system

Abstract

This innovative system enables efficient collaboration between caregivers and support robots, reducing the burden of caregiving tasks. [Solution] A care support robot system comprising: means for acquiring instructions from a caregiver via a voice input device; voice recognition means for converting acquired voice instructions into text data; natural language processing means for analyzing the content of the caregiver's instructions and identifying care tasks; task scheduling means for optimizing tasks and determining the execution order; instruction transmission means for transmitting the identified tasks to the support robot and causing it to execute; situation awareness means for monitoring the surrounding environment using sensors and adjusting its actions according to the situation; and anomaly detection means for sending feedback to the care support system and monitoring in real time.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, in the field of caregiving, the burden of dealing with the elderly and providing services has increased, and the overwork and stress of caregivers have also increased. Behind this background, it can be cited that caregivers often work alone, and it is difficult to efficiently manage and perform tasks. Also, despite the progress of robot technology, there is a problem in that there is a lack of an appropriate interface and task management method for caregivers to cooperate smoothly with robots. As a result, caregivers spend a lot of time and effort, and as a result, the quality of care may decline. Therefore, there is a demand for providing a system in which a caregiver and a support robot can effectively cooperate with each other.

Means for Solving the Problems

[0005] This invention provides an innovative system for efficiently coordinating caregivers and support robots to reduce the burden of caregiving tasks. Specifically, it acquires voice instructions from caregivers through a voice input device and converts them into text data using a voice recognition means. Furthermore, this text data is analyzed by a natural language processing means to identify caregiving tasks. These identified tasks are optimized by a task scheduling means and transmitted as instructions to the caregiving robot. The robot monitors the environment using a situation awareness means and adjusts its actions as needed. It also sends real-time feedback using an anomaly detection means, and can immediately notify the caregiver if an anomaly occurs. As a result, the caregiver's work becomes more efficient, the quality of care improves, and a comfortable environment is created for both caregivers and elderly people.

[0006] A "care support robot" is a robotic device developed to assist or replace caregiving tasks, and it has functions to efficiently carry out caregiving work in cooperation with caregivers.

[0007] A "voice input device" is a device that acquires voice information and processes it as a digital signal, and is a means of collecting user voice commands.

[0008] "Speech recognition means" refers to a technology or system that converts speech into text data, and has the function of analyzing the acquired speech information and outputting it as text information.

[0009] "Natural language processing means" refers to technologies or methods for analyzing the meaning of text data and understanding the user's intent.

[0010] A "task scheduling means" is a technology or system for determining procedures and sequences in order to efficiently manage and execute identified tasks.

[0011] "Instruction transmission means" refers to a communication means or interface for conveying analyzed tasks and schedules to a support robot.

[0012] "Situation assessment means" refers to functions and technologies that collect information about the surrounding environment using sensors and cameras to understand the current situation.

[0013] An "anomaly detection method" is a technology or system that analyzes generated data to identify unusual situations or problems and takes appropriate action as needed. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the language used in the following description will be described.

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0018] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention provides a system that introduces care support robots into caregiving tasks to reduce the burden on caregivers. This system is equipped with functions that allow the robot to appropriately perform caregiving tasks based on the caregiver's voice instructions.

[0036] First, the user (caregiver) gives voice instructions to the robot. For example, they might say, "Please give the medicine to the elderly person." The voice is then picked up by the device via the robot's built-in microphone.

[0037] This audio data is then sent to a server and converted into text data by speech recognition. For example, the audio "Please give the medicine to the elderly person" is converted into the text instruction "Prepare the medicine for the elderly person." This text is then analyzed by natural language processing to identify the intended caregiving task.

[0038] Next, the server uses task scheduling to organize the identified tasks into executable steps. This includes a process of optimization that takes into account priorities and the caregiver's schedule. This information is then sent back to the terminal using instruction transmission means, ready to be executed by the robot.

[0039] The device uses situational awareness, sensors, and cameras to take appropriate actions based on its surroundings. For example, it can determine the location of a person in a room and move to that location. The robot can retrieve medication from a shelf and safely hand it to the elderly person.

[0040] Furthermore, during task execution, the terminal sends data collected through sensors to the server in real time for safety and execution status checks. If an anomaly is detected by the anomaly detection mechanism, the user is immediately informed. This process is designed to allow the robot to react to unexpected situations, such as when an elderly person fails to respond.

[0041] Such a system allows caregivers to efficiently carry out caregiving tasks in cooperation with care robots, thereby reducing their workload. Furthermore, real-time anomaly detection and notification improve the quality and safety of care.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users give voice instructions to the caregiving robot for specific caregiving tasks. For example, they might say, "Please give medication to an elderly person." The voice is picked up by the device via the robot's built-in microphone.

[0045] Step 2:

[0046] The terminal processes the acquired voice input in real time and prepares it to be sent to the server as digital audio data. The audio data is compressed and securely transferred.

[0047] Step 3:

[0048] The server converts the received voice data into text data using speech recognition technology. Here, the instruction "hand over the medicine" is translated into a specific action such as "take it out and take it to the designated location."

[0049] Step 4:

[0050] The server inputs the converted text into a natural language processing model, which then analyzes the caregiver's instructions in detail. The analysis results identify the most appropriate care tasks and necessary resources.

[0051] Step 5:

[0052] The server applies a task scheduling algorithm to optimize the execution order of identified tasks. If necessary, it coordinates with other priority tasks and incorporates them into the daily task schedule.

[0053] Step 6:

[0054] The server sends the analysis results and details of the scheduled tasks to the terminal and provides the instructions necessary for the robot to perform specific actions.

[0055] Step 7:

[0056] Before starting to operate according to instructions, the device uses its built-in sensors and camera to assess its surroundings. It verifies the location of the subject and prepares to safely perform the task.

[0057] Step 8:

[0058] The device begins performing a task. For example, it might retrieve medication from a shelf and deliver it to an elderly person. While operating, it monitors its surroundings with sensors and takes evasive action as needed.

[0059] Step 9:

[0060] The terminal sends data related to the task being run to the server in real time. The server analyzes the data using an anomaly detection algorithm and quickly notifies the user if an anomaly occurs.

[0061] Step 10:

[0062] Users can check notifications from the server and, if necessary, issue modifications or additional instructions to caregiving tasks via their devices. This process ensures the safety and efficiency of caregiving operations.

[0063] (Example 1)

[0064] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0065] In modern care settings, caregivers are busy and required to efficiently manage multiple care tasks. However, many tasks are manual, placing a heavy burden on caregivers. Furthermore, ensuring that care support equipment operates correctly while maintaining real-time safety is challenging, highlighting the need to improve both the quality and safety of care.

[0066] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0067] In this invention, the server includes a natural language processing means for analyzing the caregiver's instructions and identifying caregiving tasks, a task scheduling means for optimizing tasks and determining the execution order, and a means for collecting execution status data and providing information feedback to the caregiver. This makes it possible to reduce the workload of caregivers while enabling real-time situation monitoring and ensuring safety.

[0068] "Care support equipment" refers to hardware and software systems used to support and streamline the caregiving tasks performed by caregivers.

[0069] A "voice input mechanism" refers to a device or system that acquires voice data and processes it as digital information.

[0070] "Voice conversion means" refers to a technology or device that analyzes acquired voice data and converts it into text data.

[0071] "Natural language processing" refers to technologies that analyze text data to understand its intent and meaning, and then link that understanding to specific actions or tasks.

[0072] "Task scheduling methods" refer to techniques and methods for determining the order and priority of tasks in order to efficiently carry out specified caregiving tasks.

[0073] The "instruction transmission means" is a function that transmits necessary work instructions from the server to the support equipment and controls the equipment to ensure it operates properly.

[0074] "Environmental recognition means" refers to technologies that use sensors and cameras to monitor the surrounding environment and appropriately adjust the operation of equipment.

[0075] An "anomaly detection method" is a technology for monitoring the operation of systems and equipment in real time and detecting potential problems or anomalies.

[0076] This invention is a system that uses care support equipment. This system receives voice instructions from the caregiver and automatically performs specific care tasks using the support equipment. A specific embodiment of this system is described below.

[0077] First, the user gives voice commands to the care support device. These voice commands are given in the form of, "Please give the medicine to the elderly person." This voice data is captured through a voice input mechanism built into the device. The captured voice data is then converted into text data by a voice conversion means. Specifically, voice recognition software can be used, and for example, by using a commercially available voice recognition API, highly accurate voice input becomes possible.

[0078] The converted text data is analyzed by a natural language processing (NLP) system on the server. This system may utilize a natural language processing library; for example, an open-source NLP library could be used. This process identifies specific caregiving tasks from the voice instructions.

[0079] Next, the identified tasks are optimized by the server's task scheduling mechanism. This ensures that tasks are executed in the optimal order based on task priority and the caregiver's schedule.

[0080] The device uses environmental awareness to monitor its surroundings with sensors and cameras and adjust its movements accordingly. This allows the assistive device to safely move to the elderly person and perform designated tasks. Specifically, this includes actions such as a robotic arm retrieving medication from a shelf and safely handing it to the elderly person.

[0081] Furthermore, the system's anomaly detection mechanism monitors task progress in real time and immediately provides feedback to the user if an anomaly is detected. This allows for a quick response even if unexpected problems occur.

[0082] As a concrete example, consider a scenario where the instruction is given, "Please deliver the remote control to the elderly person." Upon receiving this instruction, the system identifies the location of the remote control, the robot travels to that location, and performs a series of actions to deliver the remote control to the elderly person.

[0083] For generated AI models, the system's operation can be verified using the following prompts: "Describe how the robot would deliver lunch to an elderly person," and "Explain the response procedure when a caregiving robot detects an anomaly."

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] The user gives voice commands to the caregiving support device. The input is a voice command such as, "Please give the medicine to the elderly person." The output is recorded as voice data in the terminal. Here, the terminal uses its built-in voice input mechanism to acquire the voice in digital format.

[0087] Step 2:

[0088] The terminal sends the acquired audio data to the server. The server receives the audio data and converts it into text data using a speech-to-text conversion device. The input is digital audio data, and the output is the text "Please give the medicine to the elderly person." This process uses speech recognition software to convert the audio information into text information.

[0089] Step 3:

[0090] The server receives text data and uses natural language processing to identify caregiving tasks. The input is text data converted from speech, and the output is the content of the identified caregiving tasks. This process uses a natural language processing library to analyze the intent and purpose of the instructions and connect them to specific tasks.

[0091] Step 4:

[0092] The server optimizes the identified care tasks using a task scheduling mechanism. The input is the identified tasks, and the output is a task list with the execution order determined. At this stage, the server optimizes the execution procedure, taking into account task priorities and feasible schedules.

[0093] Step 5:

[0094] The terminal receives instructions from the server and uses environmental recognition means to understand its surroundings. The input is a task execution instruction from the server, and the output is the executed action. In this case, the assistive device uses sensors and cameras to recognize its surroundings and safely moves to the person in need.

[0095] Step 6:

[0096] The terminal sends data obtained during task execution to the server, which is then monitored in real time by anomaly detection measures. The input is execution status data collected by sensors, and the output is detected anomalies or problems. If an anomaly is detected, the server immediately provides feedback to the user and takes action to prompt necessary responses.

[0097] (Application Example 1)

[0098] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0099] In modern caregiving, there is a need to reduce the burden on caregivers and improve the efficiency of their work. However, caregivers are overwhelmed with many tasks, making it particularly difficult to efficiently perform daily caregiving tasks while ensuring the safety of the elderly. Furthermore, the caregiving environment is constantly changing, requiring real-time situational awareness. However, there is a lack of visual information, making it difficult for caregivers to intuitively grasp the situation.

[0100] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0101] In this invention, the server includes means for acquiring information via an acoustic input mechanism, acoustic recognition means for converting it into text data, natural language processing means for identifying caregiving tasks, and dynamic visualization means. This allows caregivers to always have access to smart visual information, enabling the safe and efficient performance of caregiving tasks.

[0102] A "care support equipment system" is a group of devices with a set of functions designed to support caregivers in care settings.

[0103] An "acoustic input mechanism" refers to a device that uses a microphone and its peripherals to acquire voice commands.

[0104] "Text data" refers to an information format in which instructions obtained via voice are converted into textual information.

[0105] "Acoustic recognition means" refers to a technical method that analyzes acquired audio data and converts it into text data.

[0106] "Natural language processing means" refers to information processing techniques that analyze text data, understand instructions, and identify caregiving tasks.

[0107] A "task planning method" is a technique for planning the execution of tasks in the optimal order, based on the caregiver's instructions.

[0108] A "means of instruction transmission" refers to a communication method for transmitting and executing a specified task to a physical assistive device.

[0109] A "sensing device" is a mechanism that includes sensors for monitoring the surrounding environment.

[0110] A "situation assessment means" is a means of adjusting the operation of equipment based on information acquired from a sensing device.

[0111] An "anomaly detection method" is a technical technique for monitoring and detecting anomalies and problems in a care support system in real time.

[0112] "Dynamic visualization means" refers to displays and methods of displaying information for providing real-time visual information to caregivers.

[0113] This invention realizes advanced functions to support caregivers in a care support equipment system. The system consists of a voice input mechanism, acoustic recognition means, natural language processing means, task planning means, instruction transmission means, sensing device, situation understanding means, anomaly detection means, and dynamic visualization means.

[0114] The server acquires speech emitted by the caregiver via an acoustic input mechanism and converts that speech into text data using an acoustic recognition means. Next, a natural language processing means identifies care tasks from the text data. The identified tasks are organized into the optimal execution order by a task planning means and communicated to the support equipment by an instruction transmission means. A sensing device monitors the surrounding environment, and a situation awareness means adjusts the operation of the equipment accordingly. An anomaly detection means monitors for anomalies in real time and notifies the caregiver as needed. Furthermore, a dynamic visualization means provides the caregiver with real-time visual information to support their understanding of the situation.

[0115] This system functions effectively when caregivers use smart glasses such as Google Glass®. The Google Cloud Speech-to-Text API is used for speech recognition, and the Python spaCy library is used for natural language processing. The display on the smart glasses is utilized to provide visual information.

[0116] For example, if a caregiver instructs, "Prepare the medication after breakfast," the system will select the appropriate medication from its inventory and safely deliver it to the elderly person while providing visual information. An example of a prompt sentence to input into the generating AI model would be, "Schedule a task to prepare the elderly person's medication after breakfast. Please be aware of soy allergies."

[0117] In this way, the system reduces the burden on caregivers and improves the efficiency of caregiving tasks.

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] The user inputs voice commands using the acoustic input mechanism of the smart glasses. This voice data is captured by the device via the microphone. The input is in voice format.

[0121] Step 2:

[0122] The device sends audio data to the server. The server uses the Google Cloud Speech-to-Text API to convert the audio data into text data. This process outputs text data from the audio data.

[0123] Step 3:

[0124] The server analyzes the acquired text data using natural language processing. Through the spaCy library, the text data is analyzed, and the user's intended caregiving task is identified. The text data is then output as caregiving task information.

[0125] Step 4:

[0126] The server uses task planning tools to optimize identified caregiving tasks into executable procedures. Prioritization and time allocation are considered when determining the execution order. The input is task information, and the output is optimized execution instructions.

[0127] Step 5:

[0128] These operational instructions are transmitted to the assistive device through an instruction transmission means. The assistive device performs the identified care task, translating the instructions as input into physical actions.

[0129] Step 6:

[0130] The terminal monitors the surrounding environment using sensing devices and adjusts the operation of assistive devices through situational awareness mechanisms. The input is environmental data, and the output is operation instructions.

[0131] Step 7:

[0132] The server monitors the situation in real time through anomaly detection mechanisms and notifies the user if a problem is found. It analyzes data from sensors to identify anomalies or problems. The input is sensor data, and the output is notification information.

[0133] Step 8:

[0134] Users can obtain visual information on the smart glasses' display through dynamic visualization means. This visual information is updated in real time, helping users understand their surroundings. The input is visual data, and the output is displayed information.

[0135] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0136] This invention incorporates an emotion engine into a care support robot system to evaluate the caregiver's emotional state in real time and provide task management and feedback tailored to that state. This system enables more flexible and responsive collaboration between caregivers and robots, thereby reducing the burden on caregivers and improving the quality of care.

[0137] Specifically, the user, a caregiver, gives voice commands to the care support robot. For example, they might say, "Please prepare tea for the elderly person." This voice is received by the microphone in the device. Furthermore, an emotion engine analyzes the caregiver's emotional state in real time from the voice.

[0138] This audio data is sent to a server and converted into text data by speech recognition. The converted text data is input into a natural language processing system to identify the caregiving task intended by the caregiver. Simultaneously, the output of the emotion engine is analyzed on the server to understand the caregiver's current emotional state (e.g., stress, fatigue, relief).

[0139] The server adjusts task scheduling based on the acquired emotional information. If the emotional state indicates stress, the server can change the priority of tasks and delay the execution of some tasks to reduce the caregiver's burden. Once tasks are determined, the results are sent back to the terminal, and the robot begins its specific actions.

[0140] The device prepares tasks to be performed upon instruction and uses sensors and cameras to understand the surrounding environment. For example, it can locate the elderly person in question and choose a safe route to move along. Based on the output of its emotion engine, the robot can also respond to caregivers using appropriate words and voice tones.

[0141] As the task is actually executed, the device continuously sends data to the server via sensors. An anomaly detection algorithm is then used to immediately notify the user if any problems occur. In addition, if the emotion engine indicates that the caregiver's emotions are particularly unstable, the server can suggest relaxation methods and support to the caregiver.

[0142] This entire process allows caregivers to provide care in a more efficient and less psychologically burdensome way, thereby creating an optimal care environment.

[0143] The following describes the processing flow.

[0144] Step 1:

[0145] The user gives voice commands to the caregiving robot. These voice commands are specific, such as "Please prepare tea for the elderly person." The voice is recorded on the device via the robot's microphone.

[0146] Step 2:

[0147] The device converts voice input into digital voice data and inputs it into the emotion engine. The emotion engine analyzes the user's emotional state from the voice and generates the results as data.

[0148] Step 3:

[0149] Voice data and emotional state data are sent to the server. The server uses speech recognition to convert the voice data into text, and then performs natural language processing to identify caregiving tasks.

[0150] Step 4:

[0151] The server analyzes the output of the emotion engine to identify the user's emotional state (e.g., stress, exhilaration, fatigue). Based on this emotional information, it re-evaluates the task priorities and execution order.

[0152] Step 5:

[0153] If the identified emotional state indicates stress, the server uses task scheduling mechanisms to optimize the order in which tasks are executed, taking measures such as postponing less important tasks.

[0154] Step 6:

[0155] The server sends optimized task instructions to the terminal. Based on these instructions, the terminal prepares to perform the specific caregiving tasks.

[0156] Step 7:

[0157] The device uses built-in sensors and cameras to assess the surrounding environment in real time. For example, it can determine the current location of an elderly person and decide on the safest route to take.

[0158] Step 8:

[0159] When performing caregiving tasks, the device selects appropriate voice tones and words based on the output of the emotion engine, responding to the user. This facilitates effective communication between the caregiver and the user.

[0160] Step 9:

[0161] While the task is running, the terminal continuously sends data collected by its sensors to the server. The server executes an anomaly detection algorithm and immediately notifies the user if an anomaly occurs.

[0162] Step 10:

[0163] If the emotion engine detects that the caregiver is experiencing unstable emotions, the server will offer suggestions for relaxation techniques and support. This feedback aims to further reduce the caregiver's psychological burden.

[0164] (Example 2)

[0165] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0166] In the field of elderly care, the psychological and physical burden on caregivers is increasing, and in particular, the significant impact of their emotional state on the efficiency and quality of caregiving tasks is a major problem. Conventional care support systems do not take into account the emotional state of caregivers in their task management and feedback, which poses a risk of lowering the quality of care. Therefore, there is a need for a system that enables flexible and efficient task management that responds to the emotional state of caregivers, thereby reducing their burden and improving the quality of care.

[0167] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0168] In this invention, the server includes emotion analysis means for evaluating the caregiver's emotional state from voice data, task scheduling means for optimizing tasks and determining the execution order based on the caregiver's emotional state, and means for suggesting relaxation methods to the caregiver when it is determined that the caregiver's emotional state is unstable. This enables task management and feedback that takes into account the caregiver's emotional state, thereby reducing the caregiver's burden and improving the quality of care.

[0169] A "care support robot system" is a system that includes automated mechanisms designed to assist caregivers and utilizes robotic technology to perform caregiving tasks.

[0170] An "audio input device" is a device used to convert audio signals into digital data, and microphones are among the devices that fall under this category.

[0171] "Speech recognition means" refers to technology that analyzes acquired speech data and converts it into corresponding text data.

[0172] "Natural language processing" refers to technologies that analyze text data, interpret human language, and extract intended commands or information.

[0173] "Emotional analysis methods" refer to techniques that evaluate a caregiver's emotional state from their voice and behavior, and infer psychological states such as stress and fatigue.

[0174] A "task scheduling method" is a method for determining the execution order of identified tasks and arranging them in an efficient and optimal manner.

[0175] "Instruction transmission means" refers to technology that transmits a determined task to a support robot and provides instructions for performing specific actions.

[0176] "Situation awareness means" refers to technology that uses sensors and cameras to collect information about the surrounding environment and adjusts the robot's movements based on the information obtained.

[0177] An "anomaly detection method" is a technology that analyzes sensor data to detect anomalies and problems in real time.

[0178] "Methods for suggesting relaxation techniques" refer to techniques that take into account the caregiver's emotional state and provide advice and methods to reduce psychological stress.

[0179] This invention combines a voice input device, voice recognition means, natural language processing means, emotion analysis means, task scheduling means, instruction transmission means, situation understanding means, anomaly detection means, and means for proposing relaxation methods in a care support robot system. The aim of this system is to reduce the burden on caregivers and improve the quality of care.

[0180] The voice input device is used to obtain instructions from the caregiver, and this is the microphone built into the terminal. The caregiver gives voice instructions into the terminal, and the system receives that information.

[0181] The speech recognition system performs the process of converting speech data sent to the server into text data. To achieve high-precision speech recognition, for example, a general-purpose cloud-based speech recognition API is utilized.

[0182] The natural language processing system analyzes the caregiver's instructions using text data generated by the speech recognition system. In this process, it identifies caregiving tasks and derives specific steps for their execution.

[0183] The emotion analysis system is designed to assess the caregiver's emotional state by analyzing their tone of voice and word choice, and measuring emotions such as stress and fatigue in real time. This assessment allows the system to understand the caregiver's psychological state.

[0184] The task scheduling mechanism optimizes identified caregiving tasks and determines their execution priority according to the caregiver's emotional state. This provides a method to reduce the burden on caregivers.

[0185] The instruction transmission means transmits the determined task to the terminal and gives specific instructions to the robot to start its operation.

[0186] Situational awareness measures involve using sensors and cameras to monitor the environment and adjust the robot's actions accordingly. This allows tasks to be performed safely and efficiently.

[0187] The anomaly detection system analyzes sensor data during operation and has the function to immediately notify the user if an anomaly occurs. Furthermore, if the system determines through emotion analysis that the caregiver's emotions are unstable, it supports the caregiver by suggesting relaxation methods.

[0188] For example, if a user gives an instruction to a care support robot such as "Prepare Mr. Tanaka's medicine," the voice input device acquires this instruction and sends the data to a server. The server converts it to text using speech recognition, identifies the task using natural language processing, and evaluates the stress level through sentiment analysis. A task scheduling system provides flexible task management that takes the user's emotional state into consideration, and a command transmission system instructs the robot to take specific actions. An example of a prompt message is: "Voice instruction: You have been asked to deliver tea to an elderly person. If the user may be experiencing stress, please adjust the task schedule."

[0189] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0190] Step 1:

[0191] The user gives voice commands to the caregiving robot to request a task. The device's microphone receives these voice commands and records them as digital audio data. In this step, the input is the user's voice, and the output is the digitized audio data.

[0192] Step 2:

[0193] The terminal sends the acquired digital audio data to the server. The server receives this data as input and uses a speech recognition engine to convert the audio into text data. In this case, the audio waveform becomes a string of characters. The output is the converted text data.

[0194] Step 3:

[0195] The server inputs the text data generated by speech recognition into a natural language processing engine. As part of the data processing, it performs calculations to extract the most appropriate caregiving task from the text. The output of this step is task identification. For example, a specific action such as "serve tea" is determined.

[0196] Step 4:

[0197] The server inputs text data along with the tone and speed of the voice into an emotion analysis system to evaluate the emotional state. Data calculations determine whether the user is experiencing stress. The output of this step is an indicator of the user's emotional state.

[0198] Step 5:

[0199] The server performs task scheduling based on acquired emotional information. It optimizes the priority of multiple tasks, taking into account the output of the emotional analysis tool. For example, if the stress level is high, some tasks can be postponed. The output is the optimized task schedule.

[0200] Step 6:

[0201] The server transmits the coordinated task schedule to the terminal using a command transmission mechanism. The terminal uses this as input to instruct the robot to perform specific actions. For example, it might be instructed to prepare tea and deliver it safely. The output is the robot starting its operation.

[0202] Step 7:

[0203] The device monitors the environment using sensors and cameras and adjusts its operation as needed. Sensor data is sent to a server, where an anomaly detection algorithm identifies problems. The input is environmental information, and the output is adjusted operation or anomaly notifications.

[0204] Step 8:

[0205] If any abnormalities or problems are detected, or if the sentiment analysis indicates that the user is in an unstable state, the server will notify the user accordingly. In particular, actions will be taken to suggest relaxation methods based on the user's emotional state. The output will consist of notifications and suggestions to the user.

[0206] (Application Example 2)

[0207] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0208] In the work environment, the increasing physical and mental burden on workers is a problem. Furthermore, in situations where work efficiency is low and errors are more likely, there is a lack of mechanisms to appropriately assess workers' emotional states and provide corresponding support. Conventional systems cannot consider changes in workers' emotions when scheduling tasks or providing appropriate support, making it difficult to optimize the work environment. Therefore, there is a need to understand workers' emotional states in real time and optimize their work accordingly.

[0209] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0210] In this invention, the server includes an emotion analysis means for evaluating the worker's emotional state in real time, a task scheduling means for optimizing tasks and determining the execution order based on emotions, and a means for proposing support methods based on the worker's emotional state. This makes it possible to improve work efficiency and reduce stress while taking the worker's emotional state into consideration.

[0211] A "care support robot system" is a robot designed to assist caregivers, reducing the burden on caregivers when giving instructions and enabling them to efficiently perform care tasks.

[0212] A "voice input device" is a device used to obtain voice instructions from caregivers, and its role is to convert voice into digital data using hardware such as a microphone.

[0213] "Speech recognition means" refers to a technology that analyzes acquired speech data and converts it into text data; it is a process of extracting meaning from speech.

[0214] "Natural language processing means" refers to technology that analyzes text data to understand the instructions given by caregivers and identify the necessary care tasks.

[0215] "Emotion analysis means" refers to a technology that evaluates the emotional state of workers and caregivers in real time, identifying emotions based on voice and behavioral data.

[0216] A "task scheduling method" is a process that optimizes tasks based on collected information and determines the optimal execution order.

[0217] A "means of transmitting instructions" is a mechanism for transmitting specific tasks to support devices or robots and causing them to perform those tasks.

[0218] "Environmental recognition means" refers to technology that uses sensors to monitor the surrounding environment and adjusts actions according to the situation.

[0219] An "anomaly detection method" is a technology that monitors the environment and work status in real time within a support system and detects anomalies or problems when they occur.

[0220] "Means of proposing support methods" refers to techniques for proposing appropriate support and relaxation methods based on the emotional state of the worker obtained through emotion analysis.

[0221] The system that realizes this invention is designed for caregiving support robot systems, enabling caregivers to perform tasks in a more efficient and less stressful way. The system mainly consists of a server, terminals, and users, each executing specific processes based on their respective roles.

[0222] The server receives voice instructions from caregivers via a voice input device and converts them into text data using speech recognition technology. The text data is then analyzed using natural language processing technology to derive specific care tasks. The emotional state of the worker or caregiver is evaluated in real time by an emotion analysis system, and scheduling is performed to reflect this emotional state. This scheduling optimizes tasks and provides the most efficient route to reduce the caregiver's burden.

[0223] The terminal monitors the surrounding environment through sensors and adjusts the robot's movements accordingly. It also incorporates anomaly detection mechanisms, immediately notifying the user (caregiver) if an anomaly or problem occurs. If the caregiver's emotional state is unstable, a system for suggesting support methods is activated, providing the worker with appropriate relaxation techniques and assistance.

[0224] Users input voice commands into a terminal and perform tasks according to the task scheduling suggested by the care support robot. The quality of care is improved and stress is reduced through the provision of feedback based on emotional state. For example, if instructed to prepare tea for an elderly person, the robot will include procedures to locate the elderly person and deliver the tea via a safe route.

[0225] An example of an input prompt for a generating AI model is, "Generate logic that identifies work instructions and emotional states from voice input and proposes an optimal work schedule based on that information," which is used in system design.

[0226] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0227] Step 1:

[0228] The server receives voice instructions given by the caregiver (user) through the terminal's voice input device. Voice data is acquired as input, and this voice is converted into text data by speech recognition software. The instructions are then returned as output in text format.

[0229] Step 2:

[0230] The server processes the text data obtained in Step 1 using natural language processing techniques. From the text data as input, it identifies caregiving tasks and identifies corresponding tasks. A specific task list is created as output. Contextual analysis is performed by a generative AI model during this process.

[0231] Step 3:

[0232] The server applies emotion analysis tools to analyze emotional information such as the tone and speed of the user's voice collected by the terminal. Based on the input voice information, the emotion engine evaluates the user's emotional state in real time and quantifies emotional states such as stress and relief. The output is the user's emotional state data.

[0233] Step 4:

[0234] The server uses a task scheduling mechanism to determine task priorities based on the task list created in step 2 and the emotional state data obtained in step 3. It receives the user's emotional state and task information as input and outputs a task order optimized based on the emotional state. If the emotional state indicates stress, less burdensome tasks are prioritized.

[0235] Step 5:

[0236] The terminal transmits the task sequence determined in step 4 to the support device, and the robot performs actions based on the user's instructions. Receiving the task sequence as input, the robot performs specific care actions. For example, if the task is "prepare and deliver tea to an elderly person," the robot will choose a safe route and move accordingly.

[0237] Step 6:

[0238] The terminal uses sensors to monitor its surroundings and transmits information to the server in real time using situational awareness tools. It takes environmental condition data as input and uses it for safety assessment. The output ensures safe and effective robot actions.

[0239] Step 7:

[0240] The terminal uses anomaly detection means to detect anomalies during task execution and reports them to the server. Patterns of normal and abnormal states are provided as input, and when an anomaly is confirmed, an alert is output to notify the user. This allows for immediate action.

[0241] Example prompt for using a generative AI model: "Generate logic that identifies work instructions and emotional states from voice input and proposes an optimal work schedule based on that information."

[0242] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0243] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0244] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0245] [Second Embodiment]

[0246] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0247] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0248] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0249] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0250] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0251] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0252] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0253] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0254] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0255] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0256] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0257] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0258] This invention provides a system that introduces care support robots into caregiving tasks to reduce the burden on caregivers. This system is equipped with functions that allow the robot to appropriately perform caregiving tasks based on the caregiver's voice instructions.

[0259] First, the user (caregiver) gives voice instructions to the robot. For example, they might say, "Please give the medicine to the elderly person." The voice is then picked up by the device via the robot's built-in microphone.

[0260] This audio data is then sent to a server and converted into text data by speech recognition. For example, the audio "Please give the medicine to the elderly person" is converted into the text instruction "Prepare the medicine for the elderly person." This text is then analyzed by natural language processing to identify the intended caregiving task.

[0261] Next, the server uses task scheduling to organize the identified tasks into executable steps. This includes a process of optimization that takes into account priorities and the caregiver's schedule. This information is then sent back to the terminal using instruction transmission means, ready to be executed by the robot.

[0262] The device uses situational awareness, sensors, and cameras to take appropriate actions based on its surroundings. For example, it can determine the location of a person in a room and move to that location. The robot can retrieve medication from a shelf and safely hand it to the elderly person.

[0263] Furthermore, during task execution, the terminal sends data collected through sensors to the server in real time for safety and execution status checks. If an anomaly is detected by the anomaly detection mechanism, the user is immediately informed. This process is designed to allow the robot to react to unexpected situations, such as when an elderly person fails to respond.

[0264] Such a system allows caregivers to efficiently carry out caregiving tasks in cooperation with care robots, thereby reducing their workload. Furthermore, real-time anomaly detection and notification improve the quality and safety of care.

[0265] The following describes the processing flow.

[0266] Step 1:

[0267] Users give voice instructions to the caregiving robot for specific caregiving tasks. For example, they might say, "Please give medication to an elderly person." The voice is picked up by the device via the robot's built-in microphone.

[0268] Step 2:

[0269] The terminal processes the acquired voice input in real time and prepares it to be sent to the server as digital audio data. The audio data is compressed and securely transferred.

[0270] Step 3:

[0271] The server converts the received voice data into text data using speech recognition technology. Here, the instruction "hand over the medicine" is translated into a specific action such as "take it out and take it to the designated location."

[0272] Step 4:

[0273] The server inputs the converted text into a natural language processing model, which then analyzes the caregiver's instructions in detail. The analysis results identify the most appropriate care tasks and necessary resources.

[0274] Step 5:

[0275] The server applies a task scheduling algorithm to optimize the execution order of identified tasks. If necessary, it coordinates with other priority tasks and incorporates them into the daily task schedule.

[0276] Step 6:

[0277] The server sends the analysis results and details of the scheduled tasks to the terminal and provides the instructions necessary for the robot to perform specific actions.

[0278] Step 7:

[0279] Before starting to operate according to the instructions, the terminal evaluates the surrounding environment using built-in sensors and a camera. It confirms the location of the target person and prepares to safely perform the task.

[0280] Step 8:

[0281] The terminal starts to perform the task. For example, it takes medicine from the shelf and delivers it to the elderly person. During the operation, it monitors the surroundings with sensors and performs avoidance operations as necessary.

[0282] Step 9:

[0283] The terminal transmits the data related to the ongoing task to the server in real time. The server analyzes the data using an anomaly detection algorithm and promptly notifies the user if an anomaly occurs.

[0284] Step 10:

[0285] The user can check the notification from the server and issue corrections or additional instructions for the care task to the terminal as necessary. This process ensures the safety and efficiency of the care operation.

[0286] (Example 1)

[0287] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0288] In modern care settings, caregivers are busy and are required to efficiently manage multiple care tasks. However, there are many manual operations, which poses the problem of a large burden on caregivers. Also, it is difficult to ensure safety in real time while the care support equipment operates properly, and it is necessary to improve the quality and safety of care.

[0289] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0290] In this invention, the server includes a natural language processing means for analyzing the caregiver's instructions and identifying caregiving tasks, a task scheduling means for optimizing tasks and determining the execution order, and a means for collecting execution status data and providing information feedback to the caregiver. This makes it possible to reduce the workload of caregivers while enabling real-time situation monitoring and ensuring safety.

[0291] "Care support equipment" refers to hardware and software systems used to support and streamline the caregiving tasks performed by caregivers.

[0292] A "voice input mechanism" refers to a device or system that acquires voice data and processes it as digital information.

[0293] "Voice conversion means" refers to a technology or device that analyzes acquired voice data and converts it into text data.

[0294] "Natural language processing" refers to technologies that analyze text data to understand its intent and meaning, and then link that understanding to specific actions or tasks.

[0295] "Task scheduling methods" refer to techniques and methods for determining the order and priority of tasks in order to efficiently carry out specified caregiving tasks.

[0296] The "instruction transmission means" is a function that transmits necessary work instructions from the server to the support equipment and controls the equipment to ensure it operates properly.

[0297] "Environmental recognition means" refers to technologies that use sensors and cameras to monitor the surrounding environment and appropriately adjust the operation of equipment.

[0298] An "anomaly detection method" is a technology for monitoring the operation of systems and equipment in real time and detecting potential problems or anomalies.

[0299] This invention is a system that uses care support equipment. This system receives voice instructions from the caregiver and automatically performs specific care tasks using the support equipment. A specific embodiment of this system is described below.

[0300] First, the user gives voice commands to the care support device. These voice commands are given in the form of, "Please give the medicine to the elderly person." This voice data is captured through a voice input mechanism built into the device. The captured voice data is then converted into text data by a voice conversion means. Specifically, voice recognition software can be used, and for example, by using a commercially available voice recognition API, highly accurate voice input becomes possible.

[0301] The converted text data is analyzed by a natural language processing (NLP) system on the server. This system may utilize a natural language processing library; for example, an open-source NLP library could be used. This process identifies specific caregiving tasks from the voice instructions.

[0302] Next, the identified tasks are optimized by the server's task scheduling mechanism. This ensures that tasks are executed in the optimal order based on task priority and the caregiver's schedule.

[0303] The device uses environmental awareness to monitor its surroundings with sensors and cameras and adjust its movements accordingly. This allows the assistive device to safely move to the elderly person and perform designated tasks. Specifically, this includes actions such as a robotic arm retrieving medication from a shelf and safely handing it to the elderly person.

[0304] Furthermore, the system's anomaly detection mechanism monitors task progress in real time and immediately provides feedback to the user if an anomaly is detected. This allows for a quick response even if unexpected problems occur.

[0305] As a specific example, consider the case where an instruction "Please bring the remote control to the elderly person" is given. Upon receiving this instruction, the system identifies the location of the remote control, and the robot moves to that location and performs a series of operations to deliver the remote control to the elderly person.

[0306] For the generative AI model, the operation of the system can be verified with the following prompt sentences: "Please describe how the robot delivers lunch to the elderly person." and "Please explain the response procedure when the care robot detects an abnormality."

[0307] The flow of the specific process in Example 1 will be described using FIG. 11.

[0308] Step 1:

[0309] The user gives a voice instruction to the care support device. The input is a voice instruction such as "Please hand the medicine to the elderly person." The output is recorded as voice data on the terminal. Here, the terminal uses the built-in voice input mechanism to acquire the voice in digital form.

[0310] Step 2:

[0311] The terminal sends the acquired voice data to the server. The server receives the voice data and converts it into text data using voice conversion means. The input is digital voice data, and the output is the text "Please hand the medicine to the elderly person." Voice recognition software is used for this process to perform the operation of converting voice information into text information.

[0312] Step 3:

[0313] The server receives the text data and identifies the care task using natural language analysis means. The input is the text data converted from voice, and the output is the content of the identified care task. In this process, a natural language processing library is used to analyze the intention and purpose of the instruction content and link it to a specific task.

[0314] Step 4:

[0315] The server optimizes the identified care tasks using a task scheduling mechanism. The input is the identified tasks, and the output is a task list with the execution order determined. At this stage, the server optimizes the execution procedure, taking into account task priorities and feasible schedules.

[0316] Step 5:

[0317] The terminal receives instructions from the server and uses environmental recognition means to understand its surroundings. The input is a task execution instruction from the server, and the output is the executed action. In this case, the assistive device uses sensors and cameras to recognize its surroundings and safely moves to the person in need.

[0318] Step 6:

[0319] The terminal sends data obtained during task execution to the server, which is then monitored in real time by anomaly detection measures. The input is execution status data collected by sensors, and the output is detected anomalies or problems. If an anomaly is detected, the server immediately provides feedback to the user and takes action to prompt necessary responses.

[0320] (Application Example 1)

[0321] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0322] In modern caregiving, there is a need to reduce the burden on caregivers and improve the efficiency of their work. However, caregivers are overwhelmed with many tasks, making it particularly difficult to efficiently perform daily caregiving tasks while ensuring the safety of the elderly. Furthermore, the caregiving environment is constantly changing, requiring real-time situational awareness. However, there is a lack of visual information, making it difficult for caregivers to intuitively grasp the situation.

[0323] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0324] In this invention, the server includes means for acquiring information via an acoustic input mechanism, acoustic recognition means for converting it into text data, natural language processing means for identifying caregiving tasks, and dynamic visualization means. This allows caregivers to always have access to smart visual information, enabling the safe and efficient performance of caregiving tasks.

[0325] A "care support equipment system" is a group of devices with a set of functions designed to support caregivers in care settings.

[0326] An "acoustic input mechanism" refers to a device that uses a microphone and its peripherals to acquire voice commands.

[0327] "Text data" refers to an information format in which instructions obtained via voice are converted into textual information.

[0328] "Acoustic recognition means" refers to a technical method that analyzes acquired audio data and converts it into text data.

[0329] "Natural language processing means" refers to information processing techniques that analyze text data, understand instructions, and identify caregiving tasks.

[0330] A "task planning method" is a technique for planning the execution of tasks in the optimal order, based on the caregiver's instructions.

[0331] A "means of instruction transmission" refers to a communication method for transmitting and executing a specified task to a physical assistive device.

[0332] A "sensing device" is a mechanism that includes sensors for monitoring the surrounding environment.

[0333] A "situation assessment means" is a means of adjusting the operation of equipment based on information acquired from a sensing device.

[0334] An "anomaly detection method" is a technical technique for monitoring and detecting anomalies and problems in a care support system in real time.

[0335] "Dynamic visualization means" refers to displays and methods of displaying information for providing real-time visual information to caregivers.

[0336] This invention realizes advanced functions to support caregivers in a care support equipment system. The system consists of a voice input mechanism, acoustic recognition means, natural language processing means, task planning means, instruction transmission means, sensing device, situation understanding means, anomaly detection means, and dynamic visualization means.

[0337] The server acquires speech emitted by the caregiver via an acoustic input mechanism and converts that speech into text data using an acoustic recognition means. Next, a natural language processing means identifies care tasks from the text data. The identified tasks are organized into the optimal execution order by a task planning means and communicated to the support equipment by an instruction transmission means. A sensing device monitors the surrounding environment, and a situation awareness means adjusts the operation of the equipment accordingly. An anomaly detection means monitors for anomalies in real time and notifies the caregiver as needed. Furthermore, a dynamic visualization means provides the caregiver with real-time visual information to support their understanding of the situation.

[0338] This system functions effectively when caregivers use smart glasses such as Google Glass. The Google Cloud Speech-to-Text API is used for speech recognition, and the Python spaCy library is used for natural language processing. The display on the smart glasses is utilized to provide visual information.

[0339] For example, if a caregiver instructs, "Prepare the medication after breakfast," the system will select the appropriate medication from its inventory and safely deliver it to the elderly person while providing visual information. An example of a prompt sentence to input into the generating AI model would be, "Schedule a task to prepare the elderly person's medication after breakfast. Please be aware of soy allergies."

[0340] In this way, the system reduces the burden on caregivers and improves the efficiency of caregiving tasks.

[0341] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0342] Step 1:

[0343] The user inputs voice commands using the acoustic input mechanism of the smart glasses. This voice data is captured by the device via the microphone. The input is in voice format.

[0344] Step 2:

[0345] The device sends audio data to the server. The server uses the Google Cloud Speech-to-Text API to convert the audio data into text data. This process outputs text data from the audio data.

[0346] Step 3:

[0347] The server analyzes the acquired text data using natural language processing. Through the spaCy library, the text data is analyzed, and the user's intended caregiving task is identified. The text data is then output as caregiving task information.

[0348] Step 4:

[0349] The server uses task planning tools to optimize identified caregiving tasks into executable procedures. Prioritization and time allocation are considered when determining the execution order. The input is task information, and the output is optimized execution instructions.

[0350] Step 5:

[0351] These operational instructions are transmitted to the assistive device through an instruction transmission means. The assistive device performs the identified care task, translating the instructions as input into physical actions.

[0352] Step 6:

[0353] The terminal monitors the surrounding environment using sensing devices and adjusts the operation of assistive devices through situational awareness mechanisms. The input is environmental data, and the output is operation instructions.

[0354] Step 7:

[0355] The server monitors the situation in real time through anomaly detection mechanisms and notifies the user if a problem is found. It analyzes data from sensors to identify anomalies or problems. The input is sensor data, and the output is notification information.

[0356] Step 8:

[0357] Users can obtain visual information on the smart glasses' display through dynamic visualization means. This visual information is updated in real time, helping users understand their surroundings. The input is visual data, and the output is displayed information.

[0358] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0359] This invention incorporates an emotion engine into a care support robot system to evaluate the caregiver's emotional state in real time and provide task management and feedback tailored to that state. This system enables more flexible and responsive collaboration between caregivers and robots, thereby reducing the burden on caregivers and improving the quality of care.

[0360] Specifically, the user, a caregiver, gives voice commands to the care support robot. For example, they might say, "Please prepare tea for the elderly person." This voice is received by the microphone in the device. Furthermore, an emotion engine analyzes the caregiver's emotional state in real time from the voice.

[0361] This audio data is sent to a server and converted into text data by speech recognition. The converted text data is input into a natural language processing system to identify the caregiving task intended by the caregiver. Simultaneously, the output of the emotion engine is analyzed on the server to understand the caregiver's current emotional state (e.g., stress, fatigue, relief).

[0362] The server adjusts task scheduling based on the acquired emotional information. If the emotional state indicates stress, the server can change the priority of tasks and delay the execution of some tasks to reduce the caregiver's burden. Once tasks are determined, the results are sent back to the terminal, and the robot begins its specific actions.

[0363] The device prepares tasks to be performed upon instruction and uses sensors and cameras to understand the surrounding environment. For example, it can locate the elderly person in question and choose a safe route to move along. Based on the output of its emotion engine, the robot can also respond to caregivers using appropriate words and voice tones.

[0364] As the task is actually executed, the device continuously sends data to the server via sensors. An anomaly detection algorithm is then used to immediately notify the user if any problems occur. In addition, if the emotion engine indicates that the caregiver's emotions are particularly unstable, the server can suggest relaxation methods and support to the caregiver.

[0365] This entire process allows caregivers to provide care in a more efficient and less psychologically burdensome way, thereby creating an optimal care environment.

[0366] The following describes the processing flow.

[0367] Step 1:

[0368] The user gives voice commands to the caregiving robot. These voice commands are specific, such as "Please prepare tea for the elderly person." The voice is recorded on the device via the robot's microphone.

[0369] Step 2:

[0370] The device converts voice input into digital voice data and inputs it into the emotion engine. The emotion engine analyzes the user's emotional state from the voice and generates the results as data.

[0371] Step 3:

[0372] Voice data and emotional state data are sent to the server. The server uses speech recognition to convert the voice data into text, and then performs natural language processing to identify caregiving tasks.

[0373] Step 4:

[0374] The server analyzes the output of the emotion engine to identify the user's emotional state (e.g., stress, exhilaration, fatigue). Based on this emotional information, it re-evaluates the task priorities and execution order.

[0375] Step 5:

[0376] If the identified emotional state indicates stress, the server uses task scheduling mechanisms to optimize the order in which tasks are executed, taking measures such as postponing less important tasks.

[0377] Step 6:

[0378] The server sends optimized task instructions to the terminal. Based on these instructions, the terminal prepares to perform the specific caregiving tasks.

[0379] Step 7:

[0380] The device uses built-in sensors and cameras to assess the surrounding environment in real time. For example, it can determine the current location of an elderly person and decide on the safest route to take.

[0381] Step 8:

[0382] When performing caregiving tasks, the device selects appropriate voice tones and words based on the output of the emotion engine, responding to the user. This facilitates effective communication between the caregiver and the user.

[0383] Step 9:

[0384] While the task is running, the terminal continuously sends data collected by its sensors to the server. The server executes an anomaly detection algorithm and immediately notifies the user if an anomaly occurs.

[0385] Step 10:

[0386] If the emotion engine detects that the caregiver is experiencing unstable emotions, the server will offer suggestions for relaxation techniques and support. This feedback aims to further reduce the caregiver's psychological burden.

[0387] (Example 2)

[0388] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0389] In the field of elderly care, the psychological and physical burden on caregivers is increasing, and in particular, the significant impact of their emotional state on the efficiency and quality of caregiving tasks is a major problem. Conventional care support systems do not take into account the emotional state of caregivers in their task management and feedback, which poses a risk of lowering the quality of care. Therefore, there is a need for a system that enables flexible and efficient task management that responds to the emotional state of caregivers, thereby reducing their burden and improving the quality of care.

[0390] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0391] In this invention, the server includes emotion analysis means for evaluating the caregiver's emotional state from voice data, task scheduling means for optimizing tasks and determining the execution order based on the caregiver's emotional state, and means for suggesting relaxation methods to the caregiver when it is determined that the caregiver's emotional state is unstable. This enables task management and feedback that takes into account the caregiver's emotional state, thereby reducing the caregiver's burden and improving the quality of care.

[0392] A "care support robot system" is a system that includes automated mechanisms designed to assist caregivers and utilizes robotic technology to perform caregiving tasks.

[0393] An "audio input device" is a device used to convert audio signals into digital data, and microphones are among the devices that fall under this category.

[0394] "Speech recognition means" refers to technology that analyzes acquired speech data and converts it into corresponding text data.

[0395] "Natural language processing" refers to technologies that analyze text data, interpret human language, and extract intended commands or information.

[0396] "Emotional analysis methods" refer to techniques that evaluate a caregiver's emotional state from their voice and behavior, and infer psychological states such as stress and fatigue.

[0397] A "task scheduling method" is a method for determining the execution order of identified tasks and arranging them in an efficient and optimal manner.

[0398] "Instruction transmission means" refers to technology that transmits a determined task to a support robot and provides instructions for performing specific actions.

[0399] "Situation awareness means" refers to technology that uses sensors and cameras to collect information about the surrounding environment and adjusts the robot's movements based on the information obtained.

[0400] An "anomaly detection method" is a technology that analyzes sensor data to detect anomalies and problems in real time.

[0401] "Methods for suggesting relaxation techniques" refer to techniques that take into account the caregiver's emotional state and provide advice and methods to reduce psychological stress.

[0402] This invention combines a voice input device, voice recognition means, natural language processing means, emotion analysis means, task scheduling means, instruction transmission means, situation understanding means, anomaly detection means, and means for proposing relaxation methods in a care support robot system. The aim of this system is to reduce the burden on caregivers and improve the quality of care.

[0403] The voice input device is used to obtain instructions from the caregiver, and this is the microphone built into the terminal. The caregiver gives voice instructions into the terminal, and the system receives that information.

[0404] The speech recognition system performs the process of converting speech data sent to the server into text data. To achieve high-precision speech recognition, for example, a general-purpose cloud-based speech recognition API is utilized.

[0405] The natural language processing system analyzes the caregiver's instructions using text data generated by the speech recognition system. In this process, it identifies caregiving tasks and derives specific steps for their execution.

[0406] The emotion analysis system is designed to assess the caregiver's emotional state by analyzing their tone of voice and word choice, and measuring emotions such as stress and fatigue in real time. This assessment allows the system to understand the caregiver's psychological state.

[0407] The task scheduling mechanism optimizes identified caregiving tasks and determines their execution priority according to the caregiver's emotional state. This provides a method to reduce the burden on caregivers.

[0408] The instruction transmission means transmits the determined task to the terminal and gives specific instructions to the robot to start its operation.

[0409] Situational awareness measures involve using sensors and cameras to monitor the environment and adjust the robot's actions accordingly. This allows tasks to be performed safely and efficiently.

[0410] The anomaly detection system analyzes sensor data during operation and has the function to immediately notify the user if an anomaly occurs. Furthermore, if the system determines through emotion analysis that the caregiver's emotions are unstable, it supports the caregiver by suggesting relaxation methods.

[0411] For example, if a user gives an instruction to a care support robot such as "Prepare Mr. Tanaka's medicine," the voice input device acquires this instruction and sends the data to a server. The server converts it to text using speech recognition, identifies the task using natural language processing, and evaluates the stress level through sentiment analysis. A task scheduling system provides flexible task management that takes the user's emotional state into consideration, and a command transmission system instructs the robot to take specific actions. An example of a prompt message is: "Voice instruction: You have been asked to deliver tea to an elderly person. If the user may be experiencing stress, please adjust the task schedule."

[0412] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0413] Step 1:

[0414] The user gives voice commands to the caregiving robot to request a task. The device's microphone receives these voice commands and records them as digital audio data. In this step, the input is the user's voice, and the output is the digitized audio data.

[0415] Step 2:

[0416] The terminal sends the acquired digital audio data to the server. The server receives this data as input and uses a speech recognition engine to convert the audio into text data. In this case, the audio waveform becomes a string of characters. The output is the converted text data.

[0417] Step 3:

[0418] The server inputs the text data generated by speech recognition into a natural language processing engine. As part of the data processing, it performs calculations to extract the most appropriate caregiving task from the text. The output of this step is task identification. For example, a specific action such as "serve tea" is determined.

[0419] Step 4:

[0420] The server inputs text data along with the tone and speed of the voice into an emotion analysis system to evaluate the emotional state. Data calculations determine whether the user is experiencing stress. The output of this step is an indicator of the user's emotional state.

[0421] Step 5:

[0422] The server performs task scheduling based on acquired emotional information. It optimizes the priority of multiple tasks, taking into account the output of the emotional analysis tool. For example, if the stress level is high, some tasks can be postponed. The output is the optimized task schedule.

[0423] Step 6:

[0424] The server transmits the coordinated task schedule to the terminal using a command transmission mechanism. The terminal uses this as input to instruct the robot to perform specific actions. For example, it might be instructed to prepare tea and deliver it safely. The output is the robot starting its operation.

[0425] Step 7:

[0426] The device monitors the environment using sensors and cameras and adjusts its operation as needed. Sensor data is sent to a server, where an anomaly detection algorithm identifies problems. The input is environmental information, and the output is adjusted operation or anomaly notifications.

[0427] Step 8:

[0428] If any abnormalities or problems are detected, or if the sentiment analysis indicates that the user is in an unstable state, the server will notify the user accordingly. In particular, actions will be taken to suggest relaxation methods based on the user's emotional state. The output will consist of notifications and suggestions to the user.

[0429] (Application Example 2)

[0430] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0431] In the work environment, the increasing physical and mental burden on workers is a problem. Furthermore, in situations where work efficiency is low and errors are more likely, there is a lack of mechanisms to appropriately assess workers' emotional states and provide corresponding support. Conventional systems cannot consider changes in workers' emotions when scheduling tasks or providing appropriate support, making it difficult to optimize the work environment. Therefore, there is a need to understand workers' emotional states in real time and optimize their work accordingly.

[0432] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0433] In this invention, the server includes an emotion analysis means for evaluating the worker's emotional state in real time, a task scheduling means for optimizing tasks and determining the execution order based on emotions, and a means for proposing support methods based on the worker's emotional state. This makes it possible to improve work efficiency and reduce stress while taking the worker's emotional state into consideration.

[0434] A "care support robot system" is a robot designed to assist caregivers, reducing the burden on caregivers when giving instructions and enabling them to efficiently perform care tasks.

[0435] A "voice input device" is a device used to obtain voice instructions from caregivers, and its role is to convert voice into digital data using hardware such as a microphone.

[0436] "Speech recognition means" refers to a technology that analyzes acquired speech data and converts it into text data; it is a process of extracting meaning from speech.

[0437] "Natural language processing means" refers to technology that analyzes text data to understand the instructions given by caregivers and identify the necessary care tasks.

[0438] "Emotion analysis means" refers to a technology that evaluates the emotional state of workers and caregivers in real time, identifying emotions based on voice and behavioral data.

[0439] A "task scheduling method" is a process that optimizes tasks based on collected information and determines the optimal execution order.

[0440] A "means of transmitting instructions" is a mechanism for transmitting specific tasks to support devices or robots and causing them to perform those tasks.

[0441] "Environmental recognition means" refers to technology that uses sensors to monitor the surrounding environment and adjusts actions according to the situation.

[0442] An "anomaly detection method" is a technology that monitors the environment and work status in real time within a support system and detects anomalies or problems when they occur.

[0443] "Means of proposing support methods" refers to techniques for proposing appropriate support and relaxation methods based on the emotional state of the worker obtained through emotion analysis.

[0444] The system that realizes this invention is designed for caregiving support robot systems, enabling caregivers to perform tasks in a more efficient and less stressful way. The system mainly consists of a server, terminals, and users, each executing specific processes based on their respective roles.

[0445] The server receives voice instructions from caregivers via a voice input device and converts them into text data using speech recognition technology. The text data is then analyzed using natural language processing technology to derive specific care tasks. The emotional state of the worker or caregiver is evaluated in real time by an emotion analysis system, and scheduling is performed to reflect this emotional state. This scheduling optimizes tasks and provides the most efficient route to reduce the caregiver's burden.

[0446] The terminal monitors the surrounding environment through sensors and adjusts the robot's movements accordingly. It also incorporates anomaly detection mechanisms, immediately notifying the user (caregiver) if an anomaly or problem occurs. If the caregiver's emotional state is unstable, a system for suggesting support methods is activated, providing the worker with appropriate relaxation techniques and assistance.

[0447] Users input voice commands into a terminal and perform tasks according to the task scheduling suggested by the care support robot. The quality of care is improved and stress is reduced through the provision of feedback based on emotional state. For example, if instructed to prepare tea for an elderly person, the robot will include procedures to locate the elderly person and deliver the tea via a safe route.

[0448] An example of an input prompt for a generating AI model is, "Generate logic that identifies work instructions and emotional states from voice input and proposes an optimal work schedule based on that information," which is used in system design.

[0449] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0450] Step 1:

[0451] The server receives voice instructions given by the caregiver (user) through the terminal's voice input device. Voice data is acquired as input, and this voice is converted into text data by speech recognition software. The instructions are then returned as output in text format.

[0452] Step 2:

[0453] The server processes the text data obtained in Step 1 using natural language processing techniques. From the text data as input, it identifies caregiving tasks and identifies corresponding tasks. A specific task list is created as output. Contextual analysis is performed by a generative AI model during this process.

[0454] Step 3:

[0455] The server applies emotion analysis tools to analyze emotional information such as the tone and speed of the user's voice collected by the terminal. Based on the input voice information, the emotion engine evaluates the user's emotional state in real time and quantifies emotional states such as stress and relief. The output is the user's emotional state data.

[0456] Step 4:

[0457] The server uses a task scheduling mechanism to determine task priorities based on the task list created in step 2 and the emotional state data obtained in step 3. It receives the user's emotional state and task information as input and outputs a task order optimized based on the emotional state. If the emotional state indicates stress, less burdensome tasks are prioritized.

[0458] Step 5:

[0459] The terminal transmits the task sequence determined in step 4 to the support device, and the robot performs actions based on the user's instructions. Receiving the task sequence as input, the robot performs specific care actions. For example, if the task is "prepare and deliver tea to an elderly person," the robot will choose a safe route and move accordingly.

[0460] Step 6:

[0461] The terminal uses sensors to monitor its surroundings and transmits information to the server in real time using situational awareness tools. It takes environmental condition data as input and uses it for safety assessment. The output ensures safe and effective robot actions.

[0462] Step 7:

[0463] The terminal uses anomaly detection means to detect anomalies during task execution and reports them to the server. Patterns of normal and abnormal states are provided as input, and when an anomaly is confirmed, an alert is output to notify the user. This allows for immediate action.

[0464] Example prompt for using a generative AI model: "Generate logic that identifies work instructions and emotional states from voice input and proposes an optimal work schedule based on that information."

[0465] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0466] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0467] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0468] [Third Embodiment]

[0469] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0470] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0471] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0472] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0473] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0474] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0475] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0476] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0477] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0478] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0479] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0480] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0481] This invention provides a system that introduces care support robots into caregiving tasks to reduce the burden on caregivers. This system is equipped with functions that allow the robot to appropriately perform caregiving tasks based on the caregiver's voice instructions.

[0482] First, the user (caregiver) gives voice instructions to the robot. For example, they might say, "Please give the medicine to the elderly person." The voice is then picked up by the device via the robot's built-in microphone.

[0483] This audio data is then sent to a server and converted into text data by speech recognition. For example, the audio "Please give the medicine to the elderly person" is converted into the text instruction "Prepare the medicine for the elderly person." This text is then analyzed by natural language processing to identify the intended caregiving task.

[0484] Next, the server uses task scheduling to organize the identified tasks into executable steps. This includes a process of optimization that takes into account priorities and the caregiver's schedule. This information is then sent back to the terminal using instruction transmission means, ready to be executed by the robot.

[0485] The device uses situational awareness, sensors, and cameras to take appropriate actions based on its surroundings. For example, it can determine the location of a person in a room and move to that location. The robot can retrieve medication from a shelf and safely hand it to the elderly person.

[0486] Furthermore, during task execution, the terminal sends data collected through sensors to the server in real time for safety and execution status checks. If an anomaly is detected by the anomaly detection mechanism, the user is immediately informed. This process is designed to allow the robot to react to unexpected situations, such as when an elderly person fails to respond.

[0487] Such a system allows caregivers to efficiently carry out caregiving tasks in cooperation with care robots, thereby reducing their workload. Furthermore, real-time anomaly detection and notification improve the quality and safety of care.

[0488] The following describes the processing flow.

[0489] Step 1:

[0490] Users give voice instructions to the caregiving robot for specific caregiving tasks. For example, they might say, "Please give medication to an elderly person." The voice is picked up by the device via the robot's built-in microphone.

[0491] Step 2:

[0492] The terminal processes the acquired voice input in real time and prepares it to be sent to the server as digital audio data. The audio data is compressed and securely transferred.

[0493] Step 3:

[0494] The server converts the received voice data into text data using speech recognition technology. Here, the instruction "hand over the medicine" is translated into a specific action such as "take it out and take it to the designated location."

[0495] Step 4:

[0496] The server inputs the converted text into a natural language processing model, which then analyzes the caregiver's instructions in detail. The analysis results identify the most appropriate care tasks and necessary resources.

[0497] Step 5:

[0498] The server applies a task scheduling algorithm to optimize the execution order of identified tasks. If necessary, it coordinates with other priority tasks and incorporates them into the daily task schedule.

[0499] Step 6:

[0500] The server sends the analysis results and details of the scheduled tasks to the terminal and provides the instructions necessary for the robot to perform specific actions.

[0501] Step 7:

[0502] Before starting to operate according to instructions, the device uses its built-in sensors and camera to assess its surroundings. It verifies the location of the subject and prepares to safely perform the task.

[0503] Step 8:

[0504] The device begins performing a task. For example, it might retrieve medication from a shelf and deliver it to an elderly person. While operating, it monitors its surroundings with sensors and takes evasive action as needed.

[0505] Step 9:

[0506] The terminal sends data related to the task being run to the server in real time. The server analyzes the data using an anomaly detection algorithm and quickly notifies the user if an anomaly occurs.

[0507] Step 10:

[0508] Users can check notifications from the server and, if necessary, issue modifications or additional instructions to caregiving tasks via their devices. This process ensures the safety and efficiency of caregiving operations.

[0509] (Example 1)

[0510] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0511] In modern care settings, caregivers are busy and required to efficiently manage multiple care tasks. However, many tasks are manual, placing a heavy burden on caregivers. Furthermore, ensuring that care support equipment operates correctly while maintaining real-time safety is challenging, highlighting the need to improve both the quality and safety of care.

[0512] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0513] In this invention, the server includes a natural language processing means for analyzing the caregiver's instructions and identifying caregiving tasks, a task scheduling means for optimizing tasks and determining the execution order, and a means for collecting execution status data and providing information feedback to the caregiver. This makes it possible to reduce the workload of caregivers while enabling real-time situation monitoring and ensuring safety.

[0514] "Care support equipment" refers to hardware and software systems used to support and streamline the caregiving tasks performed by caregivers.

[0515] A "voice input mechanism" refers to a device or system that acquires voice data and processes it as digital information.

[0516] "Voice conversion means" refers to a technology or device that analyzes acquired voice data and converts it into text data.

[0517] "Natural language processing" refers to technologies that analyze text data to understand its intent and meaning, and then link that understanding to specific actions or tasks.

[0518] "Task scheduling methods" refer to techniques and methods for determining the order and priority of tasks in order to efficiently carry out specified caregiving tasks.

[0519] The "instruction transmission means" is a function that transmits necessary work instructions from the server to the support equipment and controls the equipment to ensure it operates properly.

[0520] "Environmental recognition means" refers to technologies that use sensors and cameras to monitor the surrounding environment and appropriately adjust the operation of equipment.

[0521] An "anomaly detection method" is a technology for monitoring the operation of systems and equipment in real time and detecting potential problems or anomalies.

[0522] This invention is a system that uses care support equipment. This system receives voice instructions from the caregiver and automatically performs specific care tasks using the support equipment. A specific embodiment of this system is described below.

[0523] First, the user gives voice commands to the care support device. These voice commands are given in the form of, "Please give the medicine to the elderly person." This voice data is captured through a voice input mechanism built into the device. The captured voice data is then converted into text data by a voice conversion means. Specifically, voice recognition software can be used, and for example, by using a commercially available voice recognition API, highly accurate voice input becomes possible.

[0524] The converted text data is analyzed by a natural language processing (NLP) system on the server. This system may utilize a natural language processing library; for example, an open-source NLP library could be used. This process identifies specific caregiving tasks from the voice instructions.

[0525] Next, the identified tasks are optimized by the server's task scheduling mechanism. This ensures that tasks are executed in the optimal order based on task priority and the caregiver's schedule.

[0526] The device uses environmental awareness to monitor its surroundings with sensors and cameras and adjust its movements accordingly. This allows the assistive device to safely move to the elderly person and perform designated tasks. Specifically, this includes actions such as a robotic arm retrieving medication from a shelf and safely handing it to the elderly person.

[0527] Furthermore, the system's anomaly detection mechanism monitors task progress in real time and immediately provides feedback to the user if an anomaly is detected. This allows for a quick response even if unexpected problems occur.

[0528] As a concrete example, consider a scenario where the instruction is given, "Please deliver the remote control to the elderly person." Upon receiving this instruction, the system identifies the location of the remote control, the robot travels to that location, and performs a series of actions to deliver the remote control to the elderly person.

[0529] For generated AI models, the system's operation can be verified using the following prompts: "Describe how the robot would deliver lunch to an elderly person," and "Explain the response procedure when a caregiving robot detects an anomaly."

[0530] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0531] Step 1:

[0532] The user gives voice commands to the caregiving support device. The input is a voice command such as, "Please give the medicine to the elderly person." The output is recorded as voice data in the terminal. Here, the terminal uses its built-in voice input mechanism to acquire the voice in digital format.

[0533] Step 2:

[0534] The terminal sends the acquired audio data to the server. The server receives the audio data and converts it into text data using a speech-to-text conversion device. The input is digital audio data, and the output is the text "Please give the medicine to the elderly person." This process uses speech recognition software to convert the audio information into text information.

[0535] Step 3:

[0536] The server receives text data and uses natural language processing to identify caregiving tasks. The input is text data converted from speech, and the output is the content of the identified caregiving tasks. This process uses a natural language processing library to analyze the intent and purpose of the instructions and connect them to specific tasks.

[0537] Step 4:

[0538] The server optimizes the identified care tasks using a task scheduling mechanism. The input is the identified tasks, and the output is a task list with the execution order determined. At this stage, the server optimizes the execution procedure, taking into account task priorities and feasible schedules.

[0539] Step 5:

[0540] The terminal receives instructions from the server and uses environmental recognition means to understand its surroundings. The input is a task execution instruction from the server, and the output is the executed action. In this case, the assistive device uses sensors and cameras to recognize its surroundings and safely moves to the person in need.

[0541] Step 6:

[0542] The terminal sends data obtained during task execution to the server, which is then monitored in real time by anomaly detection measures. The input is execution status data collected by sensors, and the output is detected anomalies or problems. If an anomaly is detected, the server immediately provides feedback to the user and takes action to prompt necessary responses.

[0543] (Application Example 1)

[0544] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0545] In modern caregiving, there is a need to reduce the burden on caregivers and improve the efficiency of their work. However, caregivers are overwhelmed with many tasks, making it particularly difficult to efficiently perform daily caregiving tasks while ensuring the safety of the elderly. Furthermore, the caregiving environment is constantly changing, requiring real-time situational awareness. However, there is a lack of visual information, making it difficult for caregivers to intuitively grasp the situation.

[0546] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0547] In this invention, the server includes means for acquiring information via an acoustic input mechanism, acoustic recognition means for converting it into text data, natural language processing means for identifying caregiving tasks, and dynamic visualization means. This allows caregivers to always have access to smart visual information, enabling the safe and efficient performance of caregiving tasks.

[0548] A "care support equipment system" is a group of devices with a set of functions designed to support caregivers in care settings.

[0549] An "acoustic input mechanism" refers to a device that uses a microphone and its peripherals to acquire voice commands.

[0550] "Text data" refers to an information format in which instructions obtained via voice are converted into textual information.

[0551] "Acoustic recognition means" refers to a technical method that analyzes acquired audio data and converts it into text data.

[0552] "Natural language processing means" refers to information processing techniques that analyze text data, understand instructions, and identify caregiving tasks.

[0553] A "task planning method" is a technique for planning the execution of tasks in the optimal order, based on the caregiver's instructions.

[0554] A "means of instruction transmission" refers to a communication method for transmitting and executing a specified task to a physical assistive device.

[0555] A "sensing device" is a mechanism that includes sensors for monitoring the surrounding environment.

[0556] A "situation assessment means" is a means of adjusting the operation of equipment based on information acquired from a sensing device.

[0557] An "anomaly detection method" is a technical technique for monitoring and detecting anomalies and problems in a care support system in real time.

[0558] "Dynamic visualization means" refers to displays and methods of displaying information for providing real-time visual information to caregivers.

[0559] This invention realizes advanced functions to support caregivers in a care support equipment system. The system consists of a voice input mechanism, acoustic recognition means, natural language processing means, task planning means, instruction transmission means, sensing device, situation understanding means, anomaly detection means, and dynamic visualization means.

[0560] The server acquires speech emitted by the caregiver via an acoustic input mechanism and converts that speech into text data using an acoustic recognition means. Next, a natural language processing means identifies care tasks from the text data. The identified tasks are organized into the optimal execution order by a task planning means and communicated to the support equipment by an instruction transmission means. A sensing device monitors the surrounding environment, and a situation awareness means adjusts the operation of the equipment accordingly. An anomaly detection means monitors for anomalies in real time and notifies the caregiver as needed. Furthermore, a dynamic visualization means provides the caregiver with real-time visual information to support their understanding of the situation.

[0561] This system functions effectively when caregivers use smart glasses such as Google Glass. The Google Cloud Speech-to-Text API is used for speech recognition, and the Python spaCy library is used for natural language processing. The display on the smart glasses is utilized to provide visual information.

[0562] For example, if a caregiver instructs, "Prepare the medication after breakfast," the system will select the appropriate medication from its inventory and safely deliver it to the elderly person while providing visual information. An example of a prompt sentence to input into the generating AI model would be, "Schedule a task to prepare the elderly person's medication after breakfast. Please be aware of soy allergies."

[0563] In this way, the system reduces the burden on caregivers and improves the efficiency of caregiving tasks.

[0564] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0565] Step 1:

[0566] The user inputs voice commands using the acoustic input mechanism of the smart glasses. This voice data is captured by the device via the microphone. The input is in voice format.

[0567] Step 2:

[0568] The device sends audio data to the server. The server uses the Google Cloud Speech-to-Text API to convert the audio data into text data. This process outputs text data from the audio data.

[0569] Step 3:

[0570] The server analyzes the acquired text data using natural language processing. Through the spaCy library, the text data is analyzed, and the user's intended caregiving task is identified. The text data is then output as caregiving task information.

[0571] Step 4:

[0572] The server uses task planning tools to optimize identified caregiving tasks into executable procedures. Prioritization and time allocation are considered when determining the execution order. The input is task information, and the output is optimized execution instructions.

[0573] Step 5:

[0574] These operational instructions are transmitted to the assistive device through an instruction transmission means. The assistive device performs the identified care task, translating the instructions as input into physical actions.

[0575] Step 6:

[0576] The terminal monitors the surrounding environment using sensing devices and adjusts the operation of assistive devices through situational awareness mechanisms. The input is environmental data, and the output is operation instructions.

[0577] Step 7:

[0578] The server monitors the situation in real time through anomaly detection mechanisms and notifies the user if a problem is found. It analyzes data from sensors to identify anomalies or problems. The input is sensor data, and the output is notification information.

[0579] Step 8:

[0580] Users can obtain visual information on the smart glasses' display through dynamic visualization means. This visual information is updated in real time, helping users understand their surroundings. The input is visual data, and the output is displayed information.

[0581] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0582] This invention incorporates an emotion engine into a care support robot system to evaluate the caregiver's emotional state in real time and provide task management and feedback tailored to that state. This system enables more flexible and responsive collaboration between caregivers and robots, thereby reducing the burden on caregivers and improving the quality of care.

[0583] Specifically, the user, a caregiver, gives voice commands to the care support robot. For example, they might say, "Please prepare tea for the elderly person." This voice is received by the microphone in the device. Furthermore, an emotion engine analyzes the caregiver's emotional state in real time from the voice.

[0584] This audio data is sent to a server and converted into text data by speech recognition. The converted text data is input into a natural language processing system to identify the caregiving task intended by the caregiver. Simultaneously, the output of the emotion engine is analyzed on the server to understand the caregiver's current emotional state (e.g., stress, fatigue, relief).

[0585] The server adjusts task scheduling based on the acquired emotional information. If the emotional state indicates stress, the server can change the priority of tasks and delay the execution of some tasks to reduce the caregiver's burden. Once tasks are determined, the results are sent back to the terminal, and the robot begins its specific actions.

[0586] The device prepares tasks to be performed upon instruction and uses sensors and cameras to understand the surrounding environment. For example, it can locate the elderly person in question and choose a safe route to move along. Based on the output of its emotion engine, the robot can also respond to caregivers using appropriate words and voice tones.

[0587] As the task is actually executed, the device continuously sends data to the server via sensors. An anomaly detection algorithm is then used to immediately notify the user if any problems occur. In addition, if the emotion engine indicates that the caregiver's emotions are particularly unstable, the server can suggest relaxation methods and support to the caregiver.

[0588] This entire process allows caregivers to provide care in a more efficient and less psychologically burdensome way, thereby creating an optimal care environment.

[0589] The following describes the processing flow.

[0590] Step 1:

[0591] The user gives voice commands to the caregiving robot. These voice commands are specific, such as "Please prepare tea for the elderly person." The voice is recorded on the device via the robot's microphone.

[0592] Step 2:

[0593] The device converts voice input into digital voice data and inputs it into the emotion engine. The emotion engine analyzes the user's emotional state from the voice and generates the results as data.

[0594] Step 3:

[0595] Voice data and emotional state data are sent to the server. The server uses speech recognition to convert the voice data into text, and then performs natural language processing to identify caregiving tasks.

[0596] Step 4:

[0597] The server analyzes the output of the emotion engine to identify the user's emotional state (e.g., stress, exhilaration, fatigue). Based on this emotional information, it re-evaluates the task priorities and execution order.

[0598] Step 5:

[0599] If the identified emotional state indicates stress, the server uses task scheduling mechanisms to optimize the order in which tasks are executed, taking measures such as postponing less important tasks.

[0600] Step 6:

[0601] The server sends optimized task instructions to the terminal. Based on these instructions, the terminal prepares to perform the specific caregiving tasks.

[0602] Step 7:

[0603] The device uses built-in sensors and cameras to assess the surrounding environment in real time. For example, it can determine the current location of an elderly person and decide on the safest route to take.

[0604] Step 8:

[0605] When performing caregiving tasks, the device selects appropriate voice tones and words based on the output of the emotion engine, responding to the user. This facilitates effective communication between the caregiver and the user.

[0606] Step 9:

[0607] While the task is running, the terminal continuously sends data collected by its sensors to the server. The server executes an anomaly detection algorithm and immediately notifies the user if an anomaly occurs.

[0608] Step 10:

[0609] If the emotion engine detects that the caregiver is experiencing unstable emotions, the server will offer suggestions for relaxation techniques and support. This feedback aims to further reduce the caregiver's psychological burden.

[0610] (Example 2)

[0611] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0612] In the field of elderly care, the psychological and physical burden on caregivers is increasing, and in particular, the significant impact of their emotional state on the efficiency and quality of caregiving tasks is a major problem. Conventional care support systems do not take into account the emotional state of caregivers in their task management and feedback, which poses a risk of lowering the quality of care. Therefore, there is a need for a system that enables flexible and efficient task management that responds to the emotional state of caregivers, thereby reducing their burden and improving the quality of care.

[0613] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0614] In this invention, the server includes emotion analysis means for evaluating the caregiver's emotional state from voice data, task scheduling means for optimizing tasks and determining the execution order based on the caregiver's emotional state, and means for suggesting relaxation methods to the caregiver when it is determined that the caregiver's emotional state is unstable. This enables task management and feedback that takes into account the caregiver's emotional state, thereby reducing the caregiver's burden and improving the quality of care.

[0615] A "care support robot system" is a system that includes automated mechanisms designed to assist caregivers and utilizes robotic technology to perform caregiving tasks.

[0616] An "audio input device" is a device used to convert audio signals into digital data, and microphones are among the devices that fall under this category.

[0617] "Speech recognition means" refers to technology that analyzes acquired speech data and converts it into corresponding text data.

[0618] "Natural language processing" refers to technologies that analyze text data, interpret human language, and extract intended commands or information.

[0619] "Emotional analysis methods" refer to techniques that evaluate a caregiver's emotional state from their voice and behavior, and infer psychological states such as stress and fatigue.

[0620] A "task scheduling method" is a method for determining the execution order of identified tasks and arranging them in an efficient and optimal manner.

[0621] "Instruction transmission means" refers to technology that transmits a determined task to a support robot and provides instructions for performing specific actions.

[0622] "Situation awareness means" refers to technology that uses sensors and cameras to collect information about the surrounding environment and adjusts the robot's movements based on the information obtained.

[0623] An "anomaly detection method" is a technology that analyzes sensor data to detect anomalies and problems in real time.

[0624] "Methods for suggesting relaxation techniques" refer to techniques that take into account the caregiver's emotional state and provide advice and methods to reduce psychological stress.

[0625] This invention combines a voice input device, voice recognition means, natural language processing means, emotion analysis means, task scheduling means, instruction transmission means, situation understanding means, anomaly detection means, and means for proposing relaxation methods in a care support robot system. The aim of this system is to reduce the burden on caregivers and improve the quality of care.

[0626] The voice input device is used to obtain instructions from the caregiver, and this is the microphone built into the terminal. The caregiver gives voice instructions into the terminal, and the system receives that information.

[0627] The speech recognition system performs the process of converting speech data sent to the server into text data. To achieve high-precision speech recognition, for example, a general-purpose cloud-based speech recognition API is utilized.

[0628] The natural language processing system analyzes the caregiver's instructions using text data generated by the speech recognition system. In this process, it identifies caregiving tasks and derives specific steps for their execution.

[0629] The emotion analysis system is designed to assess the caregiver's emotional state by analyzing their tone of voice and word choice, and measuring emotions such as stress and fatigue in real time. This assessment allows the system to understand the caregiver's psychological state.

[0630] The task scheduling mechanism optimizes identified caregiving tasks and determines their execution priority according to the caregiver's emotional state. This provides a method to reduce the burden on caregivers.

[0631] The instruction transmission means transmits the determined task to the terminal and gives specific instructions to the robot to start its operation.

[0632] Situational awareness measures involve using sensors and cameras to monitor the environment and adjust the robot's actions accordingly. This allows tasks to be performed safely and efficiently.

[0633] The anomaly detection system analyzes sensor data during operation and has the function to immediately notify the user if an anomaly occurs. Furthermore, if the system determines through emotion analysis that the caregiver's emotions are unstable, it supports the caregiver by suggesting relaxation methods.

[0634] For example, if a user gives an instruction to a care support robot such as "Prepare Mr. Tanaka's medicine," the voice input device acquires this instruction and sends the data to a server. The server converts it to text using speech recognition, identifies the task using natural language processing, and evaluates the stress level through sentiment analysis. A task scheduling system provides flexible task management that takes the user's emotional state into consideration, and a command transmission system instructs the robot to take specific actions. An example of a prompt message is: "Voice instruction: You have been asked to deliver tea to an elderly person. If the user may be experiencing stress, please adjust the task schedule."

[0635] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0636] Step 1:

[0637] The user gives voice commands to the caregiving robot to request a task. The device's microphone receives these voice commands and records them as digital audio data. In this step, the input is the user's voice, and the output is the digitized audio data.

[0638] Step 2:

[0639] The terminal sends the acquired digital audio data to the server. The server receives this data as input and uses a speech recognition engine to convert the audio into text data. In this case, the audio waveform becomes a string of characters. The output is the converted text data.

[0640] Step 3:

[0641] The server inputs the text data generated by speech recognition into a natural language processing engine. As part of the data processing, it performs calculations to extract the most appropriate caregiving task from the text. The output of this step is task identification. For example, a specific action such as "serve tea" is determined.

[0642] Step 4:

[0643] The server inputs text data along with the tone and speed of the voice into an emotion analysis system to evaluate the emotional state. Data calculations determine whether the user is experiencing stress. The output of this step is an indicator of the user's emotional state.

[0644] Step 5:

[0645] The server performs task scheduling based on acquired emotional information. It optimizes the priority of multiple tasks, taking into account the output of the emotional analysis tool. For example, if the stress level is high, some tasks can be postponed. The output is the optimized task schedule.

[0646] Step 6:

[0647] The server transmits the coordinated task schedule to the terminal using a command transmission mechanism. The terminal uses this as input to instruct the robot to perform specific actions. For example, it might be instructed to prepare tea and deliver it safely. The output is the robot starting its operation.

[0648] Step 7:

[0649] The device monitors the environment using sensors and cameras and adjusts its operation as needed. Sensor data is sent to a server, where an anomaly detection algorithm identifies problems. The input is environmental information, and the output is adjusted operation or anomaly notifications.

[0650] Step 8:

[0651] If any abnormalities or problems are detected, or if the sentiment analysis indicates that the user is in an unstable state, the server will notify the user accordingly. In particular, actions will be taken to suggest relaxation methods based on the user's emotional state. The output will consist of notifications and suggestions to the user.

[0652] (Application Example 2)

[0653] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0654] In the work environment, the increasing physical and mental burden on workers is a problem. Furthermore, in situations where work efficiency is low and errors are more likely, there is a lack of mechanisms to appropriately assess workers' emotional states and provide corresponding support. Conventional systems cannot consider changes in workers' emotions when scheduling tasks or providing appropriate support, making it difficult to optimize the work environment. Therefore, there is a need to understand workers' emotional states in real time and optimize their work accordingly.

[0655] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0656] In this invention, the server includes an emotion analysis means for evaluating the worker's emotional state in real time, a task scheduling means for optimizing tasks and determining the execution order based on emotions, and a means for proposing support methods based on the worker's emotional state. This makes it possible to improve work efficiency and reduce stress while taking the worker's emotional state into consideration.

[0657] A "care support robot system" is a robot designed to assist caregivers, reducing the burden on caregivers when giving instructions and enabling them to efficiently perform care tasks.

[0658] A "voice input device" is a device used to obtain voice instructions from caregivers, and its role is to convert voice into digital data using hardware such as a microphone.

[0659] "Speech recognition means" refers to a technology that analyzes acquired speech data and converts it into text data; it is a process of extracting meaning from speech.

[0660] "Natural language processing means" refers to technology that analyzes text data to understand the instructions given by caregivers and identify the necessary care tasks.

[0661] "Emotion analysis means" refers to a technology that evaluates the emotional state of workers and caregivers in real time, identifying emotions based on voice and behavioral data.

[0662] A "task scheduling method" is a process that optimizes tasks based on collected information and determines the optimal execution order.

[0663] A "means of transmitting instructions" is a mechanism for transmitting specific tasks to support devices or robots and causing them to perform those tasks.

[0664] "Environmental recognition means" refers to technology that uses sensors to monitor the surrounding environment and adjusts actions according to the situation.

[0665] An "anomaly detection method" is a technology that monitors the environment and work status in real time within a support system and detects anomalies or problems when they occur.

[0666] "Means of proposing support methods" refers to techniques for proposing appropriate support and relaxation methods based on the emotional state of the worker obtained through emotion analysis.

[0667] The system that realizes this invention is designed for caregiving support robot systems, enabling caregivers to perform tasks in a more efficient and less stressful way. The system mainly consists of a server, terminals, and users, each executing specific processes based on their respective roles.

[0668] The server receives voice instructions from caregivers via a voice input device and converts them into text data using speech recognition technology. The text data is then analyzed using natural language processing technology to derive specific care tasks. The emotional state of the worker or caregiver is evaluated in real time by an emotion analysis system, and scheduling is performed to reflect this emotional state. This scheduling optimizes tasks and provides the most efficient route to reduce the caregiver's burden.

[0669] The terminal monitors the surrounding environment through sensors and adjusts the robot's movements accordingly. It also incorporates anomaly detection mechanisms, immediately notifying the user (caregiver) if an anomaly or problem occurs. If the caregiver's emotional state is unstable, a system for suggesting support methods is activated, providing the worker with appropriate relaxation techniques and assistance.

[0670] Users input voice commands into a terminal and perform tasks according to the task scheduling suggested by the care support robot. The quality of care is improved and stress is reduced through the provision of feedback based on emotional state. For example, if instructed to prepare tea for an elderly person, the robot will include procedures to locate the elderly person and deliver the tea via a safe route.

[0671] An example of an input prompt for a generating AI model is, "Generate logic that identifies work instructions and emotional states from voice input and proposes an optimal work schedule based on that information," which is used in system design.

[0672] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0673] Step 1:

[0674] The server receives voice instructions given by the caregiver (user) through the terminal's voice input device. Voice data is acquired as input, and this voice is converted into text data by speech recognition software. The instructions are then returned as output in text format.

[0675] Step 2:

[0676] The server processes the text data obtained in Step 1 using natural language processing techniques. From the text data as input, it identifies caregiving tasks and identifies corresponding tasks. A specific task list is created as output. Contextual analysis is performed by a generative AI model during this process.

[0677] Step 3:

[0678] The server applies emotion analysis tools to analyze emotional information such as the tone and speed of the user's voice collected by the terminal. Based on the input voice information, the emotion engine evaluates the user's emotional state in real time and quantifies emotional states such as stress and relief. The output is the user's emotional state data.

[0679] Step 4:

[0680] The server uses a task scheduling mechanism to determine task priorities based on the task list created in step 2 and the emotional state data obtained in step 3. It receives the user's emotional state and task information as input and outputs a task order optimized based on the emotional state. If the emotional state indicates stress, less burdensome tasks are prioritized.

[0681] Step 5:

[0682] The terminal transmits the task sequence determined in step 4 to the support device, and the robot performs actions based on the user's instructions. Receiving the task sequence as input, the robot performs specific care actions. For example, if the task is "prepare and deliver tea to an elderly person," the robot will choose a safe route and move accordingly.

[0683] Step 6:

[0684] The terminal uses sensors to monitor its surroundings and transmits information to the server in real time using situational awareness tools. It takes environmental condition data as input and uses it for safety assessment. The output ensures safe and effective robot actions.

[0685] Step 7:

[0686] The terminal uses anomaly detection means to detect anomalies during task execution and reports them to the server. Patterns of normal and abnormal states are provided as input, and when an anomaly is confirmed, an alert is output to notify the user. This allows for immediate action.

[0687] Example prompt for using a generative AI model: "Generate logic that identifies work instructions and emotional states from voice input and proposes an optimal work schedule based on that information."

[0688] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0689] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0690] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0691] [Fourth Embodiment]

[0692] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0693] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0694] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0695] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0696] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0697] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0698] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0699] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0700] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0701] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0702] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0703] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0704] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0705] This invention provides a system that introduces care support robots into caregiving tasks to reduce the burden on caregivers. This system is equipped with functions that allow the robot to appropriately perform caregiving tasks based on the caregiver's voice instructions.

[0706] First, the user (caregiver) gives voice instructions to the robot. For example, they might say, "Please give the medicine to the elderly person." The voice is then picked up by the device via the robot's built-in microphone.

[0707] This audio data is then sent to a server and converted into text data by speech recognition. For example, the audio "Please give the medicine to the elderly person" is converted into the text instruction "Prepare the medicine for the elderly person." This text is then analyzed by natural language processing to identify the intended caregiving task.

[0708] Next, the server uses task scheduling to organize the identified tasks into executable steps. This includes a process of optimization that takes into account priorities and the caregiver's schedule. This information is then sent back to the terminal using instruction transmission means, ready to be executed by the robot.

[0709] The device uses situational awareness, sensors, and cameras to take appropriate actions based on its surroundings. For example, it can determine the location of a person in a room and move to that location. The robot can retrieve medication from a shelf and safely hand it to the elderly person.

[0710] Furthermore, during task execution, the terminal sends data collected through sensors to the server in real time for safety and execution status checks. If an anomaly is detected by the anomaly detection mechanism, the user is immediately informed. This process is designed to allow the robot to react to unexpected situations, such as when an elderly person fails to respond.

[0711] Such a system allows caregivers to efficiently carry out caregiving tasks in cooperation with care robots, thereby reducing their workload. Furthermore, real-time anomaly detection and notification improve the quality and safety of care.

[0712] The following describes the processing flow.

[0713] Step 1:

[0714] Users give voice instructions to the caregiving robot for specific caregiving tasks. For example, they might say, "Please give medication to an elderly person." The voice is picked up by the device via the robot's built-in microphone.

[0715] Step 2:

[0716] The terminal processes the acquired voice input in real time and prepares it to be sent to the server as digital audio data. The audio data is compressed and securely transferred.

[0717] Step 3:

[0718] The server converts the received voice data into text data using speech recognition technology. Here, the instruction "hand over the medicine" is translated into a specific action such as "take it out and take it to the designated location."

[0719] Step 4:

[0720] The server inputs the converted text into a natural language processing model, which then analyzes the caregiver's instructions in detail. The analysis results identify the most appropriate care tasks and necessary resources.

[0721] Step 5:

[0722] The server applies a task scheduling algorithm to optimize the execution order of identified tasks. If necessary, it coordinates with other priority tasks and incorporates them into the daily task schedule.

[0723] Step 6:

[0724] The server sends the analysis results and details of the scheduled tasks to the terminal and provides the instructions necessary for the robot to perform specific actions.

[0725] Step 7:

[0726] Before starting to operate according to instructions, the device uses its built-in sensors and camera to assess its surroundings. It verifies the location of the subject and prepares to safely perform the task.

[0727] Step 8:

[0728] The device begins performing a task. For example, it might retrieve medication from a shelf and deliver it to an elderly person. While operating, it monitors its surroundings with sensors and takes evasive action as needed.

[0729] Step 9:

[0730] The terminal sends data related to the task being run to the server in real time. The server analyzes the data using an anomaly detection algorithm and quickly notifies the user if an anomaly occurs.

[0731] Step 10:

[0732] Users can check notifications from the server and, if necessary, issue modifications or additional instructions to caregiving tasks via their devices. This process ensures the safety and efficiency of caregiving operations.

[0733] (Example 1)

[0734] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0735] In modern care settings, caregivers are busy and required to efficiently manage multiple care tasks. However, many tasks are manual, placing a heavy burden on caregivers. Furthermore, ensuring that care support equipment operates correctly while maintaining real-time safety is challenging, highlighting the need to improve both the quality and safety of care.

[0736] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0737] In this invention, the server includes a natural language processing means for analyzing the caregiver's instructions and identifying caregiving tasks, a task scheduling means for optimizing tasks and determining the execution order, and a means for collecting execution status data and providing information feedback to the caregiver. This makes it possible to reduce the workload of caregivers while enabling real-time situation monitoring and ensuring safety.

[0738] "Care support equipment" refers to hardware and software systems used to support and streamline the caregiving tasks performed by caregivers.

[0739] A "voice input mechanism" refers to a device or system that acquires voice data and processes it as digital information.

[0740] "Voice conversion means" refers to a technology or device that analyzes acquired voice data and converts it into text data.

[0741] "Natural language processing" refers to technologies that analyze text data to understand its intent and meaning, and then link that understanding to specific actions or tasks.

[0742] "Task scheduling methods" refer to techniques and methods for determining the order and priority of tasks in order to efficiently carry out specified caregiving tasks.

[0743] The "instruction transmission means" is a function that transmits necessary work instructions from the server to the support equipment and controls the equipment to ensure it operates properly.

[0744] "Environmental recognition means" refers to technologies that use sensors and cameras to monitor the surrounding environment and appropriately adjust the operation of equipment.

[0745] An "anomaly detection method" is a technology for monitoring the operation of systems and equipment in real time and detecting potential problems or anomalies.

[0746] This invention is a system that uses care support equipment. This system receives voice instructions from the caregiver and automatically performs specific care tasks using the support equipment. A specific embodiment of this system is described below.

[0747] First, the user gives voice commands to the care support device. These voice commands are given in the form of, "Please give the medicine to the elderly person." This voice data is captured through a voice input mechanism built into the device. The captured voice data is then converted into text data by a voice conversion means. Specifically, voice recognition software can be used, and for example, by using a commercially available voice recognition API, highly accurate voice input becomes possible.

[0748] The converted text data is analyzed by a natural language processing (NLP) system on the server. This system may utilize a natural language processing library; for example, an open-source NLP library could be used. This process identifies specific caregiving tasks from the voice instructions.

[0749] Next, the identified tasks are optimized by the server's task scheduling mechanism. This ensures that tasks are executed in the optimal order based on task priority and the caregiver's schedule.

[0750] The device uses environmental awareness to monitor its surroundings with sensors and cameras and adjust its movements accordingly. This allows the assistive device to safely move to the elderly person and perform designated tasks. Specifically, this includes actions such as a robotic arm retrieving medication from a shelf and safely handing it to the elderly person.

[0751] Furthermore, the system's anomaly detection mechanism monitors task progress in real time and immediately provides feedback to the user if an anomaly is detected. This allows for a quick response even if unexpected problems occur.

[0752] As a concrete example, consider a scenario where the instruction is given, "Please deliver the remote control to the elderly person." Upon receiving this instruction, the system identifies the location of the remote control, the robot travels to that location, and performs a series of actions to deliver the remote control to the elderly person.

[0753] For generated AI models, the system's operation can be verified using the following prompts: "Describe how the robot would deliver lunch to an elderly person," and "Explain the response procedure when a caregiving robot detects an anomaly."

[0754] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0755] Step 1:

[0756] The user gives voice commands to the caregiving support device. The input is a voice command such as, "Please give the medicine to the elderly person." The output is recorded as voice data in the terminal. Here, the terminal uses its built-in voice input mechanism to acquire the voice in digital format.

[0757] Step 2:

[0758] The terminal sends the acquired audio data to the server. The server receives the audio data and converts it into text data using a speech-to-text conversion device. The input is digital audio data, and the output is the text "Please give the medicine to the elderly person." This process uses speech recognition software to convert the audio information into text information.

[0759] Step 3:

[0760] The server receives text data and uses natural language processing to identify caregiving tasks. The input is text data converted from speech, and the output is the content of the identified caregiving tasks. This process uses a natural language processing library to analyze the intent and purpose of the instructions and connect them to specific tasks.

[0761] Step 4:

[0762] The server optimizes the identified care tasks using a task scheduling mechanism. The input is the identified tasks, and the output is a task list with the execution order determined. At this stage, the server optimizes the execution procedure, taking into account task priorities and feasible schedules.

[0763] Step 5:

[0764] The terminal receives instructions from the server and uses environmental recognition means to understand its surroundings. The input is a task execution instruction from the server, and the output is the executed action. In this case, the assistive device uses sensors and cameras to recognize its surroundings and safely moves to the person in need.

[0765] Step 6:

[0766] The terminal sends data obtained during task execution to the server, which is then monitored in real time by anomaly detection measures. The input is execution status data collected by sensors, and the output is detected anomalies or problems. If an anomaly is detected, the server immediately provides feedback to the user and takes action to prompt necessary responses.

[0767] (Application Example 1)

[0768] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0769] In modern caregiving, there is a need to reduce the burden on caregivers and improve the efficiency of their work. However, caregivers are overwhelmed with many tasks, making it particularly difficult to efficiently perform daily caregiving tasks while ensuring the safety of the elderly. Furthermore, the caregiving environment is constantly changing, requiring real-time situational awareness. However, there is a lack of visual information, making it difficult for caregivers to intuitively grasp the situation.

[0770] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0771] In this invention, the server includes means for acquiring information via an acoustic input mechanism, acoustic recognition means for converting it into text data, natural language processing means for identifying caregiving tasks, and dynamic visualization means. This allows caregivers to always have access to smart visual information, enabling the safe and efficient performance of caregiving tasks.

[0772] A "care support equipment system" is a group of devices with a set of functions designed to support caregivers in care settings.

[0773] An "acoustic input mechanism" refers to a device that uses a microphone and its peripherals to acquire voice commands.

[0774] "Text data" refers to an information format in which instructions obtained via voice are converted into textual information.

[0775] "Acoustic recognition means" refers to a technical method that analyzes acquired audio data and converts it into text data.

[0776] "Natural language processing means" refers to information processing techniques that analyze text data, understand instructions, and identify caregiving tasks.

[0777] A "task planning method" is a technique for planning the execution of tasks in the optimal order, based on the caregiver's instructions.

[0778] A "means of instruction transmission" refers to a communication method for transmitting and executing a specified task to a physical assistive device.

[0779] A "sensing device" is a mechanism that includes sensors for monitoring the surrounding environment.

[0780] A "situation assessment means" is a means of adjusting the operation of equipment based on information acquired from a sensing device.

[0781] An "anomaly detection method" is a technical technique for monitoring and detecting anomalies and problems in a care support system in real time.

[0782] "Dynamic visualization means" refers to displays and methods of displaying information for providing real-time visual information to caregivers.

[0783] This invention realizes advanced functions to support caregivers in a care support equipment system. The system consists of a voice input mechanism, acoustic recognition means, natural language processing means, task planning means, instruction transmission means, sensing device, situation understanding means, anomaly detection means, and dynamic visualization means.

[0784] The server acquires speech emitted by the caregiver via an acoustic input mechanism and converts that speech into text data using an acoustic recognition means. Next, a natural language processing means identifies care tasks from the text data. The identified tasks are organized into the optimal execution order by a task planning means and communicated to the support equipment by an instruction transmission means. A sensing device monitors the surrounding environment, and a situation awareness means adjusts the operation of the equipment accordingly. An anomaly detection means monitors for anomalies in real time and notifies the caregiver as needed. Furthermore, a dynamic visualization means provides the caregiver with real-time visual information to support their understanding of the situation.

[0785] This system functions effectively when caregivers use smart glasses such as Google Glass. The Google Cloud Speech-to-Text API is used for speech recognition, and the Python spaCy library is used for natural language processing. The display on the smart glasses is utilized to provide visual information.

[0786] For example, if a caregiver instructs, "Prepare the medication after breakfast," the system will select the appropriate medication from its inventory and safely deliver it to the elderly person while providing visual information. An example of a prompt sentence to input into the generating AI model would be, "Schedule a task to prepare the elderly person's medication after breakfast. Please be aware of soy allergies."

[0787] In this way, the system reduces the burden on caregivers and improves the efficiency of caregiving tasks.

[0788] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0789] Step 1:

[0790] The user inputs voice commands using the acoustic input mechanism of the smart glasses. This voice data is captured by the device via the microphone. The input is in voice format.

[0791] Step 2:

[0792] The device sends audio data to the server. The server uses the Google Cloud Speech-to-Text API to convert the audio data into text data. This process outputs text data from the audio data.

[0793] Step 3:

[0794] The server analyzes the acquired text data using natural language processing. Through the spaCy library, the text data is analyzed, and the user's intended caregiving task is identified. The text data is then output as caregiving task information.

[0795] Step 4:

[0796] The server uses task planning tools to optimize identified caregiving tasks into executable procedures. Prioritization and time allocation are considered when determining the execution order. The input is task information, and the output is optimized execution instructions.

[0797] Step 5:

[0798] These operational instructions are transmitted to the assistive device through an instruction transmission means. The assistive device performs the identified care task, translating the instructions as input into physical actions.

[0799] Step 6:

[0800] The terminal monitors the surrounding environment using sensing devices and adjusts the operation of assistive devices through situational awareness mechanisms. The input is environmental data, and the output is operation instructions.

[0801] Step 7:

[0802] The server monitors the situation in real time through anomaly detection mechanisms and notifies the user if a problem is found. It analyzes data from sensors to identify anomalies or problems. The input is sensor data, and the output is notification information.

[0803] Step 8:

[0804] Users can obtain visual information on the smart glasses' display through dynamic visualization means. This visual information is updated in real time, helping users understand their surroundings. The input is visual data, and the output is displayed information.

[0805] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0806] This invention incorporates an emotion engine into a care support robot system to evaluate the caregiver's emotional state in real time and provide task management and feedback tailored to that state. This system enables more flexible and responsive collaboration between caregivers and robots, thereby reducing the burden on caregivers and improving the quality of care.

[0807] Specifically, the user, a caregiver, gives voice commands to the care support robot. For example, they might say, "Please prepare tea for the elderly person." This voice is received by the microphone in the device. Furthermore, an emotion engine analyzes the caregiver's emotional state in real time from the voice.

[0808] This audio data is sent to a server and converted into text data by speech recognition. The converted text data is input into a natural language processing system to identify the caregiving task intended by the caregiver. Simultaneously, the output of the emotion engine is analyzed on the server to understand the caregiver's current emotional state (e.g., stress, fatigue, relief).

[0809] The server adjusts task scheduling based on the acquired emotional information. If the emotional state indicates stress, the server can change the priority of tasks and delay the execution of some tasks to reduce the caregiver's burden. Once tasks are determined, the results are sent back to the terminal, and the robot begins its specific actions.

[0810] The device prepares tasks to be performed upon instruction and uses sensors and cameras to understand the surrounding environment. For example, it can locate the elderly person in question and choose a safe route to move along. Based on the output of its emotion engine, the robot can also respond to caregivers using appropriate words and voice tones.

[0811] As the task is actually executed, the device continuously sends data to the server via sensors. An anomaly detection algorithm is then used to immediately notify the user if any problems occur. In addition, if the emotion engine indicates that the caregiver's emotions are particularly unstable, the server can suggest relaxation methods and support to the caregiver.

[0812] This entire process allows caregivers to provide care in a more efficient and less psychologically burdensome way, thereby creating an optimal care environment.

[0813] The following describes the processing flow.

[0814] Step 1:

[0815] The user gives voice commands to the caregiving robot. These voice commands are specific, such as "Please prepare tea for the elderly person." The voice is recorded on the device via the robot's microphone.

[0816] Step 2:

[0817] The device converts voice input into digital voice data and inputs it into the emotion engine. The emotion engine analyzes the user's emotional state from the voice and generates the results as data.

[0818] Step 3:

[0819] Voice data and emotional state data are sent to the server. The server uses speech recognition to convert the voice data into text, and then performs natural language processing to identify caregiving tasks.

[0820] Step 4:

[0821] The server analyzes the output of the emotion engine to identify the user's emotional state (e.g., stress, exhilaration, fatigue). Based on this emotional information, it re-evaluates the task priorities and execution order.

[0822] Step 5:

[0823] If the identified emotional state indicates stress, the server uses task scheduling mechanisms to optimize the order in which tasks are executed, taking measures such as postponing less important tasks.

[0824] Step 6:

[0825] The server sends optimized task instructions to the terminal. Based on these instructions, the terminal prepares to perform the specific caregiving tasks.

[0826] Step 7:

[0827] The device uses built-in sensors and cameras to assess the surrounding environment in real time. For example, it can determine the current location of an elderly person and decide on the safest route to take.

[0828] Step 8:

[0829] When performing caregiving tasks, the device selects appropriate voice tones and words based on the output of the emotion engine, responding to the user. This facilitates effective communication between the caregiver and the user.

[0830] Step 9:

[0831] While the task is running, the terminal continuously sends data collected by its sensors to the server. The server executes an anomaly detection algorithm and immediately notifies the user if an anomaly occurs.

[0832] Step 10:

[0833] If the emotion engine detects that the caregiver is experiencing unstable emotions, the server will offer suggestions for relaxation techniques and support. This feedback aims to further reduce the caregiver's psychological burden.

[0834] (Example 2)

[0835] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0836] In the field of elderly care, the psychological and physical burden on caregivers is increasing, and in particular, the significant impact of their emotional state on the efficiency and quality of caregiving tasks is a major problem. Conventional care support systems do not take into account the emotional state of caregivers in their task management and feedback, which poses a risk of lowering the quality of care. Therefore, there is a need for a system that enables flexible and efficient task management that responds to the emotional state of caregivers, thereby reducing their burden and improving the quality of care.

[0837] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0838] In this invention, the server includes emotion analysis means for evaluating the caregiver's emotional state from voice data, task scheduling means for optimizing tasks and determining the execution order based on the caregiver's emotional state, and means for suggesting relaxation methods to the caregiver when it is determined that the caregiver's emotional state is unstable. This enables task management and feedback that takes into account the caregiver's emotional state, thereby reducing the caregiver's burden and improving the quality of care.

[0839] A "care support robot system" is a system that includes automated mechanisms designed to assist caregivers and utilizes robotic technology to perform caregiving tasks.

[0840] An "audio input device" is a device used to convert audio signals into digital data, and microphones are among the devices that fall under this category.

[0841] "Speech recognition means" refers to technology that analyzes acquired speech data and converts it into corresponding text data.

[0842] "Natural language processing" refers to technologies that analyze text data, interpret human language, and extract intended commands or information.

[0843] "Emotional analysis methods" refer to techniques that evaluate a caregiver's emotional state from their voice and behavior, and infer psychological states such as stress and fatigue.

[0844] A "task scheduling method" is a method for determining the execution order of identified tasks and arranging them in an efficient and optimal manner.

[0845] "Instruction transmission means" refers to technology that transmits a determined task to a support robot and provides instructions for performing specific actions.

[0846] "Situation awareness means" refers to technology that uses sensors and cameras to collect information about the surrounding environment and adjusts the robot's movements based on the information obtained.

[0847] An "anomaly detection method" is a technology that analyzes sensor data to detect anomalies and problems in real time.

[0848] "Methods for suggesting relaxation techniques" refer to techniques that take into account the caregiver's emotional state and provide advice and methods to reduce psychological stress.

[0849] This invention combines a voice input device, voice recognition means, natural language processing means, emotion analysis means, task scheduling means, instruction transmission means, situation understanding means, anomaly detection means, and means for proposing relaxation methods in a care support robot system. The aim of this system is to reduce the burden on caregivers and improve the quality of care.

[0850] The voice input device is used to obtain instructions from the caregiver, and this is the microphone built into the terminal. The caregiver gives voice instructions into the terminal, and the system receives that information.

[0851] The speech recognition system performs the process of converting speech data sent to the server into text data. To achieve high-precision speech recognition, for example, a general-purpose cloud-based speech recognition API is utilized.

[0852] The natural language processing system analyzes the caregiver's instructions using text data generated by the speech recognition system. In this process, it identifies caregiving tasks and derives specific steps for their execution.

[0853] The emotion analysis system is designed to assess the caregiver's emotional state by analyzing their tone of voice and word choice, and measuring emotions such as stress and fatigue in real time. This assessment allows the system to understand the caregiver's psychological state.

[0854] The task scheduling mechanism optimizes identified caregiving tasks and determines their execution priority according to the caregiver's emotional state. This provides a method to reduce the burden on caregivers.

[0855] The instruction transmission means transmits the determined task to the terminal and gives specific instructions to the robot to start its operation.

[0856] Situational awareness measures involve using sensors and cameras to monitor the environment and adjust the robot's actions accordingly. This allows tasks to be performed safely and efficiently.

[0857] The anomaly detection system analyzes sensor data during operation and has the function to immediately notify the user if an anomaly occurs. Furthermore, if the system determines through emotion analysis that the caregiver's emotions are unstable, it supports the caregiver by suggesting relaxation methods.

[0858] For example, if a user gives an instruction to a care support robot such as "Prepare Mr. Tanaka's medicine," the voice input device acquires this instruction and sends the data to a server. The server converts it to text using speech recognition, identifies the task using natural language processing, and evaluates the stress level through sentiment analysis. A task scheduling system provides flexible task management that takes the user's emotional state into consideration, and a command transmission system instructs the robot to take specific actions. An example of a prompt message is: "Voice instruction: You have been asked to deliver tea to an elderly person. If the user may be experiencing stress, please adjust the task schedule."

[0859] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0860] Step 1:

[0861] The user gives voice commands to the caregiving robot to request a task. The device's microphone receives these voice commands and records them as digital audio data. In this step, the input is the user's voice, and the output is the digitized audio data.

[0862] Step 2:

[0863] The terminal sends the acquired digital audio data to the server. The server receives this data as input and uses a speech recognition engine to convert the audio into text data. In this case, the audio waveform becomes a string of characters. The output is the converted text data.

[0864] Step 3:

[0865] The server inputs the text data generated by speech recognition into a natural language processing engine. As part of the data processing, it performs calculations to extract the most appropriate caregiving task from the text. The output of this step is task identification. For example, a specific action such as "serve tea" is determined.

[0866] Step 4:

[0867] The server inputs text data along with the tone and speed of the voice into an emotion analysis system to evaluate the emotional state. Data calculations determine whether the user is experiencing stress. The output of this step is an indicator of the user's emotional state.

[0868] Step 5:

[0869] The server performs task scheduling based on acquired emotional information. It optimizes the priority of multiple tasks, taking into account the output of the emotional analysis tool. For example, if the stress level is high, some tasks can be postponed. The output is the optimized task schedule.

[0870] Step 6:

[0871] The server transmits the coordinated task schedule to the terminal using a command transmission mechanism. The terminal uses this as input to instruct the robot to perform specific actions. For example, it might be instructed to prepare tea and deliver it safely. The output is the robot starting its operation.

[0872] Step 7:

[0873] The device monitors the environment using sensors and cameras and adjusts its operation as needed. Sensor data is sent to a server, where an anomaly detection algorithm identifies problems. The input is environmental information, and the output is adjusted operation or anomaly notifications.

[0874] Step 8:

[0875] If any abnormalities or problems are detected, or if the sentiment analysis indicates that the user is in an unstable state, the server will notify the user accordingly. In particular, actions will be taken to suggest relaxation methods based on the user's emotional state. The output will consist of notifications and suggestions to the user.

[0876] (Application Example 2)

[0877] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0878] In the work environment, the increasing physical and mental burden on workers is a problem. Furthermore, in situations where work efficiency is low and errors are more likely, there is a lack of mechanisms to appropriately assess workers' emotional states and provide corresponding support. Conventional systems cannot consider changes in workers' emotions when scheduling tasks or providing appropriate support, making it difficult to optimize the work environment. Therefore, there is a need to understand workers' emotional states in real time and optimize their work accordingly.

[0879] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0880] In this invention, the server includes an emotion analysis means for evaluating the worker's emotional state in real time, a task scheduling means for optimizing tasks and determining the execution order based on emotions, and a means for proposing support methods based on the worker's emotional state. This makes it possible to improve work efficiency and reduce stress while taking the worker's emotional state into consideration.

[0881] A "care support robot system" is a robot designed to assist caregivers, reducing the burden on caregivers when giving instructions and enabling them to efficiently perform care tasks.

[0882] A "voice input device" is a device used to obtain voice instructions from caregivers, and its role is to convert voice into digital data using hardware such as a microphone.

[0883] "Speech recognition means" refers to a technology that analyzes acquired speech data and converts it into text data; it is a process of extracting meaning from speech.

[0884] "Natural language processing means" refers to technology that analyzes text data to understand the instructions given by caregivers and identify the necessary care tasks.

[0885] "Emotion analysis means" refers to a technology that evaluates the emotional state of workers and caregivers in real time, identifying emotions based on voice and behavioral data.

[0886] A "task scheduling method" is a process that optimizes tasks based on collected information and determines the optimal execution order.

[0887] A "means of transmitting instructions" is a mechanism for transmitting specific tasks to support devices or robots and causing them to perform those tasks.

[0888] "Environmental recognition means" refers to technology that uses sensors to monitor the surrounding environment and adjusts actions according to the situation.

[0889] An "anomaly detection method" is a technology that monitors the environment and work status in real time within a support system and detects anomalies or problems when they occur.

[0890] "Means of proposing support methods" refers to techniques for proposing appropriate support and relaxation methods based on the emotional state of the worker obtained through emotion analysis.

[0891] The system that realizes this invention is designed for caregiving support robot systems, enabling caregivers to perform tasks in a more efficient and less stressful way. The system mainly consists of a server, terminals, and users, each executing specific processes based on their respective roles.

[0892] The server receives voice instructions from caregivers via a voice input device and converts them into text data using speech recognition technology. The text data is then analyzed using natural language processing technology to derive specific care tasks. The emotional state of the worker or caregiver is evaluated in real time by an emotion analysis system, and scheduling is performed to reflect this emotional state. This scheduling optimizes tasks and provides the most efficient route to reduce the caregiver's burden.

[0893] The terminal monitors the surrounding environment through sensors and adjusts the robot's movements accordingly. It also incorporates anomaly detection mechanisms, immediately notifying the user (caregiver) if an anomaly or problem occurs. If the caregiver's emotional state is unstable, a system for suggesting support methods is activated, providing the worker with appropriate relaxation techniques and assistance.

[0894] Users input voice commands into a terminal and perform tasks according to the task scheduling suggested by the care support robot. The quality of care is improved and stress is reduced through the provision of feedback based on emotional state. For example, if instructed to prepare tea for an elderly person, the robot will include procedures to locate the elderly person and deliver the tea via a safe route.

[0895] An example of an input prompt for a generating AI model is, "Generate logic that identifies work instructions and emotional states from voice input and proposes an optimal work schedule based on that information," which is used in system design.

[0896] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0897] Step 1:

[0898] The server receives voice instructions given by the caregiver (user) through the terminal's voice input device. Voice data is acquired as input, and this voice is converted into text data by speech recognition software. The instructions are then returned as output in text format.

[0899] Step 2:

[0900] The server processes the text data obtained in Step 1 using natural language processing techniques. From the text data as input, it identifies caregiving tasks and identifies corresponding tasks. A specific task list is created as output. Contextual analysis is performed by a generative AI model during this process.

[0901] Step 3:

[0902] The server applies emotion analysis tools to analyze emotional information such as the tone and speed of the user's voice collected by the terminal. Based on the input voice information, the emotion engine evaluates the user's emotional state in real time and quantifies emotional states such as stress and relief. The output is the user's emotional state data.

[0903] Step 4:

[0904] The server uses a task scheduling mechanism to determine task priorities based on the task list created in step 2 and the emotional state data obtained in step 3. It receives the user's emotional state and task information as input and outputs a task order optimized based on the emotional state. If the emotional state indicates stress, less burdensome tasks are prioritized.

[0905] Step 5:

[0906] The terminal transmits the task sequence determined in step 4 to the support device, and the robot performs actions based on the user's instructions. Receiving the task sequence as input, the robot performs specific care actions. For example, if the task is "prepare and deliver tea to an elderly person," the robot will choose a safe route and move accordingly.

[0907] Step 6:

[0908] The terminal uses sensors to monitor its surroundings and transmits information to the server in real time using situational awareness tools. It takes environmental condition data as input and uses it for safety assessment. The output ensures safe and effective robot actions.

[0909] Step 7:

[0910] The terminal uses anomaly detection means to detect anomalies during task execution and reports them to the server. Patterns of normal and abnormal states are provided as input, and when an anomaly is confirmed, an alert is output to notify the user. This allows for immediate action.

[0911] Example prompt for using a generative AI model: "Generate logic that identifies work instructions and emotional states from voice input and proposes an optimal work schedule based on that information."

[0912] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0913] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0914] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0915] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0916] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0917] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0918] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0919] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0920] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0921] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0922] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0923] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0924] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0925] 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.

[0926] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0927] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0928] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0929] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0930] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0931] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0932] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0933] The following is further disclosed regarding the embodiments described above.

[0934] (Claim 1)

[0935] In a care support robot system,

[0936] A means of obtaining instructions from a caregiver via a voice input device,

[0937] A speech recognition means that converts acquired voice commands into text data,

[0938] A natural language processing tool that analyzes the instructions given by caregivers and identifies care tasks,

[0939] A task scheduling means for optimizing tasks and determining their execution order,

[0940] A means for transmitting instructions to a support robot to execute a specified task,

[0941] A situation awareness means that monitors the surrounding environment using sensors and adjusts its operation according to the situation,

[0942] An anomaly detection method that sends feedback to the care support system and monitors it in real time,

[0943] A system that includes this.

[0944] (Claim 2)

[0945] The system according to claim 1, comprising means for analyzing the instructions of a caregiver and automatically generating care tasks.

[0946] (Claim 3)

[0947] The system according to claim 1, further comprising means for notifying a caregiver when an abnormality or problem is detected.

[0948] "Example 1"

[0949] (Claim 1)

[0950] In care support equipment systems,

[0951] A means of obtaining instructions from a caregiver via a voice input mechanism,

[0952] A voice conversion means that converts acquired voice commands into text data,

[0953] A natural language processing tool that analyzes the instructions given by caregivers and identifies caregiving tasks,

[0954] A business scheduling method that optimizes business processes and determines the execution order,

[0955] A means for transmitting instructions to support equipment to communicate and execute the specified tasks,

[0956] An environmental recognition means that monitors the surrounding situation using a detector and adjusts its operation according to the situation,

[0957] An anomaly detection method that sends feedback to the care support system and monitors it in real time,

[0958] A means of collecting data on the status of execution and providing information feedback to caregivers,

[0959] A system that includes this.

[0960] (Claim 2)

[0961] The system according to claim 1, comprising means for automatically generating relevant caregiving tasks based on voice instructions.

[0962] (Claim 3)

[0963] The system according to claim 1, further comprising means for promptly notifying a caregiver of information when an abnormality or problem is detected.

[0964] "Application Example 1"

[0965] (Claim 1)

[0966] In care support equipment systems,

[0967] A means of obtaining instructions from a caregiver via an acoustic input mechanism,

[0968] A sound recognition means that converts acquired voice commands into text data,

[0969] A natural language processing tool that analyzes the instructions given by caregivers and identifies care tasks,

[0970] A task planning means for optimizing tasks and determining the execution order,

[0971] A means for transmitting instructions to an assistive device to execute an identified task,

[0972] A situation awareness means that monitors the surrounding environment using a sensing device and adjusts its operation according to the situation,

[0973] An anomaly detection method that sends feedback to the care support system and monitors it in real time,

[0974] A dynamic visualization means that provides visual information to caregivers via an information display,

[0975] A system that includes this.

[0976] (Claim 2)

[0977] The system according to claim 1, comprising means for analyzing the instructions of a caregiver and automatically generating care tasks.

[0978] (Claim 3)

[0979] The system according to claim 1, further comprising means for notifying a caregiver when an abnormality or problem is detected.

[0980] "Example 2 of combining an emotion engine"

[0981] (Claim 1)

[0982] In a care support robot system,

[0983] A means of obtaining instructions from a caregiver via a voice input device,

[0984] A means of transmitting the acquired audio data to a server in digital format,

[0985] A speech recognition means that converts transmitted audio data into text data,

[0986] A natural language processing tool that analyzes the instructions given by caregivers and identifies care tasks,

[0987] An emotion analysis method for evaluating the emotional state of caregivers from audio data,

[0988] A task scheduling means that optimizes tasks and determines the execution order based on the caregiver's emotional state,

[0989] A means for transmitting instructions to a support robot to execute a specified task,

[0990] A situation awareness means that monitors the surrounding environment using sensors and adjusts its operation according to the situation,

[0991] An anomaly detection means that sends feedback to the care support system and monitors anomalies in real time,

[0992] A system that includes this.

[0993] (Claim 2)

[0994] The system according to claim 1, further comprising means for suggesting relaxation methods to a caregiver when it is determined that the caregiver's emotional state is unstable.

[0995] (Claim 3)

[0996] The system according to claim 1, further comprising means for notifying a caregiver when an abnormality or problem is detected.

[0997] "Application example 2 when combining with an emotional engine"

[0998] (Claim 1)

[0999] In a care support robot system,

[1000] A means of obtaining instructions from a caregiver via a voice input device,

[1001] A speech recognition means that converts acquired voice commands into text data,

[1002] A natural language processing tool that analyzes the instructions given by caregivers and identifies care tasks,

[1003] A means of emotional analysis that evaluates the emotional state of workers in real time,

[1004] A task scheduling method that optimizes tasks and determines the execution order based on emotions,

[1005] An instruction transmission means for communicating the identified task to a support device and causing it to execute,

[1006] An environmental awareness means that monitors the surrounding situation using sensors and adjusts its operation according to the situation,

[1007] An anomaly detection method that sends feedback to the support system and monitors in real time,

[1008] A system that includes this.

[1009] (Claim 2)

[1010] The system according to claim 1, comprising means for proposing a support method based on the emotional state of the worker.

[1011] (Claim 3)

[1012] The system according to claim 1, further comprising means for notifying an operator when an abnormality or problem is detected and for suggesting a support method appropriate to their emotional state. [Explanation of Symbols]

[1013] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. In a care support robot system, A means of obtaining instructions from a caregiver via a voice input device, A speech recognition means that converts acquired voice commands into text data, A natural language processing tool that analyzes the instructions given by caregivers and identifies care tasks, A task scheduling means for optimizing tasks and determining their execution order, A means for transmitting instructions to a support robot to execute a specified task, A situation awareness means that monitors the surrounding environment using sensors and adjusts its operation according to the situation, An anomaly detection method that sends feedback to the care support system and monitors it in real time, A system that includes this.

2. The system according to claim 1, comprising means for analyzing the instructions given by a caregiver and automatically generating care tasks.

3. The system according to claim 1, further comprising means for notifying a caregiver when an abnormality or problem is detected.

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