and warnings to protect employee safety and health based on the results of the analysis by the analysis unit. For example, it may suggest taking a break if there are signs of overwork, suggest redistribution of work if a particular task is too burdensome for an employee, or warn of health risks.

[0056] (Example 2) A system according to an embodiment of the present invention is a system that uses an AI assistant to assist with work in medical and nursing care settings and protects employees by utilizing accumulated behavioral records. This system not only uses the AI ​​assistant to improve work efficiency and reduce the burden on employees, but also analyzes the behavioral records to make suggestions and warnings to protect the safety and health of employees. As a result, the system can improve work efficiency, reduce the burden on employees, and protect their safety and health.

[0057] The system according to the embodiment includes a task support unit, an action record storage unit, an analysis unit, and a suggestion unit. The task support unit uses an AI assistant to assist with tasks. For example, the task support unit monitors patients' vital signs. The task support unit can also manage medication administration schedules. The task support unit can also provide transportation support for patients. The action record storage unit stores employee action records. For example, the action record storage unit records which tasks were performed and when. The action record storage unit can also record what care was provided to which patient. The action record storage unit can also record the movement history of employees. The analysis unit analyzes the action records stored by the action record storage unit. For example, the analysis unit analyzes the work patterns of employees. The analysis unit can also analyze the workload of employees. The analysis unit can also analyze health data of employees. The suggestion unit makes suggestions and warnings to protect the safety and health of employees based on the results of the analysis by the analysis unit. For example, the suggestion unit may suggest taking a break if signs of overwork are observed. The suggestion unit may also suggest reviewing the allocation of work if a particular task is too burdensome for an employee. The suggestion unit may also warn of health risks to employees. This allows the system according to the embodiment to assist with work using an AI assistant and protect the safety and health of employees by analyzing behavioral records. For example, the system may analyze an employee's work patterns and suggest an efficient work schedule. The system may also analyze an employee's health data and warn of health risks. The system may also analyze an employee's behavioral records to detect signs of overwork and suggest taking a break.

[0058] The task support unit can monitor a patient's vital signs, manage a medication administration schedule, and assist with patient transportation. The task support unit, for example, monitors a patient's vital signs. For example, the task support unit monitors a patient's heart rate. The task support unit can also monitor a patient's blood pressure. The task support unit can also monitor a patient's body temperature. The task support unit, for example, manages a medication administration schedule. For example, the task support unit manages a schedule of medications to be administered to a patient. The task support unit can also record the time of medication administration. The task support unit can also manage the dosage of medication. The task support unit, for example, supports patient transportation. For example, the task support unit assists in operating a wheelchair. The task support unit can also guide the patient's travel route. The task support unit can also support patient transportation. In this way, by monitoring a patient's vital signs, managing a medication administration schedule, and assisting with patient transportation, it is possible to improve work efficiency and the quality of patient care.

[0059] The analysis unit can analyze the behavioral records to understand the work patterns and workload of employees. The analysis unit, for example, analyzes the behavioral records to understand the work patterns of employees. For example, the analysis unit analyzes which tasks the employees performed at what time. The analysis unit can also analyze what kind of care the employees provided to which patients. The analysis unit can also analyze the movement history of employees. The analysis unit, for example, analyzes the behavioral records to understand the workload of employees. For example, the analysis unit analyzes how much time the employees spent on each task. The analysis unit can also analyze how much effort the employees spent on each task. The analysis unit can also analyze the health data of employees to understand the workload of employees. In this way, by understanding the work patterns and workload of employees, it is possible to improve work efficiency and reduce the workload of employees.

[0060] The suggestion unit can suggest that an employee take a break when signs of overwork are observed. The suggestion unit, for example, suggests that an employee take a break when signs of overwork are observed. For example, the suggestion unit analyzes the employee's behavioral records to detect signs of overwork. The suggestion unit can also analyze the employee's health data to detect signs of overwork. The suggestion unit can also analyze the employee's stress level to detect signs of overwork. This makes it possible to prevent employees from overworking and protect their health.

[0061] The suggestion unit can automatically set the priority of work and propose a schedule for efficiently progressing the work. The suggestion unit, for example, automatically sets the priority of work and proposes a schedule for efficiently progressing the work. For example, the suggestion unit analyzes employee behavior records to set the priority of work. The suggestion unit can also analyze employee work content and propose an efficient schedule. The suggestion unit can also analyze employee work patterns and propose an efficient schedule. This can improve work efficiency and reduce the burden on employees.

[0062] The suggestion unit can monitor the patient's condition in real time and immediately issue a warning if an abnormality occurs. The suggestion unit, for example, monitors the patient's condition in real time and immediately issue a warning if an abnormality occurs. For example, the suggestion unit monitors the patient's vital signs and detects abnormalities. The suggestion unit can also monitor the patient's behavior and detect abnormalities. The suggestion unit can also monitor the patient's health data and detect abnormalities. This makes it possible to monitor the patient's condition in real time and respond immediately if an abnormality occurs.

[0063] The suggestion unit can analyze the stress level of an employee and make suggestions for stress reduction. The suggestion unit can, for example, analyze the stress level of an employee and make suggestions for stress reduction. For example, the suggestion unit can analyze the employee's behavioral record and estimate the stress level. The suggestion unit can also analyze the employee's health data and estimate the stress level. The suggestion unit can also analyze the employee's emotional data and estimate the stress level. This can reduce the employee's stress and protect their health.

[0064] The suggestion unit can estimate an employee's level of fatigue and suggest appropriate timing for taking a break. The suggestion unit, for example, estimates an employee's level of fatigue and suggests appropriate timing for taking a break. For example, the suggestion unit analyzes the employee's behavioral record to estimate the level of fatigue. The suggestion unit can also analyze the employee's health data to estimate the level of fatigue. The suggestion unit can also analyze the employee's self-reported data to estimate the level of fatigue. This makes it possible to appropriately manage employee fatigue and protect their health.

[0065] The suggestion unit can analyze the behavioral records to identify bottlenecks in operations and propose improvement measures. The suggestion unit, for example, analyzes the behavioral records to identify bottlenecks in operations and propose improvement measures. For example, the suggestion unit analyzes the behavioral records of employees to identify bottlenecks in operations. The suggestion unit can also analyze the content of employees' work to identify bottlenecks in operations. The suggestion unit can also analyze employees' work patterns to identify bottlenecks in operations. This makes it possible to identify bottlenecks in operations and improve efficiency.

[0066] The suggestion unit can evaluate the skill level of an employee based on the behavioral record and suggest an appropriate training program. The suggestion unit, for example, evaluates the skill level of an employee based on the behavioral record and suggests an appropriate training program. For example, the suggestion unit analyzes the behavioral record of an employee and evaluates the skill level. The suggestion unit can also analyze the work content of an employee and evaluate the skill level. The suggestion unit can also analyze the work pattern of an employee and evaluate the skill level. In this way, the quality of work can be improved by evaluating the skill level of an employee and providing appropriate training.

[0067] The proposal unit can design a career path for an employee based on the behavioral record and support long-term growth. The proposal unit can, for example, design a career path for an employee based on the behavioral record and support long-term growth. For example, the proposal unit can analyze the behavioral record of an employee and design a career path. The proposal unit can also analyze the work content of an employee and design a career path. The proposal unit can also analyze the work patterns of an employee and design a career path. In this way, by designing a career path for an employee and supporting their long-term growth, it is possible to improve employee motivation and the quality of work.

[0068] The proposal unit can share behavioral records with other medical institutions and introduce best practices. For example, the proposal unit can share business procedures that have been successful at other medical institutions and introduce them at its own facility. The proposal unit can also propose business improvement measures based on successful cases at other medical institutions. The proposal unit can also formulate and introduce best practices in collaboration with other medical institutions. In this way, the quality of business operations can be improved by sharing behavioral records and introducing best practices.

[0069] The suggestion unit can analyze employee motivation using the emotion estimation function and propose measures to improve motivation. The suggestion unit, for example, uses the emotion estimation function to analyze employee motivation and propose measures to improve motivation. For example, the suggestion unit analyzes employee behavior records and emotion data to estimate motivation. The suggestion unit can also analyze employee work content and estimate motivation. The suggestion unit can also analyze employee work patterns and estimate motivation. In this way, the quality of work can be improved by analyzing employee motivation and proposing measures to improve it.

[0070] The suggestion unit can monitor employee health data in real time and immediately issue a warning if an abnormality is detected. The suggestion unit, for example, monitors employee health data in real time and immediately issue a warning if an abnormality is detected. For example, the suggestion unit monitors an employee's heart rate and blood pressure and detects abnormalities. The suggestion unit can also monitor an employee's body temperature and detect abnormalities. The suggestion unit can also monitor an employee's health check results and detect abnormalities. This makes it possible to monitor employee health data in real time and respond immediately if an abnormality occurs.

[0071] The suggestion unit can analyze employee behavior patterns, predict the risk of accidents, and suggest preventive measures. The suggestion unit, for example, analyzes employee behavior patterns, predicts the risk of accidents, and suggests preventive measures. For example, the suggestion unit analyzes employee behavior records and predicts the risk of accidents. The suggestion unit can also analyze employee work content and predict the risk of accidents. The suggestion unit can also analyze employee work patterns and predict the risk of accidents. In this way, by analyzing employee behavior patterns, predicting the risk of accidents, and suggesting preventive measures, it is possible to prevent accidents from occurring.

[0072] The suggestion unit can analyze the stress level of an employee and suggest counseling for stress reduction. The suggestion unit, for example, analyzes the stress level of an employee and suggests counseling for stress reduction. For example, the suggestion unit analyzes the employee's behavioral record and emotional data to estimate the stress level. The suggestion unit can also analyze the employee's health data to estimate the stress level. The suggestion unit can also analyze the employee's self-reported data to estimate the stress level. In this way, the employee's health can be protected by analyzing the employee's stress level and suggesting appropriate counseling.

[0073] The proposal unit can create an individual health plan based on the employee's health data and support the maintenance of health. The proposal unit, for example, creates an individual health plan based on the employee's health data and supports the maintenance of health. For example, the proposal unit analyzes the employee's health data and creates an individual health plan. The proposal unit can also analyze the employee's health checkup results and create an individual health plan. The proposal unit can also analyze the employee's self-reported data and create an individual health plan. In this way, the health of employees can be protected by creating an individual health plan based on the employee's health data and supporting the maintenance of health.

[0074] The suggestion unit can analyze employee behavior records, automatically generate a safety training program, and periodically update it. The suggestion unit, for example, analyzes employee behavior records, automatically generate a safety training program, and periodically update it. For example, the suggestion unit analyzes employee behavior records and creates a training program for high-risk behavior. The suggestion unit can also analyze past accident data and create a safety training program. The suggestion unit can also analyze employee work content and create a safety training program. In this way, by analyzing employee behavior records, automatically generating a safety training program, and periodically updating it, employee safety can be protected.

[0075] The suggestion unit can use the emotion estimation function to analyze the emotional state of an employee and provide emotional support. The suggestion unit, for example, uses the emotion estimation function to analyze the emotional state of an employee and provide emotional support. For example, the suggestion unit analyzes the employee's behavioral record and emotion data to estimate the emotional state. The suggestion unit can also analyze the employee's work content and estimate the emotional state. The suggestion unit can also analyze the employee's work pattern and estimate the emotional state. In this way, by analyzing the employee's emotional state and providing emotional support, it is possible to improve the health and motivation of employees.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The system can further include a voice recognition unit. The voice recognition unit recognizes voice instructions from employees and transmits the instructions to the work support unit. For example, an employee can give voice instructions to start monitoring a patient's vital signs. The voice recognition unit can also accept voice instructions to change a medication administration schedule. Furthermore, the voice recognition unit can also provide voice instructions to assist with patient transportation. This allows employees to give instructions without using their hands, improving work efficiency.

[0078] The system may further include an environmental monitoring unit. The environmental monitoring unit monitors the temperature, humidity, and lighting conditions in the patient room and makes suggestions to maintain an appropriate environment. For example, the environmental monitoring unit may suggest turning on the air conditioner if the temperature in the room is too high, or suggest using a humidifier if the humidity is too low, or suggest adjusting the lighting if the lighting is too dim. This helps maintain a comfortable environment for the patient and improves the quality of care.

[0079] The system can further include a prediction unit. The prediction unit predicts future workloads based on employee behavior records and proposes countermeasures in advance. For example, the prediction unit can analyze employees' past work patterns and predict that workloads will be concentrated at specific times. The prediction unit can also predict future health risks based on employees' health data. Furthermore, the prediction unit can propose efficient travel routes based on employees' movement history. This reduces employees' workloads in advance and enables efficient business operations.

[0080] The system may further include a feedback section. The feedback section collects feedback from employees and uses it to improve the system. For example, the feedback section allows employees to submit opinions about the functions of the business support section. The feedback section also allows employees to evaluate the proposals made by the proposal section. The feedback section also allows employees to give their opinions about the analysis results of the analysis section. This allows the system to reflect employee opinions and evolve into a more user-friendly and effective system.

[0081] The system can further include a learning unit. The learning unit provides individual learning programs based on employee behavior records. For example, the learning unit can analyze an employee's work patterns and suggest training to improve necessary skills. The learning unit can also provide knowledge for maintaining health based on the employee's health data. Furthermore, the learning unit can teach efficient transportation methods based on the employee's movement history. This can improve the employee's skills and knowledge and increase the quality of work.

[0082] The suggestion unit can estimate the user's emotions and suggest appropriate relaxation methods based on the estimated user's emotions. For example, the suggestion unit can analyze the employee's behavioral record and emotional data and suggest music for relaxation when stress levels are high. The suggestion unit can also analyze the employee's health data and suggest deep breathing or stretching for relaxation. Furthermore, the suggestion unit can suggest short breaks for relaxation based on the employee's emotional data. This can reduce employee stress and protect their health.

[0083] The suggestion unit can estimate the user's emotions and suggest an appropriate communication method based on the estimated user's emotions. For example, the suggestion unit can analyze an employee's behavioral record and emotional data and suggest communication in a relaxed atmosphere when the employee is emotionally unstable. The suggestion unit can also analyze an employee's health data and suggest a calm response when the employee is emotionally excited. Furthermore, the suggestion unit can suggest words of encouragement when the employee is feeling down based on the employee's emotional data. This can promote appropriate communication according to the employee's emotions and improve the workplace atmosphere.

[0084] The suggestion unit can estimate the user's emotions and provide appropriate feedback based on the estimated user's emotions. For example, the suggestion unit can analyze the employee's behavioral record and emotional data and provide calm feedback when the employee is emotionally excited. The suggestion unit can also analyze the employee's health data and provide encouraging feedback when the employee is emotionally depressed. Furthermore, the suggestion unit can provide constructive feedback based on the employee's emotional data when the employee is emotionally stable. This makes it possible to provide appropriate feedback according to the employee's emotions and improve the quality of work.

[0085] The suggestion unit can estimate the user's emotions and suggest appropriate break timing based on the estimated user's emotions. For example, the suggestion unit can analyze the employee's behavioral record and emotional data to suggest a short break if the employee is emotionally excited. The suggestion unit can also analyze the employee's health data to suggest a break to refresh if the employee is emotionally depressed. Furthermore, the suggestion unit can also suggest regular breaks based on the employee's emotional data if the employee's emotions are stable. This makes it possible to provide appropriate breaks according to the employee's emotions and protect their health.

[0086] The suggestion unit can estimate the user's emotions and suggest appropriate work allocation based on the estimated user's emotions. For example, the suggestion unit can analyze an employee's behavioral record and emotional data to suggest less burdensome work when the employee is emotionally charged. The suggestion unit can also analyze an employee's health data to suggest relaxing work when the employee is emotionally depressed. Furthermore, the suggestion unit can also suggest normal work when the employee's emotions are stable based on the employee's emotional data. This allows for appropriate work allocation based on the employee's emotions, improving work efficiency and protecting the employee's health.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The operational support department uses an AI assistant to assist with operations. For example, the operational support department monitors patients' vital signs, manages medication schedules, and assists patients with transportation. Step 2: The behavior record storage unit stores employee behavior records, such as which tasks were performed at what times, what kind of care was provided to which patients, and the employee's movement history. Step 3: The analysis unit analyzes the behavioral records stored by the behavioral record storage unit, for example, analyzing the employee's work patterns, workload, and health data. Step 4: The suggestion unit makes suggestions and warnings to protect employee safety and health based on the results of the analysis by the analysis unit. For example, it may suggest taking a break if there are signs of overwork, suggest redistribution of work if a particular task is too burdensome for an employee, or warn of health risks.

[0089] 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.

[0090] 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.

[0091] 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.

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

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

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

[0105] 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.

[0106] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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."

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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]

[0156] 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. The Business Support Department uses AI assistants to assist with business operations, an action record storage unit that stores employee action records; an analysis unit that analyzes the behavior records stored by the behavior record storage unit; a suggestion unit that makes suggestions and warnings to protect the safety and health of employees based on the results of the analysis by the analysis unit. A system characterized by:

2. The business support department Monitor the patient's vital signs, manage the administration schedule of the medication, and assist with the patient's mobility.

2. The system of claim 1.

3. The analysis unit Analyze the behavioral records to understand the employee's work patterns and workload.

2. The system of claim 1.

4. The proposal unit Automatically prioritize the above tasks and propose a schedule for efficiently progressing the work.

2. The system of claim 1.

5. The proposal unit Analyze the employee's emotional state and provide emotional support 2. The system of claim 1.

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